From 0612c103ed42308787bb1085303c49509549c4f9 Mon Sep 17 00:00:00 2001
From: nathanlyons <nathan.j.lyons@gmail.com>
Date: Wed, 2 May 2018 17:33:35 -0500
Subject: [PATCH 01/15] clade identifiers explained

---
 biota_evolution/BiotaEvolver_tutorial.ipynb  | 146 +++++++++++++++++++
 biota_evolution/images/clade_definitions.eps | Bin 0 -> 1731494 bytes
 biota_evolution/images/clade_definitions.png | Bin 0 -> 8510 bytes
 3 files changed, 146 insertions(+)
 create mode 100644 biota_evolution/BiotaEvolver_tutorial.ipynb
 create mode 100644 biota_evolution/images/clade_definitions.eps
 create mode 100644 biota_evolution/images/clade_definitions.png

diff --git a/biota_evolution/BiotaEvolver_tutorial.ipynb b/biota_evolution/BiotaEvolver_tutorial.ipynb
new file mode 100644
index 0000000..5a4f708
--- /dev/null
+++ b/biota_evolution/BiotaEvolver_tutorial.ipynb
@@ -0,0 +1,146 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"http://landlab.github.io\"><img style=\"float: left\" src=\"https://raw.githubusercontent.com/landlab/tutorials/master/landlab_header.png\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using the Landlab BiotaEvolver component\n",
+    "\n",
+    "<hr>\n",
+    "<small> For instructions on how to run an interactive iPython notebook, click here: <a href=\"https://github.com/landlab/tutorials/blob/master/README.md\">https://github.com/landlab/tutorials/blob/master/README.md</a></small><br>\n",
+    "<small>For more Landlab tutorials, click here: <a href=\"https://github.com/landlab/landlab/wiki/Tutorials\">https://github.com/landlab/landlab/wiki/Tutorials</a></small>\n",
+    "<hr>\n",
+    "\n",
+    "In this tutorial we will:\n",
+    "* Introduce species into a Landlab model.\n",
+    "* Set the zones in which the species operate.\n",
+    "* Evolve a landscape and the species over time.\n",
+    "* Explore BiotaEvolver output data structures.\n",
+    "\n",
+    "This tutorial loosely follows the fault throw experiment in Lyons et al., in preperation for Earth Surface Dynamics."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Import modules."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from landlab import RasterModelGrid, CLOSED_BOUNDARY, FIXED_VALUE_BOUNDARY\n",
+    "from landlab.components import (BiotaEvolver, FastscapeEroder, FlowRouter,\n",
+    "                                LinearDiffuser, NormalFault)\n",
+    "from landlab.components.biota_macroevolution import Species, Zone\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A bit of magic so that we can plot within this notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from __future__ import print_function\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## This\n",
+    "\n",
+    "Here we will do this."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## That\n",
+    "\n",
+    "Now we will do that."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Identifiers of species\n",
+    "\n",
+    "Species identifiers are automatically generated. They are stored as tuples in species objects. The first element of the identifier tuple is the clade name, and the second element is the species number. For example, the first species of clade A is 0, so the identifier is (A, 0) and the tenth species of this clade is (A, 9).\n",
+    "\n",
+    "### Clade name\n",
+    "\n",
+    "A clade is a group of species that share a common ancestor. Species may share multiple common ancestors, so clades can be defined differently for the same tree. The clade(s) of this tree can be defined in multiple ways:\n",
+    "<img src=\"files/images/clade_definitions.png\">\n",
+    "\n",
+    "In BiotaEvolver, the common ancestor that defines a clade is the least recent common ancestor. This is the species at the root node that is at the base of the phylogenentic tree. The species at the root node is typically introduced to a BiotaEvolver model using the ```introduce_species``` function.\n",
+    "\n",
+    "The clades of the first 26 species are labeled alphabetically A thru Z. The next 26 are labeled AB thru AZ. \n",
+    "\n",
+    "1-26: A thru Z\n",
+    "27-53: AA thru AZ\n",
+    "BA thru BZ\n",
+    "...\n",
+    "AAA thru AAZ\n",
+    "ABA thru ABZ\n",
+    "ACA thru ACZ\n",
+    "\n",
+    "### Species number\n",
+    "\n",
+    "Species are numbered sequently from 0 by clade."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Click here for more <a href=\"https://github.com/landlab/landlab/wiki/Tutorials\">Landlab tutorials</a>"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/biota_evolution/images/clade_definitions.eps b/biota_evolution/images/clade_definitions.eps
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From 012cd55709e4eb5de7e91b1754018bcabf5b36f8 Mon Sep 17 00:00:00 2001
From: nathanlyons <nathan.j.lyons@gmail.com>
Date: Wed, 2 May 2018 23:33:02 -0500
Subject: [PATCH 02/15] presented species numbering

---
 biota_evolution/BiotaEvolver_tutorial.ipynb  |  31 +-
 biota_evolution/biota_evolver_tutorial.ipynb | 102 +++++
 biota_evolution/images/clade_definitions.eps | Bin 1731494 -> 1738554 bytes
 biota_evolution/images/clade_definitions.png | Bin 8510 -> 12267 bytes
 biota_evolution/images/species_number.eps    | Bin 0 -> 1724066 bytes
 biota_evolution/images/species_number.png    | Bin 0 -> 2672 bytes
 biota_evolution/notebook.tex                 | 383 +++++++++++++++++++
 7 files changed, 504 insertions(+), 12 deletions(-)
 create mode 100644 biota_evolution/biota_evolver_tutorial.ipynb
 create mode 100644 biota_evolution/images/species_number.eps
 create mode 100644 biota_evolution/images/species_number.png
 create mode 100644 biota_evolution/notebook.tex

diff --git a/biota_evolution/BiotaEvolver_tutorial.ipynb b/biota_evolution/BiotaEvolver_tutorial.ipynb
index 5a4f708..67d86be 100644
--- a/biota_evolution/BiotaEvolver_tutorial.ipynb
+++ b/biota_evolution/BiotaEvolver_tutorial.ipynb
@@ -88,30 +88,37 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Identifiers of species\n",
+    "## Species identifiers and phylogenetic trees\n",
     "\n",
     "Species identifiers are automatically generated. They are stored as tuples in species objects. The first element of the identifier tuple is the clade name, and the second element is the species number. For example, the first species of clade A is 0, so the identifier is (A, 0) and the tenth species of this clade is (A, 9).\n",
     "\n",
     "### Clade name\n",
     "\n",
-    "A clade is a group of species that share a common ancestor. Species may share multiple common ancestors, so clades can be defined differently for the same tree. The clade(s) of this tree can be defined in multiple ways:\n",
-    "<img src=\"files/images/clade_definitions.png\">\n",
+    "A clade is a group of species that share a common ancestor. Species may share multiple common ancestors, so clades can be defined differently in the same tree. The clade(s) of this example tree can be defined in multiple ways:\n",
     "\n",
-    "In BiotaEvolver, the common ancestor that defines a clade is the least recent common ancestor. This is the species at the root node that is at the base of the phylogenentic tree. The species at the root node is typically introduced to a BiotaEvolver model using the ```introduce_species``` function.\n",
+    "![](images/clade_definitions.png)\n",
     "\n",
-    "The clades of the first 26 species are labeled alphabetically A thru Z. The next 26 are labeled AB thru AZ. \n",
+    "In BiotaEvolver, clades are defined by the root node for the purpose of assigning species identifiers. The species at the root node is:\n",
+    "* the least recent common ancestor of all species in the tree, and\n",
+    "* typically introduced to a BiotaEvolver model using the ```introduce_species``` function.\n",
     "\n",
-    "1-26: A thru Z\n",
-    "27-53: AA thru AZ\n",
-    "BA thru BZ\n",
+    "The clades of the first 26 species are labeled alphabetically from A to Z. The next 26 are labeled AB thru AZ. \n",
+    "\n",
+    "1-26: A to Z\n",
+    "27-53: AA to AZ\n",
+    "BA to BZ\n",
     "...\n",
-    "AAA thru AAZ\n",
-    "ABA thru ABZ\n",
-    "ACA thru ACZ\n",
+    "AAA to AAZ\n",
+    "ABA to ABZ\n",
+    "ACA to ACZ\n",
     "\n",
     "### Species number\n",
     "\n",
-    "Species are numbered sequently from 0 by clade."
+    "Species are numbered sequently by clade beginning with 0.\n",
+    "\n",
+    "In the example tree below, species A0 produced 4 child species. Only one species, B0 exists in clade B because child species were not produced by B0.\n",
+    "\n",
+    "![](images/species_number.png)"
    ]
   },
   {
diff --git a/biota_evolution/biota_evolver_tutorial.ipynb b/biota_evolution/biota_evolver_tutorial.ipynb
new file mode 100644
index 0000000..4609643
--- /dev/null
+++ b/biota_evolution/biota_evolver_tutorial.ipynb
@@ -0,0 +1,102 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"http://landlab.github.io\"><img style=\"float: left\" src=\"https://raw.githubusercontent.com/landlab/tutorials/master/landlab_header.png\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using the Landlab flexure component\n",
+    "\n",
+    "<hr>\n",
+    "<small> For instructions on how to run an interactive iPython notebook, click here: <a href=\"https://github.com/landlab/tutorials/blob/master/README.md\">https://github.com/landlab/tutorials/blob/master/README.md</a></small><br>\n",
+    "<small>For more Landlab tutorials, click here: <a href=\"https://github.com/landlab/landlab/wiki/Tutorials\">https://github.com/landlab/landlab/wiki/Tutorials</a></small>\n",
+    "<hr>\n",
+    "\n",
+    "In this tutorial we will:\n",
+    "* do this\n",
+    "* do that\n",
+    "* do the other thing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A bit of magic so that we can plot within this notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from __future__ import print_function\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## This\n",
+    "\n",
+    "Here we will do this."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## That\n",
+    "\n",
+    "Now we will do that."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The other thing\n",
+    "\n",
+    "Finally we will do the other thing."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Click here for more <a href=\"https://github.com/landlab/landlab/wiki/Tutorials\">Landlab tutorials</a>"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/biota_evolution/images/clade_definitions.eps b/biota_evolution/images/clade_definitions.eps
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diff --git a/biota_evolution/notebook.tex b/biota_evolution/notebook.tex
new file mode 100644
index 0000000..3ec7fd0
--- /dev/null
+++ b/biota_evolution/notebook.tex
@@ -0,0 +1,383 @@
+
+% Default to the notebook output style
+
+    
+
+
+% Inherit from the specified cell style.
+
+
+
+
+    
+\documentclass[11pt]{article}
+
+    
+    
+    \usepackage[T1]{fontenc}
+    % Nicer default font (+ math font) than Computer Modern for most use cases
+    \usepackage{mathpazo}
+
+    % Basic figure setup, for now with no caption control since it's done
+    % automatically by Pandoc (which extracts ![](path) syntax from Markdown).
+    \usepackage{graphicx}
+    % We will generate all images so they have a width \maxwidth. This means
+    % that they will get their normal width if they fit onto the page, but
+    % are scaled down if they would overflow the margins.
+    \makeatletter
+    \def\maxwidth{\ifdim\Gin@nat@width>\linewidth\linewidth
+    \else\Gin@nat@width\fi}
+    \makeatother
+    \let\Oldincludegraphics\includegraphics
+    % Set max figure width to be 80% of text width, for now hardcoded.
+    \renewcommand{\includegraphics}[1]{\Oldincludegraphics[width=.8\maxwidth]{#1}}
+    % Ensure that by default, figures have no caption (until we provide a
+    % proper Figure object with a Caption API and a way to capture that
+    % in the conversion process - todo).
+    \usepackage{caption}
+    \DeclareCaptionLabelFormat{nolabel}{}
+    \captionsetup{labelformat=nolabel}
+
+    \usepackage{adjustbox} % Used to constrain images to a maximum size 
+    \usepackage{xcolor} % Allow colors to be defined
+    \usepackage{enumerate} % Needed for markdown enumerations to work
+    \usepackage{geometry} % Used to adjust the document margins
+    \usepackage{amsmath} % Equations
+    \usepackage{amssymb} % Equations
+    \usepackage{textcomp} % defines textquotesingle
+    % Hack from http://tex.stackexchange.com/a/47451/13684:
+    \AtBeginDocument{%
+        \def\PYZsq{\textquotesingle}% Upright quotes in Pygmentized code
+    }
+    \usepackage{upquote} % Upright quotes for verbatim code
+    \usepackage{eurosym} % defines \euro
+    \usepackage[mathletters]{ucs} % Extended unicode (utf-8) support
+    \usepackage[utf8x]{inputenc} % Allow utf-8 characters in the tex document
+    \usepackage{fancyvrb} % verbatim replacement that allows latex
+    \usepackage{grffile} % extends the file name processing of package graphics 
+                         % to support a larger range 
+    % The hyperref package gives us a pdf with properly built
+    % internal navigation ('pdf bookmarks' for the table of contents,
+    % internal cross-reference links, web links for URLs, etc.)
+    \usepackage{hyperref}
+    \usepackage{longtable} % longtable support required by pandoc >1.10
+    \usepackage{booktabs}  % table support for pandoc > 1.12.2
+    \usepackage[inline]{enumitem} % IRkernel/repr support (it uses the enumerate* environment)
+    \usepackage[normalem]{ulem} % ulem is needed to support strikethroughs (\sout)
+                                % normalem makes italics be italics, not underlines
+    
+
+    
+    
+    % Colors for the hyperref package
+    \definecolor{urlcolor}{rgb}{0,.145,.698}
+    \definecolor{linkcolor}{rgb}{.71,0.21,0.01}
+    \definecolor{citecolor}{rgb}{.12,.54,.11}
+
+    % ANSI colors
+    \definecolor{ansi-black}{HTML}{3E424D}
+    \definecolor{ansi-black-intense}{HTML}{282C36}
+    \definecolor{ansi-red}{HTML}{E75C58}
+    \definecolor{ansi-red-intense}{HTML}{B22B31}
+    \definecolor{ansi-green}{HTML}{00A250}
+    \definecolor{ansi-green-intense}{HTML}{007427}
+    \definecolor{ansi-yellow}{HTML}{DDB62B}
+    \definecolor{ansi-yellow-intense}{HTML}{B27D12}
+    \definecolor{ansi-blue}{HTML}{208FFB}
+    \definecolor{ansi-blue-intense}{HTML}{0065CA}
+    \definecolor{ansi-magenta}{HTML}{D160C4}
+    \definecolor{ansi-magenta-intense}{HTML}{A03196}
+    \definecolor{ansi-cyan}{HTML}{60C6C8}
+    \definecolor{ansi-cyan-intense}{HTML}{258F8F}
+    \definecolor{ansi-white}{HTML}{C5C1B4}
+    \definecolor{ansi-white-intense}{HTML}{A1A6B2}
+
+    % commands and environments needed by pandoc snippets
+    % extracted from the output of `pandoc -s`
+    \providecommand{\tightlist}{%
+      \setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}
+    \DefineVerbatimEnvironment{Highlighting}{Verbatim}{commandchars=\\\{\}}
+    % Add ',fontsize=\small' for more characters per line
+    \newenvironment{Shaded}{}{}
+    \newcommand{\KeywordTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{\textbf{{#1}}}}
+    \newcommand{\DataTypeTok}[1]{\textcolor[rgb]{0.56,0.13,0.00}{{#1}}}
+    \newcommand{\DecValTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
+    \newcommand{\BaseNTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
+    \newcommand{\FloatTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
+    \newcommand{\CharTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
+    \newcommand{\StringTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
+    \newcommand{\CommentTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textit{{#1}}}}
+    \newcommand{\OtherTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{{#1}}}
+    \newcommand{\AlertTok}[1]{\textcolor[rgb]{1.00,0.00,0.00}{\textbf{{#1}}}}
+    \newcommand{\FunctionTok}[1]{\textcolor[rgb]{0.02,0.16,0.49}{{#1}}}
+    \newcommand{\RegionMarkerTok}[1]{{#1}}
+    \newcommand{\ErrorTok}[1]{\textcolor[rgb]{1.00,0.00,0.00}{\textbf{{#1}}}}
+    \newcommand{\NormalTok}[1]{{#1}}
+    
+    % Additional commands for more recent versions of Pandoc
+    \newcommand{\ConstantTok}[1]{\textcolor[rgb]{0.53,0.00,0.00}{{#1}}}
+    \newcommand{\SpecialCharTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
+    \newcommand{\VerbatimStringTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
+    \newcommand{\SpecialStringTok}[1]{\textcolor[rgb]{0.73,0.40,0.53}{{#1}}}
+    \newcommand{\ImportTok}[1]{{#1}}
+    \newcommand{\DocumentationTok}[1]{\textcolor[rgb]{0.73,0.13,0.13}{\textit{{#1}}}}
+    \newcommand{\AnnotationTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
+    \newcommand{\CommentVarTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
+    \newcommand{\VariableTok}[1]{\textcolor[rgb]{0.10,0.09,0.49}{{#1}}}
+    \newcommand{\ControlFlowTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{\textbf{{#1}}}}
+    \newcommand{\OperatorTok}[1]{\textcolor[rgb]{0.40,0.40,0.40}{{#1}}}
+    \newcommand{\BuiltInTok}[1]{{#1}}
+    \newcommand{\ExtensionTok}[1]{{#1}}
+    \newcommand{\PreprocessorTok}[1]{\textcolor[rgb]{0.74,0.48,0.00}{{#1}}}
+    \newcommand{\AttributeTok}[1]{\textcolor[rgb]{0.49,0.56,0.16}{{#1}}}
+    \newcommand{\InformationTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
+    \newcommand{\WarningTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
+    
+    
+    % Define a nice break command that doesn't care if a line doesn't already
+    % exist.
+    \def\br{\hspace*{\fill} \\* }
+    % Math Jax compatability definitions
+    \def\gt{>}
+    \def\lt{<}
+    % Document parameters
+    \title{BiotaEvolver\_tutorial}
+    
+    
+    
+
+    % Pygments definitions
+    
+\makeatletter
+\def\PY@reset{\let\PY@it=\relax \let\PY@bf=\relax%
+    \let\PY@ul=\relax \let\PY@tc=\relax%
+    \let\PY@bc=\relax \let\PY@ff=\relax}
+\def\PY@tok#1{\csname PY@tok@#1\endcsname}
+\def\PY@toks#1+{\ifx\relax#1\empty\else%
+    \PY@tok{#1}\expandafter\PY@toks\fi}
+\def\PY@do#1{\PY@bc{\PY@tc{\PY@ul{%
+    \PY@it{\PY@bf{\PY@ff{#1}}}}}}}
+\def\PY#1#2{\PY@reset\PY@toks#1+\relax+\PY@do{#2}}
+
+\expandafter\def\csname PY@tok@w\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.73,0.73}{##1}}}
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+\expandafter\def\csname PY@tok@se\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.73,0.40,0.13}{##1}}}
+\expandafter\def\csname PY@tok@sr\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.40,0.53}{##1}}}
+\expandafter\def\csname PY@tok@ss\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}}
+\expandafter\def\csname PY@tok@sx\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
+\expandafter\def\csname PY@tok@m\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
+\expandafter\def\csname PY@tok@gh\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.00,0.50}{##1}}}
+\expandafter\def\csname PY@tok@gu\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.50,0.00,0.50}{##1}}}
+\expandafter\def\csname PY@tok@gd\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.63,0.00,0.00}{##1}}}
+\expandafter\def\csname PY@tok@gi\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.63,0.00}{##1}}}
+\expandafter\def\csname PY@tok@gr\endcsname{\def\PY@tc##1{\textcolor[rgb]{1.00,0.00,0.00}{##1}}}
+\expandafter\def\csname PY@tok@ge\endcsname{\let\PY@it=\textit}
+\expandafter\def\csname PY@tok@gs\endcsname{\let\PY@bf=\textbf}
+\expandafter\def\csname PY@tok@gp\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.00,0.50}{##1}}}
+\expandafter\def\csname PY@tok@go\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.53,0.53,0.53}{##1}}}
+\expandafter\def\csname PY@tok@gt\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.27,0.87}{##1}}}
+\expandafter\def\csname PY@tok@err\endcsname{\def\PY@bc##1{\setlength{\fboxsep}{0pt}\fcolorbox[rgb]{1.00,0.00,0.00}{1,1,1}{\strut ##1}}}
+\expandafter\def\csname PY@tok@kc\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
+\expandafter\def\csname PY@tok@kd\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
+\expandafter\def\csname PY@tok@kn\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
+\expandafter\def\csname PY@tok@kr\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
+\expandafter\def\csname PY@tok@bp\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
+\expandafter\def\csname PY@tok@fm\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.00,1.00}{##1}}}
+\expandafter\def\csname PY@tok@vc\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}}
+\expandafter\def\csname PY@tok@vg\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}}
+\expandafter\def\csname PY@tok@vi\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}}
+\expandafter\def\csname PY@tok@vm\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}}
+\expandafter\def\csname PY@tok@sa\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
+\expandafter\def\csname PY@tok@sb\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
+\expandafter\def\csname PY@tok@sc\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
+\expandafter\def\csname PY@tok@dl\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
+\expandafter\def\csname PY@tok@s2\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
+\expandafter\def\csname PY@tok@sh\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
+\expandafter\def\csname PY@tok@s1\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
+\expandafter\def\csname PY@tok@mb\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
+\expandafter\def\csname PY@tok@mf\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
+\expandafter\def\csname PY@tok@mh\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
+\expandafter\def\csname PY@tok@mi\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
+\expandafter\def\csname PY@tok@il\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
+\expandafter\def\csname PY@tok@mo\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
+\expandafter\def\csname PY@tok@ch\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
+\expandafter\def\csname PY@tok@cm\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
+\expandafter\def\csname PY@tok@cpf\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
+\expandafter\def\csname PY@tok@c1\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
+\expandafter\def\csname PY@tok@cs\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
+
+\def\PYZbs{\char`\\}
+\def\PYZus{\char`\_}
+\def\PYZob{\char`\{}
+\def\PYZcb{\char`\}}
+\def\PYZca{\char`\^}
+\def\PYZam{\char`\&}
+\def\PYZlt{\char`\<}
+\def\PYZgt{\char`\>}
+\def\PYZsh{\char`\#}
+\def\PYZpc{\char`\%}
+\def\PYZdl{\char`\$}
+\def\PYZhy{\char`\-}
+\def\PYZsq{\char`\'}
+\def\PYZdq{\char`\"}
+\def\PYZti{\char`\~}
+% for compatibility with earlier versions
+\def\PYZat{@}
+\def\PYZlb{[}
+\def\PYZrb{]}
+\makeatother
+
+
+    % Exact colors from NB
+    \definecolor{incolor}{rgb}{0.0, 0.0, 0.5}
+    \definecolor{outcolor}{rgb}{0.545, 0.0, 0.0}
+
+
+
+    
+    % Prevent overflowing lines due to hard-to-break entities
+    \sloppy 
+    % Setup hyperref package
+    \hypersetup{
+      breaklinks=true,  % so long urls are correctly broken across lines
+      colorlinks=true,
+      urlcolor=urlcolor,
+      linkcolor=linkcolor,
+      citecolor=citecolor,
+      }
+    % Slightly bigger margins than the latex defaults
+    
+    \geometry{verbose,tmargin=1in,bmargin=1in,lmargin=1in,rmargin=1in}
+    
+    
+
+    \begin{document}
+    
+    
+    \maketitle
+    
+    
+
+    
+    
+
+    \section{Using the Landlab BiotaEvolver
+component}\label{using-the-landlab-biotaevolver-component}
+
+ For instructions on how to run an interactive iPython notebook, click
+here: https://github.com/landlab/tutorials/blob/master/README.md For
+more Landlab tutorials, click here:
+https://github.com/landlab/landlab/wiki/Tutorials
+
+In this tutorial we will: * Introduce species into a Landlab model. *
+Set the zones in which the species operate. * Evolve a landscape and the
+species over time. * Explore BiotaEvolver output data structures.
+
+This tutorial loosely follows the fault throw experiment in Lyons et
+al., in preperation for Earth Surface Dynamics.
+
+    Import modules.
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}1}]:} \PY{k+kn}{from} \PY{n+nn}{landlab} \PY{k}{import} \PY{n}{RasterModelGrid}\PY{p}{,} \PY{n}{CLOSED\PYZus{}BOUNDARY}\PY{p}{,} \PY{n}{FIXED\PYZus{}VALUE\PYZus{}BOUNDARY}
+        \PY{k+kn}{from} \PY{n+nn}{landlab}\PY{n+nn}{.}\PY{n+nn}{components} \PY{k}{import} \PY{p}{(}\PY{n}{BiotaEvolver}\PY{p}{,} \PY{n}{FastscapeEroder}\PY{p}{,} \PY{n}{FlowRouter}\PY{p}{,}
+                                        \PY{n}{LinearDiffuser}\PY{p}{,} \PY{n}{NormalFault}\PY{p}{)}
+        \PY{k+kn}{from} \PY{n+nn}{landlab}\PY{n+nn}{.}\PY{n+nn}{components}\PY{n+nn}{.}\PY{n+nn}{biota\PYZus{}macroevolution} \PY{k}{import} \PY{n}{Species}\PY{p}{,} \PY{n}{Zone}
+        \PY{k+kn}{import} \PY{n+nn}{numpy} \PY{k}{as} \PY{n+nn}{np}
+\end{Verbatim}
+
+
+    A bit of magic so that we can plot within this notebook.
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor} }]:} \PY{k+kn}{from} \PY{n+nn}{\PYZus{}\PYZus{}future\PYZus{}\PYZus{}} \PY{k}{import} \PY{n}{print\PYZus{}function}
+        \PY{o}{\PYZpc{}}\PY{k}{matplotlib} inline
+\end{Verbatim}
+
+
+    \subsection{This}\label{this}
+
+Here we will do this.
+
+    \subsection{That}\label{that}
+
+Now we will do that.
+
+    \subsection{Species identifiers and phylogenetic
+trees}\label{species-identifiers-and-phylogenetic-trees}
+
+Species identifiers are automatically generated. They are stored as
+tuples in species objects. The first element of the identifier tuple is
+the clade name, and the second element is the species number. For
+example, the first species of clade A is 0, so the identifier is (A, 0)
+and the tenth species of this clade is (A, 9).
+
+\subsubsection{Clade name}\label{clade-name}
+
+A clade is a group of species that share a common ancestor. Species may
+share multiple common ancestors, so clades can be defined differently in
+the same tree. The clade(s) of this example tree can be defined in
+multiple ways:
+
+\begin{figure}
+\centering
+\includegraphics{images/clade_definitions.png}
+\caption{}
+\end{figure}
+
+In BiotaEvolver, clades are defined by the root node for the purpose of
+assigning species identifiers. The species at the root node is: * the
+least recent common ancestor of all species in the tree, and * typically
+introduced to a BiotaEvolver model using the \texttt{introduce\_species}
+function.
+
+The clades of the first 26 species are labeled alphabetically A thru Z.
+The next 26 are labeled AB thru AZ.
+
+1-26: A thru Z 27-53: AA thru AZ BA thru BZ ... AAA thru AAZ ABA thru
+ABZ ACA thru ACZ
+
+\subsubsection{Species number}\label{species-number}
+
+Species are numbered sequently by clade beginning with 0.
+
+In the example tree below, species A0 produced 4 child species. Only one
+species, B0 exists in clade B because no child species were produced by
+B0.
+
+\begin{figure}
+\centering
+\includegraphics{images/species_number.png}
+\caption{}
+\end{figure}
+
+    \subsubsection{Click here for more Landlab
+tutorials}\label{click-here-for-more-landlab-tutorials}
+
+
+    % Add a bibliography block to the postdoc
+    
+    
+    
+    \end{document}

From d6791efcaf4422e5b8df925a9a50924ab5e4d0cb Mon Sep 17 00:00:00 2001
From: nathanlyons <nathan.j.lyons@gmail.com>
Date: Thu, 3 May 2018 17:08:02 -0500
Subject: [PATCH 03/15] added steady state and faulting code blocks

---
 biota_evolution/BiotaEvolver_tutorial.ipynb  | 230 ++++++++++-
 biota_evolution/biota_evolver_tutorial.ipynb | 102 -----
 biota_evolution/images/clade_definitions.eps | Bin 1738554 -> 0 bytes
 biota_evolution/images/clade_definitions.png | Bin 12267 -> 12951 bytes
 biota_evolution/images/species_number.eps    | Bin 1724066 -> 0 bytes
 biota_evolution/images/species_number.png    | Bin 2672 -> 2512 bytes
 biota_evolution/notebook.tex                 | 383 -------------------
 7 files changed, 215 insertions(+), 500 deletions(-)
 delete mode 100644 biota_evolution/biota_evolver_tutorial.ipynb
 delete mode 100644 biota_evolution/images/clade_definitions.eps
 delete mode 100644 biota_evolution/images/species_number.eps
 delete mode 100644 biota_evolution/notebook.tex

diff --git a/biota_evolution/BiotaEvolver_tutorial.ipynb b/biota_evolution/BiotaEvolver_tutorial.ipynb
index 67d86be..d9a8cab 100644
--- a/biota_evolution/BiotaEvolver_tutorial.ipynb
+++ b/biota_evolution/BiotaEvolver_tutorial.ipynb
@@ -24,66 +24,252 @@
     "* Evolve a landscape and the species over time.\n",
     "* Explore BiotaEvolver output data structures.\n",
     "\n",
-    "This tutorial loosely follows the fault throw experiment in Lyons et al., in preperation for Earth Surface Dynamics."
+    "The configuration of the model in this tutorial loosely follows the fault throw experiment in Lyons et al., in preperation for Earth Surface Dynamics."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Import modules."
+    "Import modules and set the environment to show plots inline in this notebook."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
     "from landlab import RasterModelGrid, CLOSED_BOUNDARY, FIXED_VALUE_BOUNDARY\n",
     "from landlab.components import (BiotaEvolver, FastscapeEroder, FlowRouter,\n",
-    "                                LinearDiffuser, NormalFault)\n",
+    "                                LinearDiffuser)\n",
     "from landlab.components.biota_macroevolution import Species, Zone\n",
-    "import numpy as np"
+    "import numpy as np\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a model grid with steady state topography\n",
+    "\n",
+    "Here the topography of a Landlab model grid brought to topographic steady state."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a grid with random initial topography."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dx = 200\n",
+    "nrows = 100\n",
+    "ncols = 20\n",
+    "mg_ss = RasterModelGrid(nrows, ncols, dx)\n",
+    "mg_ss.set_status_at_node_on_edges(right=CLOSED_BOUNDARY,\n",
+    "                                  top=FIXED_VALUE_BOUNDARY,\n",
+    "                                  left=CLOSED_BOUNDARY,\n",
+    "                                  bottom=FIXED_VALUE_BOUNDARY)\n",
+    "z_ss = mg_ss.add_zeros('node', 'topographic__elevation')\n",
+    "np.random.seed(500)\n",
+    "z_ss += np.random.rand(z_ss.size)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "A bit of magic so that we can plot within this notebook."
+    "Set parameters and initialize components."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "from __future__ import print_function\n",
-    "%matplotlib inline"
+    "dt = 1000\n",
+    "total_time = int(4e6)\n",
+    "t = range(0, total_time, dt)\n",
+    "U = 7e-5\n",
+    "uplift_per_step = U * dt\n",
+    "K_sp = 10e-5\n",
+    "m_sp = 0.5\n",
+    "n_sp = 1\n",
+    "k_d = 0.0001\n",
+    "\n",
+    "fr = FlowRouter(mg_ss)\n",
+    "sp = FastscapeEroder(mg_ss, K_sp=K_sp, m_sp=m_sp, n_sp=n_sp)\n",
+    "lf = LinearDiffuser(mg_ss, linear_diffusivity=k_d, deposit=False)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## This\n",
+    "Run the model until topography reaches steady state (ss)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for t_i in t:\n",
+    "    z_ss[mg_ss.core_nodes] += uplift_per_step\n",
     "\n",
-    "Here we will do this."
+    "    fr.run_one_step()\n",
+    "    sp.run_one_step(dt)\n",
+    "    lf.run_one_step(dt)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## That\n",
+    "## Fault the grid and evolve species"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a copy of the steady state grid."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mg = deepcopy(mg_ss)\n",
+    "z = mg.at_node['topographic__elevation']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the fault."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grid_width = mg.number_of_node_columns * mg.dx\n",
+    "x_of_fault = grid_width * 0.5\n",
+    "core_mask = mg.node_is_core()\n",
+    "upthown_block_mask = mg.x_of_node >= x_of_fault\n",
+    "upthown_block_mask = np.all([core_mask, upthown_block_mask], 0)\n",
+    "z[upthown_block_mask] += 100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Initialize components."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fr = FlowRouter(mg)\n",
+    "sp = FastscapeEroder(mg, K_sp=K_sp, m_sp=m_sp, n_sp=n_sp)\n",
+    "lf = LinearDiffuser(mg, linear_diffusivity=k_d, deposit=False)\n",
+    "\n",
+    "be = BiotaEvolver(mg)\n",
+    "dt_be = 1e5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Populate BiotaEvolver zones with species.\n",
+    "\n",
+    "Zones are portions of a model grid. A Species object exists throughout a zone. At each timestep, the spatial intersection of the zones at the prior time and current time are identified and typed. For example, a zone in the prior time intersects two zones in the current time. This zone path is typed one-to-many.\n",
+    "\n",
+    "Macroevolutionary processes are carried out by the intersection of zones over time. The connectivity of zones in BiotaEvolver are called 'paths', and the relationship of zones is described by the path type. Not all combinations of none, one, and many are considered at the time of this tutorial. All none-to-n and some many-to-n are not considered.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "min_stream_area = 1e6\n",
+    "zones = Zone.get_zones_with_area_threshold(mg, min_stream_area)\n",
+    "for zone in zones:\n",
+    "    new_species = Species(0, zone)\n",
+    "    be.introduce_species(new_species, 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "total_time = 5e5\n",
+    "nt = int(total_time // dt)\n",
+    "for time_i in range(nt):\n",
+    "    # Uniformly uplift.\n",
+    "    z[mg.core_nodes] += uplift_per_step\n",
+    "\n",
+    "    # Increment components.\n",
+    "    fr.run_one_step()\n",
+    "    sp.run_one_step(dt)\n",
+    "    lf.run_one_step(dt)\n",
+    "\n",
+    "    elapsed_time = dt * (time_i + 1)\n",
     "\n",
-    "Now we will do that."
+    "    if elapsed_time % dt_be == 0:\n",
+    "        # Run BiotaEvolver with updated zones.\n",
+    "        print('BiotaEvolver time', elapsed_time, '---------------------------')\n",
+    "        zones = Zone.get_zones_with_area_threshold(mg, min_stream_area)\n",
+    "        be.run_one_step(elapsed_time, zones)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -121,6 +307,20 @@
     "![](images/species_number.png)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/biota_evolution/biota_evolver_tutorial.ipynb b/biota_evolution/biota_evolver_tutorial.ipynb
deleted file mode 100644
index 4609643..0000000
--- a/biota_evolution/biota_evolver_tutorial.ipynb
+++ /dev/null
@@ -1,102 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a href=\"http://landlab.github.io\"><img style=\"float: left\" src=\"https://raw.githubusercontent.com/landlab/tutorials/master/landlab_header.png\"></a>\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Using the Landlab flexure component\n",
-    "\n",
-    "<hr>\n",
-    "<small> For instructions on how to run an interactive iPython notebook, click here: <a href=\"https://github.com/landlab/tutorials/blob/master/README.md\">https://github.com/landlab/tutorials/blob/master/README.md</a></small><br>\n",
-    "<small>For more Landlab tutorials, click here: <a href=\"https://github.com/landlab/landlab/wiki/Tutorials\">https://github.com/landlab/landlab/wiki/Tutorials</a></small>\n",
-    "<hr>\n",
-    "\n",
-    "In this tutorial we will:\n",
-    "* do this\n",
-    "* do that\n",
-    "* do the other thing"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A bit of magic so that we can plot within this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from __future__ import print_function\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## This\n",
-    "\n",
-    "Here we will do this."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## That\n",
-    "\n",
-    "Now we will do that."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## The other thing\n",
-    "\n",
-    "Finally we will do the other thing."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Click here for more <a href=\"https://github.com/landlab/landlab/wiki/Tutorials\">Landlab tutorials</a>"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/biota_evolution/images/clade_definitions.eps b/biota_evolution/images/clade_definitions.eps
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diff --git a/biota_evolution/images/species_number.png b/biota_evolution/images/species_number.png
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diff --git a/biota_evolution/notebook.tex b/biota_evolution/notebook.tex
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-
-    \section{Using the Landlab BiotaEvolver
-component}\label{using-the-landlab-biotaevolver-component}
-
- For instructions on how to run an interactive iPython notebook, click
-here: https://github.com/landlab/tutorials/blob/master/README.md For
-more Landlab tutorials, click here:
-https://github.com/landlab/landlab/wiki/Tutorials
-
-In this tutorial we will: * Introduce species into a Landlab model. *
-Set the zones in which the species operate. * Evolve a landscape and the
-species over time. * Explore BiotaEvolver output data structures.
-
-This tutorial loosely follows the fault throw experiment in Lyons et
-al., in preperation for Earth Surface Dynamics.
-
-    Import modules.
-
-    \begin{Verbatim}[commandchars=\\\{\}]
-{\color{incolor}In [{\color{incolor}1}]:} \PY{k+kn}{from} \PY{n+nn}{landlab} \PY{k}{import} \PY{n}{RasterModelGrid}\PY{p}{,} \PY{n}{CLOSED\PYZus{}BOUNDARY}\PY{p}{,} \PY{n}{FIXED\PYZus{}VALUE\PYZus{}BOUNDARY}
-        \PY{k+kn}{from} \PY{n+nn}{landlab}\PY{n+nn}{.}\PY{n+nn}{components} \PY{k}{import} \PY{p}{(}\PY{n}{BiotaEvolver}\PY{p}{,} \PY{n}{FastscapeEroder}\PY{p}{,} \PY{n}{FlowRouter}\PY{p}{,}
-                                        \PY{n}{LinearDiffuser}\PY{p}{,} \PY{n}{NormalFault}\PY{p}{)}
-        \PY{k+kn}{from} \PY{n+nn}{landlab}\PY{n+nn}{.}\PY{n+nn}{components}\PY{n+nn}{.}\PY{n+nn}{biota\PYZus{}macroevolution} \PY{k}{import} \PY{n}{Species}\PY{p}{,} \PY{n}{Zone}
-        \PY{k+kn}{import} \PY{n+nn}{numpy} \PY{k}{as} \PY{n+nn}{np}
-\end{Verbatim}
-
-
-    A bit of magic so that we can plot within this notebook.
-
-    \begin{Verbatim}[commandchars=\\\{\}]
-{\color{incolor}In [{\color{incolor} }]:} \PY{k+kn}{from} \PY{n+nn}{\PYZus{}\PYZus{}future\PYZus{}\PYZus{}} \PY{k}{import} \PY{n}{print\PYZus{}function}
-        \PY{o}{\PYZpc{}}\PY{k}{matplotlib} inline
-\end{Verbatim}
-
-
-    \subsection{This}\label{this}
-
-Here we will do this.
-
-    \subsection{That}\label{that}
-
-Now we will do that.
-
-    \subsection{Species identifiers and phylogenetic
-trees}\label{species-identifiers-and-phylogenetic-trees}
-
-Species identifiers are automatically generated. They are stored as
-tuples in species objects. The first element of the identifier tuple is
-the clade name, and the second element is the species number. For
-example, the first species of clade A is 0, so the identifier is (A, 0)
-and the tenth species of this clade is (A, 9).
-
-\subsubsection{Clade name}\label{clade-name}
-
-A clade is a group of species that share a common ancestor. Species may
-share multiple common ancestors, so clades can be defined differently in
-the same tree. The clade(s) of this example tree can be defined in
-multiple ways:
-
-\begin{figure}
-\centering
-\includegraphics{images/clade_definitions.png}
-\caption{}
-\end{figure}
-
-In BiotaEvolver, clades are defined by the root node for the purpose of
-assigning species identifiers. The species at the root node is: * the
-least recent common ancestor of all species in the tree, and * typically
-introduced to a BiotaEvolver model using the \texttt{introduce\_species}
-function.
-
-The clades of the first 26 species are labeled alphabetically A thru Z.
-The next 26 are labeled AB thru AZ.
-
-1-26: A thru Z 27-53: AA thru AZ BA thru BZ ... AAA thru AAZ ABA thru
-ABZ ACA thru ACZ
-
-\subsubsection{Species number}\label{species-number}
-
-Species are numbered sequently by clade beginning with 0.
-
-In the example tree below, species A0 produced 4 child species. Only one
-species, B0 exists in clade B because no child species were produced by
-B0.
-
-\begin{figure}
-\centering
-\includegraphics{images/species_number.png}
-\caption{}
-\end{figure}
-
-    \subsubsection{Click here for more Landlab
-tutorials}\label{click-here-for-more-landlab-tutorials}
-
-
-    % Add a bibliography block to the postdoc
-    
-    
-    
-    \end{document}

From ec22336b2a002448f20a7f9c1fedbd4bd447a315 Mon Sep 17 00:00:00 2001
From: nathanlyons <nathan.j.lyons@gmail.com>
Date: Thu, 3 May 2018 23:47:39 -0500
Subject: [PATCH 04/15] zones explained in a table

---
 biota_evolution/BiotaEvolver_tutorial.ipynb   |  30 +-
 biota_evolution/images/Untitled-1.png         | Bin 0 -> 515 bytes
 biota_evolution/images/zone__later_time.png   | Bin 0 -> 572 bytes
 biota_evolution/images/zone__many_to_many.png | Bin 0 -> 1897 bytes
 biota_evolution/images/zone__many_to_one.png  | Bin 0 -> 1431 bytes
 biota_evolution/images/zone__one_to_many.png  | Bin 0 -> 1459 bytes
 biota_evolution/images/zone__one_to_none.png  | Bin 0 -> 515 bytes
 biota_evolution/images/zone__one_to_one.png   | Bin 0 -> 1035 bytes
 biota_evolution/notebook.tex                  | 565 ++++++++++++++++++
 9 files changed, 592 insertions(+), 3 deletions(-)
 create mode 100644 biota_evolution/images/Untitled-1.png
 create mode 100644 biota_evolution/images/zone__later_time.png
 create mode 100644 biota_evolution/images/zone__many_to_many.png
 create mode 100644 biota_evolution/images/zone__many_to_one.png
 create mode 100644 biota_evolution/images/zone__one_to_many.png
 create mode 100644 biota_evolution/images/zone__one_to_none.png
 create mode 100644 biota_evolution/images/zone__one_to_one.png
 create mode 100644 biota_evolution/notebook.tex

diff --git a/biota_evolution/BiotaEvolver_tutorial.ipynb b/biota_evolution/BiotaEvolver_tutorial.ipynb
index d9a8cab..e7573df 100644
--- a/biota_evolution/BiotaEvolver_tutorial.ipynb
+++ b/biota_evolution/BiotaEvolver_tutorial.ipynb
@@ -207,14 +207,38 @@
     "\n",
     "Zones are portions of a model grid. A Species object exists throughout a zone. At each timestep, the spatial intersection of the zones at the prior time and current time are identified and typed. For example, a zone in the prior time intersects two zones in the current time. This zone path is typed one-to-many.\n",
     "\n",
-    "Macroevolutionary processes are carried out by the intersection of zones over time. The connectivity of zones in BiotaEvolver are called 'paths', and the relationship of zones is described by the path type. Not all combinations of none, one, and many are considered at the time of this tutorial. All none-to-n and some many-to-n are not considered.  "
+    "Macroevolutionary processes are carried out by the intersection of zones over time. The connectivity of zones in BiotaEvolver are called 'paths', and the relationship of zones is described by the path type. Not all combinations of none, one, and many are considered at the time of this tutorial. All none-to-n and some many-to-n are not considered. \n",
+    "\n",
+    "<img style=\"float: left;\" src=\"images/zone__one_to_none.png\"> &nbsp; zone<sub>*t*</sub>: a zone in time, *t*.<br>\n",
+    "\n",
+    "<img style=\"float: left;\" src=\"images/zone__later_time.png\"> &nbsp; zone<sub>*t*+1</sub>: a zone in the time following *t*.\n",
+    "\n",
+    "<center>path type</center> | <center>graphical<br>represention</center> | <center>zone temporal connectivity</center> | <center>macroevolution implications</center>\n",
+    "--- | --- | --- | ---\n",
+    "one-to-none | ![](images/zone__one_to_none.png) | zone<sub>*t*</sub> does not intersect a zone in *t*+1. | The species in the zone of the earlier time will go extinct. \n",
+    "one-to-one | ![](images/zone__one_to_one.png) | zone<sub>*t*</sub> intersects zone<sub>*t*+1</sub>. | The species in the zone of the earlier time will relocate to the zone of the later time.\n",
+    "one-to-many | ![](images/zone__one_to_many.png) | zone<sub>*t*</sub> intersects multiple zones in *t*+1. | The species in the zone of the earlier time disperses across the multiple zones in the later time. The species in the zones of the later time are geographical disconnected, thus speciation occurs.\n",
+    "many-to-one | ![](images/zone__many_to_one.png) | Multiple zones in *t* intersect zone<sub>*t*+1</sub>. | Species density increases locally.\n",
+    "many-to-many | ![](images/zone__many_to_many.png) | Multiple zones in *t* intersect multiple zones in *t*+1. | Species distribution shifts. Speciation occurs where the zone of a species at *t* intersects more than one zone at *t*+1.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'Zone' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-1-53c90586ee9c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mmin_stream_area\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1e6\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mzones\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mZone\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_zones_with_area_threshold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_stream_area\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mzone\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzones\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mnew_species\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSpecies\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mbe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintroduce_species\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_species\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'Zone' is not defined"
+     ]
+    }
+   ],
    "source": [
     "min_stream_area = 1e6\n",
     "zones = Zone.get_zones_with_area_threshold(mg, min_stream_area)\n",
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diff --git a/biota_evolution/notebook.tex b/biota_evolution/notebook.tex
new file mode 100644
index 0000000..618cd06
--- /dev/null
+++ b/biota_evolution/notebook.tex
@@ -0,0 +1,565 @@
+
+% Default to the notebook output style
+
+    
+
+
+% Inherit from the specified cell style.
+
+
+
+
+    
+\documentclass[11pt]{article}
+
+    
+    
+    \usepackage[T1]{fontenc}
+    % Nicer default font (+ math font) than Computer Modern for most use cases
+    \usepackage{mathpazo}
+
+    % Basic figure setup, for now with no caption control since it's done
+    % automatically by Pandoc (which extracts ![](path) syntax from Markdown).
+    \usepackage{graphicx}
+    % We will generate all images so they have a width \maxwidth. This means
+    % that they will get their normal width if they fit onto the page, but
+    % are scaled down if they would overflow the margins.
+    \makeatletter
+    \def\maxwidth{\ifdim\Gin@nat@width>\linewidth\linewidth
+    \else\Gin@nat@width\fi}
+    \makeatother
+    \let\Oldincludegraphics\includegraphics
+    % Set max figure width to be 80% of text width, for now hardcoded.
+    \renewcommand{\includegraphics}[1]{\Oldincludegraphics[width=.8\maxwidth]{#1}}
+    % Ensure that by default, figures have no caption (until we provide a
+    % proper Figure object with a Caption API and a way to capture that
+    % in the conversion process - todo).
+    \usepackage{caption}
+    \DeclareCaptionLabelFormat{nolabel}{}
+    \captionsetup{labelformat=nolabel}
+
+    \usepackage{adjustbox} % Used to constrain images to a maximum size 
+    \usepackage{xcolor} % Allow colors to be defined
+    \usepackage{enumerate} % Needed for markdown enumerations to work
+    \usepackage{geometry} % Used to adjust the document margins
+    \usepackage{amsmath} % Equations
+    \usepackage{amssymb} % Equations
+    \usepackage{textcomp} % defines textquotesingle
+    % Hack from http://tex.stackexchange.com/a/47451/13684:
+    \AtBeginDocument{%
+        \def\PYZsq{\textquotesingle}% Upright quotes in Pygmentized code
+    }
+    \usepackage{upquote} % Upright quotes for verbatim code
+    \usepackage{eurosym} % defines \euro
+    \usepackage[mathletters]{ucs} % Extended unicode (utf-8) support
+    \usepackage[utf8x]{inputenc} % Allow utf-8 characters in the tex document
+    \usepackage{fancyvrb} % verbatim replacement that allows latex
+    \usepackage{grffile} % extends the file name processing of package graphics 
+                         % to support a larger range 
+    % The hyperref package gives us a pdf with properly built
+    % internal navigation ('pdf bookmarks' for the table of contents,
+    % internal cross-reference links, web links for URLs, etc.)
+    \usepackage{hyperref}
+    \usepackage{longtable} % longtable support required by pandoc >1.10
+    \usepackage{booktabs}  % table support for pandoc > 1.12.2
+    \usepackage[inline]{enumitem} % IRkernel/repr support (it uses the enumerate* environment)
+    \usepackage[normalem]{ulem} % ulem is needed to support strikethroughs (\sout)
+                                % normalem makes italics be italics, not underlines
+    
+
+    
+    
+    % Colors for the hyperref package
+    \definecolor{urlcolor}{rgb}{0,.145,.698}
+    \definecolor{linkcolor}{rgb}{.71,0.21,0.01}
+    \definecolor{citecolor}{rgb}{.12,.54,.11}
+
+    % ANSI colors
+    \definecolor{ansi-black}{HTML}{3E424D}
+    \definecolor{ansi-black-intense}{HTML}{282C36}
+    \definecolor{ansi-red}{HTML}{E75C58}
+    \definecolor{ansi-red-intense}{HTML}{B22B31}
+    \definecolor{ansi-green}{HTML}{00A250}
+    \definecolor{ansi-green-intense}{HTML}{007427}
+    \definecolor{ansi-yellow}{HTML}{DDB62B}
+    \definecolor{ansi-yellow-intense}{HTML}{B27D12}
+    \definecolor{ansi-blue}{HTML}{208FFB}
+    \definecolor{ansi-blue-intense}{HTML}{0065CA}
+    \definecolor{ansi-magenta}{HTML}{D160C4}
+    \definecolor{ansi-magenta-intense}{HTML}{A03196}
+    \definecolor{ansi-cyan}{HTML}{60C6C8}
+    \definecolor{ansi-cyan-intense}{HTML}{258F8F}
+    \definecolor{ansi-white}{HTML}{C5C1B4}
+    \definecolor{ansi-white-intense}{HTML}{A1A6B2}
+
+    % commands and environments needed by pandoc snippets
+    % extracted from the output of `pandoc -s`
+    \providecommand{\tightlist}{%
+      \setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}
+    \DefineVerbatimEnvironment{Highlighting}{Verbatim}{commandchars=\\\{\}}
+    % Add ',fontsize=\small' for more characters per line
+    \newenvironment{Shaded}{}{}
+    \newcommand{\KeywordTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{\textbf{{#1}}}}
+    \newcommand{\DataTypeTok}[1]{\textcolor[rgb]{0.56,0.13,0.00}{{#1}}}
+    \newcommand{\DecValTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
+    \newcommand{\BaseNTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
+    \newcommand{\FloatTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
+    \newcommand{\CharTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
+    \newcommand{\StringTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
+    \newcommand{\CommentTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textit{{#1}}}}
+    \newcommand{\OtherTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{{#1}}}
+    \newcommand{\AlertTok}[1]{\textcolor[rgb]{1.00,0.00,0.00}{\textbf{{#1}}}}
+    \newcommand{\FunctionTok}[1]{\textcolor[rgb]{0.02,0.16,0.49}{{#1}}}
+    \newcommand{\RegionMarkerTok}[1]{{#1}}
+    \newcommand{\ErrorTok}[1]{\textcolor[rgb]{1.00,0.00,0.00}{\textbf{{#1}}}}
+    \newcommand{\NormalTok}[1]{{#1}}
+    
+    % Additional commands for more recent versions of Pandoc
+    \newcommand{\ConstantTok}[1]{\textcolor[rgb]{0.53,0.00,0.00}{{#1}}}
+    \newcommand{\SpecialCharTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
+    \newcommand{\VerbatimStringTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
+    \newcommand{\SpecialStringTok}[1]{\textcolor[rgb]{0.73,0.40,0.53}{{#1}}}
+    \newcommand{\ImportTok}[1]{{#1}}
+    \newcommand{\DocumentationTok}[1]{\textcolor[rgb]{0.73,0.13,0.13}{\textit{{#1}}}}
+    \newcommand{\AnnotationTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
+    \newcommand{\CommentVarTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
+    \newcommand{\VariableTok}[1]{\textcolor[rgb]{0.10,0.09,0.49}{{#1}}}
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+    \newcommand{\WarningTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
+    
+    
+    % Define a nice break command that doesn't care if a line doesn't already
+    % exist.
+    \def\br{\hspace*{\fill} \\* }
+    % Math Jax compatability definitions
+    \def\gt{>}
+    \def\lt{<}
+    % Document parameters
+    \title{BiotaEvolver\_tutorial}
+    
+    
+    
+
+    % Pygments definitions
+    
+\makeatletter
+\def\PY@reset{\let\PY@it=\relax \let\PY@bf=\relax%
+    \let\PY@ul=\relax \let\PY@tc=\relax%
+    \let\PY@bc=\relax \let\PY@ff=\relax}
+\def\PY@tok#1{\csname PY@tok@#1\endcsname}
+\def\PY@toks#1+{\ifx\relax#1\empty\else%
+    \PY@tok{#1}\expandafter\PY@toks\fi}
+\def\PY@do#1{\PY@bc{\PY@tc{\PY@ul{%
+    \PY@it{\PY@bf{\PY@ff{#1}}}}}}}
+\def\PY#1#2{\PY@reset\PY@toks#1+\relax+\PY@do{#2}}
+
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+\expandafter\def\csname PY@tok@gu\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.50,0.00,0.50}{##1}}}
+\expandafter\def\csname PY@tok@gd\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.63,0.00,0.00}{##1}}}
+\expandafter\def\csname PY@tok@gi\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.63,0.00}{##1}}}
+\expandafter\def\csname PY@tok@gr\endcsname{\def\PY@tc##1{\textcolor[rgb]{1.00,0.00,0.00}{##1}}}
+\expandafter\def\csname PY@tok@ge\endcsname{\let\PY@it=\textit}
+\expandafter\def\csname PY@tok@gs\endcsname{\let\PY@bf=\textbf}
+\expandafter\def\csname PY@tok@gp\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.00,0.50}{##1}}}
+\expandafter\def\csname PY@tok@go\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.53,0.53,0.53}{##1}}}
+\expandafter\def\csname PY@tok@gt\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.27,0.87}{##1}}}
+\expandafter\def\csname PY@tok@err\endcsname{\def\PY@bc##1{\setlength{\fboxsep}{0pt}\fcolorbox[rgb]{1.00,0.00,0.00}{1,1,1}{\strut ##1}}}
+\expandafter\def\csname PY@tok@kc\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
+\expandafter\def\csname PY@tok@kd\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
+\expandafter\def\csname PY@tok@kn\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
+\expandafter\def\csname PY@tok@kr\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
+\expandafter\def\csname PY@tok@bp\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
+\expandafter\def\csname PY@tok@fm\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.00,1.00}{##1}}}
+\expandafter\def\csname PY@tok@vc\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}}
+\expandafter\def\csname PY@tok@vg\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}}
+\expandafter\def\csname PY@tok@vi\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}}
+\expandafter\def\csname PY@tok@vm\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}}
+\expandafter\def\csname PY@tok@sa\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
+\expandafter\def\csname PY@tok@sb\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
+\expandafter\def\csname PY@tok@sc\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
+\expandafter\def\csname PY@tok@dl\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
+\expandafter\def\csname PY@tok@s2\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
+\expandafter\def\csname PY@tok@sh\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
+\expandafter\def\csname PY@tok@s1\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
+\expandafter\def\csname PY@tok@mb\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
+\expandafter\def\csname PY@tok@mf\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
+\expandafter\def\csname PY@tok@mh\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
+\expandafter\def\csname PY@tok@mi\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
+\expandafter\def\csname PY@tok@il\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
+\expandafter\def\csname PY@tok@mo\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
+\expandafter\def\csname PY@tok@ch\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
+\expandafter\def\csname PY@tok@cm\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
+\expandafter\def\csname PY@tok@cpf\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
+\expandafter\def\csname PY@tok@c1\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
+\expandafter\def\csname PY@tok@cs\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
+
+\def\PYZbs{\char`\\}
+\def\PYZus{\char`\_}
+\def\PYZob{\char`\{}
+\def\PYZcb{\char`\}}
+\def\PYZca{\char`\^}
+\def\PYZam{\char`\&}
+\def\PYZlt{\char`\<}
+\def\PYZgt{\char`\>}
+\def\PYZsh{\char`\#}
+\def\PYZpc{\char`\%}
+\def\PYZdl{\char`\$}
+\def\PYZhy{\char`\-}
+\def\PYZsq{\char`\'}
+\def\PYZdq{\char`\"}
+\def\PYZti{\char`\~}
+% for compatibility with earlier versions
+\def\PYZat{@}
+\def\PYZlb{[}
+\def\PYZrb{]}
+\makeatother
+
+
+    % Exact colors from NB
+    \definecolor{incolor}{rgb}{0.0, 0.0, 0.5}
+    \definecolor{outcolor}{rgb}{0.545, 0.0, 0.0}
+
+
+
+    
+    % Prevent overflowing lines due to hard-to-break entities
+    \sloppy 
+    % Setup hyperref package
+    \hypersetup{
+      breaklinks=true,  % so long urls are correctly broken across lines
+      colorlinks=true,
+      urlcolor=urlcolor,
+      linkcolor=linkcolor,
+      citecolor=citecolor,
+      }
+    % Slightly bigger margins than the latex defaults
+    
+    \geometry{verbose,tmargin=1in,bmargin=1in,lmargin=1in,rmargin=1in}
+    
+    
+
+    \begin{document}
+    
+    
+    \maketitle
+    
+    
+
+    
+    
+
+    \section{Using the Landlab BiotaEvolver
+component}\label{using-the-landlab-biotaevolver-component}
+
+ For instructions on how to run an interactive iPython notebook, click
+here: https://github.com/landlab/tutorials/blob/master/README.md For
+more Landlab tutorials, click here:
+https://github.com/landlab/landlab/wiki/Tutorials
+
+In this tutorial we will: * Introduce species into a Landlab model. *
+Set the zones in which the species operate. * Evolve a landscape and the
+species over time. * Explore BiotaEvolver output data structures.
+
+The configuration of the model in this tutorial loosely follows the
+fault throw experiment in Lyons et al., in preperation for Earth Surface
+Dynamics.
+
+    Import modules and set the environment to show plots inline in this
+notebook.
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}2}]:} \PY{k+kn}{from} \PY{n+nn}{landlab} \PY{k}{import} \PY{n}{RasterModelGrid}\PY{p}{,} \PY{n}{CLOSED\PYZus{}BOUNDARY}\PY{p}{,} \PY{n}{FIXED\PYZus{}VALUE\PYZus{}BOUNDARY}
+        \PY{k+kn}{from} \PY{n+nn}{landlab}\PY{n+nn}{.}\PY{n+nn}{components} \PY{k}{import} \PY{p}{(}\PY{n}{BiotaEvolver}\PY{p}{,} \PY{n}{FastscapeEroder}\PY{p}{,} \PY{n}{FlowRouter}\PY{p}{,}
+                                        \PY{n}{LinearDiffuser}\PY{p}{)}
+        \PY{k+kn}{from} \PY{n+nn}{landlab}\PY{n+nn}{.}\PY{n+nn}{components}\PY{n+nn}{.}\PY{n+nn}{biota\PYZus{}macroevolution} \PY{k}{import} \PY{n}{Species}\PY{p}{,} \PY{n}{Zone}
+        \PY{k+kn}{import} \PY{n+nn}{numpy} \PY{k}{as} \PY{n+nn}{np}
+        
+        \PY{o}{\PYZpc{}}\PY{k}{matplotlib} inline
+\end{Verbatim}
+
+
+    \subsection{Create a model grid with steady state
+topography}\label{create-a-model-grid-with-steady-state-topography}
+
+Here the topography of a Landlab model grid brought to topographic
+steady state.
+
+    Create a grid with random initial topography.
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}3}]:} \PY{n}{dx} \PY{o}{=} \PY{l+m+mi}{200}
+        \PY{n}{nrows} \PY{o}{=} \PY{l+m+mi}{100}
+        \PY{n}{ncols} \PY{o}{=} \PY{l+m+mi}{20}
+        \PY{n}{mg\PYZus{}ss} \PY{o}{=} \PY{n}{RasterModelGrid}\PY{p}{(}\PY{n}{nrows}\PY{p}{,} \PY{n}{ncols}\PY{p}{,} \PY{n}{dx}\PY{p}{)}
+        \PY{n}{mg\PYZus{}ss}\PY{o}{.}\PY{n}{set\PYZus{}status\PYZus{}at\PYZus{}node\PYZus{}on\PYZus{}edges}\PY{p}{(}\PY{n}{right}\PY{o}{=}\PY{n}{CLOSED\PYZus{}BOUNDARY}\PY{p}{,}
+                                          \PY{n}{top}\PY{o}{=}\PY{n}{FIXED\PYZus{}VALUE\PYZus{}BOUNDARY}\PY{p}{,}
+                                          \PY{n}{left}\PY{o}{=}\PY{n}{CLOSED\PYZus{}BOUNDARY}\PY{p}{,}
+                                          \PY{n}{bottom}\PY{o}{=}\PY{n}{FIXED\PYZus{}VALUE\PYZus{}BOUNDARY}\PY{p}{)}
+        \PY{n}{z\PYZus{}ss} \PY{o}{=} \PY{n}{mg\PYZus{}ss}\PY{o}{.}\PY{n}{add\PYZus{}zeros}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{node}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{topographic\PYZus{}\PYZus{}elevation}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
+        \PY{n}{np}\PY{o}{.}\PY{n}{random}\PY{o}{.}\PY{n}{seed}\PY{p}{(}\PY{l+m+mi}{500}\PY{p}{)}
+        \PY{n}{z\PYZus{}ss} \PY{o}{+}\PY{o}{=} \PY{n}{np}\PY{o}{.}\PY{n}{random}\PY{o}{.}\PY{n}{rand}\PY{p}{(}\PY{n}{z\PYZus{}ss}\PY{o}{.}\PY{n}{size}\PY{p}{)}
+\end{Verbatim}
+
+
+    Set parameters and initialize components.
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor} }]:} \PY{n}{dt} \PY{o}{=} \PY{l+m+mi}{1000}
+        \PY{n}{total\PYZus{}time} \PY{o}{=} \PY{n+nb}{int}\PY{p}{(}\PY{l+m+mf}{4e6}\PY{p}{)}
+        \PY{n}{t} \PY{o}{=} \PY{n+nb}{range}\PY{p}{(}\PY{l+m+mi}{0}\PY{p}{,} \PY{n}{total\PYZus{}time}\PY{p}{,} \PY{n}{dt}\PY{p}{)}
+        \PY{n}{U} \PY{o}{=} \PY{l+m+mf}{7e\PYZhy{}5}
+        \PY{n}{uplift\PYZus{}per\PYZus{}step} \PY{o}{=} \PY{n}{U} \PY{o}{*} \PY{n}{dt}
+        \PY{n}{K\PYZus{}sp} \PY{o}{=} \PY{l+m+mf}{10e\PYZhy{}5}
+        \PY{n}{m\PYZus{}sp} \PY{o}{=} \PY{l+m+mf}{0.5}
+        \PY{n}{n\PYZus{}sp} \PY{o}{=} \PY{l+m+mi}{1}
+        \PY{n}{k\PYZus{}d} \PY{o}{=} \PY{l+m+mf}{0.0001}
+        
+        \PY{n}{fr} \PY{o}{=} \PY{n}{FlowRouter}\PY{p}{(}\PY{n}{mg\PYZus{}ss}\PY{p}{)}
+        \PY{n}{sp} \PY{o}{=} \PY{n}{FastscapeEroder}\PY{p}{(}\PY{n}{mg\PYZus{}ss}\PY{p}{,} \PY{n}{K\PYZus{}sp}\PY{o}{=}\PY{n}{K\PYZus{}sp}\PY{p}{,} \PY{n}{m\PYZus{}sp}\PY{o}{=}\PY{n}{m\PYZus{}sp}\PY{p}{,} \PY{n}{n\PYZus{}sp}\PY{o}{=}\PY{n}{n\PYZus{}sp}\PY{p}{)}
+        \PY{n}{lf} \PY{o}{=} \PY{n}{LinearDiffuser}\PY{p}{(}\PY{n}{mg\PYZus{}ss}\PY{p}{,} \PY{n}{linear\PYZus{}diffusivity}\PY{o}{=}\PY{n}{k\PYZus{}d}\PY{p}{,} \PY{n}{deposit}\PY{o}{=}\PY{k+kc}{False}\PY{p}{)}
+\end{Verbatim}
+
+
+    Run the model until topography reaches steady state (ss).
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor} }]:} \PY{k}{for} \PY{n}{t\PYZus{}i} \PY{o+ow}{in} \PY{n}{t}\PY{p}{:}
+            \PY{n}{z\PYZus{}ss}\PY{p}{[}\PY{n}{mg\PYZus{}ss}\PY{o}{.}\PY{n}{core\PYZus{}nodes}\PY{p}{]} \PY{o}{+}\PY{o}{=} \PY{n}{uplift\PYZus{}per\PYZus{}step}
+        
+            \PY{n}{fr}\PY{o}{.}\PY{n}{run\PYZus{}one\PYZus{}step}\PY{p}{(}\PY{p}{)}
+            \PY{n}{sp}\PY{o}{.}\PY{n}{run\PYZus{}one\PYZus{}step}\PY{p}{(}\PY{n}{dt}\PY{p}{)}
+            \PY{n}{lf}\PY{o}{.}\PY{n}{run\PYZus{}one\PYZus{}step}\PY{p}{(}\PY{n}{dt}\PY{p}{)}
+\end{Verbatim}
+
+
+    \subsection{Fault the grid and evolve
+species}\label{fault-the-grid-and-evolve-species}
+
+    Create a copy of the steady state grid.
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor} }]:} \PY{n}{mg} \PY{o}{=} \PY{n}{deepcopy}\PY{p}{(}\PY{n}{mg\PYZus{}ss}\PY{p}{)}
+        \PY{n}{z} \PY{o}{=} \PY{n}{mg}\PY{o}{.}\PY{n}{at\PYZus{}node}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{topographic\PYZus{}\PYZus{}elevation}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}
+\end{Verbatim}
+
+
+    Define the fault.
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor} }]:} \PY{n}{grid\PYZus{}width} \PY{o}{=} \PY{n}{mg}\PY{o}{.}\PY{n}{number\PYZus{}of\PYZus{}node\PYZus{}columns} \PY{o}{*} \PY{n}{mg}\PY{o}{.}\PY{n}{dx}
+        \PY{n}{x\PYZus{}of\PYZus{}fault} \PY{o}{=} \PY{n}{grid\PYZus{}width} \PY{o}{*} \PY{l+m+mf}{0.5}
+        \PY{n}{core\PYZus{}mask} \PY{o}{=} \PY{n}{mg}\PY{o}{.}\PY{n}{node\PYZus{}is\PYZus{}core}\PY{p}{(}\PY{p}{)}
+        \PY{n}{upthown\PYZus{}block\PYZus{}mask} \PY{o}{=} \PY{n}{mg}\PY{o}{.}\PY{n}{x\PYZus{}of\PYZus{}node} \PY{o}{\PYZgt{}}\PY{o}{=} \PY{n}{x\PYZus{}of\PYZus{}fault}
+        \PY{n}{upthown\PYZus{}block\PYZus{}mask} \PY{o}{=} \PY{n}{np}\PY{o}{.}\PY{n}{all}\PY{p}{(}\PY{p}{[}\PY{n}{core\PYZus{}mask}\PY{p}{,} \PY{n}{upthown\PYZus{}block\PYZus{}mask}\PY{p}{]}\PY{p}{,} \PY{l+m+mi}{0}\PY{p}{)}
+        \PY{n}{z}\PY{p}{[}\PY{n}{upthown\PYZus{}block\PYZus{}mask}\PY{p}{]} \PY{o}{+}\PY{o}{=} \PY{l+m+mi}{100}
+\end{Verbatim}
+
+
+    Initialize components.
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor} }]:} \PY{n}{fr} \PY{o}{=} \PY{n}{FlowRouter}\PY{p}{(}\PY{n}{mg}\PY{p}{)}
+        \PY{n}{sp} \PY{o}{=} \PY{n}{FastscapeEroder}\PY{p}{(}\PY{n}{mg}\PY{p}{,} \PY{n}{K\PYZus{}sp}\PY{o}{=}\PY{n}{K\PYZus{}sp}\PY{p}{,} \PY{n}{m\PYZus{}sp}\PY{o}{=}\PY{n}{m\PYZus{}sp}\PY{p}{,} \PY{n}{n\PYZus{}sp}\PY{o}{=}\PY{n}{n\PYZus{}sp}\PY{p}{)}
+        \PY{n}{lf} \PY{o}{=} \PY{n}{LinearDiffuser}\PY{p}{(}\PY{n}{mg}\PY{p}{,} \PY{n}{linear\PYZus{}diffusivity}\PY{o}{=}\PY{n}{k\PYZus{}d}\PY{p}{,} \PY{n}{deposit}\PY{o}{=}\PY{k+kc}{False}\PY{p}{)}
+        
+        \PY{n}{be} \PY{o}{=} \PY{n}{BiotaEvolver}\PY{p}{(}\PY{n}{mg}\PY{p}{)}
+        \PY{n}{dt\PYZus{}be} \PY{o}{=} \PY{l+m+mf}{1e5}
+\end{Verbatim}
+
+
+    \begin{verbatim}
+<tr>
+    <th>Title</th>
+</tr>
+<tr>
+    <td>Data</td>
+</tr>
+\end{verbatim}
+
+    Populate BiotaEvolver zones with species.
+
+Zones are portions of a model grid. A Species object exists throughout a
+zone. At each timestep, the spatial intersection of the zones at the
+prior time and current time are identified and typed. For example, a
+zone in the prior time intersects two zones in the current time. This
+zone path is typed one-to-many.
+
+Macroevolutionary processes are carried out by the intersection of zones
+over time. The connectivity of zones in BiotaEvolver are called 'paths',
+and the relationship of zones is described by the path type. Not all
+combinations of none, one, and many are considered at the time of this
+tutorial. All none-to-n and some many-to-n are not considered.
+
+\begin{longtable}[]{@{}llc@{}}
+\toprule
+Name & Description & age\tabularnewline
+\midrule
+\endhead
+Mary & She is a nice girl. & 20\tabularnewline
+Jackie Junior & He is a very naughty boy. & 5\tabularnewline
+\bottomrule
+\end{longtable}
+
+path type \textbar{}
+
+graphicalrepresention
+
+\textbar{}
+
+zone description
+
+\textbar{}
+
+macroevolution implications
+
+-\/-\/- \textbar{} -\/-\/- \textbar{} -\/-\/- \textbar{} -\/-\/-
+one-to-none \textbar{} \includegraphics{images/zone__one_to_none.png}
+\textbar{} The zone in the earlier time does not intersect a zone in a
+later time. \textbar{} The species in the zone of the earlier time will
+go extinct. one-to-one \textbar{} 2 \textbar{} 3 \textbar{} 4
+one-to-many \textbar{} 2 \textbar{} 3 \textbar{} 4 many-to-one
+\textbar{} 2 \textbar{} 3 \textbar{} 4 many-to-many \textbar{} 2
+\textbar{} 3 \textbar{} 4
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}1}]:} \PY{n}{min\PYZus{}stream\PYZus{}area} \PY{o}{=} \PY{l+m+mf}{1e6}
+        \PY{n}{zones} \PY{o}{=} \PY{n}{Zone}\PY{o}{.}\PY{n}{get\PYZus{}zones\PYZus{}with\PYZus{}area\PYZus{}threshold}\PY{p}{(}\PY{n}{mg}\PY{p}{,} \PY{n}{min\PYZus{}stream\PYZus{}area}\PY{p}{)}
+        \PY{k}{for} \PY{n}{zone} \PY{o+ow}{in} \PY{n}{zones}\PY{p}{:}
+            \PY{n}{new\PYZus{}species} \PY{o}{=} \PY{n}{Species}\PY{p}{(}\PY{l+m+mi}{0}\PY{p}{,} \PY{n}{zone}\PY{p}{)}
+            \PY{n}{be}\PY{o}{.}\PY{n}{introduce\PYZus{}species}\PY{p}{(}\PY{n}{new\PYZus{}species}\PY{p}{,} \PY{l+m+mi}{0}\PY{p}{)}
+\end{Verbatim}
+
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+
+        ---------------------------------------------------------------------------
+
+        NameError                                 Traceback (most recent call last)
+
+        <ipython-input-1-53c90586ee9c> in <module>()
+          1 min\_stream\_area = 1e6
+    ----> 2 zones = Zone.get\_zones\_with\_area\_threshold(mg, min\_stream\_area)
+          3 for zone in zones:
+          4     new\_species = Species(0, zone)
+          5     be.introduce\_species(new\_species, 0)
+
+
+        NameError: name 'Zone' is not defined
+
+    \end{Verbatim}
+
+    Run model.
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor} }]:} \PY{n}{total\PYZus{}time} \PY{o}{=} \PY{l+m+mf}{5e5}
+        \PY{n}{nt} \PY{o}{=} \PY{n+nb}{int}\PY{p}{(}\PY{n}{total\PYZus{}time} \PY{o}{/}\PY{o}{/} \PY{n}{dt}\PY{p}{)}
+        \PY{k}{for} \PY{n}{time\PYZus{}i} \PY{o+ow}{in} \PY{n+nb}{range}\PY{p}{(}\PY{n}{nt}\PY{p}{)}\PY{p}{:}
+            \PY{c+c1}{\PYZsh{} Uniformly uplift.}
+            \PY{n}{z}\PY{p}{[}\PY{n}{mg}\PY{o}{.}\PY{n}{core\PYZus{}nodes}\PY{p}{]} \PY{o}{+}\PY{o}{=} \PY{n}{uplift\PYZus{}per\PYZus{}step}
+        
+            \PY{c+c1}{\PYZsh{} Increment components.}
+            \PY{n}{fr}\PY{o}{.}\PY{n}{run\PYZus{}one\PYZus{}step}\PY{p}{(}\PY{p}{)}
+            \PY{n}{sp}\PY{o}{.}\PY{n}{run\PYZus{}one\PYZus{}step}\PY{p}{(}\PY{n}{dt}\PY{p}{)}
+            \PY{n}{lf}\PY{o}{.}\PY{n}{run\PYZus{}one\PYZus{}step}\PY{p}{(}\PY{n}{dt}\PY{p}{)}
+        
+            \PY{n}{elapsed\PYZus{}time} \PY{o}{=} \PY{n}{dt} \PY{o}{*} \PY{p}{(}\PY{n}{time\PYZus{}i} \PY{o}{+} \PY{l+m+mi}{1}\PY{p}{)}
+        
+            \PY{k}{if} \PY{n}{elapsed\PYZus{}time} \PY{o}{\PYZpc{}} \PY{n}{dt\PYZus{}be} \PY{o}{==} \PY{l+m+mi}{0}\PY{p}{:}
+                \PY{c+c1}{\PYZsh{} Run BiotaEvolver with updated zones.}
+                \PY{n+nb}{print}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{BiotaEvolver time}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{n}{elapsed\PYZus{}time}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
+                \PY{n}{zones} \PY{o}{=} \PY{n}{Zone}\PY{o}{.}\PY{n}{get\PYZus{}zones\PYZus{}with\PYZus{}area\PYZus{}threshold}\PY{p}{(}\PY{n}{mg}\PY{p}{,} \PY{n}{min\PYZus{}stream\PYZus{}area}\PY{p}{)}
+                \PY{n}{be}\PY{o}{.}\PY{n}{run\PYZus{}one\PYZus{}step}\PY{p}{(}\PY{n}{elapsed\PYZus{}time}\PY{p}{,} \PY{n}{zones}\PY{p}{)}
+\end{Verbatim}
+
+
+    \subsection{Species identifiers and phylogenetic
+trees}\label{species-identifiers-and-phylogenetic-trees}
+
+Species identifiers are automatically generated. They are stored as
+tuples in species objects. The first element of the identifier tuple is
+the clade name, and the second element is the species number. For
+example, the first species of clade A is 0, so the identifier is (A, 0)
+and the tenth species of this clade is (A, 9).
+
+\subsubsection{Clade name}\label{clade-name}
+
+A clade is a group of species that share a common ancestor. Species may
+share multiple common ancestors, so clades can be defined differently in
+the same tree. The clade(s) of this example tree can be defined in
+multiple ways:
+
+\begin{figure}
+\centering
+\includegraphics{images/clade_definitions.png}
+\caption{}
+\end{figure}
+
+In BiotaEvolver, clades are defined by the root node for the purpose of
+assigning species identifiers. The species at the root node is: * the
+least recent common ancestor of all species in the tree, and * typically
+introduced to a BiotaEvolver model using the \texttt{introduce\_species}
+function.
+
+The clades of the first 26 species are labeled alphabetically from A to
+Z. The next 26 are labeled AB thru AZ.
+
+1-26: A to Z 27-53: AA to AZ BA to BZ ... AAA to AAZ ABA to ABZ ACA to
+ACZ
+
+\subsubsection{Species number}\label{species-number}
+
+Species are numbered sequently by clade beginning with 0.
+
+In the example tree below, species A0 produced 4 child species. Only one
+species, B0 exists in clade B because child species were not produced by
+B0.
+
+\begin{figure}
+\centering
+\includegraphics{images/species_number.png}
+\caption{}
+\end{figure}
+
+    \subsubsection{Click here for more Landlab
+tutorials}\label{click-here-for-more-landlab-tutorials}
+
+
+    % Add a bibliography block to the postdoc
+    
+    
+    
+    \end{document}

From c8dcd19013bde2bde380eeafa49ac44076c51e4a Mon Sep 17 00:00:00 2001
From: nathanlyons <nathan.j.lyons@gmail.com>
Date: Sun, 6 May 2018 17:51:53 -0500
Subject: [PATCH 05/15] BiotaEvolver intro

---
 biota_evolution/BiotaEvolver_tutorial.ipynb | 257 +++++++++++++++++---
 1 file changed, 222 insertions(+), 35 deletions(-)

diff --git a/biota_evolution/BiotaEvolver_tutorial.ipynb b/biota_evolution/BiotaEvolver_tutorial.ipynb
index e7573df..0c84c6f 100644
--- a/biota_evolution/BiotaEvolver_tutorial.ipynb
+++ b/biota_evolution/BiotaEvolver_tutorial.ipynb
@@ -24,7 +24,14 @@
     "* Evolve a landscape and the species over time.\n",
     "* Explore BiotaEvolver output data structures.\n",
     "\n",
-    "The configuration of the model in this tutorial loosely follows the fault throw experiment in Lyons et al., in preperation for Earth Surface Dynamics."
+    "The configuration of the model in this tutorial loosely follows the fault throw experiment in Lyons et al., in preperation for Earth Surface Dynamics.\n",
+    "\n",
+    "## Species macroevolution in the model component\n",
+    "\n",
+    "The base species class evolves in these processes:\n",
+    "* dispersal: geographic range expansion\n",
+    "* speciation: lineage splitting\n",
+    "* extinction: lineage termination"
    ]
   },
   {
@@ -36,10 +43,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
+    "from copy import deepcopy\n",
     "from landlab import RasterModelGrid, CLOSED_BOUNDARY, FIXED_VALUE_BOUNDARY\n",
     "from landlab.components import (BiotaEvolver, FastscapeEroder, FlowRouter,\n",
     "                                LinearDiffuser)\n",
@@ -67,7 +75,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -93,7 +101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -121,7 +129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -149,7 +157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -166,7 +174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -187,7 +195,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -224,21 +232,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'Zone' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-1-53c90586ee9c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mmin_stream_area\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1e6\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mzones\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mZone\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_zones_with_area_threshold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_stream_area\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mzone\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzones\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mnew_species\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSpecies\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mbe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintroduce_species\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_species\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'Zone' is not defined"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "min_stream_area = 1e6\n",
     "zones = Zone.get_zones_with_area_threshold(mg, min_stream_area)\n",
@@ -256,9 +252,58 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "BiotaEvolver time 100000 ---------------------------\n",
+      "many-to-one\n",
+      "many-to-many\n",
+      "many-to-one\n",
+      "one-to-one\n",
+      "many-to-many\n",
+      "BiotaEvolver time 200000 ---------------------------\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "BiotaEvolver time 300000 ---------------------------\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "BiotaEvolver time 400000 ---------------------------\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "BiotaEvolver time 500000 ---------------------------\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n"
+     ]
+    }
+   ],
    "source": [
     "total_time = 5e5\n",
     "nt = int(total_time // dt)\n",
@@ -282,10 +327,150 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>time_appeared</th>\n",
+       "      <th>time_disappeared</th>\n",
+       "      <th>subtype</th>\n",
+       "      <th>object</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>clade</th>\n",
+       "      <th>number</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x1c10c57630&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>100000</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x1c10c57320&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x1c10c40518&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x1c10c40048&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>E</th>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>100000</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x1c10c123c8&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">B</th>\n",
+       "      <th>1</th>\n",
+       "      <td>100000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x1c10cde828&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>100000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x1c10cdebe0&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">E</th>\n",
+       "      <th>1</th>\n",
+       "      <td>100000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x1c10c40c18&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>100000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x1c10cfc2e8&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>100000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x1c10cdecf8&gt;</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             time_appeared time_disappeared subtype                     object\n",
+       "clade number                                                                  \n",
+       "A     0                  0              NaN    base  <Species at 0x1c10c57630>\n",
+       "B     0                  0           100000    base  <Species at 0x1c10c57320>\n",
+       "C     0                  0              NaN    base  <Species at 0x1c10c40518>\n",
+       "D     0                  0              NaN    base  <Species at 0x1c10c40048>\n",
+       "E     0                  0           100000    base  <Species at 0x1c10c123c8>\n",
+       "B     1             100000              NaN    base  <Species at 0x1c10cde828>\n",
+       "      2             100000              NaN    base  <Species at 0x1c10cdebe0>\n",
+       "E     1             100000              NaN    base  <Species at 0x1c10c40c18>\n",
+       "      2             100000              NaN    base  <Species at 0x1c10cfc2e8>\n",
+       "      3             100000              NaN    base  <Species at 0x1c10cdecf8>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "be.species"
+   ]
   },
   {
    "cell_type": "code",
@@ -312,15 +497,17 @@
     "* the least recent common ancestor of all species in the tree, and\n",
     "* typically introduced to a BiotaEvolver model using the ```introduce_species``` function.\n",
     "\n",
-    "The clades of the first 26 species are labeled alphabetically from A to Z. The next 26 are labeled AB thru AZ. \n",
+    "The clades of the first 26 species are named alphabetically from A to Z. The alphabet is repeated for subsequent species following this schema:\n",
     "\n",
-    "1-26: A to Z\n",
-    "27-53: AA to AZ\n",
-    "BA to BZ\n",
-    "...\n",
-    "AAA to AAZ\n",
-    "ABA to ABZ\n",
-    "ACA to ACZ\n",
+    "<center>clade sequence</center> | <center>clade name</center>\n",
+    "--- | ---\n",
+    "1–26 | A–Z\n",
+    "27–52 | AA–AZ\n",
+    "53–78 | BA–BZ\n",
+    "... | ...\n",
+    "703–728 | AAA–AAZ\n",
+    "729–754 | ABA–ABZ\n",
+    "... | ...\n",
     "\n",
     "### Species number\n",
     "\n",

From 2a80de099ac70e8e5cd28679d9152d39e18e4eca Mon Sep 17 00:00:00 2001
From: nathanlyons <nathan.j.lyons@gmail.com>
Date: Sun, 6 May 2018 18:01:06 -0500
Subject: [PATCH 06/15] species panda dataframe explanation

---
 biota_evolution/BiotaEvolver_tutorial.ipynb   | 275 +++++++++++++++---
 .../images/zone_path__one_to_many.png         | Bin 0 -> 1829 bytes
 2 files changed, 239 insertions(+), 36 deletions(-)
 create mode 100644 biota_evolution/images/zone_path__one_to_many.png

diff --git a/biota_evolution/BiotaEvolver_tutorial.ipynb b/biota_evolution/BiotaEvolver_tutorial.ipynb
index e7573df..bc57529 100644
--- a/biota_evolution/BiotaEvolver_tutorial.ipynb
+++ b/biota_evolution/BiotaEvolver_tutorial.ipynb
@@ -36,10 +36,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
+    "from copy import deepcopy\n",
     "from landlab import RasterModelGrid, CLOSED_BOUNDARY, FIXED_VALUE_BOUNDARY\n",
     "from landlab.components import (BiotaEvolver, FastscapeEroder, FlowRouter,\n",
     "                                LinearDiffuser)\n",
@@ -55,7 +56,7 @@
    "source": [
     "## Create a model grid with steady state topography\n",
     "\n",
-    "Here the topography of a Landlab model grid brought to topographic steady state."
+    "Here, the topography of a Landlab model grid brought to topographic steady state."
    ]
   },
   {
@@ -67,7 +68,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -93,7 +94,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -102,9 +103,12 @@
     "t = range(0, total_time, dt)\n",
     "U = 7e-5\n",
     "uplift_per_step = U * dt\n",
+    "\n",
+    "# Set stream power model parameters.\n",
     "K_sp = 10e-5\n",
     "m_sp = 0.5\n",
     "n_sp = 1\n",
+    "\n",
     "k_d = 0.0001\n",
     "\n",
     "fr = FlowRouter(mg_ss)\n",
@@ -121,7 +125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -149,7 +153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -166,7 +170,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -187,7 +191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -203,9 +207,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Populate BiotaEvolver zones with species.\n",
+    "Species and zones are the two object types central to BiotaEvolver.\n",
     "\n",
-    "Zones are portions of a model grid. A Species object exists throughout a zone. At each timestep, the spatial intersection of the zones at the prior time and current time are identified and typed. For example, a zone in the prior time intersects two zones in the current time. This zone path is typed one-to-many.\n",
+    "### Zones\n",
+    "\n",
+    "Zones are portions of a model grid. A Species object exists throughout a zone.\n",
+    "\n",
+    "At each timestep, the spatial intersection of the zones at the prior time and current time are identified and typed. For example, a zone in the prior time intersects two zones in the current time. This zone path is typed one-to-many.\n",
     "\n",
     "Macroevolutionary processes are carried out by the intersection of zones over time. The connectivity of zones in BiotaEvolver are called 'paths', and the relationship of zones is described by the path type. Not all combinations of none, one, and many are considered at the time of this tutorial. All none-to-n and some many-to-n are not considered. \n",
     "\n",
@@ -219,26 +227,32 @@
     "one-to-one | ![](images/zone__one_to_one.png) | zone<sub>*t*</sub> intersects zone<sub>*t*+1</sub>. | The species in the zone of the earlier time will relocate to the zone of the later time.\n",
     "one-to-many | ![](images/zone__one_to_many.png) | zone<sub>*t*</sub> intersects multiple zones in *t*+1. | The species in the zone of the earlier time disperses across the multiple zones in the later time. The species in the zones of the later time are geographical disconnected, thus speciation occurs.\n",
     "many-to-one | ![](images/zone__many_to_one.png) | Multiple zones in *t* intersect zone<sub>*t*+1</sub>. | Species density increases locally.\n",
-    "many-to-many | ![](images/zone__many_to_many.png) | Multiple zones in *t* intersect multiple zones in *t*+1. | Species distribution shifts. Speciation occurs where the zone of a species at *t* intersects more than one zone at *t*+1.\n"
+    "many-to-many | ![](images/zone__many_to_many.png) | Multiple zones in *t* intersect multiple zones in *t*+1. | Species distribution shifts. Speciation occurs where the zone of a species at *t* intersects more than one zone at *t*+1.\n",
+    "\n",
+    "### Species\n",
+    "\n",
+    "The model domain is populated with species by the user.\n",
+    "\n",
+    "The rules of macroevolution are programmed into species. Behind the scenes, zone paths are the conditions evaluated by the macroevolution rules. The outcome of the rules given the zone paths are described in the zone table above. For example, a one-to-many path:\n",
+    "\n",
+    "![](images/zone_path__one_to_many.png)\n",
+    "\n",
+    "The base species class \n",
+    "\n",
+    "Users may develop species with different behaviors and macroevolution rules.\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "Populate BiotaEvolver zones with species.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'Zone' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-1-53c90586ee9c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mmin_stream_area\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1e6\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mzones\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mZone\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_zones_with_area_threshold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_stream_area\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mzone\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzones\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mnew_species\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSpecies\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mbe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintroduce_species\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_species\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'Zone' is not defined"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "min_stream_area = 1e6\n",
     "zones = Zone.get_zones_with_area_threshold(mg, min_stream_area)\n",
@@ -251,14 +265,63 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Run model."
+    "Run model. The ```run_one_step``` function of BiotaEvolver takes the new zones of the time step."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "BiotaEvolver time 100000 ---------------------------\n",
+      "many-to-one\n",
+      "many-to-many\n",
+      "many-to-one\n",
+      "one-to-one\n",
+      "many-to-many\n",
+      "BiotaEvolver time 200000 ---------------------------\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "BiotaEvolver time 300000 ---------------------------\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "BiotaEvolver time 400000 ---------------------------\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "BiotaEvolver time 500000 ---------------------------\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n"
+     ]
+    }
+   ],
    "source": [
     "total_time = 5e5\n",
     "nt = int(total_time // dt)\n",
@@ -266,7 +329,7 @@
     "    # Uniformly uplift.\n",
     "    z[mg.core_nodes] += uplift_per_step\n",
     "\n",
-    "    # Increment components.\n",
+    "    # Increment earth surface process components.\n",
     "    fr.run_one_step()\n",
     "    sp.run_one_step(dt)\n",
     "    lf.run_one_step(dt)\n",
@@ -281,18 +344,158 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "Show species DataFrame."
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>time_appeared</th>\n",
+       "      <th>time_disappeared</th>\n",
+       "      <th>subtype</th>\n",
+       "      <th>object</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>clade</th>\n",
+       "      <th>number</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x1c150a58d0&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>100000</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x1c15090a58&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x1c15098128&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x1c150afef0&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>E</th>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>100000</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x1c150afe10&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">B</th>\n",
+       "      <th>1</th>\n",
+       "      <td>100000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x1c15150048&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>100000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x1c15150278&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">E</th>\n",
+       "      <th>1</th>\n",
+       "      <td>100000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x1c15150940&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>100000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x1c151525f8&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>100000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x1c151520f0&gt;</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             time_appeared time_disappeared subtype                     object\n",
+       "clade number                                                                  \n",
+       "A     0                  0              NaN    base  <Species at 0x1c150a58d0>\n",
+       "B     0                  0           100000    base  <Species at 0x1c15090a58>\n",
+       "C     0                  0              NaN    base  <Species at 0x1c15098128>\n",
+       "D     0                  0              NaN    base  <Species at 0x1c150afef0>\n",
+       "E     0                  0           100000    base  <Species at 0x1c150afe10>\n",
+       "B     1             100000              NaN    base  <Species at 0x1c15150048>\n",
+       "      2             100000              NaN    base  <Species at 0x1c15150278>\n",
+       "E     1             100000              NaN    base  <Species at 0x1c15150940>\n",
+       "      2             100000              NaN    base  <Species at 0x1c151525f8>\n",
+       "      3             100000              NaN    base  <Species at 0x1c151520f0>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "be.species"
+   ]
   },
   {
    "cell_type": "markdown",
diff --git a/biota_evolution/images/zone_path__one_to_many.png b/biota_evolution/images/zone_path__one_to_many.png
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From 06fd0a1bd18ba0b10b7f9e75d4e3d7a47970330f Mon Sep 17 00:00:00 2001
From: nathanlyons <nathan.j.lyons@gmail.com>
Date: Sun, 6 May 2018 18:15:38 -0500
Subject: [PATCH 07/15] fixed conflicts in last commit

---
 biota_evolution/BiotaEvolver_tutorial.ipynb | 391 ++------------------
 1 file changed, 21 insertions(+), 370 deletions(-)

diff --git a/biota_evolution/BiotaEvolver_tutorial.ipynb b/biota_evolution/BiotaEvolver_tutorial.ipynb
index 1d70a89..4ceadbe 100644
--- a/biota_evolution/BiotaEvolver_tutorial.ipynb
+++ b/biota_evolution/BiotaEvolver_tutorial.ipynb
@@ -43,11 +43,7 @@
   },
   {
    "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 7,
-=======
-   "execution_count": 6,
->>>>>>> c8dcd19013bde2bde380eeafa49ac44076c51e4a
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -79,7 +75,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -105,7 +101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -136,7 +132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -164,11 +160,7 @@
   },
   {
    "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 8,
-=======
-   "execution_count": 7,
->>>>>>> c8dcd19013bde2bde380eeafa49ac44076c51e4a
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -185,11 +177,7 @@
   },
   {
    "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 9,
-=======
-   "execution_count": 8,
->>>>>>> c8dcd19013bde2bde380eeafa49ac44076c51e4a
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -210,11 +198,7 @@
   },
   {
    "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 10,
-=======
-   "execution_count": 9,
->>>>>>> c8dcd19013bde2bde380eeafa49ac44076c51e4a
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -273,11 +257,7 @@
   },
   {
    "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 11,
-=======
-   "execution_count": 10,
->>>>>>> c8dcd19013bde2bde380eeafa49ac44076c51e4a
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -297,62 +277,9 @@
   },
   {
    "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 12,
-=======
-   "execution_count": 11,
->>>>>>> c8dcd19013bde2bde380eeafa49ac44076c51e4a
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "BiotaEvolver time 100000 ---------------------------\n",
-      "many-to-one\n",
-      "many-to-many\n",
-      "many-to-one\n",
-      "one-to-one\n",
-      "many-to-many\n",
-      "BiotaEvolver time 200000 ---------------------------\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "BiotaEvolver time 300000 ---------------------------\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "BiotaEvolver time 400000 ---------------------------\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "BiotaEvolver time 500000 ---------------------------\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "total_time = 5e5\n",
     "nt = int(total_time // dt)\n",
@@ -375,302 +302,19 @@
    ]
   },
   {
-<<<<<<< HEAD
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Show species DataFrame."
-=======
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
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-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>time_appeared</th>\n",
-       "      <th>time_disappeared</th>\n",
-       "      <th>subtype</th>\n",
-       "      <th>object</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>clade</th>\n",
-       "      <th>number</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>A</th>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x1c10c57630&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>B</th>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>100000</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x1c10c57320&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>C</th>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x1c10c40518&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>D</th>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x1c10c40048&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>E</th>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>100000</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x1c10c123c8&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">B</th>\n",
-       "      <th>1</th>\n",
-       "      <td>100000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x1c10cde828&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>100000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x1c10cdebe0&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"3\" valign=\"top\">E</th>\n",
-       "      <th>1</th>\n",
-       "      <td>100000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x1c10c40c18&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>100000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x1c10cfc2e8&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>100000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x1c10cdecf8&gt;</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             time_appeared time_disappeared subtype                     object\n",
-       "clade number                                                                  \n",
-       "A     0                  0              NaN    base  <Species at 0x1c10c57630>\n",
-       "B     0                  0           100000    base  <Species at 0x1c10c57320>\n",
-       "C     0                  0              NaN    base  <Species at 0x1c10c40518>\n",
-       "D     0                  0              NaN    base  <Species at 0x1c10c40048>\n",
-       "E     0                  0           100000    base  <Species at 0x1c10c123c8>\n",
-       "B     1             100000              NaN    base  <Species at 0x1c10cde828>\n",
-       "      2             100000              NaN    base  <Species at 0x1c10cdebe0>\n",
-       "E     1             100000              NaN    base  <Species at 0x1c10c40c18>\n",
-       "      2             100000              NaN    base  <Species at 0x1c10cfc2e8>\n",
-       "      3             100000              NaN    base  <Species at 0x1c10cdecf8>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "be.species"
->>>>>>> c8dcd19013bde2bde380eeafa49ac44076c51e4a
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>time_appeared</th>\n",
-       "      <th>time_disappeared</th>\n",
-       "      <th>subtype</th>\n",
-       "      <th>object</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>clade</th>\n",
-       "      <th>number</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>A</th>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x1c150a58d0&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>B</th>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>100000</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x1c15090a58&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>C</th>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x1c15098128&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>D</th>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x1c150afef0&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>E</th>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>100000</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x1c150afe10&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">B</th>\n",
-       "      <th>1</th>\n",
-       "      <td>100000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x1c15150048&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>100000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x1c15150278&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"3\" valign=\"top\">E</th>\n",
-       "      <th>1</th>\n",
-       "      <td>100000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x1c15150940&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>100000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x1c151525f8&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>100000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x1c151520f0&gt;</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             time_appeared time_disappeared subtype                     object\n",
-       "clade number                                                                  \n",
-       "A     0                  0              NaN    base  <Species at 0x1c150a58d0>\n",
-       "B     0                  0           100000    base  <Species at 0x1c15090a58>\n",
-       "C     0                  0              NaN    base  <Species at 0x1c15098128>\n",
-       "D     0                  0              NaN    base  <Species at 0x1c150afef0>\n",
-       "E     0                  0           100000    base  <Species at 0x1c150afe10>\n",
-       "B     1             100000              NaN    base  <Species at 0x1c15150048>\n",
-       "      2             100000              NaN    base  <Species at 0x1c15150278>\n",
-       "E     1             100000              NaN    base  <Species at 0x1c15150940>\n",
-       "      2             100000              NaN    base  <Species at 0x1c151525f8>\n",
-       "      3             100000              NaN    base  <Species at 0x1c151520f0>"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "be.species"
    ]
@@ -734,6 +378,13 @@
    "source": [
     "### Click here for more <a href=\"https://github.com/landlab/landlab/wiki/Tutorials\">Landlab tutorials</a>"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

From d6c22ea0ac6dc3e3465bf28ae8e58d260d0d962c Mon Sep 17 00:00:00 2001
From: nathanlyons <nathan.j.lyons@gmail.com>
Date: Sun, 6 May 2018 19:02:42 -0500
Subject: [PATCH 08/15] removed conflicts in last commit

---
 biota_evolution/BiotaEvolver_tutorial.ipynb | 211 ++++++++++++++++++--
 1 file changed, 199 insertions(+), 12 deletions(-)

diff --git a/biota_evolution/BiotaEvolver_tutorial.ipynb b/biota_evolution/BiotaEvolver_tutorial.ipynb
index 4ceadbe..d9b2edb 100644
--- a/biota_evolution/BiotaEvolver_tutorial.ipynb
+++ b/biota_evolution/BiotaEvolver_tutorial.ipynb
@@ -43,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -75,7 +75,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -101,7 +101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -132,7 +132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -160,7 +160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -177,7 +177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -198,7 +198,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -257,7 +257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -277,9 +277,58 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "BiotaEvolver time 100000 ---------------------------\n",
+      "many-to-one\n",
+      "many-to-many\n",
+      "many-to-one\n",
+      "one-to-one\n",
+      "many-to-many\n",
+      "BiotaEvolver time 200000 ---------------------------\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "BiotaEvolver time 300000 ---------------------------\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "BiotaEvolver time 400000 ---------------------------\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "BiotaEvolver time 500000 ---------------------------\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n",
+      "many-to-one\n"
+     ]
+    }
+   ],
    "source": [
     "total_time = 5e5\n",
     "nt = int(total_time // dt)\n",
@@ -303,9 +352,147 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>time_appeared</th>\n",
+       "      <th>time_disappeared</th>\n",
+       "      <th>subtype</th>\n",
+       "      <th>object</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>clade</th>\n",
+       "      <th>number</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x1c186fd668&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>100000</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x10faba390&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x10d554278&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x10e70ad68&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>E</th>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>100000</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x1c18703d68&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">B</th>\n",
+       "      <th>1</th>\n",
+       "      <td>100000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x1c18764080&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>100000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x1c1874f518&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">E</th>\n",
+       "      <th>1</th>\n",
+       "      <td>100000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x1c18785400&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>100000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x1c18764eb8&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>100000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x1c18764748&gt;</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             time_appeared time_disappeared subtype                     object\n",
+       "clade number                                                                  \n",
+       "A     0                  0              NaN    base  <Species at 0x1c186fd668>\n",
+       "B     0                  0           100000    base   <Species at 0x10faba390>\n",
+       "C     0                  0              NaN    base   <Species at 0x10d554278>\n",
+       "D     0                  0              NaN    base   <Species at 0x10e70ad68>\n",
+       "E     0                  0           100000    base  <Species at 0x1c18703d68>\n",
+       "B     1             100000              NaN    base  <Species at 0x1c18764080>\n",
+       "      2             100000              NaN    base  <Species at 0x1c1874f518>\n",
+       "E     1             100000              NaN    base  <Species at 0x1c18785400>\n",
+       "      2             100000              NaN    base  <Species at 0x1c18764eb8>\n",
+       "      3             100000              NaN    base  <Species at 0x1c18764748>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "be.species"
    ]

From 426795b1513589ec5b7732b06e9a4e4025b6b050 Mon Sep 17 00:00:00 2001
From: nathanlyons <nathan.j.lyons@gmail.com>
Date: Thu, 10 May 2018 17:42:17 -0500
Subject: [PATCH 09/15] DataFrames explained

---
 biota_evolution/BiotaEvolver_tutorial.ipynb   | 514 ++++++----------
 biota_evolution/images/Untitled-1.png         | Bin 515 -> 0 bytes
 biota_evolution/images/species_number.png     | Bin 2512 -> 5293 bytes
 .../model_grid_steady_state_elevation.txt     | 105 ++++
 biota_evolution/notebook.tex                  | 565 ------------------
 5 files changed, 294 insertions(+), 890 deletions(-)
 delete mode 100644 biota_evolution/images/Untitled-1.png
 create mode 100644 biota_evolution/model_grid_steady_state_elevation.txt
 delete mode 100644 biota_evolution/notebook.tex

diff --git a/biota_evolution/BiotaEvolver_tutorial.ipynb b/biota_evolution/BiotaEvolver_tutorial.ipynb
index d9b2edb..de4092c 100644
--- a/biota_evolution/BiotaEvolver_tutorial.ipynb
+++ b/biota_evolution/BiotaEvolver_tutorial.ipynb
@@ -26,9 +26,9 @@
     "\n",
     "The configuration of the model in this tutorial loosely follows the fault throw experiment in Lyons et al., in preperation for Earth Surface Dynamics.\n",
     "\n",
-    "## Species macroevolution in the model component\n",
+    "## Macroevolution processes built into species\n",
     "\n",
-    "The base species class evolves in these processes:\n",
+    "The evolution of the base species is driven by these processes:\n",
     "* dispersal: geographic range expansion\n",
     "* speciation: lineage splitting\n",
     "* extinction: lineage termination"
@@ -43,15 +43,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from copy import deepcopy\n",
-    "from landlab import RasterModelGrid, CLOSED_BOUNDARY, FIXED_VALUE_BOUNDARY\n",
+    "from landlab import CLOSED_BOUNDARY, FIXED_VALUE_BOUNDARY\n",
     "from landlab.components import (BiotaEvolver, FastscapeEroder, FlowRouter,\n",
     "                                LinearDiffuser)\n",
-    "from landlab.components.biota_macroevolution import Species, Zone\n",
+    "from landlab.components.biota_macroevolution import plot, Species, Zone\n",
+    "from landlab.io import read_esri_ascii\n",
+    "from landlab.plot import imshow_grid\n",
+    "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "\n",
     "%matplotlib inline"
@@ -61,111 +63,115 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Create a model grid with steady state topography\n",
-    "\n",
-    "Here, the topography of a Landlab model grid brought to topographic steady state."
+    "## Prepare the grid and components of the model."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Create a grid with random initial topography."
+    "Load a model grid that has steady state topography. The top and bottom boundaries are set to open."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "dx = 200\n",
-    "nrows = 100\n",
-    "ncols = 20\n",
-    "mg_ss = RasterModelGrid(nrows, ncols, dx)\n",
-    "mg_ss.set_status_at_node_on_edges(right=CLOSED_BOUNDARY,\n",
-    "                                  top=FIXED_VALUE_BOUNDARY,\n",
-    "                                  left=CLOSED_BOUNDARY,\n",
-    "                                  bottom=FIXED_VALUE_BOUNDARY)\n",
-    "z_ss = mg_ss.add_zeros('node', 'topographic__elevation')\n",
-    "np.random.seed(500)\n",
-    "z_ss += np.random.rand(z_ss.size)"
+    "(mg, z) = read_esri_ascii('model_grid_steady_state_elevation.txt')\n",
+    "mg.at_node['topographic__elevation'] = z\n",
+    "mg.set_status_at_node_on_edges(right=CLOSED_BOUNDARY, top=FIXED_VALUE_BOUNDARY,\n",
+    "                               left=CLOSED_BOUNDARY,\n",
+    "                               bottom=FIXED_VALUE_BOUNDARY)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Set parameters and initialize components."
+    "Initialize components. BiotaEvolver requires only one parameter, a model grid."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "dt = 1000\n",
-    "total_time = int(4e6)\n",
-    "t = range(0, total_time, dt)\n",
-    "U = 7e-5\n",
-    "uplift_per_step = U * dt\n",
-    "\n",
-    "# Set stream power model parameters.\n",
-    "K_sp = 10e-5\n",
-    "m_sp = 0.5\n",
-    "n_sp = 1\n",
-    "\n",
-    "k_d = 0.0001\n",
-    "\n",
-    "fr = FlowRouter(mg_ss)\n",
-    "sp = FastscapeEroder(mg_ss, K_sp=K_sp, m_sp=m_sp, n_sp=n_sp)\n",
-    "lf = LinearDiffuser(mg_ss, linear_diffusivity=k_d, deposit=False)"
+    "fr = FlowRouter(mg)\n",
+    "sp = FastscapeEroder(mg, K_sp=10e-5, m_sp=0.5, n_sp=1.0)\n",
+    "lf = LinearDiffuser(mg, linear_diffusivity=0.0001)\n",
+    "be = BiotaEvolver(mg)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Run the model until topography reaches steady state (ss)."
+    "## Prepare BiotaEvolver objects\n",
+    "\n",
+    "Species and zones are the two object types central to BiotaEvolver.\n",
+    "\n",
+    "### Zones\n",
+    "\n",
+    "Zones are portions of a model grid. A Species object exists throughout a zone.\n",
+    "\n",
+    "At each timestep, the spatial intersection of the zones at the prior time and current time are identified and typed. For example, a zone in the prior time intersects two zones in the current time. This zone path is typed one-to-many.\n",
+    "\n",
+    "Macroevolutionary processes are carried out by the intersection of zones over time. The connectivity of zones in BiotaEvolver are called 'paths', and the relationship of zones is described by the path type. Not all combinations of none, one, and many are considered at the time of this tutorial. All none-to-n and some many-to-n are not considered. \n",
+    "\n",
+    "<img style=\"float: left;\" src=\"images/zone__one_to_none.png\"> &nbsp; zone<sub>*t*</sub>: a zone in time, *t*.<br>\n",
+    "\n",
+    "<img style=\"float: left;\" src=\"images/zone__later_time.png\"> &nbsp; zone<sub>*t*+1</sub>: a zone in the time following *t*.\n",
+    "\n",
+    "<center>path type</center> | <center>graphical<br>represention</center> | <center>zone temporal connectivity</center> | <center>macroevolution implications</center>\n",
+    "--- | --- | --- | ---\n",
+    "one-to-none | ![](images/zone__one_to_none.png) | zone<sub>*t*</sub> does not intersect a zone in *t*+1. | The species in the zone of the earlier time will go extinct. \n",
+    "one-to-one | ![](images/zone__one_to_one.png) | zone<sub>*t*</sub> intersects zone<sub>*t*+1</sub>. | The species in the zone of the earlier time will relocate to the zone of the later time.\n",
+    "one-to-many | ![](images/zone__one_to_many.png) | zone<sub>*t*</sub> intersects multiple zones in *t*+1. | The species in the zone of the earlier time disperses across the multiple zones in the later time. The species in the zones of the later time are geographical disconnected, thus speciation occurs.\n",
+    "many-to-one | ![](images/zone__many_to_one.png) | Multiple zones in *t* intersect zone<sub>*t*+1</sub>. | Species density increases locally.\n",
+    "many-to-many | ![](images/zone__many_to_many.png) | Multiple zones in *t* intersect multiple zones in *t*+1. | Species distribution shifts. Speciation occurs where the zone of a species at *t* intersects more than one zone at *t*+1.\n",
+    "\n",
+    "### Species\n",
+    "\n",
+    "The model domain is populated with species by the user.\n",
+    "\n",
+    "The rules of macroevolution are programmed into species. Behind the scenes, zone paths are the conditions evaluated by the macroevolution rules. The outcome of the rules given the zone paths are described in the zone table above. For example, a one-to-many path:\n",
+    "\n",
+    "![](images/zone_path__one_to_many.png)\n",
+    "\n",
+    "The base species class \n",
+    "\n",
+    "Users may develop species with different behaviors and macroevolution rules.\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "Populate BiotaEvolver zones with species.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "for t_i in t:\n",
-    "    z_ss[mg_ss.core_nodes] += uplift_per_step\n",
+    "fr.run_one_step()\n",
     "\n",
-    "    fr.run_one_step()\n",
-    "    sp.run_one_step(dt)\n",
-    "    lf.run_one_step(dt)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Fault the grid and evolve species"
+    "min_stream_area = 1e6\n",
+    "zones = Zone.get_zones_with_area_threshold(mg, min_stream_area)\n",
+    "for zone in zones:\n",
+    "    new_species = Species(0, zone)\n",
+    "    be.introduce_species(new_species, 0)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Create a copy of the steady state grid."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mg = deepcopy(mg_ss)\n",
-    "z = mg.at_node['topographic__elevation']"
+    "## Initiate a fault"
    ]
   },
   {
@@ -177,161 +183,77 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "grid_width = mg.number_of_node_columns * mg.dx\n",
     "x_of_fault = grid_width * 0.5\n",
     "core_mask = mg.node_is_core()\n",
-    "upthown_block_mask = mg.x_of_node >= x_of_fault\n",
-    "upthown_block_mask = np.all([core_mask, upthown_block_mask], 0)\n",
-    "z[upthown_block_mask] += 100"
+    "upthown_nodes = mg.x_of_node >= x_of_fault\n",
+    "upthown_block_mask = np.all([core_mask, upthown_nodes], 0)\n",
+    "\n",
+    "imshow_grid(mg, 'topographic__elevation')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Initialize components."
+    "Uplift the nodes of the upthrown block by 20 m."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "fr = FlowRouter(mg)\n",
-    "sp = FastscapeEroder(mg, K_sp=K_sp, m_sp=m_sp, n_sp=n_sp)\n",
-    "lf = LinearDiffuser(mg, linear_diffusivity=k_d, deposit=False)\n",
-    "\n",
-    "be = BiotaEvolver(mg)\n",
-    "dt_be = 1e5"
+    "z[upthown_block_mask] += 20"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Species and zones are the two object types central to BiotaEvolver.\n",
-    "\n",
-    "### Zones\n",
+    "## Model species evolution\n",
     "\n",
-    "Zones are portions of a model grid. A Species object exists throughout a zone.\n",
-    "\n",
-    "At each timestep, the spatial intersection of the zones at the prior time and current time are identified and typed. For example, a zone in the prior time intersects two zones in the current time. This zone path is typed one-to-many.\n",
-    "\n",
-    "Macroevolutionary processes are carried out by the intersection of zones over time. The connectivity of zones in BiotaEvolver are called 'paths', and the relationship of zones is described by the path type. Not all combinations of none, one, and many are considered at the time of this tutorial. All none-to-n and some many-to-n are not considered. \n",
-    "\n",
-    "<img style=\"float: left;\" src=\"images/zone__one_to_none.png\"> &nbsp; zone<sub>*t*</sub>: a zone in time, *t*.<br>\n",
-    "\n",
-    "<img style=\"float: left;\" src=\"images/zone__later_time.png\"> &nbsp; zone<sub>*t*+1</sub>: a zone in the time following *t*.\n",
-    "\n",
-    "<center>path type</center> | <center>graphical<br>represention</center> | <center>zone temporal connectivity</center> | <center>macroevolution implications</center>\n",
-    "--- | --- | --- | ---\n",
-    "one-to-none | ![](images/zone__one_to_none.png) | zone<sub>*t*</sub> does not intersect a zone in *t*+1. | The species in the zone of the earlier time will go extinct. \n",
-    "one-to-one | ![](images/zone__one_to_one.png) | zone<sub>*t*</sub> intersects zone<sub>*t*+1</sub>. | The species in the zone of the earlier time will relocate to the zone of the later time.\n",
-    "one-to-many | ![](images/zone__one_to_many.png) | zone<sub>*t*</sub> intersects multiple zones in *t*+1. | The species in the zone of the earlier time disperses across the multiple zones in the later time. The species in the zones of the later time are geographical disconnected, thus speciation occurs.\n",
-    "many-to-one | ![](images/zone__many_to_one.png) | Multiple zones in *t* intersect zone<sub>*t*+1</sub>. | Species density increases locally.\n",
-    "many-to-many | ![](images/zone__many_to_many.png) | Multiple zones in *t* intersect multiple zones in *t*+1. | Species distribution shifts. Speciation occurs where the zone of a species at *t* intersects more than one zone at *t*+1.\n",
-    "\n",
-    "### Species\n",
-    "\n",
-    "The model domain is populated with species by the user.\n",
-    "\n",
-    "The rules of macroevolution are programmed into species. Behind the scenes, zone paths are the conditions evaluated by the macroevolution rules. The outcome of the rules given the zone paths are described in the zone table above. For example, a one-to-many path:\n",
-    "\n",
-    "![](images/zone_path__one_to_many.png)\n",
+    "First, set model time step and uplift parameters.\n",
     "\n",
-    "The base species class \n",
-    "\n",
-    "Users may develop species with different behaviors and macroevolution rules.\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "Populate BiotaEvolver zones with species.\n"
+    "BiotaEvolver runs every 10 timesteps of the other components."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "min_stream_area = 1e6\n",
-    "zones = Zone.get_zones_with_area_threshold(mg, min_stream_area)\n",
-    "for zone in zones:\n",
-    "    new_species = Species(0, zone)\n",
-    "    be.introduce_species(new_species, 0)"
+    "dt = 1000\n",
+    "total_time = int(1e5)\n",
+    "nt = int(total_time // dt)\n",
+    "\n",
+    "dt_be = 10 * dt\n",
+    "\n",
+    "uplift_rate = 7e-5\n",
+    "uplift_per_step = uplift_rate * dt"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Run model. The ```run_one_step``` function of BiotaEvolver takes the new zones of the time step."
+    "Run model. The ```run_one_step``` function of BiotaEvolver takes the new zones of the time step.\n",
+    "\n",
+    "This code block may take a couple minutes to run."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "BiotaEvolver time 100000 ---------------------------\n",
-      "many-to-one\n",
-      "many-to-many\n",
-      "many-to-one\n",
-      "one-to-one\n",
-      "many-to-many\n",
-      "BiotaEvolver time 200000 ---------------------------\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "BiotaEvolver time 300000 ---------------------------\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "BiotaEvolver time 400000 ---------------------------\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "BiotaEvolver time 500000 ---------------------------\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n",
-      "many-to-one\n"
-     ]
-    }
-   ],
-   "source": [
-    "total_time = 5e5\n",
-    "nt = int(total_time // dt)\n",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "for time_i in range(nt):\n",
     "    # Uniformly uplift.\n",
     "    z[mg.core_nodes] += uplift_per_step\n",
@@ -345,156 +267,16 @@
     "\n",
     "    if elapsed_time % dt_be == 0:\n",
     "        # Run BiotaEvolver with updated zones.\n",
-    "        print('BiotaEvolver time', elapsed_time, '---------------------------')\n",
     "        zones = Zone.get_zones_with_area_threshold(mg, min_stream_area)\n",
     "        be.run_one_step(elapsed_time, zones)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>time_appeared</th>\n",
-       "      <th>time_disappeared</th>\n",
-       "      <th>subtype</th>\n",
-       "      <th>object</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>clade</th>\n",
-       "      <th>number</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>A</th>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x1c186fd668&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>B</th>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>100000</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x10faba390&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>C</th>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x10d554278&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>D</th>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x10e70ad68&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>E</th>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>100000</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x1c18703d68&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">B</th>\n",
-       "      <th>1</th>\n",
-       "      <td>100000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x1c18764080&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>100000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x1c1874f518&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"3\" valign=\"top\">E</th>\n",
-       "      <th>1</th>\n",
-       "      <td>100000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x1c18785400&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>100000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x1c18764eb8&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>100000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x1c18764748&gt;</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             time_appeared time_disappeared subtype                     object\n",
-       "clade number                                                                  \n",
-       "A     0                  0              NaN    base  <Species at 0x1c186fd668>\n",
-       "B     0                  0           100000    base   <Species at 0x10faba390>\n",
-       "C     0                  0              NaN    base   <Species at 0x10d554278>\n",
-       "D     0                  0              NaN    base   <Species at 0x10e70ad68>\n",
-       "E     0                  0           100000    base  <Species at 0x1c18703d68>\n",
-       "B     1             100000              NaN    base  <Species at 0x1c18764080>\n",
-       "      2             100000              NaN    base  <Species at 0x1c1874f518>\n",
-       "E     1             100000              NaN    base  <Species at 0x1c18785400>\n",
-       "      2             100000              NaN    base  <Species at 0x1c18764eb8>\n",
-       "      3             100000              NaN    base  <Species at 0x1c18764748>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "be.species"
+    "## Plot results\n",
+    "\n"
    ]
   },
   {
@@ -503,9 +285,22 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "be.species"
+    "plt.subplot(1, 2, 1)\n",
+    "imshow_grid(mg, 'topographic__elevation', colorbar_label='elevation (m)')\n",
+    "\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plot.imshow_zones(mg, zones)\n",
+    "\n",
+    "plot.plot_number_of_species(be.record, be.species)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -545,25 +340,51 @@
     "![](images/species_number.png)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## DataFrames\n",
+    "\n",
+    "Data are stored in Pandas DataFrames."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "be.species"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "be.zones"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "be.zone_paths"
+   ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Click here for more <a href=\"https://github.com/landlab/landlab/wiki/Tutorials\">Landlab tutorials</a>"
+    "### Record DataFrames\n",
+    "\n",
+    "Instances of BiotaEvolver, species, and zones each have DataFrames called ```record```. Rows are data stored at each time the model is ran.\n",
+    "\n",
+    "Examine the BiotaEvolver record."
    ]
   },
   {
@@ -571,7 +392,50 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "be.record"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Examine the record of a species."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s = be.species.iloc[0].object\n",
+    "s.record"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Examine the record of a zone."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "z = be.zones.iloc[0].object\n",
+    "z.record"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Click here for more <a href=\"https://github.com/landlab/landlab/wiki/Tutorials\">Landlab tutorials</a>"
+   ]
   }
  ],
  "metadata": {
diff --git a/biota_evolution/images/Untitled-1.png b/biota_evolution/images/Untitled-1.png
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diff --git a/biota_evolution/model_grid_steady_state_elevation.txt b/biota_evolution/model_grid_steady_state_elevation.txt
new file mode 100644
index 0000000..5ced41f
--- /dev/null
+++ b/biota_evolution/model_grid_steady_state_elevation.txt
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diff --git a/biota_evolution/notebook.tex b/biota_evolution/notebook.tex
deleted file mode 100644
index 618cd06..0000000
--- a/biota_evolution/notebook.tex
+++ /dev/null
@@ -1,565 +0,0 @@
-
-% Default to the notebook output style
-
-    
-
-
-% Inherit from the specified cell style.
-
-
-
-
-    
-\documentclass[11pt]{article}
-
-    
-    
-    \usepackage[T1]{fontenc}
-    % Nicer default font (+ math font) than Computer Modern for most use cases
-    \usepackage{mathpazo}
-
-    % Basic figure setup, for now with no caption control since it's done
-    % automatically by Pandoc (which extracts ![](path) syntax from Markdown).
-    \usepackage{graphicx}
-    % We will generate all images so they have a width \maxwidth. This means
-    % that they will get their normal width if they fit onto the page, but
-    % are scaled down if they would overflow the margins.
-    \makeatletter
-    \def\maxwidth{\ifdim\Gin@nat@width>\linewidth\linewidth
-    \else\Gin@nat@width\fi}
-    \makeatother
-    \let\Oldincludegraphics\includegraphics
-    % Set max figure width to be 80% of text width, for now hardcoded.
-    \renewcommand{\includegraphics}[1]{\Oldincludegraphics[width=.8\maxwidth]{#1}}
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-    % proper Figure object with a Caption API and a way to capture that
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-    
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-    
-    
-
-    \section{Using the Landlab BiotaEvolver
-component}\label{using-the-landlab-biotaevolver-component}
-
- For instructions on how to run an interactive iPython notebook, click
-here: https://github.com/landlab/tutorials/blob/master/README.md For
-more Landlab tutorials, click here:
-https://github.com/landlab/landlab/wiki/Tutorials
-
-In this tutorial we will: * Introduce species into a Landlab model. *
-Set the zones in which the species operate. * Evolve a landscape and the
-species over time. * Explore BiotaEvolver output data structures.
-
-The configuration of the model in this tutorial loosely follows the
-fault throw experiment in Lyons et al., in preperation for Earth Surface
-Dynamics.
-
-    Import modules and set the environment to show plots inline in this
-notebook.
-
-    \begin{Verbatim}[commandchars=\\\{\}]
-{\color{incolor}In [{\color{incolor}2}]:} \PY{k+kn}{from} \PY{n+nn}{landlab} \PY{k}{import} \PY{n}{RasterModelGrid}\PY{p}{,} \PY{n}{CLOSED\PYZus{}BOUNDARY}\PY{p}{,} \PY{n}{FIXED\PYZus{}VALUE\PYZus{}BOUNDARY}
-        \PY{k+kn}{from} \PY{n+nn}{landlab}\PY{n+nn}{.}\PY{n+nn}{components} \PY{k}{import} \PY{p}{(}\PY{n}{BiotaEvolver}\PY{p}{,} \PY{n}{FastscapeEroder}\PY{p}{,} \PY{n}{FlowRouter}\PY{p}{,}
-                                        \PY{n}{LinearDiffuser}\PY{p}{)}
-        \PY{k+kn}{from} \PY{n+nn}{landlab}\PY{n+nn}{.}\PY{n+nn}{components}\PY{n+nn}{.}\PY{n+nn}{biota\PYZus{}macroevolution} \PY{k}{import} \PY{n}{Species}\PY{p}{,} \PY{n}{Zone}
-        \PY{k+kn}{import} \PY{n+nn}{numpy} \PY{k}{as} \PY{n+nn}{np}
-        
-        \PY{o}{\PYZpc{}}\PY{k}{matplotlib} inline
-\end{Verbatim}
-
-
-    \subsection{Create a model grid with steady state
-topography}\label{create-a-model-grid-with-steady-state-topography}
-
-Here the topography of a Landlab model grid brought to topographic
-steady state.
-
-    Create a grid with random initial topography.
-
-    \begin{Verbatim}[commandchars=\\\{\}]
-{\color{incolor}In [{\color{incolor}3}]:} \PY{n}{dx} \PY{o}{=} \PY{l+m+mi}{200}
-        \PY{n}{nrows} \PY{o}{=} \PY{l+m+mi}{100}
-        \PY{n}{ncols} \PY{o}{=} \PY{l+m+mi}{20}
-        \PY{n}{mg\PYZus{}ss} \PY{o}{=} \PY{n}{RasterModelGrid}\PY{p}{(}\PY{n}{nrows}\PY{p}{,} \PY{n}{ncols}\PY{p}{,} \PY{n}{dx}\PY{p}{)}
-        \PY{n}{mg\PYZus{}ss}\PY{o}{.}\PY{n}{set\PYZus{}status\PYZus{}at\PYZus{}node\PYZus{}on\PYZus{}edges}\PY{p}{(}\PY{n}{right}\PY{o}{=}\PY{n}{CLOSED\PYZus{}BOUNDARY}\PY{p}{,}
-                                          \PY{n}{top}\PY{o}{=}\PY{n}{FIXED\PYZus{}VALUE\PYZus{}BOUNDARY}\PY{p}{,}
-                                          \PY{n}{left}\PY{o}{=}\PY{n}{CLOSED\PYZus{}BOUNDARY}\PY{p}{,}
-                                          \PY{n}{bottom}\PY{o}{=}\PY{n}{FIXED\PYZus{}VALUE\PYZus{}BOUNDARY}\PY{p}{)}
-        \PY{n}{z\PYZus{}ss} \PY{o}{=} \PY{n}{mg\PYZus{}ss}\PY{o}{.}\PY{n}{add\PYZus{}zeros}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{node}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{topographic\PYZus{}\PYZus{}elevation}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
-        \PY{n}{np}\PY{o}{.}\PY{n}{random}\PY{o}{.}\PY{n}{seed}\PY{p}{(}\PY{l+m+mi}{500}\PY{p}{)}
-        \PY{n}{z\PYZus{}ss} \PY{o}{+}\PY{o}{=} \PY{n}{np}\PY{o}{.}\PY{n}{random}\PY{o}{.}\PY{n}{rand}\PY{p}{(}\PY{n}{z\PYZus{}ss}\PY{o}{.}\PY{n}{size}\PY{p}{)}
-\end{Verbatim}
-
-
-    Set parameters and initialize components.
-
-    \begin{Verbatim}[commandchars=\\\{\}]
-{\color{incolor}In [{\color{incolor} }]:} \PY{n}{dt} \PY{o}{=} \PY{l+m+mi}{1000}
-        \PY{n}{total\PYZus{}time} \PY{o}{=} \PY{n+nb}{int}\PY{p}{(}\PY{l+m+mf}{4e6}\PY{p}{)}
-        \PY{n}{t} \PY{o}{=} \PY{n+nb}{range}\PY{p}{(}\PY{l+m+mi}{0}\PY{p}{,} \PY{n}{total\PYZus{}time}\PY{p}{,} \PY{n}{dt}\PY{p}{)}
-        \PY{n}{U} \PY{o}{=} \PY{l+m+mf}{7e\PYZhy{}5}
-        \PY{n}{uplift\PYZus{}per\PYZus{}step} \PY{o}{=} \PY{n}{U} \PY{o}{*} \PY{n}{dt}
-        \PY{n}{K\PYZus{}sp} \PY{o}{=} \PY{l+m+mf}{10e\PYZhy{}5}
-        \PY{n}{m\PYZus{}sp} \PY{o}{=} \PY{l+m+mf}{0.5}
-        \PY{n}{n\PYZus{}sp} \PY{o}{=} \PY{l+m+mi}{1}
-        \PY{n}{k\PYZus{}d} \PY{o}{=} \PY{l+m+mf}{0.0001}
-        
-        \PY{n}{fr} \PY{o}{=} \PY{n}{FlowRouter}\PY{p}{(}\PY{n}{mg\PYZus{}ss}\PY{p}{)}
-        \PY{n}{sp} \PY{o}{=} \PY{n}{FastscapeEroder}\PY{p}{(}\PY{n}{mg\PYZus{}ss}\PY{p}{,} \PY{n}{K\PYZus{}sp}\PY{o}{=}\PY{n}{K\PYZus{}sp}\PY{p}{,} \PY{n}{m\PYZus{}sp}\PY{o}{=}\PY{n}{m\PYZus{}sp}\PY{p}{,} \PY{n}{n\PYZus{}sp}\PY{o}{=}\PY{n}{n\PYZus{}sp}\PY{p}{)}
-        \PY{n}{lf} \PY{o}{=} \PY{n}{LinearDiffuser}\PY{p}{(}\PY{n}{mg\PYZus{}ss}\PY{p}{,} \PY{n}{linear\PYZus{}diffusivity}\PY{o}{=}\PY{n}{k\PYZus{}d}\PY{p}{,} \PY{n}{deposit}\PY{o}{=}\PY{k+kc}{False}\PY{p}{)}
-\end{Verbatim}
-
-
-    Run the model until topography reaches steady state (ss).
-
-    \begin{Verbatim}[commandchars=\\\{\}]
-{\color{incolor}In [{\color{incolor} }]:} \PY{k}{for} \PY{n}{t\PYZus{}i} \PY{o+ow}{in} \PY{n}{t}\PY{p}{:}
-            \PY{n}{z\PYZus{}ss}\PY{p}{[}\PY{n}{mg\PYZus{}ss}\PY{o}{.}\PY{n}{core\PYZus{}nodes}\PY{p}{]} \PY{o}{+}\PY{o}{=} \PY{n}{uplift\PYZus{}per\PYZus{}step}
-        
-            \PY{n}{fr}\PY{o}{.}\PY{n}{run\PYZus{}one\PYZus{}step}\PY{p}{(}\PY{p}{)}
-            \PY{n}{sp}\PY{o}{.}\PY{n}{run\PYZus{}one\PYZus{}step}\PY{p}{(}\PY{n}{dt}\PY{p}{)}
-            \PY{n}{lf}\PY{o}{.}\PY{n}{run\PYZus{}one\PYZus{}step}\PY{p}{(}\PY{n}{dt}\PY{p}{)}
-\end{Verbatim}
-
-
-    \subsection{Fault the grid and evolve
-species}\label{fault-the-grid-and-evolve-species}
-
-    Create a copy of the steady state grid.
-
-    \begin{Verbatim}[commandchars=\\\{\}]
-{\color{incolor}In [{\color{incolor} }]:} \PY{n}{mg} \PY{o}{=} \PY{n}{deepcopy}\PY{p}{(}\PY{n}{mg\PYZus{}ss}\PY{p}{)}
-        \PY{n}{z} \PY{o}{=} \PY{n}{mg}\PY{o}{.}\PY{n}{at\PYZus{}node}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{topographic\PYZus{}\PYZus{}elevation}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}
-\end{Verbatim}
-
-
-    Define the fault.
-
-    \begin{Verbatim}[commandchars=\\\{\}]
-{\color{incolor}In [{\color{incolor} }]:} \PY{n}{grid\PYZus{}width} \PY{o}{=} \PY{n}{mg}\PY{o}{.}\PY{n}{number\PYZus{}of\PYZus{}node\PYZus{}columns} \PY{o}{*} \PY{n}{mg}\PY{o}{.}\PY{n}{dx}
-        \PY{n}{x\PYZus{}of\PYZus{}fault} \PY{o}{=} \PY{n}{grid\PYZus{}width} \PY{o}{*} \PY{l+m+mf}{0.5}
-        \PY{n}{core\PYZus{}mask} \PY{o}{=} \PY{n}{mg}\PY{o}{.}\PY{n}{node\PYZus{}is\PYZus{}core}\PY{p}{(}\PY{p}{)}
-        \PY{n}{upthown\PYZus{}block\PYZus{}mask} \PY{o}{=} \PY{n}{mg}\PY{o}{.}\PY{n}{x\PYZus{}of\PYZus{}node} \PY{o}{\PYZgt{}}\PY{o}{=} \PY{n}{x\PYZus{}of\PYZus{}fault}
-        \PY{n}{upthown\PYZus{}block\PYZus{}mask} \PY{o}{=} \PY{n}{np}\PY{o}{.}\PY{n}{all}\PY{p}{(}\PY{p}{[}\PY{n}{core\PYZus{}mask}\PY{p}{,} \PY{n}{upthown\PYZus{}block\PYZus{}mask}\PY{p}{]}\PY{p}{,} \PY{l+m+mi}{0}\PY{p}{)}
-        \PY{n}{z}\PY{p}{[}\PY{n}{upthown\PYZus{}block\PYZus{}mask}\PY{p}{]} \PY{o}{+}\PY{o}{=} \PY{l+m+mi}{100}
-\end{Verbatim}
-
-
-    Initialize components.
-
-    \begin{Verbatim}[commandchars=\\\{\}]
-{\color{incolor}In [{\color{incolor} }]:} \PY{n}{fr} \PY{o}{=} \PY{n}{FlowRouter}\PY{p}{(}\PY{n}{mg}\PY{p}{)}
-        \PY{n}{sp} \PY{o}{=} \PY{n}{FastscapeEroder}\PY{p}{(}\PY{n}{mg}\PY{p}{,} \PY{n}{K\PYZus{}sp}\PY{o}{=}\PY{n}{K\PYZus{}sp}\PY{p}{,} \PY{n}{m\PYZus{}sp}\PY{o}{=}\PY{n}{m\PYZus{}sp}\PY{p}{,} \PY{n}{n\PYZus{}sp}\PY{o}{=}\PY{n}{n\PYZus{}sp}\PY{p}{)}
-        \PY{n}{lf} \PY{o}{=} \PY{n}{LinearDiffuser}\PY{p}{(}\PY{n}{mg}\PY{p}{,} \PY{n}{linear\PYZus{}diffusivity}\PY{o}{=}\PY{n}{k\PYZus{}d}\PY{p}{,} \PY{n}{deposit}\PY{o}{=}\PY{k+kc}{False}\PY{p}{)}
-        
-        \PY{n}{be} \PY{o}{=} \PY{n}{BiotaEvolver}\PY{p}{(}\PY{n}{mg}\PY{p}{)}
-        \PY{n}{dt\PYZus{}be} \PY{o}{=} \PY{l+m+mf}{1e5}
-\end{Verbatim}
-
-
-    \begin{verbatim}
-<tr>
-    <th>Title</th>
-</tr>
-<tr>
-    <td>Data</td>
-</tr>
-\end{verbatim}
-
-    Populate BiotaEvolver zones with species.
-
-Zones are portions of a model grid. A Species object exists throughout a
-zone. At each timestep, the spatial intersection of the zones at the
-prior time and current time are identified and typed. For example, a
-zone in the prior time intersects two zones in the current time. This
-zone path is typed one-to-many.
-
-Macroevolutionary processes are carried out by the intersection of zones
-over time. The connectivity of zones in BiotaEvolver are called 'paths',
-and the relationship of zones is described by the path type. Not all
-combinations of none, one, and many are considered at the time of this
-tutorial. All none-to-n and some many-to-n are not considered.
-
-\begin{longtable}[]{@{}llc@{}}
-\toprule
-Name & Description & age\tabularnewline
-\midrule
-\endhead
-Mary & She is a nice girl. & 20\tabularnewline
-Jackie Junior & He is a very naughty boy. & 5\tabularnewline
-\bottomrule
-\end{longtable}
-
-path type \textbar{}
-
-graphicalrepresention
-
-\textbar{}
-
-zone description
-
-\textbar{}
-
-macroevolution implications
-
--\/-\/- \textbar{} -\/-\/- \textbar{} -\/-\/- \textbar{} -\/-\/-
-one-to-none \textbar{} \includegraphics{images/zone__one_to_none.png}
-\textbar{} The zone in the earlier time does not intersect a zone in a
-later time. \textbar{} The species in the zone of the earlier time will
-go extinct. one-to-one \textbar{} 2 \textbar{} 3 \textbar{} 4
-one-to-many \textbar{} 2 \textbar{} 3 \textbar{} 4 many-to-one
-\textbar{} 2 \textbar{} 3 \textbar{} 4 many-to-many \textbar{} 2
-\textbar{} 3 \textbar{} 4
-
-    \begin{Verbatim}[commandchars=\\\{\}]
-{\color{incolor}In [{\color{incolor}1}]:} \PY{n}{min\PYZus{}stream\PYZus{}area} \PY{o}{=} \PY{l+m+mf}{1e6}
-        \PY{n}{zones} \PY{o}{=} \PY{n}{Zone}\PY{o}{.}\PY{n}{get\PYZus{}zones\PYZus{}with\PYZus{}area\PYZus{}threshold}\PY{p}{(}\PY{n}{mg}\PY{p}{,} \PY{n}{min\PYZus{}stream\PYZus{}area}\PY{p}{)}
-        \PY{k}{for} \PY{n}{zone} \PY{o+ow}{in} \PY{n}{zones}\PY{p}{:}
-            \PY{n}{new\PYZus{}species} \PY{o}{=} \PY{n}{Species}\PY{p}{(}\PY{l+m+mi}{0}\PY{p}{,} \PY{n}{zone}\PY{p}{)}
-            \PY{n}{be}\PY{o}{.}\PY{n}{introduce\PYZus{}species}\PY{p}{(}\PY{n}{new\PYZus{}species}\PY{p}{,} \PY{l+m+mi}{0}\PY{p}{)}
-\end{Verbatim}
-
-
-    \begin{Verbatim}[commandchars=\\\{\}]
-
-        ---------------------------------------------------------------------------
-
-        NameError                                 Traceback (most recent call last)
-
-        <ipython-input-1-53c90586ee9c> in <module>()
-          1 min\_stream\_area = 1e6
-    ----> 2 zones = Zone.get\_zones\_with\_area\_threshold(mg, min\_stream\_area)
-          3 for zone in zones:
-          4     new\_species = Species(0, zone)
-          5     be.introduce\_species(new\_species, 0)
-
-
-        NameError: name 'Zone' is not defined
-
-    \end{Verbatim}
-
-    Run model.
-
-    \begin{Verbatim}[commandchars=\\\{\}]
-{\color{incolor}In [{\color{incolor} }]:} \PY{n}{total\PYZus{}time} \PY{o}{=} \PY{l+m+mf}{5e5}
-        \PY{n}{nt} \PY{o}{=} \PY{n+nb}{int}\PY{p}{(}\PY{n}{total\PYZus{}time} \PY{o}{/}\PY{o}{/} \PY{n}{dt}\PY{p}{)}
-        \PY{k}{for} \PY{n}{time\PYZus{}i} \PY{o+ow}{in} \PY{n+nb}{range}\PY{p}{(}\PY{n}{nt}\PY{p}{)}\PY{p}{:}
-            \PY{c+c1}{\PYZsh{} Uniformly uplift.}
-            \PY{n}{z}\PY{p}{[}\PY{n}{mg}\PY{o}{.}\PY{n}{core\PYZus{}nodes}\PY{p}{]} \PY{o}{+}\PY{o}{=} \PY{n}{uplift\PYZus{}per\PYZus{}step}
-        
-            \PY{c+c1}{\PYZsh{} Increment components.}
-            \PY{n}{fr}\PY{o}{.}\PY{n}{run\PYZus{}one\PYZus{}step}\PY{p}{(}\PY{p}{)}
-            \PY{n}{sp}\PY{o}{.}\PY{n}{run\PYZus{}one\PYZus{}step}\PY{p}{(}\PY{n}{dt}\PY{p}{)}
-            \PY{n}{lf}\PY{o}{.}\PY{n}{run\PYZus{}one\PYZus{}step}\PY{p}{(}\PY{n}{dt}\PY{p}{)}
-        
-            \PY{n}{elapsed\PYZus{}time} \PY{o}{=} \PY{n}{dt} \PY{o}{*} \PY{p}{(}\PY{n}{time\PYZus{}i} \PY{o}{+} \PY{l+m+mi}{1}\PY{p}{)}
-        
-            \PY{k}{if} \PY{n}{elapsed\PYZus{}time} \PY{o}{\PYZpc{}} \PY{n}{dt\PYZus{}be} \PY{o}{==} \PY{l+m+mi}{0}\PY{p}{:}
-                \PY{c+c1}{\PYZsh{} Run BiotaEvolver with updated zones.}
-                \PY{n+nb}{print}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{BiotaEvolver time}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{n}{elapsed\PYZus{}time}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
-                \PY{n}{zones} \PY{o}{=} \PY{n}{Zone}\PY{o}{.}\PY{n}{get\PYZus{}zones\PYZus{}with\PYZus{}area\PYZus{}threshold}\PY{p}{(}\PY{n}{mg}\PY{p}{,} \PY{n}{min\PYZus{}stream\PYZus{}area}\PY{p}{)}
-                \PY{n}{be}\PY{o}{.}\PY{n}{run\PYZus{}one\PYZus{}step}\PY{p}{(}\PY{n}{elapsed\PYZus{}time}\PY{p}{,} \PY{n}{zones}\PY{p}{)}
-\end{Verbatim}
-
-
-    \subsection{Species identifiers and phylogenetic
-trees}\label{species-identifiers-and-phylogenetic-trees}
-
-Species identifiers are automatically generated. They are stored as
-tuples in species objects. The first element of the identifier tuple is
-the clade name, and the second element is the species number. For
-example, the first species of clade A is 0, so the identifier is (A, 0)
-and the tenth species of this clade is (A, 9).
-
-\subsubsection{Clade name}\label{clade-name}
-
-A clade is a group of species that share a common ancestor. Species may
-share multiple common ancestors, so clades can be defined differently in
-the same tree. The clade(s) of this example tree can be defined in
-multiple ways:
-
-\begin{figure}
-\centering
-\includegraphics{images/clade_definitions.png}
-\caption{}
-\end{figure}
-
-In BiotaEvolver, clades are defined by the root node for the purpose of
-assigning species identifiers. The species at the root node is: * the
-least recent common ancestor of all species in the tree, and * typically
-introduced to a BiotaEvolver model using the \texttt{introduce\_species}
-function.
-
-The clades of the first 26 species are labeled alphabetically from A to
-Z. The next 26 are labeled AB thru AZ.
-
-1-26: A to Z 27-53: AA to AZ BA to BZ ... AAA to AAZ ABA to ABZ ACA to
-ACZ
-
-\subsubsection{Species number}\label{species-number}
-
-Species are numbered sequently by clade beginning with 0.
-
-In the example tree below, species A0 produced 4 child species. Only one
-species, B0 exists in clade B because child species were not produced by
-B0.
-
-\begin{figure}
-\centering
-\includegraphics{images/species_number.png}
-\caption{}
-\end{figure}
-
-    \subsubsection{Click here for more Landlab
-tutorials}\label{click-here-for-more-landlab-tutorials}
-
-
-    % Add a bibliography block to the postdoc
-    
-    
-    
-    \end{document}

From 5967b6f6115d6b2cee4f5e4d5dcf5caa2a469db1 Mon Sep 17 00:00:00 2001
From: nathanlyons <nathan.j.lyons@gmail.com>
Date: Mon, 14 May 2018 23:36:27 -0500
Subject: [PATCH 10/15] BiotaEvolver tutorial renamed SpeciesEvolver

---
 biota_evolution/BiotaEvolver_tutorial.ipynb   |  462 -----
 mappers/notebook.tex                          |  760 ++++++++
 .../SpeciesEvolver_tutorial.ipynb             | 1587 +++++++++++++++++
 .../images/clade_definitions.png              |  Bin
 .../images/species_number.png                 |  Bin
 .../images/zone__later_time.png               |  Bin
 .../images/zone__many_to_many.png             |  Bin
 .../images/zone__many_to_one.png              |  Bin
 .../images/zone__one_to_many.png              |  Bin
 .../images/zone__one_to_none.png              |  Bin
 .../images/zone__one_to_one.png               |  Bin
 .../images/zone_path__one_to_many.png         |  Bin
 .../model_grid_steady_state_elevation.txt     |    0
 13 files changed, 2347 insertions(+), 462 deletions(-)
 delete mode 100644 biota_evolution/BiotaEvolver_tutorial.ipynb
 create mode 100644 mappers/notebook.tex
 create mode 100644 species_evolution/SpeciesEvolver_tutorial.ipynb
 rename {biota_evolution => species_evolution}/images/clade_definitions.png (100%)
 rename {biota_evolution => species_evolution}/images/species_number.png (100%)
 rename {biota_evolution => species_evolution}/images/zone__later_time.png (100%)
 rename {biota_evolution => species_evolution}/images/zone__many_to_many.png (100%)
 rename {biota_evolution => species_evolution}/images/zone__many_to_one.png (100%)
 rename {biota_evolution => species_evolution}/images/zone__one_to_many.png (100%)
 rename {biota_evolution => species_evolution}/images/zone__one_to_none.png (100%)
 rename {biota_evolution => species_evolution}/images/zone__one_to_one.png (100%)
 rename {biota_evolution => species_evolution}/images/zone_path__one_to_many.png (100%)
 rename {biota_evolution => species_evolution}/model_grid_steady_state_elevation.txt (100%)

diff --git a/biota_evolution/BiotaEvolver_tutorial.ipynb b/biota_evolution/BiotaEvolver_tutorial.ipynb
deleted file mode 100644
index de4092c..0000000
--- a/biota_evolution/BiotaEvolver_tutorial.ipynb
+++ /dev/null
@@ -1,462 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a href=\"http://landlab.github.io\"><img style=\"float: left\" src=\"https://raw.githubusercontent.com/landlab/tutorials/master/landlab_header.png\"></a>\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Using the Landlab BiotaEvolver component\n",
-    "\n",
-    "<hr>\n",
-    "<small> For instructions on how to run an interactive iPython notebook, click here: <a href=\"https://github.com/landlab/tutorials/blob/master/README.md\">https://github.com/landlab/tutorials/blob/master/README.md</a></small><br>\n",
-    "<small>For more Landlab tutorials, click here: <a href=\"https://github.com/landlab/landlab/wiki/Tutorials\">https://github.com/landlab/landlab/wiki/Tutorials</a></small>\n",
-    "<hr>\n",
-    "\n",
-    "In this tutorial we will:\n",
-    "* Introduce species into a Landlab model.\n",
-    "* Set the zones in which the species operate.\n",
-    "* Evolve a landscape and the species over time.\n",
-    "* Explore BiotaEvolver output data structures.\n",
-    "\n",
-    "The configuration of the model in this tutorial loosely follows the fault throw experiment in Lyons et al., in preperation for Earth Surface Dynamics.\n",
-    "\n",
-    "## Macroevolution processes built into species\n",
-    "\n",
-    "The evolution of the base species is driven by these processes:\n",
-    "* dispersal: geographic range expansion\n",
-    "* speciation: lineage splitting\n",
-    "* extinction: lineage termination"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Import modules and set the environment to show plots inline in this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from landlab import CLOSED_BOUNDARY, FIXED_VALUE_BOUNDARY\n",
-    "from landlab.components import (BiotaEvolver, FastscapeEroder, FlowRouter,\n",
-    "                                LinearDiffuser)\n",
-    "from landlab.components.biota_macroevolution import plot, Species, Zone\n",
-    "from landlab.io import read_esri_ascii\n",
-    "from landlab.plot import imshow_grid\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Prepare the grid and components of the model."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Load a model grid that has steady state topography. The top and bottom boundaries are set to open."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "(mg, z) = read_esri_ascii('model_grid_steady_state_elevation.txt')\n",
-    "mg.at_node['topographic__elevation'] = z\n",
-    "mg.set_status_at_node_on_edges(right=CLOSED_BOUNDARY, top=FIXED_VALUE_BOUNDARY,\n",
-    "                               left=CLOSED_BOUNDARY,\n",
-    "                               bottom=FIXED_VALUE_BOUNDARY)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Initialize components. BiotaEvolver requires only one parameter, a model grid."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fr = FlowRouter(mg)\n",
-    "sp = FastscapeEroder(mg, K_sp=10e-5, m_sp=0.5, n_sp=1.0)\n",
-    "lf = LinearDiffuser(mg, linear_diffusivity=0.0001)\n",
-    "be = BiotaEvolver(mg)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Prepare BiotaEvolver objects\n",
-    "\n",
-    "Species and zones are the two object types central to BiotaEvolver.\n",
-    "\n",
-    "### Zones\n",
-    "\n",
-    "Zones are portions of a model grid. A Species object exists throughout a zone.\n",
-    "\n",
-    "At each timestep, the spatial intersection of the zones at the prior time and current time are identified and typed. For example, a zone in the prior time intersects two zones in the current time. This zone path is typed one-to-many.\n",
-    "\n",
-    "Macroevolutionary processes are carried out by the intersection of zones over time. The connectivity of zones in BiotaEvolver are called 'paths', and the relationship of zones is described by the path type. Not all combinations of none, one, and many are considered at the time of this tutorial. All none-to-n and some many-to-n are not considered. \n",
-    "\n",
-    "<img style=\"float: left;\" src=\"images/zone__one_to_none.png\"> &nbsp; zone<sub>*t*</sub>: a zone in time, *t*.<br>\n",
-    "\n",
-    "<img style=\"float: left;\" src=\"images/zone__later_time.png\"> &nbsp; zone<sub>*t*+1</sub>: a zone in the time following *t*.\n",
-    "\n",
-    "<center>path type</center> | <center>graphical<br>represention</center> | <center>zone temporal connectivity</center> | <center>macroevolution implications</center>\n",
-    "--- | --- | --- | ---\n",
-    "one-to-none | ![](images/zone__one_to_none.png) | zone<sub>*t*</sub> does not intersect a zone in *t*+1. | The species in the zone of the earlier time will go extinct. \n",
-    "one-to-one | ![](images/zone__one_to_one.png) | zone<sub>*t*</sub> intersects zone<sub>*t*+1</sub>. | The species in the zone of the earlier time will relocate to the zone of the later time.\n",
-    "one-to-many | ![](images/zone__one_to_many.png) | zone<sub>*t*</sub> intersects multiple zones in *t*+1. | The species in the zone of the earlier time disperses across the multiple zones in the later time. The species in the zones of the later time are geographical disconnected, thus speciation occurs.\n",
-    "many-to-one | ![](images/zone__many_to_one.png) | Multiple zones in *t* intersect zone<sub>*t*+1</sub>. | Species density increases locally.\n",
-    "many-to-many | ![](images/zone__many_to_many.png) | Multiple zones in *t* intersect multiple zones in *t*+1. | Species distribution shifts. Speciation occurs where the zone of a species at *t* intersects more than one zone at *t*+1.\n",
-    "\n",
-    "### Species\n",
-    "\n",
-    "The model domain is populated with species by the user.\n",
-    "\n",
-    "The rules of macroevolution are programmed into species. Behind the scenes, zone paths are the conditions evaluated by the macroevolution rules. The outcome of the rules given the zone paths are described in the zone table above. For example, a one-to-many path:\n",
-    "\n",
-    "![](images/zone_path__one_to_many.png)\n",
-    "\n",
-    "The base species class \n",
-    "\n",
-    "Users may develop species with different behaviors and macroevolution rules.\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "Populate BiotaEvolver zones with species.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fr.run_one_step()\n",
-    "\n",
-    "min_stream_area = 1e6\n",
-    "zones = Zone.get_zones_with_area_threshold(mg, min_stream_area)\n",
-    "for zone in zones:\n",
-    "    new_species = Species(0, zone)\n",
-    "    be.introduce_species(new_species, 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initiate a fault"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Define the fault."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "grid_width = mg.number_of_node_columns * mg.dx\n",
-    "x_of_fault = grid_width * 0.5\n",
-    "core_mask = mg.node_is_core()\n",
-    "upthown_nodes = mg.x_of_node >= x_of_fault\n",
-    "upthown_block_mask = np.all([core_mask, upthown_nodes], 0)\n",
-    "\n",
-    "imshow_grid(mg, 'topographic__elevation')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Uplift the nodes of the upthrown block by 20 m."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "z[upthown_block_mask] += 20"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Model species evolution\n",
-    "\n",
-    "First, set model time step and uplift parameters.\n",
-    "\n",
-    "BiotaEvolver runs every 10 timesteps of the other components."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dt = 1000\n",
-    "total_time = int(1e5)\n",
-    "nt = int(total_time // dt)\n",
-    "\n",
-    "dt_be = 10 * dt\n",
-    "\n",
-    "uplift_rate = 7e-5\n",
-    "uplift_per_step = uplift_rate * dt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Run model. The ```run_one_step``` function of BiotaEvolver takes the new zones of the time step.\n",
-    "\n",
-    "This code block may take a couple minutes to run."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for time_i in range(nt):\n",
-    "    # Uniformly uplift.\n",
-    "    z[mg.core_nodes] += uplift_per_step\n",
-    "\n",
-    "    # Increment earth surface process components.\n",
-    "    fr.run_one_step()\n",
-    "    sp.run_one_step(dt)\n",
-    "    lf.run_one_step(dt)\n",
-    "\n",
-    "    elapsed_time = dt * (time_i + 1)\n",
-    "\n",
-    "    if elapsed_time % dt_be == 0:\n",
-    "        # Run BiotaEvolver with updated zones.\n",
-    "        zones = Zone.get_zones_with_area_threshold(mg, min_stream_area)\n",
-    "        be.run_one_step(elapsed_time, zones)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plot results\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.subplot(1, 2, 1)\n",
-    "imshow_grid(mg, 'topographic__elevation', colorbar_label='elevation (m)')\n",
-    "\n",
-    "plt.subplot(1, 2, 2)\n",
-    "plot.imshow_zones(mg, zones)\n",
-    "\n",
-    "plot.plot_number_of_species(be.record, be.species)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Species identifiers and phylogenetic trees\n",
-    "\n",
-    "Species identifiers are automatically generated. They are stored as tuples in species objects. The first element of the identifier tuple is the clade name, and the second element is the species number. For example, the first species of clade A is 0, so the identifier is (A, 0) and the tenth species of this clade is (A, 9).\n",
-    "\n",
-    "### Clade name\n",
-    "\n",
-    "A clade is a group of species that share a common ancestor. Species may share multiple common ancestors, so clades can be defined differently in the same tree. The clade(s) of this example tree can be defined in multiple ways:\n",
-    "\n",
-    "![](images/clade_definitions.png)\n",
-    "\n",
-    "In BiotaEvolver, clades are defined by the root node for the purpose of assigning species identifiers. The species at the root node is:\n",
-    "* the least recent common ancestor of all species in the tree, and\n",
-    "* typically introduced to a BiotaEvolver model using the ```introduce_species``` function.\n",
-    "\n",
-    "The clades of the first 26 species are named alphabetically from A to Z. The alphabet is repeated for subsequent species following this schema:\n",
-    "\n",
-    "<center>clade sequence</center> | <center>clade name</center>\n",
-    "--- | ---\n",
-    "1–26 | A–Z\n",
-    "27–52 | AA–AZ\n",
-    "53–78 | BA–BZ\n",
-    "... | ...\n",
-    "703–728 | AAA–AAZ\n",
-    "729–754 | ABA–ABZ\n",
-    "... | ...\n",
-    "\n",
-    "### Species number\n",
-    "\n",
-    "Species are numbered sequently by clade beginning with 0.\n",
-    "\n",
-    "In the example tree below, species A0 produced 4 child species. Only one species, B0 exists in clade B because child species were not produced by B0.\n",
-    "\n",
-    "![](images/species_number.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## DataFrames\n",
-    "\n",
-    "Data are stored in Pandas DataFrames."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "be.species"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "be.zones"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "be.zone_paths"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Record DataFrames\n",
-    "\n",
-    "Instances of BiotaEvolver, species, and zones each have DataFrames called ```record```. Rows are data stored at each time the model is ran.\n",
-    "\n",
-    "Examine the BiotaEvolver record."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "be.record"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Examine the record of a species."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s = be.species.iloc[0].object\n",
-    "s.record"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Examine the record of a zone."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "z = be.zones.iloc[0].object\n",
-    "z.record"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Click here for more <a href=\"https://github.com/landlab/landlab/wiki/Tutorials\">Landlab tutorials</a>"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/mappers/notebook.tex b/mappers/notebook.tex
new file mode 100644
index 0000000..d55f500
--- /dev/null
+++ b/mappers/notebook.tex
@@ -0,0 +1,760 @@
+
+% Default to the notebook output style
+
+    
+
+
+% Inherit from the specified cell style.
+
+
+
+
+    
+\documentclass[11pt]{article}
+
+    
+    
+    \usepackage[T1]{fontenc}
+    % Nicer default font (+ math font) than Computer Modern for most use cases
+    \usepackage{mathpazo}
+
+    % Basic figure setup, for now with no caption control since it's done
+    % automatically by Pandoc (which extracts ![](path) syntax from Markdown).
+    \usepackage{graphicx}
+    % We will generate all images so they have a width \maxwidth. This means
+    % that they will get their normal width if they fit onto the page, but
+    % are scaled down if they would overflow the margins.
+    \makeatletter
+    \def\maxwidth{\ifdim\Gin@nat@width>\linewidth\linewidth
+    \else\Gin@nat@width\fi}
+    \makeatother
+    \let\Oldincludegraphics\includegraphics
+    % Set max figure width to be 80% of text width, for now hardcoded.
+    \renewcommand{\includegraphics}[1]{\Oldincludegraphics[width=.8\maxwidth]{#1}}
+    % Ensure that by default, figures have no caption (until we provide a
+    % proper Figure object with a Caption API and a way to capture that
+    % in the conversion process - todo).
+    \usepackage{caption}
+    \DeclareCaptionLabelFormat{nolabel}{}
+    \captionsetup{labelformat=nolabel}
+
+    \usepackage{adjustbox} % Used to constrain images to a maximum size 
+    \usepackage{xcolor} % Allow colors to be defined
+    \usepackage{enumerate} % Needed for markdown enumerations to work
+    \usepackage{geometry} % Used to adjust the document margins
+    \usepackage{amsmath} % Equations
+    \usepackage{amssymb} % Equations
+    \usepackage{textcomp} % defines textquotesingle
+    % Hack from http://tex.stackexchange.com/a/47451/13684:
+    \AtBeginDocument{%
+        \def\PYZsq{\textquotesingle}% Upright quotes in Pygmentized code
+    }
+    \usepackage{upquote} % Upright quotes for verbatim code
+    \usepackage{eurosym} % defines \euro
+    \usepackage[mathletters]{ucs} % Extended unicode (utf-8) support
+    \usepackage[utf8x]{inputenc} % Allow utf-8 characters in the tex document
+    \usepackage{fancyvrb} % verbatim replacement that allows latex
+    \usepackage{grffile} % extends the file name processing of package graphics 
+                         % to support a larger range 
+    % The hyperref package gives us a pdf with properly built
+    % internal navigation ('pdf bookmarks' for the table of contents,
+    % internal cross-reference links, web links for URLs, etc.)
+    \usepackage{hyperref}
+    \usepackage{longtable} % longtable support required by pandoc >1.10
+    \usepackage{booktabs}  % table support for pandoc > 1.12.2
+    \usepackage[inline]{enumitem} % IRkernel/repr support (it uses the enumerate* environment)
+    \usepackage[normalem]{ulem} % ulem is needed to support strikethroughs (\sout)
+                                % normalem makes italics be italics, not underlines
+    
+
+    
+    
+    % Colors for the hyperref package
+    \definecolor{urlcolor}{rgb}{0,.145,.698}
+    \definecolor{linkcolor}{rgb}{.71,0.21,0.01}
+    \definecolor{citecolor}{rgb}{.12,.54,.11}
+
+    % ANSI colors
+    \definecolor{ansi-black}{HTML}{3E424D}
+    \definecolor{ansi-black-intense}{HTML}{282C36}
+    \definecolor{ansi-red}{HTML}{E75C58}
+    \definecolor{ansi-red-intense}{HTML}{B22B31}
+    \definecolor{ansi-green}{HTML}{00A250}
+    \definecolor{ansi-green-intense}{HTML}{007427}
+    \definecolor{ansi-yellow}{HTML}{DDB62B}
+    \definecolor{ansi-yellow-intense}{HTML}{B27D12}
+    \definecolor{ansi-blue}{HTML}{208FFB}
+    \definecolor{ansi-blue-intense}{HTML}{0065CA}
+    \definecolor{ansi-magenta}{HTML}{D160C4}
+    \definecolor{ansi-magenta-intense}{HTML}{A03196}
+    \definecolor{ansi-cyan}{HTML}{60C6C8}
+    \definecolor{ansi-cyan-intense}{HTML}{258F8F}
+    \definecolor{ansi-white}{HTML}{C5C1B4}
+    \definecolor{ansi-white-intense}{HTML}{A1A6B2}
+
+    % commands and environments needed by pandoc snippets
+    % extracted from the output of `pandoc -s`
+    \providecommand{\tightlist}{%
+      \setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}
+    \DefineVerbatimEnvironment{Highlighting}{Verbatim}{commandchars=\\\{\}}
+    % Add ',fontsize=\small' for more characters per line
+    \newenvironment{Shaded}{}{}
+    \newcommand{\KeywordTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{\textbf{{#1}}}}
+    \newcommand{\DataTypeTok}[1]{\textcolor[rgb]{0.56,0.13,0.00}{{#1}}}
+    \newcommand{\DecValTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
+    \newcommand{\BaseNTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
+    \newcommand{\FloatTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
+    \newcommand{\CharTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
+    \newcommand{\StringTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
+    \newcommand{\CommentTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textit{{#1}}}}
+    \newcommand{\OtherTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{{#1}}}
+    \newcommand{\AlertTok}[1]{\textcolor[rgb]{1.00,0.00,0.00}{\textbf{{#1}}}}
+    \newcommand{\FunctionTok}[1]{\textcolor[rgb]{0.02,0.16,0.49}{{#1}}}
+    \newcommand{\RegionMarkerTok}[1]{{#1}}
+    \newcommand{\ErrorTok}[1]{\textcolor[rgb]{1.00,0.00,0.00}{\textbf{{#1}}}}
+    \newcommand{\NormalTok}[1]{{#1}}
+    
+    % Additional commands for more recent versions of Pandoc
+    \newcommand{\ConstantTok}[1]{\textcolor[rgb]{0.53,0.00,0.00}{{#1}}}
+    \newcommand{\SpecialCharTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
+    \newcommand{\VerbatimStringTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
+    \newcommand{\SpecialStringTok}[1]{\textcolor[rgb]{0.73,0.40,0.53}{{#1}}}
+    \newcommand{\ImportTok}[1]{{#1}}
+    \newcommand{\DocumentationTok}[1]{\textcolor[rgb]{0.73,0.13,0.13}{\textit{{#1}}}}
+    \newcommand{\AnnotationTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
+    \newcommand{\CommentVarTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
+    \newcommand{\VariableTok}[1]{\textcolor[rgb]{0.10,0.09,0.49}{{#1}}}
+    \newcommand{\ControlFlowTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{\textbf{{#1}}}}
+    \newcommand{\OperatorTok}[1]{\textcolor[rgb]{0.40,0.40,0.40}{{#1}}}
+    \newcommand{\BuiltInTok}[1]{{#1}}
+    \newcommand{\ExtensionTok}[1]{{#1}}
+    \newcommand{\PreprocessorTok}[1]{\textcolor[rgb]{0.74,0.48,0.00}{{#1}}}
+    \newcommand{\AttributeTok}[1]{\textcolor[rgb]{0.49,0.56,0.16}{{#1}}}
+    \newcommand{\InformationTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
+    \newcommand{\WarningTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
+    
+    
+    % Define a nice break command that doesn't care if a line doesn't already
+    % exist.
+    \def\br{\hspace*{\fill} \\* }
+    % Math Jax compatability definitions
+    \def\gt{>}
+    \def\lt{<}
+    % Document parameters
+    \title{mappers}
+    
+    
+    
+
+    % Pygments definitions
+    
+\makeatletter
+\def\PY@reset{\let\PY@it=\relax \let\PY@bf=\relax%
+    \let\PY@ul=\relax \let\PY@tc=\relax%
+    \let\PY@bc=\relax \let\PY@ff=\relax}
+\def\PY@tok#1{\csname PY@tok@#1\endcsname}
+\def\PY@toks#1+{\ifx\relax#1\empty\else%
+    \PY@tok{#1}\expandafter\PY@toks\fi}
+\def\PY@do#1{\PY@bc{\PY@tc{\PY@ul{%
+    \PY@it{\PY@bf{\PY@ff{#1}}}}}}}
+\def\PY#1#2{\PY@reset\PY@toks#1+\relax+\PY@do{#2}}
+
+\expandafter\def\csname PY@tok@w\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.73,0.73}{##1}}}
+\expandafter\def\csname PY@tok@c\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
+\expandafter\def\csname PY@tok@cp\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.74,0.48,0.00}{##1}}}
+\expandafter\def\csname PY@tok@k\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
+\expandafter\def\csname PY@tok@kp\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
+\expandafter\def\csname PY@tok@kt\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.69,0.00,0.25}{##1}}}
+\expandafter\def\csname PY@tok@o\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
+\expandafter\def\csname PY@tok@ow\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.67,0.13,1.00}{##1}}}
+\expandafter\def\csname PY@tok@nb\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
+\expandafter\def\csname PY@tok@nf\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.00,1.00}{##1}}}
+\expandafter\def\csname PY@tok@nc\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.00,1.00}{##1}}}
+\expandafter\def\csname PY@tok@nn\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.00,1.00}{##1}}}
+\expandafter\def\csname PY@tok@ne\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.82,0.25,0.23}{##1}}}
+\expandafter\def\csname PY@tok@nv\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}}
+\expandafter\def\csname PY@tok@no\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.53,0.00,0.00}{##1}}}
+\expandafter\def\csname PY@tok@nl\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.63,0.63,0.00}{##1}}}
+\expandafter\def\csname PY@tok@ni\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.60,0.60,0.60}{##1}}}
+\expandafter\def\csname PY@tok@na\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.49,0.56,0.16}{##1}}}
+\expandafter\def\csname PY@tok@nt\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
+\expandafter\def\csname PY@tok@nd\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.67,0.13,1.00}{##1}}}
+\expandafter\def\csname PY@tok@s\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
+\expandafter\def\csname PY@tok@sd\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
+\expandafter\def\csname PY@tok@si\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.73,0.40,0.53}{##1}}}
+\expandafter\def\csname PY@tok@se\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.73,0.40,0.13}{##1}}}
+\expandafter\def\csname PY@tok@sr\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.40,0.53}{##1}}}
+\expandafter\def\csname PY@tok@ss\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}}
+\expandafter\def\csname PY@tok@sx\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
+\expandafter\def\csname PY@tok@m\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
+\expandafter\def\csname PY@tok@gh\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.00,0.50}{##1}}}
+\expandafter\def\csname PY@tok@gu\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.50,0.00,0.50}{##1}}}
+\expandafter\def\csname PY@tok@gd\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.63,0.00,0.00}{##1}}}
+\expandafter\def\csname PY@tok@gi\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.63,0.00}{##1}}}
+\expandafter\def\csname PY@tok@gr\endcsname{\def\PY@tc##1{\textcolor[rgb]{1.00,0.00,0.00}{##1}}}
+\expandafter\def\csname PY@tok@ge\endcsname{\let\PY@it=\textit}
+\expandafter\def\csname PY@tok@gs\endcsname{\let\PY@bf=\textbf}
+\expandafter\def\csname PY@tok@gp\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.00,0.50}{##1}}}
+\expandafter\def\csname PY@tok@go\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.53,0.53,0.53}{##1}}}
+\expandafter\def\csname PY@tok@gt\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.27,0.87}{##1}}}
+\expandafter\def\csname PY@tok@err\endcsname{\def\PY@bc##1{\setlength{\fboxsep}{0pt}\fcolorbox[rgb]{1.00,0.00,0.00}{1,1,1}{\strut ##1}}}
+\expandafter\def\csname PY@tok@kc\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
+\expandafter\def\csname PY@tok@kd\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
+\expandafter\def\csname PY@tok@kn\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
+\expandafter\def\csname PY@tok@kr\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
+\expandafter\def\csname PY@tok@bp\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
+\expandafter\def\csname PY@tok@fm\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.00,1.00}{##1}}}
+\expandafter\def\csname PY@tok@vc\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}}
+\expandafter\def\csname PY@tok@vg\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}}
+\expandafter\def\csname PY@tok@vi\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}}
+\expandafter\def\csname PY@tok@vm\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}}
+\expandafter\def\csname PY@tok@sa\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
+\expandafter\def\csname PY@tok@sb\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
+\expandafter\def\csname PY@tok@sc\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
+\expandafter\def\csname PY@tok@dl\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
+\expandafter\def\csname PY@tok@s2\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
+\expandafter\def\csname PY@tok@sh\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
+\expandafter\def\csname PY@tok@s1\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
+\expandafter\def\csname PY@tok@mb\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
+\expandafter\def\csname PY@tok@mf\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
+\expandafter\def\csname PY@tok@mh\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
+\expandafter\def\csname PY@tok@mi\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
+\expandafter\def\csname PY@tok@il\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
+\expandafter\def\csname PY@tok@mo\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
+\expandafter\def\csname PY@tok@ch\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
+\expandafter\def\csname PY@tok@cm\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
+\expandafter\def\csname PY@tok@cpf\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
+\expandafter\def\csname PY@tok@c1\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
+\expandafter\def\csname PY@tok@cs\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
+
+\def\PYZbs{\char`\\}
+\def\PYZus{\char`\_}
+\def\PYZob{\char`\{}
+\def\PYZcb{\char`\}}
+\def\PYZca{\char`\^}
+\def\PYZam{\char`\&}
+\def\PYZlt{\char`\<}
+\def\PYZgt{\char`\>}
+\def\PYZsh{\char`\#}
+\def\PYZpc{\char`\%}
+\def\PYZdl{\char`\$}
+\def\PYZhy{\char`\-}
+\def\PYZsq{\char`\'}
+\def\PYZdq{\char`\"}
+\def\PYZti{\char`\~}
+% for compatibility with earlier versions
+\def\PYZat{@}
+\def\PYZlb{[}
+\def\PYZrb{]}
+\makeatother
+
+
+    % Exact colors from NB
+    \definecolor{incolor}{rgb}{0.0, 0.0, 0.5}
+    \definecolor{outcolor}{rgb}{0.545, 0.0, 0.0}
+
+
+
+    
+    % Prevent overflowing lines due to hard-to-break entities
+    \sloppy 
+    % Setup hyperref package
+    \hypersetup{
+      breaklinks=true,  % so long urls are correctly broken across lines
+      colorlinks=true,
+      urlcolor=urlcolor,
+      linkcolor=linkcolor,
+      citecolor=citecolor,
+      }
+    % Slightly bigger margins than the latex defaults
+    
+    \geometry{verbose,tmargin=1in,bmargin=1in,lmargin=1in,rmargin=1in}
+    
+    
+
+    \begin{document}
+    
+    
+    \maketitle
+    
+    
+
+    
+    
+
+    \section{Mapping values between grid
+elements}\label{mapping-values-between-grid-elements}
+
+ (Note: for instructions on how to run an interactive iPython notebook,
+click here: https://github.com/landlab/tutorials/blob/master/README.md
+For the unexpanded version to download and run, click here:
+https://nbviewer.jupyter.org/github/landlab/tutorials/blob/master/mappers/mappers\_unexpanded.ipynb
+For more Landlab tutorials, click here:
+https://github.com/landlab/landlab/wiki/Tutorials)
+
+Imagine that you're using Landlab to write a model of shallow water flow
+over terrain. A natural approach is to place your scalar fields, such as
+water depth, at the nodes. You then place your vector fields, such as
+water surface gradient, flow velocity, and discharge, at the links. But
+your velocity depends on both slope and depth, which means you need to
+know the depth at the links too. How do you do this?
+
+This tutorial introduces \emph{mappers}: grid functions that map
+quantities defined on one set of elements (such as nodes) onto another
+set of elements (such as links). As you'll see, there are a variety of
+mappers available.
+
+    \subsection{Mapping from nodes to
+links}\label{mapping-from-nodes-to-links}
+
+For the sake of example, we'll start with a simple 3-row by 4-column
+raster grid. The grid will contain a scalar field called
+\texttt{water\_\_depth}, abbreviated \texttt{h}. We'll populate it with
+some example values, as follows:
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}1}]:} \PY{k+kn}{from} \PY{n+nn}{\PYZus{}\PYZus{}future\PYZus{}\PYZus{}} \PY{k}{import} \PY{n}{print\PYZus{}function}
+\end{Verbatim}
+
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}2}]:} \PY{k+kn}{from} \PY{n+nn}{\PYZus{}\PYZus{}future\PYZus{}\PYZus{}} \PY{k}{import} \PY{n}{print\PYZus{}function}
+        \PY{k+kn}{from} \PY{n+nn}{landlab} \PY{k}{import} \PY{n}{RasterModelGrid}
+        \PY{k+kn}{import} \PY{n+nn}{numpy} \PY{k}{as} \PY{n+nn}{np}
+        \PY{n}{mg} \PY{o}{=} \PY{n}{RasterModelGrid}\PY{p}{(}\PY{p}{(}\PY{l+m+mi}{3}\PY{p}{,} \PY{l+m+mi}{4}\PY{p}{)}\PY{p}{,} \PY{l+m+mf}{100.0}\PY{p}{)}
+        \PY{n}{h} \PY{o}{=} \PY{n}{mg}\PY{o}{.}\PY{n}{add\PYZus{}zeros}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{node}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{surface\PYZus{}water\PYZus{}\PYZus{}depth}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
+        \PY{n}{h}\PY{p}{[}\PY{p}{:}\PY{p}{]} \PY{o}{=} \PY{l+m+mi}{7} \PY{o}{\PYZhy{}} \PY{n}{np}\PY{o}{.}\PY{n}{abs}\PY{p}{(}\PY{l+m+mi}{6} \PY{o}{\PYZhy{}} \PY{n}{np}\PY{o}{.}\PY{n}{arange}\PY{p}{(}\PY{l+m+mi}{12}\PY{p}{)}\PY{p}{)}
+\end{Verbatim}
+
+
+    For the sake of visualizing values at nodes on our grid, we'll define a
+handy little function:
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}3}]:} \PY{k}{def} \PY{n+nf}{show\PYZus{}node\PYZus{}values}\PY{p}{(}\PY{n}{mg}\PY{p}{,} \PY{n}{u}\PY{p}{)}\PY{p}{:}
+            \PY{k}{for} \PY{n}{r} \PY{o+ow}{in} \PY{n+nb}{range}\PY{p}{(}\PY{n}{mg}\PY{o}{.}\PY{n}{number\PYZus{}of\PYZus{}node\PYZus{}rows} \PY{o}{\PYZhy{}} \PY{l+m+mi}{1}\PY{p}{,} \PY{o}{\PYZhy{}}\PY{l+m+mi}{1}\PY{p}{,} \PY{o}{\PYZhy{}}\PY{l+m+mi}{1}\PY{p}{)}\PY{p}{:}
+                \PY{k}{for} \PY{n}{c} \PY{o+ow}{in} \PY{n+nb}{range}\PY{p}{(}\PY{n}{mg}\PY{o}{.}\PY{n}{number\PYZus{}of\PYZus{}node\PYZus{}columns}\PY{p}{)}\PY{p}{:}
+                    \PY{n+nb}{print}\PY{p}{(}\PY{n+nb}{int}\PY{p}{(}\PY{n}{u}\PY{p}{[}\PY{n}{c} \PY{o}{+} \PY{p}{(}\PY{n}{mg}\PY{o}{.}\PY{n}{number\PYZus{}of\PYZus{}node\PYZus{}columns} \PY{o}{*} \PY{n}{r}\PY{p}{)}\PY{p}{]}\PY{p}{)}\PY{p}{,} \PY{n}{end}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{ }\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
+                \PY{n+nb}{print}\PY{p}{(}\PY{p}{)}
+\end{Verbatim}
+
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}4}]:} \PY{n}{show\PYZus{}node\PYZus{}values}\PY{p}{(}\PY{n}{mg}\PY{p}{,} \PY{n}{h}\PY{p}{)}
+\end{Verbatim}
+
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+5 4 3 2 
+5 6 7 6 
+1 2 3 4 
+
+    \end{Verbatim}
+
+    Let's review the numbering of nodes and links. The lines below will
+print a list that shows, for each link ID, the IDs of the nodes at the
+link's tail and head:
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}5}]:} \PY{k}{for} \PY{n}{i} \PY{o+ow}{in} \PY{n+nb}{range}\PY{p}{(}\PY{n}{mg}\PY{o}{.}\PY{n}{number\PYZus{}of\PYZus{}links}\PY{p}{)}\PY{p}{:}
+            \PY{n+nb}{print}\PY{p}{(}\PY{n}{i}\PY{p}{,} \PY{n}{mg}\PY{o}{.}\PY{n}{node\PYZus{}at\PYZus{}link\PYZus{}tail}\PY{p}{[}\PY{n}{i}\PY{p}{]}\PY{p}{,} \PY{n}{mg}\PY{o}{.}\PY{n}{node\PYZus{}at\PYZus{}link\PYZus{}head}\PY{p}{[}\PY{n}{i}\PY{p}{]}\PY{p}{)}
+\end{Verbatim}
+
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+0 0 1
+1 1 2
+2 2 3
+3 0 4
+4 1 5
+5 2 6
+6 3 7
+7 4 5
+8 5 6
+9 6 7
+10 4 8
+11 5 9
+12 6 10
+13 7 11
+14 8 9
+15 9 10
+16 10 11
+
+    \end{Verbatim}
+
+    \subsubsection{Finding the mean value between two nodes on a
+link}\label{finding-the-mean-value-between-two-nodes-on-a-link}
+
+Suppose we want to have a \emph{link-based} array, called
+\emph{h\_edge}, that contains water depth at locations between adjacent
+pairs of nodes. For each link, we'll simply take the average of the
+depth at the link's two nodes. To accomplish this, we can use the
+\texttt{map\_mean\_of\_link\_nodes\_to\_link} grid method. At link 8,
+for example, we'll average the \emph{h} values at nodes 5 and 6, which
+should give us a depth of (6 + 7) / 2 = 6.5:
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}6}]:} \PY{n}{h\PYZus{}edge} \PY{o}{=} \PY{n}{mg}\PY{o}{.}\PY{n}{map\PYZus{}mean\PYZus{}of\PYZus{}link\PYZus{}nodes\PYZus{}to\PYZus{}link}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{surface\PYZus{}water\PYZus{}\PYZus{}depth}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
+        \PY{k}{for} \PY{n}{i} \PY{o+ow}{in} \PY{n+nb}{range}\PY{p}{(}\PY{n}{mg}\PY{o}{.}\PY{n}{number\PYZus{}of\PYZus{}links}\PY{p}{)}\PY{p}{:}
+            \PY{n+nb}{print}\PY{p}{(}\PY{n}{i}\PY{p}{,} \PY{n}{h\PYZus{}edge}\PY{p}{[}\PY{n}{i}\PY{p}{]}\PY{p}{)}
+\end{Verbatim}
+
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+0 1.5
+1 2.5
+2 3.5
+3 3.0
+4 4.0
+5 5.0
+6 5.0
+7 5.5
+8 6.5
+9 6.5
+10 5.0
+11 5.0
+12 5.0
+13 4.0
+14 4.5
+15 3.5
+16 2.5
+
+    \end{Verbatim}
+
+    \subsubsection{What's in a name?}\label{whats-in-a-name}
+
+The mapping function has a long name, which is designed to make it as
+clear as possible to understand what the function does. All the mappers
+start with the verb \emph{map}. Then the relationship is given; in this
+case, we are looking at the \emph{mean}. Then the elements from which a
+quantity is being mapped: we are taking values from \emph{link nodes}.
+Finally, the element to which the new values apply: \emph{link}.
+
+\subsubsection{Mapping minimum or maximum
+values}\label{mapping-minimum-or-maximum-values}
+
+We can also map the minimum value of \emph{h}:
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}7}]:} \PY{n}{h\PYZus{}edge} \PY{o}{=} \PY{n}{mg}\PY{o}{.}\PY{n}{map\PYZus{}min\PYZus{}of\PYZus{}link\PYZus{}nodes\PYZus{}to\PYZus{}link}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{surface\PYZus{}water\PYZus{}\PYZus{}depth}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
+        \PY{k}{for} \PY{n}{i} \PY{o+ow}{in} \PY{n+nb}{range}\PY{p}{(}\PY{n}{mg}\PY{o}{.}\PY{n}{number\PYZus{}of\PYZus{}links}\PY{p}{)}\PY{p}{:}
+            \PY{n+nb}{print}\PY{p}{(}\PY{n}{i}\PY{p}{,} \PY{n}{h\PYZus{}edge}\PY{p}{[}\PY{n}{i}\PY{p}{]}\PY{p}{)}
+\end{Verbatim}
+
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+0 1.0
+1 2.0
+2 3.0
+3 1.0
+4 2.0
+5 3.0
+6 4.0
+7 5.0
+8 6.0
+9 6.0
+10 5.0
+11 4.0
+12 3.0
+13 2.0
+14 4.0
+15 3.0
+16 2.0
+
+    \end{Verbatim}
+
+    ... or the maximum:
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}8}]:} \PY{n}{h\PYZus{}edge} \PY{o}{=} \PY{n}{mg}\PY{o}{.}\PY{n}{map\PYZus{}max\PYZus{}of\PYZus{}link\PYZus{}nodes\PYZus{}to\PYZus{}link}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{surface\PYZus{}water\PYZus{}\PYZus{}depth}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
+        \PY{k}{for} \PY{n}{i} \PY{o+ow}{in} \PY{n+nb}{range}\PY{p}{(}\PY{n}{mg}\PY{o}{.}\PY{n}{number\PYZus{}of\PYZus{}links}\PY{p}{)}\PY{p}{:}
+            \PY{n+nb}{print}\PY{p}{(}\PY{n}{i}\PY{p}{,} \PY{n}{h\PYZus{}edge}\PY{p}{[}\PY{n}{i}\PY{p}{]}\PY{p}{)}
+\end{Verbatim}
+
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+0 2.0
+1 3.0
+2 4.0
+3 5.0
+4 6.0
+5 7.0
+6 6.0
+7 6.0
+8 7.0
+9 7.0
+10 5.0
+11 6.0
+12 7.0
+13 6.0
+14 5.0
+15 4.0
+16 3.0
+
+    \end{Verbatim}
+
+    \subsubsection{Upwind and downwind}\label{upwind-and-downwind}
+
+Numerical schemes often use \emph{upwind differencing} or \emph{downwind
+differencing}. For example, finite difference schemes for equations that
+include advection may use "upwind" rather than centered differences, in
+which a scalar quantity (our \emph{h} for example) is taken from
+whichever side is upstream in the flow field.
+
+How do we know the flow direction? If the flow is driven by the gradient
+in some scalar field, such as pressure or elevation, one approach is to
+look at the values of this scalar on either end of the link: the end
+with the higher value is upwind, and the end with the lower value is
+downwind.
+
+Suppose for example that our water flow is driven by the water-surface
+slope (which is often a good approximation for the \emph{energy slope},
+though it omits the kinetic energy). Let's define a bed-surface
+elevation field \emph{z}:
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}9}]:} \PY{n}{z} \PY{o}{=} \PY{n}{mg}\PY{o}{.}\PY{n}{add\PYZus{}zeros}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{node}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{topographic\PYZus{}\PYZus{}elevation}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
+        \PY{n}{z}\PY{p}{[}\PY{p}{:}\PY{p}{]} \PY{o}{=} \PY{l+m+mi}{16} \PY{o}{\PYZhy{}} \PY{n}{np}\PY{o}{.}\PY{n}{abs}\PY{p}{(}\PY{l+m+mi}{7} \PY{o}{\PYZhy{}} \PY{n}{np}\PY{o}{.}\PY{n}{arange}\PY{p}{(}\PY{l+m+mi}{12}\PY{p}{)}\PY{p}{)}
+        \PY{n}{show\PYZus{}node\PYZus{}values}\PY{p}{(}\PY{n}{mg}\PY{p}{,} \PY{n}{z}\PY{p}{)}
+\end{Verbatim}
+
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+15 14 13 12 
+13 14 15 16 
+9 10 11 12 
+
+    \end{Verbatim}
+
+    The water-surface elevation is then the sum of \emph{h} and \emph{z}:
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}10}]:} \PY{n}{w} \PY{o}{=} \PY{n}{z} \PY{o}{+} \PY{n}{h}
+         \PY{n}{show\PYZus{}node\PYZus{}values}\PY{p}{(}\PY{n}{mg}\PY{p}{,} \PY{n}{w}\PY{p}{)}
+\end{Verbatim}
+
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+20 18 16 14 
+18 20 22 22 
+10 12 14 16 
+
+    \end{Verbatim}
+
+    For every link, we can assign the value of \emph{h} from whichever end
+of the link has the greater \emph{w}:
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}11}]:} \PY{n}{h\PYZus{}edge} \PY{o}{=} \PY{n}{mg}\PY{o}{.}\PY{n}{map\PYZus{}value\PYZus{}at\PYZus{}max\PYZus{}node\PYZus{}to\PYZus{}link}\PY{p}{(}\PY{n}{w}\PY{p}{,} \PY{n}{h}\PY{p}{)}
+         \PY{k}{for} \PY{n}{i} \PY{o+ow}{in} \PY{n+nb}{range}\PY{p}{(}\PY{n}{mg}\PY{o}{.}\PY{n}{number\PYZus{}of\PYZus{}links}\PY{p}{)}\PY{p}{:}
+             \PY{n+nb}{print}\PY{p}{(}\PY{n}{i}\PY{p}{,} \PY{n}{h\PYZus{}edge}\PY{p}{[}\PY{n}{i}\PY{p}{]}\PY{p}{)}
+\end{Verbatim}
+
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+0 2.0
+1 3.0
+2 4.0
+3 5.0
+4 6.0
+5 7.0
+6 6.0
+7 6.0
+8 7.0
+9 6.0
+10 5.0
+11 6.0
+12 7.0
+13 6.0
+14 5.0
+15 4.0
+16 3.0
+
+    \end{Verbatim}
+
+    Consider the middle two nodes (5 and 6). Node 6 is higher (22 versus
+20). Therefore, the link between them (link 8) should be assigned the
+value of \emph{h} at node 6. This value happens to be 7.0.
+
+Of course, we could also take the value from the \emph{lower} of the two
+nodes, which gives link 8 a value of 6.0:
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}12}]:} \PY{n}{h\PYZus{}edge} \PY{o}{=} \PY{n}{mg}\PY{o}{.}\PY{n}{map\PYZus{}value\PYZus{}at\PYZus{}min\PYZus{}node\PYZus{}to\PYZus{}link}\PY{p}{(}\PY{n}{w}\PY{p}{,} \PY{n}{h}\PY{p}{)}
+         \PY{k}{for} \PY{n}{i} \PY{o+ow}{in} \PY{n+nb}{range}\PY{p}{(}\PY{n}{mg}\PY{o}{.}\PY{n}{number\PYZus{}of\PYZus{}links}\PY{p}{)}\PY{p}{:}
+             \PY{n+nb}{print}\PY{p}{(}\PY{n}{i}\PY{p}{,} \PY{n}{h\PYZus{}edge}\PY{p}{[}\PY{n}{i}\PY{p}{]}\PY{p}{)}
+\end{Verbatim}
+
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+0 1.0
+1 2.0
+2 3.0
+3 1.0
+4 2.0
+5 3.0
+6 4.0
+7 5.0
+8 6.0
+9 6.0
+10 5.0
+11 4.0
+12 3.0
+13 2.0
+14 4.0
+15 3.0
+16 2.0
+
+    \end{Verbatim}
+
+    \subsubsection{Heads or tails?}\label{heads-or-tails}
+
+It is also possible to map the scalar quantity at either the head node
+or the tail node to the link:
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}13}]:} \PY{n}{h\PYZus{}edge} \PY{o}{=} \PY{n}{mg}\PY{o}{.}\PY{n}{map\PYZus{}link\PYZus{}head\PYZus{}node\PYZus{}to\PYZus{}link}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{surface\PYZus{}water\PYZus{}\PYZus{}depth}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
+         \PY{k}{for} \PY{n}{i} \PY{o+ow}{in} \PY{n+nb}{range}\PY{p}{(}\PY{n}{mg}\PY{o}{.}\PY{n}{number\PYZus{}of\PYZus{}links}\PY{p}{)}\PY{p}{:}
+             \PY{n+nb}{print}\PY{p}{(}\PY{n}{i}\PY{p}{,} \PY{n}{h\PYZus{}edge}\PY{p}{[}\PY{n}{i}\PY{p}{]}\PY{p}{)}
+\end{Verbatim}
+
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+0 2.0
+1 3.0
+2 4.0
+3 5.0
+4 6.0
+5 7.0
+6 6.0
+7 6.0
+8 7.0
+9 6.0
+10 5.0
+11 4.0
+12 3.0
+13 2.0
+14 4.0
+15 3.0
+16 2.0
+
+    \end{Verbatim}
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}14}]:} \PY{n}{h\PYZus{}edge} \PY{o}{=} \PY{n}{mg}\PY{o}{.}\PY{n}{map\PYZus{}link\PYZus{}tail\PYZus{}node\PYZus{}to\PYZus{}link}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{surface\PYZus{}water\PYZus{}\PYZus{}depth}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
+         \PY{k}{for} \PY{n}{i} \PY{o+ow}{in} \PY{n+nb}{range}\PY{p}{(}\PY{n}{mg}\PY{o}{.}\PY{n}{number\PYZus{}of\PYZus{}links}\PY{p}{)}\PY{p}{:}
+             \PY{n+nb}{print}\PY{p}{(}\PY{n}{i}\PY{p}{,} \PY{n}{h\PYZus{}edge}\PY{p}{[}\PY{n}{i}\PY{p}{]}\PY{p}{)}
+\end{Verbatim}
+
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+0 1.0
+1 2.0
+2 3.0
+3 1.0
+4 2.0
+5 3.0
+6 4.0
+7 5.0
+8 6.0
+9 7.0
+10 5.0
+11 6.0
+12 7.0
+13 6.0
+14 5.0
+15 4.0
+16 3.0
+
+    \end{Verbatim}
+
+    \subsubsection{Simple example using centered water
+depth}\label{simple-example-using-centered-water-depth}
+
+The following implements one time-step of a linear-viscous flow model,
+in which flow velocity is calculated with depth at the links is taken as
+the mean of depth at the two bounding nodes. To make the flow a little
+tamer, we'll have our fluid be basaltic lava instead of water, with a
+dynamic viscosity of 100 Pa s.
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}15}]:} \PY{n}{gamma} \PY{o}{=} \PY{l+m+mf}{25000.0}  \PY{c+c1}{\PYZsh{} unit weight of fluid, N/m2}
+         \PY{n}{viscosity} \PY{o}{=} \PY{l+m+mf}{100.0}  \PY{c+c1}{\PYZsh{} dynamic viscosity in Pa s}
+         \PY{n}{grad} \PY{o}{=} \PY{n}{mg}\PY{o}{.}\PY{n}{calc\PYZus{}grad\PYZus{}at\PYZus{}link}\PY{p}{(}\PY{n}{w}\PY{p}{)}
+         \PY{n}{h\PYZus{}edge} \PY{o}{=} \PY{n}{mg}\PY{o}{.}\PY{n}{map\PYZus{}mean\PYZus{}of\PYZus{}link\PYZus{}nodes\PYZus{}to\PYZus{}link}\PY{p}{(}\PY{n}{h}\PY{p}{)}
+         \PY{n}{vel} \PY{o}{=} \PY{o}{\PYZhy{}}\PY{p}{(}\PY{n}{gamma} \PY{o}{/} \PY{n}{viscosity}\PY{p}{)} \PY{o}{*} \PY{n}{h\PYZus{}edge} \PY{o}{*} \PY{n}{h\PYZus{}edge} \PY{o}{*} \PY{n}{grad}
+         \PY{k}{for} \PY{n}{ln} \PY{o+ow}{in} \PY{n+nb}{range}\PY{p}{(}\PY{n}{mg}\PY{o}{.}\PY{n}{number\PYZus{}of\PYZus{}links}\PY{p}{)}\PY{p}{:}
+             \PY{n+nb}{print}\PY{p}{(}\PY{n}{ln}\PY{p}{,} \PY{n}{h\PYZus{}edge}\PY{p}{[}\PY{n}{ln}\PY{p}{]}\PY{p}{,} \PY{n}{grad}\PY{p}{[}\PY{n}{ln}\PY{p}{]}\PY{p}{,} \PY{n}{vel}\PY{p}{[}\PY{n}{ln}\PY{p}{]}\PY{p}{)}
+\end{Verbatim}
+
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+0 1.5 0.02 -11.25
+1 2.5 0.02 -31.25
+2 3.5 0.02 -61.25
+3 3.0 0.08 -180.0
+4 4.0 0.08 -320.0
+5 5.0 0.08 -500.0
+6 5.0 0.06 -375.0
+7 5.5 0.02 -151.25
+8 6.5 0.02 -211.25
+9 6.5 0.0 -0.0
+10 5.0 0.02 -125.0
+11 5.0 -0.02 125.0
+12 5.0 -0.06 375.0
+13 4.0 -0.08 320.0
+14 4.5 -0.02 101.25
+15 3.5 -0.02 61.25
+16 2.5 -0.02 31.25
+
+    \end{Verbatim}
+
+    I'm not sure I love the idea of a 5-m thick lava flow moving at over 100
+m/s! (I guess we can take some comfort from the thought that turbulence
+would probably slow it down)
+
+How different would the numerical solution be using an upwind scheme for
+flow depth? Let's find out:
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}16}]:} \PY{n}{h\PYZus{}edge} \PY{o}{=} \PY{n}{mg}\PY{o}{.}\PY{n}{map\PYZus{}value\PYZus{}at\PYZus{}max\PYZus{}node\PYZus{}to\PYZus{}link}\PY{p}{(}\PY{n}{w}\PY{p}{,} \PY{n}{h}\PY{p}{)}
+         \PY{n}{vel} \PY{o}{=} \PY{o}{\PYZhy{}}\PY{p}{(}\PY{n}{gamma} \PY{o}{/} \PY{n}{viscosity}\PY{p}{)} \PY{o}{*} \PY{n}{h\PYZus{}edge} \PY{o}{*} \PY{n}{h\PYZus{}edge} \PY{o}{*} \PY{n}{grad}
+         \PY{k}{for} \PY{n}{ln} \PY{o+ow}{in} \PY{n+nb}{range}\PY{p}{(}\PY{n}{mg}\PY{o}{.}\PY{n}{number\PYZus{}of\PYZus{}links}\PY{p}{)}\PY{p}{:}
+             \PY{n+nb}{print}\PY{p}{(}\PY{n}{ln}\PY{p}{,} \PY{n}{h\PYZus{}edge}\PY{p}{[}\PY{n}{ln}\PY{p}{]}\PY{p}{,} \PY{n}{grad}\PY{p}{[}\PY{n}{ln}\PY{p}{]}\PY{p}{,} \PY{n}{vel}\PY{p}{[}\PY{n}{ln}\PY{p}{]}\PY{p}{)}
+\end{Verbatim}
+
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+0 2.0 0.02 -20.0
+1 3.0 0.02 -45.0
+2 4.0 0.02 -80.0
+3 5.0 0.08 -500.0
+4 6.0 0.08 -720.0
+5 7.0 0.08 -980.0
+6 6.0 0.06 -540.0
+7 6.0 0.02 -180.0
+8 7.0 0.02 -245.0
+9 6.0 0.0 -0.0
+10 5.0 0.02 -125.0
+11 6.0 -0.02 180.0
+12 7.0 -0.06 735.0
+13 6.0 -0.08 720.0
+14 5.0 -0.02 125.0
+15 4.0 -0.02 80.0
+16 3.0 -0.02 45.0
+
+    \end{Verbatim}
+
+    Still pretty scary.
+
+In any event, this example illustrates how you can use Landlab's mapping
+functions to build mass-conservation models in which the flow rate
+depends on a gradient and a scalar, both of which can be evaluated at
+links.
+
+
+    % Add a bibliography block to the postdoc
+    
+    
+    
+    \end{document}
diff --git a/species_evolution/SpeciesEvolver_tutorial.ipynb b/species_evolution/SpeciesEvolver_tutorial.ipynb
new file mode 100644
index 0000000..75f7a7d
--- /dev/null
+++ b/species_evolution/SpeciesEvolver_tutorial.ipynb
@@ -0,0 +1,1587 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"http://landlab.github.io\"><img style=\"float: left\" src=\"https://raw.githubusercontent.com/landlab/tutorials/master/landlab_header.png\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using the Landlab SpeciesEvolver component\n",
+    "\n",
+    "<hr>\n",
+    "<small> For instructions on how to run an interactive iPython notebook, click here: <a href=\"https://github.com/landlab/tutorials/blob/master/README.md\">https://github.com/landlab/tutorials/blob/master/README.md</a></small><br>\n",
+    "<small>For more Landlab tutorials, click here: <a href=\"https://github.com/landlab/landlab/wiki/Tutorials\">https://github.com/landlab/landlab/wiki/Tutorials</a></small>\n",
+    "<hr>\n",
+    "\n",
+    "In this tutorial we will:\n",
+    "* Introduce species into a Landlab model.\n",
+    "* Set the zones in which the species operate.\n",
+    "* Evolve a landscape and the species over time.\n",
+    "* Explore SpeciesEvolver output data structures.\n",
+    "\n",
+    "The configuration of the model in this tutorial loosely follows the fault throw experiment in Lyons et al., in preperation for Earth Surface Dynamics.\n",
+    "\n",
+    "## Macroevolution processes built into species\n",
+    "\n",
+    "The evolution of the base species is driven by these processes:\n",
+    "* dispersal: geographic range expansion\n",
+    "* speciation: lineage splitting\n",
+    "* extinction: lineage termination"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Import modules and set the environment to show plots inline in this notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from landlab import CLOSED_BOUNDARY, FIXED_VALUE_BOUNDARY\n",
+    "from landlab.components import (SpeciesEvolver, FastscapeEroder, FlowRouter,\n",
+    "                                LinearDiffuser)\n",
+    "from landlab.components.species_macroevolution import plot, Species, Zone\n",
+    "from landlab.io import read_esri_ascii\n",
+    "from landlab.plot import imshow_grid\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prepare the grid and components of the model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load a model grid that has steady state topography. The top and bottom boundaries are set to open."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(mg, z) = read_esri_ascii('model_grid_steady_state_elevation.txt')\n",
+    "mg.at_node['topographic__elevation'] = z\n",
+    "mg.set_status_at_node_on_edges(right=CLOSED_BOUNDARY, top=FIXED_VALUE_BOUNDARY,\n",
+    "                               left=CLOSED_BOUNDARY,\n",
+    "                               bottom=FIXED_VALUE_BOUNDARY)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Initialize components. SpeciesEvolver requires only one parameter, a model grid."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fr = FlowRouter(mg)\n",
+    "sp = FastscapeEroder(mg, K_sp=10e-5, m_sp=0.5, n_sp=1.0)\n",
+    "lf = LinearDiffuser(mg, linear_diffusivity=0.0001)\n",
+    "se = SpeciesEvolver(mg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prepare SpeciesEvolver objects\n",
+    "\n",
+    "Species and zones are the two object types central to SpeciesEvolver.\n",
+    "\n",
+    "### Zones\n",
+    "\n",
+    "Zones are portions of a model grid. A Species object exists throughout a zone.\n",
+    "\n",
+    "At each timestep, the spatial intersection of the zones at the prior time and current time are identified and typed. For example, a zone in the prior time intersects two zones in the current time. This zone path is typed one-to-many.\n",
+    "\n",
+    "Macroevolutionary processes are carried out by the intersection of zones over time. The connectivity of zones in SpeciesEvolver are called 'paths', and the relationship of zones is described by the path type. Not all combinations of none, one, and many are considered at the time of this tutorial. All none-to-n and some many-to-n are not considered. \n",
+    "\n",
+    "<img style=\"float: left;\" src=\"images/zone__one_to_none.png\"> &nbsp; zone<sub>*t*</sub>: a zone in time, *t*.<br>\n",
+    "\n",
+    "<img style=\"float: left;\" src=\"images/zone__later_time.png\"> &nbsp; zone<sub>*t*+1</sub>: a zone in the time following *t*.\n",
+    "\n",
+    "<center>path type</center> | <center>graphical<br>represention</center> | <center>zone temporal connectivity</center> | <center>macroevolution implications</center>\n",
+    "--- | --- | --- | ---\n",
+    "one-to-none | ![](images/zone__one_to_none.png) | zone<sub>*t*</sub> does not intersect a zone in *t*+1. | The species in the zone of the earlier time will go extinct. \n",
+    "one-to-one | ![](images/zone__one_to_one.png) | zone<sub>*t*</sub> intersects zone<sub>*t*+1</sub>. | The species in the zone of the earlier time will relocate to the zone of the later time.\n",
+    "one-to-many | ![](images/zone__one_to_many.png) | zone<sub>*t*</sub> intersects multiple zones in *t*+1. | The species in the zone of the earlier time disperses across the multiple zones in the later time. The species in the zones of the later time are geographical disconnected, thus speciation occurs.\n",
+    "many-to-one | ![](images/zone__many_to_one.png) | Multiple zones in *t* intersect zone<sub>*t*+1</sub>. | Species density increases locally.\n",
+    "many-to-many | ![](images/zone__many_to_many.png) | Multiple zones in *t* intersect multiple zones in *t*+1. | Species distribution shifts. Speciation occurs where the zone of a species at *t* intersects more than one zone at *t*+1.\n",
+    "\n",
+    "### Species\n",
+    "\n",
+    "The model domain is populated with species by the user.\n",
+    "\n",
+    "The rules of macroevolution are programmed into species. Behind the scenes, zone paths are the conditions evaluated by the macroevolution rules. The outcome of the rules given the zone paths are described in the zone table above. For example, a one-to-many path:\n",
+    "\n",
+    "![](images/zone_path__one_to_many.png)\n",
+    "\n",
+    "The base species class \n",
+    "\n",
+    "Users may develop species with different behaviors and macroevolution rules.\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "Populate SpeciesEvolver zones with species.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fr.run_one_step()\n",
+    "\n",
+    "min_stream_area = 1e6\n",
+    "zones = Zone.get_zones_with_area_threshold(mg, min_stream_area)\n",
+    "for zone in zones:\n",
+    "    new_species = Species(0, zone)\n",
+    "    se.introduce_species(new_species, 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initiate a fault"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the fault."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "grid_width = mg.number_of_node_columns * mg.dx\n",
+    "x_of_fault = grid_width * 0.5\n",
+    "core_mask = mg.node_is_core()\n",
+    "upthown_nodes = mg.x_of_node >= x_of_fault\n",
+    "upthown_block_mask = np.all([core_mask, upthown_nodes], 0)\n",
+    "\n",
+    "imshow_grid(mg, 'topographic__elevation')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Uplift the nodes of the upthrown block by 20 m."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "z[upthown_block_mask] += 20"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model species evolution\n",
+    "\n",
+    "First, set model time step and uplift parameters.\n",
+    "\n",
+    "SpeciesEvolver runs every 10 timesteps of the other components."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dt = 1000\n",
+    "total_time = int(1e5)\n",
+    "nt = int(total_time // dt)\n",
+    "\n",
+    "dt_se = 10 * dt\n",
+    "\n",
+    "uplift_rate = 7e-5\n",
+    "uplift_per_step = uplift_rate * dt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run model. The ```run_one_step``` function of SpeciesEvolver takes the new zones of the time step.\n",
+    "\n",
+    "This code block may take a couple minutes to run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for time_i in range(nt):\n",
+    "    # Uniformly uplift.\n",
+    "    z[mg.core_nodes] += uplift_per_step\n",
+    "\n",
+    "    # Increment earth surface process components.\n",
+    "    fr.run_one_step()\n",
+    "    sp.run_one_step(dt)\n",
+    "    lf.run_one_step(dt)\n",
+    "\n",
+    "    elapsed_time = dt * (time_i + 1)\n",
+    "\n",
+    "    if elapsed_time % dt_se == 0:\n",
+    "        # Run SpeciesEvolver with updated zones.\n",
+    "        zones = Zone.get_zones_with_area_threshold(mg, min_stream_area)\n",
+    "        se.run_one_step(elapsed_time, zones)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot results\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEKCAYAAAAfGVI8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAHHZJREFUeJzt3XuUFeWZ7/HvQzdXBUFoETEImhYQbBtOy5DR4yRG4yVHTSJRCDFZxiVRkZjM5BhjRDPJrIyuQY96MssjxsRrdFRi4niJOgx4y4wRdjU2Vy8gBmiERG7SgjT9nD92dWhbuinoXbv2rvp91tqrd1VX7/dXKbMf6n2r3jJ3R0REsqtb0gFERCRZKgQiIhmnQiAiknEqBCIiGadCICKScSoEIiIZp0IgIpJxKgQiIhmnQiAiknGVSQeIYtCgQT58+PCkY4iIlJWFCxf+2d2r9rVdWRSC4cOHs2DBgqRjiIiUFTNbHWU7dQ2JiGScCoGISMapEIiIZJwKgYhIxqkQiIhkXGyFwMx+aWYbzGxxm3WHmtnzZvZm+HNAXO2LiEg0cZ4R3AOc2W7dNcBcd68G5obLIiKSoNjuI3D3F81seLvV5wGfDd/fC8wHfhBXhix69tlneeWVV5KOISIFMmPGDKqq9nlPWJcU+4aywe7eCODujWZ2WEcbmtk0YBrAsGHDihSvvM2fP5+zzz6blpYWzCzpOCJSAF/72tdSVwgic/fZwGyAuro6TzhOyWtsbGTy5MlUV1fz2muv0bdv36QjiUiZKHYheM/MhoRnA0OADUVuP5V27drFhRdeyLZt25g7d66KgIjsl2JfPvoE8M3w/TeB3xW5/VS69tpreemll5g9ezZjxoxJOo6IlJk4Lx99CPgvYKSZrTGzS4AbgdPN7E3g9HBZuuDxxx9n1qxZXH755UydOjXpOCJShsy99Lvf6+rqXLOPftKbb75JXV0dI0eO5KWXXqJnz55JRxKREmJmC929bl/b6c7iMtXU1MSkSZOorKzk0UcfVREQkQNWslcNScfcnenTp9PQ0MDTTz/NUUcdlXQkESljOiMoQ3fffTf33HMPM2fO5Mwz29+8LSKyf1QIykwul+PKK6/kC1/4Atdff33ScUQkBVQIysimTZuYNGkSVVVVPPjgg1RUVCQdSURSQGMEZaKlpYVvfOMbrFmzhhdffJFBgwYlHUlEUkKFoEzcdNNNPPnkk9x+++1MnDgx6TgikiLqGioD8+bN47rrrmPy5MlceeWVSccRkZRRIShxa9euZfLkyRx77LHcddddmlVURApOXUMlrHUyue3btzNv3jwOPvjgpCOJSAqpEJSwa665hldeeYVf//rXHHfccUnHEZGUUtdQiZozZw633HIL06dPZ8qUKUnHEZEUUyEoQW+88QYXX3wxEyZM4Oabb046joiknApBiWmdTK5Hjx6aTE5EikJjBCXE3bn88stZvHgxzzzzjJ7VLCJFoTOCEnLXXXdx3333ccMNN3DGGWckHUdEMkKFoEQsXLiQGTNmcMYZZzBz5syk44hIhqgQlID333+fSZMmMXjwYB544AG6ddNhEZHi0RhBwlonk1u7di0vv/yyJpMTkaJTIUjYjTfeyFNPPcXPf/5zJkyYkHQcEckg9UEkaO7cucycOZMpU6ZwxRVXJB1HRDJKhSAha9euZcqUKYwcOZLZs2drMjkRSYwKQQJ27drFBRdcQFNTE3PmzNFkciKSKI0RJODqq6/mD3/4Aw8//DCjR49OOo6IZJzOCIrs0Ucf5dZbb2XGjBlceOGFSccREVEhKKYVK1bwrW99i4kTJzJr1qyk44iIACoERbN9+3bOP/98evXqxSOPPEKPHj2SjiQiAmiMoCjcncsuu4ylS5fy7LPP8qlPfSrpSCIif6VCUAR33nknDzzwAD/5yU84/fTTk44jIvIx6hqK2YIFC7jqqqs466yz+NGPfpR0HBGRT0ikEJjZVWa22MyWmNl3k8hQDH/5y1+YNGkShx9+OPfff78mkxORklT0riEzGwtcCkwAPgJ+b2ZPufubxc4Sp5aWFi666CIaGxt5+eWXGThwYNKRRET2Kol/oo4G/tvdm9y9GXgB+HICOWL1s5/9jGeeeYZbb72VE088Mek4IiIdSqIQLAZOMbOBZtYHOBtI1WU0DQ0NXH/99UydOpXLLrss6TgiIp0qeteQuy8zs5uA54EPgEVAc/vtzGwaMA0ou2f3zp8/H3fnxhtv1GRyIlLyEhm9dPe73X28u58CvA98YnzA3We7e52711VVVRU/ZBfkcjmqqqoYOnRo0lFERPYpkfsIzOwwd99gZsOArwCfSSJHXIIgYPz48TobEJGykNQNZXPMbCCwC5ju7psSylFwO3fuZMmSJZx11llJRxERiSSRQuDu/zOJdothyZIlNDc3M27cuKSjiIhEojucCiyXywEwfvz4hJOIiESjQlBgQRDQt29fjj766KSjiIhEokJQYEEQUFtbq+kkRKRs6NuqgHbv3s2iRYs0PiAiZUWFoIDeeOMNmpqaND4gImVFhaCAgiAA0BmBiJQVFYICCoKAnj17Mnr06KSjiIhEpkJQQLlcjrFjx9K9e/eko4iIRKZCUCDu/tepJUREysk+C4GZfdXM+obvrzOz35iZvu3aeffdd9m0aZPGB0Sk7EQ5I5jp7tvM7GTgDOBe4I54Y5UfDRSLSLmKUgh2hz+/CNzh7r8DesQXqTzlcjm6detGTU1N0lFERPZLlEKw1szuBC4AnjaznhH/LlOCIGDUqFH06dMn6SgiIvslyhf6BcCzwJnuvhk4FPjfsaYqQ0EQqFtIRMrSPguBuzcBG4CTw1XN7OWJYlm2YcMG1q5dq0IgImUpylVDNwA/AH4YruoOPBBnqHLTOlCsS0dFpBxF6Rr6MnAusB3A3dcBfeMMVW5aC0FtbW3CSURE9l+UQvCRuzvgAGZ2ULyRyk8QBAwfPpwBAwYkHUVEZL9FKQSPhFcN9TezS4H/AO6KN1Z5yeVyGh8QkbIVZbB4FvAYMAcYCVzv7v837mDlYuvWrbz11lsaHxCRshXp4fXu/jzwfMxZytKiRYsA3VEsIuWrw0JgZi+7+8lmto1wfKD1V4C7e7/Y05UBTS0hIuWuw0Lg7ieHP3WFUCdyuRyDBw9myJAhSUcRETkgUe4jmNg6+2i4fLCZ/U28scpH6x3FZpZ0FBGRAxLlqqE7gA/aLDeh2UcB2LFjB0uXLlW3kIiUtSiFwML7CABw9xYiDjKn3eLFi2lublYhEJGyFqUQrDSz75hZ9/B1FbAy7mDlQFNLiEgaRCkElwF/C6wF1gB/A0yLM1S5CIKAfv36MWLEiKSjiIgcsH128bj7BmByEbKUnSAIqK2tpVs3PZ5BRMpXlKuGjjWzuWa2OFyuMbPr4o9W2nbv3s2iRYvULSQiZS/KP2XvIj8F9S4Ad38dnSGwYsUKPvzwQw0Ui0jZi1II+rj7H9uta+5Ko2b2PTNbYmaLzewhM+vVlc9Lgu4oFpG0iFII/mxmx7BnGupJQOOBNmhmQ4HvAHXuPhaooAzPMIIgoGfPnowaNSrpKCIiXRLlfoDpwGxglJmtBVYBUwvQbm8z2wX0AdZ18fOKLpfLUVNTQ/fu3ZOOIiLSJVGmoV7p7qcBVcAodz/Z3VcfaIPuvhaYBbxL/sxii7s/1347M5tmZgvMbMHGjRsPtLlYuLseVi8iqRHlqqGBZnY78BIw38xuM7OBB9qgmQ0AzgNGAEcAB5nZ19tv5+6z3b3O3euqqqoOtLlYrF69ms2bN6sQiEgqRBkjeBjYCJwPTArf/1sX2jwNWOXuG919F/Ab8jeslY1cLgdooFhE0iFKITjU3X/q7qvC1z8B/bvQ5rvARDPrY/kpOz8PLOvC5xVdEARUVFRQU1OTdBQRkS6LUgjmmdlkM+sWvi4AnjrQBt39VfKPvswBDWGG2Qf6eUkIgoBRo0bRu3fvpKOIiHSZtZlYdO8b5J9QdhCwm/zTyboB28NfF+VJZXV1db5gwYK4m4ls6NChnHrqqdx///1JRxER6ZCZLXT3un1tF2WuIT2hrI333nuPdevWaWoJEUmNKFcNnWRmB4Xvv25mt5jZsPijlSbdUSwiaRP1CWVNZnYCcDWwGshsn0hrIaitrU04iYhIYUQpBM3hE8rOA25z99uAzHYXBUHAiBEj6N+/KxdOiYiUjihTTGwzsx8CXwdOMbMKILPzKuRyOY0PiEiqRDkjuBDYCVzi7uuBocC/xJqqRG3ZsoW3335b4wMikipRrhpaD9zSZvld4L44Q5WqRYsWARooFpF00TMW94OmlhCRNFIh2A9BEHD44YczZMiQpKOIiBRMh4XAzOaGP28qXpzSpqmnRSSNOhsjGGJmfweca2YPk59e4q/cPRdrshKzY8cOli5dyjnnnJN0FBGRguqsEFwPXAMcSZvB4pADp8YVqhQ1NDSwe/duXToqIqnTYSFw98eAx8xsprv/tIiZSpKmlhCRtIpy+ehPzexc4JRw1Xx3fzLeWKUnCAIOOeQQRowYkXQUEZGCijLp3D8DVwFLw9dV4bpMCYKA2tpa8s/SERFJjyiXj34RON3df+nuvwTODNdlRnNzM4sWLdL4gIikUtT7CNrOsHZIHEFK2YoVK9ixY4fGB0QklaJMOvfPQGBm88hfQnoK8MNYU5UYDRSLSJpFGSx+yMzmAyeSLwQ/COcfyoxcLkevXr0YNWpU0lFERAouyhkB7t4IPBFzlpIVBAE1NTVUVkb6n0tEpKxorqF9cHfq6+vVLSQiqaVCsA/vvPMOmzdvViEQkdTqtBCYWTczW1ysMKWodeppXToqImnVaSFw9xZgkZkNK1KekhMEARUVFRx//PFJRxERiUWU0c8hwBIz+yOwvXWlu58bW6oSEgQBo0ePplevXklHERGJRZRC8I+xpyhhQRBw2mmnJR1DRCQ2Ue4jeMHMjgKq3f0/zKwPUBF/tOStX7+exsZGjQ+ISKpFmXTuUuAx4M5w1VDgt3GGKhW6o1hEsiDK5aPTgZOArQDu/iZwWJyhSkVrIaitrU04iYhIfKIUgp3u/lHrgplVkn9CWeoFQcAxxxzDIYdkbp49EcmQKIXgBTO7FuhtZqcDjwL/fqANmtlIM6tv89pqZt890M+LUy6XU7eQiKRelEJwDbARaAC+DTwNXHegDbr7Cnevdfda4H8ATcDjB/p5cdmyZQsrV65UIRCR1Ity1VCLmd0LvEq+S2iFuxeqa+jzwNvuvrpAn1cw9fX1gAaKRST99lkIzOyLwP8D3iY/DfUIM/u2uz9TgPYnAw8V4HMKTlNLiEhWRLmh7Gbgc+7+FoCZHQM8BXSpEJhZD+BcOnjIjZlNA6YBDBtW/BkugiBgyJAhDB48uOhti4gUU5Qxgg2tRSC0EthQgLbPAnLu/t7efunus929zt3rqqqqCtDc/gmCQN1CIpIJHZ4RmNlXwrdLzOxp4BHyYwRfBV4rQNtTKNFuoQ8//JBly5Zx3nnnJR1FRCR2nXUNndPm/XvA34XvNwIDutJoOE3F6eSvQio5DQ0N7N69W+MDIpIJHRYCd784rkbdvQkYGNfnd5WmlhCRLIly1dAIYAYwvO32aZ6GOggC+vfvz/Dhw5OOIiISuyhXDf0WuJv83cQt8cYpDa0DxWaWdBQRkdhFKQQ73P322JOUiObmZl5//XWuuOKKpKOIiBRFlEJwm5ndADwH7Gxd6e652FIlaPny5ezYsUPjAyKSGVEKwfHARcCp7Oka8nA5dTRQLCJZE6UQfBk4uu1U1GmWy+Xo3bs3I0eOTDqKiEhRRLmzeBHQP+4gpSIIAmpqaqisjFIjRUTKX5Rvu8HAcjN7jY+PEaTu8lF3p76+nilTpiQdRUSkaKIUghtiT1EiVq1axZYtW3RHsYhkSpTnEbxQjCCloHXqaQ0Ui0iWRLmzeBt7nlHcA+gObHf3fnEGS0IQBFRUVDB27Niko4iIFE2UM4K+bZfN7EvAhNgSJSgIAo477jh69eqVdBQRkaKJctXQx7j7b0nxPQQaHxCRrInSNfSVNovdgDr2dBWlRmNjI+vXr9f4gIhkTpSrhto+l6AZeAdI3RNbdEexiGRVlDGC2J5LUEpaC0FtbW3CSUREiquzR1Ve38nfubv/NIY8icnlcnz605+mX7/UXQwlItKpzgaLt+/lBXAJ8IOYcxWdHlYvIlnVYSFw95tbX8BsoDdwMfAwcHSR8hXF5s2bWbVqlQqBiGRSp2MEZnYo8PfAVOBeYLy7bypGsGKqr68H0KWjIpJJnY0R/AvwFfJnA8e7+wdFS1VkmlpCRLKsszGCfwCOAK4D1pnZ1vC1zcy2FidecQRBwBFHHMFhhx2WdBQRkaLr8IzA3ff7ruNypYFiEcmyzHzZd6SpqYlly5ZpfEBEMivzhaChoYGWlhadEYhIZmW+EGhqCRHJOhWCIGDAgAEcddRRSUcREUlE5gtBLpdj3LhxmFnSUUREEpHpQrBr1y4aGhrULSQimZbpQrB8+XJ27typQiAimZbpQtA6UKxLR0UkyxIpBGbW38weM7PlZrbMzD6TRI5cLkefPn049thjk2heRKQkRHlCWRxuA37v7pPMrAfQJ4kQQRBQU1NDRUVFEs2LiJSEop8RmFk/4BTgbgB3/8jdNxc7R0tLC/X19RofEJHMS6Jr6GhgI/ArMwvM7BdmdlD7jcxsmpktMLMFGzduLHiIVatWsXXrVo0PiEjmJVEIKoHxwB3uPo78k8+uab+Ru8929zp3r6uqqip4CE09LSKSl0QhWAOscfdXw+XHyBeGogqCgMrKSsaOHVvspkVESkrRC4G7rwf+ZGYjw1WfB5YWO0cQBIwZM4aePXsWu2kRkZKS1FVDM4AHwyuGVpJ/FnLRuDu5XI6zzz67mM2KiJSkRAqBu9cDdUm0DdDY2MiGDRs0PiAiQkbvLNbU0yIie2S6ENTW1iacREQkeZksBLlcjurqavr27Zt0FBGRxGWyEOhh9SIie2SuEGzatIl33nlHhUBEJJS5QlBfXw9o6mkRkVaZKwSaWkJE5OMyVwiCIGDo0KHEMX+RiEg5ymQhULeQiMgemSoETU1NLF++XN1CIiJtZKoQvP7667S0tKgQiIi0kalCoKklREQ+KXOF4NBDD2XYsGFJRxERKRmZKgS5XI5x48ZhZklHEREpGZkpBLt27aKhoUHdQiIi7WSmECxbtoyPPvpIhUBEpJ3MFILWgWLdQyAi8nGZKQS5XI4+ffpQXV2ddBQRkZKSmUIQBAEnnHACFRUVSUcRESkpmSgELS0t1NfXq1tIRGQvMlEIVq5cybZt2zRQLCKyF5koBJp6WkSkY5koBEEQUFlZyZgxY5KOIiJScjJTCMaOHUvPnj2TjiIiUnJSXwjc/a9TS4iIyCelvhCsW7eOjRs3qhCIiHQg9YVAU0+LiHQuE4XAzDjhhBOSjiIiUpJSXwhyuRzV1dX07ds36SgiIiUp9YUgCAJ1C4mIdCKRQmBm75hZg5nVm9mCuNp5//33Wb16taaWEBHpRGWCbX/O3f8cZwP19fWABopFRDqT6q4hTS0hIrJvSRUCB54zs4VmNi2uRoIg4Mgjj2TQoEFxNSEiUvaS6ho6yd3XmdlhwPNmttzdX2y7QVggpgEMGzbsgBo5/vjjD/hvRUSywtw92QBmPwY+cPdZHW1TV1fnCxbENqYsIpJKZrbQ3ev2tV3Ru4bM7CAz69v6HvgCsLjYOUREJC+JrqHBwONm1tr+r9399wnkEBEREigE7r4S0HwPIiIlItWXj4qIyL6pEIiIZJwKgYhIxqkQiIhknAqBiEjGJX5DWRRmthFYfYB/PgiIdXK7EqR9zgbtc/p1dX+PcveqfW1UFoWgK8xsQZQ769JE+5wN2uf0K9b+qmtIRCTjVAhERDIuC4VgdtIBEqB9zgbtc/oVZX9TP0YgIiKdy8IZgYiIdCLVhcDMzjSzFWb2lpldk3SeQjOzT5nZPDNbZmZLzOyqcP2hZva8mb0Z/hyQdNZCM7MKMwvM7MlweYSZvRru87+ZWY+kMxaSmfU3s8fMbHl4vD+T9uNsZt8L/7tebGYPmVmvtB1nM/ulmW0ws8Vt1u31uFre7eH32etmNr5QOVJbCMysAvhX4CzgOGCKmR2XbKqCawb+wd1HAxOB6eE+XgPMdfdqYG64nDZXAcvaLN8E/J9wnzcBlySSKj63Ab9391HkZ+9dRoqPs5kNBb4D1Ln7WKACmEz6jvM9wJnt1nV0XM8CqsPXNOCOQoVIbSEAJgBvuftKd/8IeBg4L+FMBeXuje6eC99vI//lMJT8ft4bbnYv8KVkEsbDzI4Evgj8Ilw24FTgsXCTVO2zmfUDTgHuBnD3j9x9Myk/zuSnye9tZpVAH6CRlB3n8BG977db3dFxPQ+4z/P+G+hvZkMKkSPNhWAo8Kc2y2vCdalkZsOBccCrwGB3b4R8sQAOSy5ZLG4FrgZawuWBwGZ3bw6X03asjwY2Ar8Ku8N+ET7dL7XH2d3XArOAd8kXgC3AQtJ9nFt1dFxj+05LcyGwvaxL5SVSZnYwMAf4rrtvTTpPnMzsfwEb3H1h29V72TRNx7oSGA/c4e7jgO2kqBtob8J+8fOAEcARwEHku0baS9Nx3pfY/jtPcyFYA3yqzfKRwLqEssTGzLqTLwIPuvtvwtXvtZ4yhj83JJUvBicB55rZO+S7+04lf4bQP+xCgPQd6zXAGnd/NVx+jHxhSPNxPg1Y5e4b3X0X8Bvgb0n3cW7V0XGN7TstzYXgNaA6vMqgB/mBpicSzlRQYd/43cAyd7+lza+eAL4Zvv8m8LtiZ4uLu//Q3Y909+Hkj+l/uvtUYB4wKdwsbfu8HviTmY0MV30eWEqKjzP5LqGJZtYn/O+8dZ9Te5zb6Oi4PgF8I7x6aCKwpbULqcvcPbUv4GzgDeBt4EdJ54lh/04mf2r4OlAfvs4m32c+F3gz/Hlo0llj2v/PAk+G748G/gi8BTwK9Ew6X4H3tRZYEB7r3wID0n6cgX8ElgOLgfuBnmk7zsBD5MdAdpH/F/8lHR1X8l1D/xp+nzWQv6KqIDl0Z7GISMaluWtIREQiUCEQEck4FQIRkYxTIRARyTgVAhGRjFMhkNQzs4FmVh++1pvZ2jbLf4ipzXFm1joX0o/N7Pv78bcPm1l1HLlE9qZy35uIlDd3/wv56/Axsx8DH7j7rJibvRb4pwP82zvIz6V0aeHiiHRMZwSSaWb2Qfjzs2b2gpk9YmZvmNmNZjbVzP5oZg1mdky4XZWZzTGz18LXSXv5zL5Ajbsv2svvLjWzZ8xsjJnl2qyvNrPW+ZNeAk5rM5WCSKxUCET2OIH8cw6OBy4CjnX3CeSnu54RbnMb+fnwTwTOD3/XXh35u2E/xsyuBM4BvuTuS4AtZlYb/vpi8nPT4+4t5O+cPaEwuyXSOf2LQ2SP1zycu8XM3gaeC9c3AJ8L358GHJef/gaAfmbW1/PPg2g1hPy00W1dRH4KgS95fhI1yBeRi83s74ELyT9Do9UG8rNuLkQkZioEInvsbPO+pc1yC3v+v9IN+Iy7f9jJ53wI9Gq3bjH5cYojgVXhujnADcB/AgvDsYxWvcLPEYmduoZE9s9zwJWtC226dtpaBny63boA+DbwhJkdAeDuO4BnyQ8O/6rd9scCSwqUWaRTKgQi++c7QF348PClwGXtN3D35cAh4aBx2/UvA98HnjKzQeHqB8nPINvaDYWZDQY+9EJNMSyyD5p9VCQGZvY9YJu7720wue123wcOcfeZ7f52q7vfHXNMEUBjBCJxuQP4amcbmNnjwDHkn7LW1mby8++LFIXOCEREMk5jBCIiGadCICKScSoEIiIZp0IgIpJxKgQiIhmnQiAiknH/HwEnjY5uK1hFAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.subplot(1, 2, 1)\n",
+    "imshow_grid(mg, 'topographic__elevation', colorbar_label='elevation (m)')\n",
+    "\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plot.imshow_zones(mg, zones)\n",
+    "\n",
+    "plot.number_of_species(se.record, se.species)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Species identifiers and phylogenetic trees\n",
+    "\n",
+    "Species identifiers are automatically generated. They are stored as tuples in species objects. The first element of the identifier tuple is the clade name, and the second element is the species number. For example, the first species of clade A is 0, so the identifier is (A, 0) and the tenth species of this clade is (A, 9).\n",
+    "\n",
+    "### Clade name\n",
+    "\n",
+    "A clade is a group of species that share a common ancestor. Species may share multiple common ancestors, so clades can be defined differently in the same tree. The clade(s) of this example tree can be defined in multiple ways:\n",
+    "\n",
+    "![](images/clade_definitions.png)\n",
+    "\n",
+    "In SpeciesEvolver, clades are defined by the root node for the purpose of assigning species identifiers. The species at the root node is:\n",
+    "* the least recent common ancestor of all species in the tree, and\n",
+    "* typically introduced to a SpeciesEvolver model using the ```introduce_species``` function.\n",
+    "\n",
+    "The clades of the first 26 species are named alphabetically from A to Z. The alphabet is repeated for subsequent species following this schema:\n",
+    "\n",
+    "<center>clade sequence</center> | <center>clade name</center>\n",
+    "--- | ---\n",
+    "1–26 | A–Z\n",
+    "27–52 | AA–AZ\n",
+    "53–78 | BA–BZ\n",
+    "... | ...\n",
+    "703–728 | AAA–AAZ\n",
+    "729–754 | ABA–ABZ\n",
+    "... | ...\n",
+    "\n",
+    "### Species number\n",
+    "\n",
+    "Species are numbered sequently by clade beginning with 0.\n",
+    "\n",
+    "In the example tree below, species A0 produced 4 child species. Only one species, B0 exists in clade B because child species were not produced by B0.\n",
+    "\n",
+    "![](images/species_number.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "E ---------\n",
+      "  children number  subclades  x_max  x_min      y\n",
+      "0      NaN      4        2.0  100.0   20.0  0.500\n",
+      "1      NaN      3        2.0  100.0   20.0  1.000\n",
+      "2      NaN      1        0.0  100.0   10.0  1.500\n",
+      "3      NaN      2        0.0   10.0   10.0  0.750\n",
+      "4      NaN      0       -1.0    0.0    0.0  1.125\n",
+      "D ---------\n",
+      "  children number  subclades  x_max  x_min    y\n",
+      "0      NaN      0       -1.0  100.0    0.0  2.0\n",
+      "C ---------\n",
+      "  children number  subclades  x_max  x_min    y\n",
+      "0      NaN      0       -1.0  100.0    0.0  2.5\n",
+      "B ---------\n",
+      "  children number  subclades  x_max  x_min     y\n",
+      "0      NaN      4        0.0  100.0   10.0  3.00\n",
+      "1      NaN      3        0.0  100.0   10.0  3.50\n",
+      "2      NaN      2        0.0  100.0   10.0  4.00\n",
+      "3      NaN      1        0.0  100.0   10.0  4.50\n",
+      "4      NaN      0       -1.0    0.0    0.0  3.75\n",
+      "A ---------\n",
+      "  children number  subclades  x_max  x_min    y\n",
+      "0      NaN      0       -1.0  100.0    0.0  5.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from landlab.components.species_macroevolution import plot\n",
+    "\n",
+    "plot.tree(se.species)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## DataFrames\n",
+    "\n",
+    "Data are stored in Pandas DataFrames."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>clade</th>\n",
+       "      <th>species</th>\n",
+       "      <th>time_appeared</th>\n",
+       "      <th>latest_time</th>\n",
+       "      <th>subtype</th>\n",
+       "      <th>object</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>A</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>100000</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x1c19ad9860&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>B</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x1c19abfd30&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>C</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>100000</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x1c19ad9f28&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>D</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>100000</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x1c19ad9438&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>E</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x1c19ad96a0&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>B</td>\n",
+       "      <td>2</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>100000</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x1c25163828&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>B</td>\n",
+       "      <td>3</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>100000</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x1c25163048&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>B</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>100000</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x1c25163710&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>B</td>\n",
+       "      <td>4</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>100000</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x1c25163940&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>E</td>\n",
+       "      <td>2</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x1c25163978&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>E</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>100000</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x1c25163fd0&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>E</td>\n",
+       "      <td>3</td>\n",
+       "      <td>20000</td>\n",
+       "      <td>100000</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x1c2517dd30&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>E</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20000</td>\n",
+       "      <td>100000</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Species at 0x1c2517d240&gt;</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   clade species time_appeared latest_time subtype                     object\n",
+       "0      A       0             0      100000    base  <Species at 0x1c19ad9860>\n",
+       "1      B       0             0           0    base  <Species at 0x1c19abfd30>\n",
+       "2      C       0             0      100000    base  <Species at 0x1c19ad9f28>\n",
+       "3      D       0             0      100000    base  <Species at 0x1c19ad9438>\n",
+       "4      E       0             0           0    base  <Species at 0x1c19ad96a0>\n",
+       "5      B       2         10000      100000    base  <Species at 0x1c25163828>\n",
+       "6      B       3         10000      100000    base  <Species at 0x1c25163048>\n",
+       "7      B       1         10000      100000    base  <Species at 0x1c25163710>\n",
+       "8      B       4         10000      100000    base  <Species at 0x1c25163940>\n",
+       "9      E       2         10000       10000    base  <Species at 0x1c25163978>\n",
+       "10     E       1         10000      100000    base  <Species at 0x1c25163fd0>\n",
+       "11     E       3         20000      100000    base  <Species at 0x1c2517dd30>\n",
+       "12     E       4         20000      100000    base  <Species at 0x1c2517d240>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "se.species"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>time_appeared</th>\n",
+       "      <th>latest_time</th>\n",
+       "      <th>subtype</th>\n",
+       "      <th>object</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Zone at 0x1c19ad9978&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>100000</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Zone at 0x1c19ad9a58&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>100000</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Zone at 0x1c19ad9b38&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>100000</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Zone at 0x1c19ad9c18&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>100000</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Zone at 0x1c19ad9cf8&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>10000</td>\n",
+       "      <td>100000</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Zone at 0x1c2512f208&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>10000</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Zone at 0x1c2512f438&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>10000</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Zone at 0x1c2512f668&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>10000</td>\n",
+       "      <td>50000</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Zone at 0x1c2512f080&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10000</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Zone at 0x1c2512f898&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10000</td>\n",
+       "      <td>20000</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Zone at 0x1c2512f550&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>10000</td>\n",
+       "      <td>20000</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Zone at 0x1c2512f780&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>20000</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>base</td>\n",
+       "      <td>&lt;Zone at 0x1c2515de80&gt;</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   time_appeared latest_time subtype                  object\n",
+       "0              0       30000    base  <Zone at 0x1c19ad9978>\n",
+       "1              0      100000    base  <Zone at 0x1c19ad9a58>\n",
+       "2              0      100000    base  <Zone at 0x1c19ad9b38>\n",
+       "3              0      100000    base  <Zone at 0x1c19ad9c18>\n",
+       "4              0      100000    base  <Zone at 0x1c19ad9cf8>\n",
+       "5          10000      100000    base  <Zone at 0x1c2512f208>\n",
+       "6          10000       10000    base  <Zone at 0x1c2512f438>\n",
+       "7          10000       30000    base  <Zone at 0x1c2512f668>\n",
+       "8          10000       50000    base  <Zone at 0x1c2512f080>\n",
+       "9          10000       10000    base  <Zone at 0x1c2512f898>\n",
+       "10         10000       20000    base  <Zone at 0x1c2512f550>\n",
+       "11         10000       20000    base  <Zone at 0x1c2512f780>\n",
+       "12         20000       30000    base  <Zone at 0x1c2515de80>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "se.zones"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>time</th>\n",
+       "      <th>origin</th>\n",
+       "      <th>destinations</th>\n",
+       "      <th>path_type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10000</td>\n",
+       "      <td>&lt;Zone at 0x1c19ad9978&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c19ad9978&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10000</td>\n",
+       "      <td>&lt;Zone at 0x1c19ad9a58&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c2512f208&gt;, &lt;Zone at 0x1c19ad9a58...</td>\n",
+       "      <td>one-to-many</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10000</td>\n",
+       "      <td>&lt;Zone at 0x1c19ad9b38&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c19ad9b38&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10000</td>\n",
+       "      <td>&lt;Zone at 0x1c19ad9c18&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c19ad9c18&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10000</td>\n",
+       "      <td>&lt;Zone at 0x1c19ad9cf8&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c2512f668&gt;, &lt;Zone at 0x1c19ad9cf8&gt;]</td>\n",
+       "      <td>one-to-many</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>10000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>[&lt;Zone at 0x1c2512f080&gt;]</td>\n",
+       "      <td>one-to-many</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>10000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>[&lt;Zone at 0x1c2512f208&gt;]</td>\n",
+       "      <td>one-to-many</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>10000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>[&lt;Zone at 0x1c19ad9a58&gt;]</td>\n",
+       "      <td>one-to-many</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>10000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>[&lt;Zone at 0x1c2512f438&gt;]</td>\n",
+       "      <td>one-to-many</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>[&lt;Zone at 0x1c2512f550&gt;]</td>\n",
+       "      <td>one-to-many</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>[&lt;Zone at 0x1c2512f668&gt;]</td>\n",
+       "      <td>one-to-many</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>10000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>[&lt;Zone at 0x1c2512f780&gt;]</td>\n",
+       "      <td>one-to-many</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>10000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>[&lt;Zone at 0x1c2512f898&gt;]</td>\n",
+       "      <td>one-to-many</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>10000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>[&lt;Zone at 0x1c19ad9cf8&gt;]</td>\n",
+       "      <td>one-to-many</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>20000</td>\n",
+       "      <td>&lt;Zone at 0x1c19ad9978&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c19ad9978&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>20000</td>\n",
+       "      <td>&lt;Zone at 0x1c19ad9a58&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c19ad9a58&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>20000</td>\n",
+       "      <td>&lt;Zone at 0x1c19ad9b38&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c19ad9b38&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>20000</td>\n",
+       "      <td>&lt;Zone at 0x1c19ad9c18&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c19ad9c18&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>20000</td>\n",
+       "      <td>&lt;Zone at 0x1c19ad9cf8&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c19ad9cf8&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>20000</td>\n",
+       "      <td>&lt;Zone at 0x1c2512f208&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c2512f208&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>20000</td>\n",
+       "      <td>&lt;Zone at 0x1c2512f438&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c2512f550&gt;]</td>\n",
+       "      <td>many-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>20000</td>\n",
+       "      <td>&lt;Zone at 0x1c2512f668&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c2512f668&gt;, &lt;Zone at 0x1c2515de80&gt;]</td>\n",
+       "      <td>one-to-many</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>20000</td>\n",
+       "      <td>&lt;Zone at 0x1c2512f080&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c2512f080&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>20000</td>\n",
+       "      <td>&lt;Zone at 0x1c2512f898&gt;</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>one-to-none</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>20000</td>\n",
+       "      <td>&lt;Zone at 0x1c2512f550&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c2512f550&gt;]</td>\n",
+       "      <td>many-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>20000</td>\n",
+       "      <td>&lt;Zone at 0x1c2512f780&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c2512f780&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>20000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>[&lt;Zone at 0x1c2512f668&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>20000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>[&lt;Zone at 0x1c2515de80&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>30000</td>\n",
+       "      <td>&lt;Zone at 0x1c19ad9978&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c19ad9978&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>30000</td>\n",
+       "      <td>&lt;Zone at 0x1c19ad9a58&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c19ad9a58&gt;]</td>\n",
+       "      <td>many-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>50000</td>\n",
+       "      <td>&lt;Zone at 0x1c19ad9c18&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c19ad9c18&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>50000</td>\n",
+       "      <td>&lt;Zone at 0x1c19ad9cf8&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c19ad9cf8&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>50000</td>\n",
+       "      <td>&lt;Zone at 0x1c2512f208&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c2512f208&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53</th>\n",
+       "      <td>50000</td>\n",
+       "      <td>&lt;Zone at 0x1c2512f080&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c2512f080&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>60000</td>\n",
+       "      <td>&lt;Zone at 0x1c19ad9a58&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c19ad9a58&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>60000</td>\n",
+       "      <td>&lt;Zone at 0x1c19ad9b38&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c19ad9b38&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>60000</td>\n",
+       "      <td>&lt;Zone at 0x1c19ad9c18&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c19ad9c18&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>60000</td>\n",
+       "      <td>&lt;Zone at 0x1c19ad9cf8&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c19ad9cf8&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>60000</td>\n",
+       "      <td>&lt;Zone at 0x1c2512f208&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c2512f208&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>60000</td>\n",
+       "      <td>&lt;Zone at 0x1c2512f080&gt;</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>one-to-none</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>70000</td>\n",
+       "      <td>&lt;Zone at 0x1c19ad9a58&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c19ad9a58&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61</th>\n",
+       "      <td>70000</td>\n",
+       "      <td>&lt;Zone at 0x1c19ad9b38&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c19ad9b38&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>70000</td>\n",
+       "      <td>&lt;Zone at 0x1c19ad9c18&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c19ad9c18&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>70000</td>\n",
+       "      <td>&lt;Zone at 0x1c19ad9cf8&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c19ad9cf8&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>70000</td>\n",
+       "      <td>&lt;Zone at 0x1c2512f208&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c2512f208&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65</th>\n",
+       "      <td>80000</td>\n",
+       "      <td>&lt;Zone at 0x1c19ad9a58&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c19ad9a58&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>80000</td>\n",
+       "      <td>&lt;Zone at 0x1c19ad9b38&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c19ad9b38&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67</th>\n",
+       "      <td>80000</td>\n",
+       "      <td>&lt;Zone at 0x1c19ad9c18&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c19ad9c18&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>68</th>\n",
+       "      <td>80000</td>\n",
+       "      <td>&lt;Zone at 0x1c19ad9cf8&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c19ad9cf8&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69</th>\n",
+       "      <td>80000</td>\n",
+       "      <td>&lt;Zone at 0x1c2512f208&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c2512f208&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>90000</td>\n",
+       "      <td>&lt;Zone at 0x1c19ad9a58&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c19ad9a58&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>90000</td>\n",
+       "      <td>&lt;Zone at 0x1c19ad9b38&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c19ad9b38&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>90000</td>\n",
+       "      <td>&lt;Zone at 0x1c19ad9c18&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c19ad9c18&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>90000</td>\n",
+       "      <td>&lt;Zone at 0x1c19ad9cf8&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c19ad9cf8&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>90000</td>\n",
+       "      <td>&lt;Zone at 0x1c2512f208&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c2512f208&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75</th>\n",
+       "      <td>100000</td>\n",
+       "      <td>&lt;Zone at 0x1c19ad9a58&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c19ad9a58&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>76</th>\n",
+       "      <td>100000</td>\n",
+       "      <td>&lt;Zone at 0x1c19ad9b38&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c19ad9b38&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>77</th>\n",
+       "      <td>100000</td>\n",
+       "      <td>&lt;Zone at 0x1c19ad9c18&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c19ad9c18&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>78</th>\n",
+       "      <td>100000</td>\n",
+       "      <td>&lt;Zone at 0x1c19ad9cf8&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c19ad9cf8&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>79</th>\n",
+       "      <td>100000</td>\n",
+       "      <td>&lt;Zone at 0x1c2512f208&gt;</td>\n",
+       "      <td>[&lt;Zone at 0x1c2512f208&gt;]</td>\n",
+       "      <td>one-to-one</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>80 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      time                  origin  \\\n",
+       "0    10000  <Zone at 0x1c19ad9978>   \n",
+       "1    10000  <Zone at 0x1c19ad9a58>   \n",
+       "2    10000  <Zone at 0x1c19ad9b38>   \n",
+       "3    10000  <Zone at 0x1c19ad9c18>   \n",
+       "4    10000  <Zone at 0x1c19ad9cf8>   \n",
+       "5    10000                     NaN   \n",
+       "6    10000                     NaN   \n",
+       "7    10000                     NaN   \n",
+       "8    10000                     NaN   \n",
+       "9    10000                     NaN   \n",
+       "10   10000                     NaN   \n",
+       "11   10000                     NaN   \n",
+       "12   10000                     NaN   \n",
+       "13   10000                     NaN   \n",
+       "14   20000  <Zone at 0x1c19ad9978>   \n",
+       "15   20000  <Zone at 0x1c19ad9a58>   \n",
+       "16   20000  <Zone at 0x1c19ad9b38>   \n",
+       "17   20000  <Zone at 0x1c19ad9c18>   \n",
+       "18   20000  <Zone at 0x1c19ad9cf8>   \n",
+       "19   20000  <Zone at 0x1c2512f208>   \n",
+       "20   20000  <Zone at 0x1c2512f438>   \n",
+       "21   20000  <Zone at 0x1c2512f668>   \n",
+       "22   20000  <Zone at 0x1c2512f080>   \n",
+       "23   20000  <Zone at 0x1c2512f898>   \n",
+       "24   20000  <Zone at 0x1c2512f550>   \n",
+       "25   20000  <Zone at 0x1c2512f780>   \n",
+       "26   20000                     NaN   \n",
+       "27   20000                     NaN   \n",
+       "28   30000  <Zone at 0x1c19ad9978>   \n",
+       "29   30000  <Zone at 0x1c19ad9a58>   \n",
+       "..     ...                     ...   \n",
+       "50   50000  <Zone at 0x1c19ad9c18>   \n",
+       "51   50000  <Zone at 0x1c19ad9cf8>   \n",
+       "52   50000  <Zone at 0x1c2512f208>   \n",
+       "53   50000  <Zone at 0x1c2512f080>   \n",
+       "54   60000  <Zone at 0x1c19ad9a58>   \n",
+       "55   60000  <Zone at 0x1c19ad9b38>   \n",
+       "56   60000  <Zone at 0x1c19ad9c18>   \n",
+       "57   60000  <Zone at 0x1c19ad9cf8>   \n",
+       "58   60000  <Zone at 0x1c2512f208>   \n",
+       "59   60000  <Zone at 0x1c2512f080>   \n",
+       "60   70000  <Zone at 0x1c19ad9a58>   \n",
+       "61   70000  <Zone at 0x1c19ad9b38>   \n",
+       "62   70000  <Zone at 0x1c19ad9c18>   \n",
+       "63   70000  <Zone at 0x1c19ad9cf8>   \n",
+       "64   70000  <Zone at 0x1c2512f208>   \n",
+       "65   80000  <Zone at 0x1c19ad9a58>   \n",
+       "66   80000  <Zone at 0x1c19ad9b38>   \n",
+       "67   80000  <Zone at 0x1c19ad9c18>   \n",
+       "68   80000  <Zone at 0x1c19ad9cf8>   \n",
+       "69   80000  <Zone at 0x1c2512f208>   \n",
+       "70   90000  <Zone at 0x1c19ad9a58>   \n",
+       "71   90000  <Zone at 0x1c19ad9b38>   \n",
+       "72   90000  <Zone at 0x1c19ad9c18>   \n",
+       "73   90000  <Zone at 0x1c19ad9cf8>   \n",
+       "74   90000  <Zone at 0x1c2512f208>   \n",
+       "75  100000  <Zone at 0x1c19ad9a58>   \n",
+       "76  100000  <Zone at 0x1c19ad9b38>   \n",
+       "77  100000  <Zone at 0x1c19ad9c18>   \n",
+       "78  100000  <Zone at 0x1c19ad9cf8>   \n",
+       "79  100000  <Zone at 0x1c2512f208>   \n",
+       "\n",
+       "                                         destinations    path_type  \n",
+       "0                            [<Zone at 0x1c19ad9978>]   one-to-one  \n",
+       "1   [<Zone at 0x1c2512f208>, <Zone at 0x1c19ad9a58...  one-to-many  \n",
+       "2                            [<Zone at 0x1c19ad9b38>]   one-to-one  \n",
+       "3                            [<Zone at 0x1c19ad9c18>]   one-to-one  \n",
+       "4    [<Zone at 0x1c2512f668>, <Zone at 0x1c19ad9cf8>]  one-to-many  \n",
+       "5                            [<Zone at 0x1c2512f080>]  one-to-many  \n",
+       "6                            [<Zone at 0x1c2512f208>]  one-to-many  \n",
+       "7                            [<Zone at 0x1c19ad9a58>]  one-to-many  \n",
+       "8                            [<Zone at 0x1c2512f438>]  one-to-many  \n",
+       "9                            [<Zone at 0x1c2512f550>]  one-to-many  \n",
+       "10                           [<Zone at 0x1c2512f668>]  one-to-many  \n",
+       "11                           [<Zone at 0x1c2512f780>]  one-to-many  \n",
+       "12                           [<Zone at 0x1c2512f898>]  one-to-many  \n",
+       "13                           [<Zone at 0x1c19ad9cf8>]  one-to-many  \n",
+       "14                           [<Zone at 0x1c19ad9978>]   one-to-one  \n",
+       "15                           [<Zone at 0x1c19ad9a58>]   one-to-one  \n",
+       "16                           [<Zone at 0x1c19ad9b38>]   one-to-one  \n",
+       "17                           [<Zone at 0x1c19ad9c18>]   one-to-one  \n",
+       "18                           [<Zone at 0x1c19ad9cf8>]   one-to-one  \n",
+       "19                           [<Zone at 0x1c2512f208>]   one-to-one  \n",
+       "20                           [<Zone at 0x1c2512f550>]  many-to-one  \n",
+       "21   [<Zone at 0x1c2512f668>, <Zone at 0x1c2515de80>]  one-to-many  \n",
+       "22                           [<Zone at 0x1c2512f080>]   one-to-one  \n",
+       "23                                                 []  one-to-none  \n",
+       "24                           [<Zone at 0x1c2512f550>]  many-to-one  \n",
+       "25                           [<Zone at 0x1c2512f780>]   one-to-one  \n",
+       "26                           [<Zone at 0x1c2512f668>]   one-to-one  \n",
+       "27                           [<Zone at 0x1c2515de80>]   one-to-one  \n",
+       "28                           [<Zone at 0x1c19ad9978>]   one-to-one  \n",
+       "29                           [<Zone at 0x1c19ad9a58>]  many-to-one  \n",
+       "..                                                ...          ...  \n",
+       "50                           [<Zone at 0x1c19ad9c18>]   one-to-one  \n",
+       "51                           [<Zone at 0x1c19ad9cf8>]   one-to-one  \n",
+       "52                           [<Zone at 0x1c2512f208>]   one-to-one  \n",
+       "53                           [<Zone at 0x1c2512f080>]   one-to-one  \n",
+       "54                           [<Zone at 0x1c19ad9a58>]   one-to-one  \n",
+       "55                           [<Zone at 0x1c19ad9b38>]   one-to-one  \n",
+       "56                           [<Zone at 0x1c19ad9c18>]   one-to-one  \n",
+       "57                           [<Zone at 0x1c19ad9cf8>]   one-to-one  \n",
+       "58                           [<Zone at 0x1c2512f208>]   one-to-one  \n",
+       "59                                                 []  one-to-none  \n",
+       "60                           [<Zone at 0x1c19ad9a58>]   one-to-one  \n",
+       "61                           [<Zone at 0x1c19ad9b38>]   one-to-one  \n",
+       "62                           [<Zone at 0x1c19ad9c18>]   one-to-one  \n",
+       "63                           [<Zone at 0x1c19ad9cf8>]   one-to-one  \n",
+       "64                           [<Zone at 0x1c2512f208>]   one-to-one  \n",
+       "65                           [<Zone at 0x1c19ad9a58>]   one-to-one  \n",
+       "66                           [<Zone at 0x1c19ad9b38>]   one-to-one  \n",
+       "67                           [<Zone at 0x1c19ad9c18>]   one-to-one  \n",
+       "68                           [<Zone at 0x1c19ad9cf8>]   one-to-one  \n",
+       "69                           [<Zone at 0x1c2512f208>]   one-to-one  \n",
+       "70                           [<Zone at 0x1c19ad9a58>]   one-to-one  \n",
+       "71                           [<Zone at 0x1c19ad9b38>]   one-to-one  \n",
+       "72                           [<Zone at 0x1c19ad9c18>]   one-to-one  \n",
+       "73                           [<Zone at 0x1c19ad9cf8>]   one-to-one  \n",
+       "74                           [<Zone at 0x1c2512f208>]   one-to-one  \n",
+       "75                           [<Zone at 0x1c19ad9a58>]   one-to-one  \n",
+       "76                           [<Zone at 0x1c19ad9b38>]   one-to-one  \n",
+       "77                           [<Zone at 0x1c19ad9c18>]   one-to-one  \n",
+       "78                           [<Zone at 0x1c19ad9cf8>]   one-to-one  \n",
+       "79                           [<Zone at 0x1c2512f208>]   one-to-one  \n",
+       "\n",
+       "[80 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "se.zone_paths"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Record DataFrames\n",
+    "\n",
+    "Instances of SpeciesEvolver, species, and zones each have DataFrames called ```record```. Rows are data stored at each time the model is ran.\n",
+    "\n",
+    "Examine the SpeciesEvolver record."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>time</th>\n",
+       "      <th>number_of_captures</th>\n",
+       "      <th>sum_of_area_captured</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10000.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>240000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>20000.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>80000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>30000.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>40000.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>50000.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>60000.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>80000.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>90000.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>100000.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        time  number_of_captures  sum_of_area_captured\n",
+       "0        0.0                 NaN                   NaN\n",
+       "1    10000.0                 6.0              240000.0\n",
+       "2    20000.0                 2.0               80000.0\n",
+       "3    30000.0                 0.0                   0.0\n",
+       "4    40000.0                 0.0                   0.0\n",
+       "5    50000.0                 0.0                   0.0\n",
+       "6    60000.0                 0.0                   0.0\n",
+       "7    70000.0                 0.0                   0.0\n",
+       "8    80000.0                 0.0                   0.0\n",
+       "9    90000.0                 0.0                   0.0\n",
+       "10  100000.0                 0.0                   0.0"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "se.record"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Examine the record of a species."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'be' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-15-10af7c02b047>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspecies\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecord\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'be' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "s = be.species.iloc[0].object\n",
+    "s.record"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Examine the record of a zone."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "z = be.zones.iloc[0].object\n",
+    "z.record"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Click here for more <a href=\"https://github.com/landlab/landlab/wiki/Tutorials\">Landlab tutorials</a>"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/biota_evolution/images/clade_definitions.png b/species_evolution/images/clade_definitions.png
similarity index 100%
rename from biota_evolution/images/clade_definitions.png
rename to species_evolution/images/clade_definitions.png
diff --git a/biota_evolution/images/species_number.png b/species_evolution/images/species_number.png
similarity index 100%
rename from biota_evolution/images/species_number.png
rename to species_evolution/images/species_number.png
diff --git a/biota_evolution/images/zone__later_time.png b/species_evolution/images/zone__later_time.png
similarity index 100%
rename from biota_evolution/images/zone__later_time.png
rename to species_evolution/images/zone__later_time.png
diff --git a/biota_evolution/images/zone__many_to_many.png b/species_evolution/images/zone__many_to_many.png
similarity index 100%
rename from biota_evolution/images/zone__many_to_many.png
rename to species_evolution/images/zone__many_to_many.png
diff --git a/biota_evolution/images/zone__many_to_one.png b/species_evolution/images/zone__many_to_one.png
similarity index 100%
rename from biota_evolution/images/zone__many_to_one.png
rename to species_evolution/images/zone__many_to_one.png
diff --git a/biota_evolution/images/zone__one_to_many.png b/species_evolution/images/zone__one_to_many.png
similarity index 100%
rename from biota_evolution/images/zone__one_to_many.png
rename to species_evolution/images/zone__one_to_many.png
diff --git a/biota_evolution/images/zone__one_to_none.png b/species_evolution/images/zone__one_to_none.png
similarity index 100%
rename from biota_evolution/images/zone__one_to_none.png
rename to species_evolution/images/zone__one_to_none.png
diff --git a/biota_evolution/images/zone__one_to_one.png b/species_evolution/images/zone__one_to_one.png
similarity index 100%
rename from biota_evolution/images/zone__one_to_one.png
rename to species_evolution/images/zone__one_to_one.png
diff --git a/biota_evolution/images/zone_path__one_to_many.png b/species_evolution/images/zone_path__one_to_many.png
similarity index 100%
rename from biota_evolution/images/zone_path__one_to_many.png
rename to species_evolution/images/zone_path__one_to_many.png
diff --git a/biota_evolution/model_grid_steady_state_elevation.txt b/species_evolution/model_grid_steady_state_elevation.txt
similarity index 100%
rename from biota_evolution/model_grid_steady_state_elevation.txt
rename to species_evolution/model_grid_steady_state_elevation.txt

From 3a5575de8f6d5c70d80bcf6a4248bc1792c0d981 Mon Sep 17 00:00:00 2001
From: nathanlyons <nathan.j.lyons@gmail.com>
Date: Mon, 23 Sep 2019 12:14:34 -0400
Subject: [PATCH 11/15] initial commit of SpeciesEvolver customization

---
 species_evolution/Customize_ZoneSpecies.ipynb |  447 ++++
 .../Introduction_to_SpeciesEvolver.ipynb      | 2023 +++++++++++++++++
 .../Modeling stream-based species.ipynb       |  714 ++++++
 .../SpeciesEvolver_tutorial.ipynb             | 1587 -------------
 .../images/clade_definitions.png              |  Bin 12951 -> 18624 bytes
 .../images/species_identifier.png             |  Bin 0 -> 6338 bytes
 species_evolution/images/species_number.png   |  Bin 5293 -> 0 bytes
 .../model_grid_steady_state_elevation.txt     |  204 +-
 ...grid_steady_state_elevation_low_relief.txt |  105 +
 species_evolution/untitled.txt                |    0
 10 files changed, 3391 insertions(+), 1689 deletions(-)
 create mode 100644 species_evolution/Customize_ZoneSpecies.ipynb
 create mode 100644 species_evolution/Introduction_to_SpeciesEvolver.ipynb
 create mode 100644 species_evolution/Modeling stream-based species.ipynb
 delete mode 100644 species_evolution/SpeciesEvolver_tutorial.ipynb
 create mode 100644 species_evolution/images/species_identifier.png
 delete mode 100644 species_evolution/images/species_number.png
 create mode 100644 species_evolution/model_grid_steady_state_elevation_low_relief.txt
 create mode 100644 species_evolution/untitled.txt

diff --git a/species_evolution/Customize_ZoneSpecies.ipynb b/species_evolution/Customize_ZoneSpecies.ipynb
new file mode 100644
index 0000000..90963a8
--- /dev/null
+++ b/species_evolution/Customize_ZoneSpecies.ipynb
@@ -0,0 +1,447 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"http://landlab.github.io\"><img style=\"float: left\" src=\"https://raw.githubusercontent.com/landlab/tutorials/master/landlab_header.png\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Customize ZoneSpecies\n",
+    "\n",
+    "<hr>\n",
+    "<small> For instructions on how to run an interactive iPython notebook, click here: <a href=\"https://github.com/landlab/tutorials/blob/master/README.md\">https://github.com/landlab/tutorials/blob/master/README.md</a></small><br>\n",
+    "<small>For more Landlab tutorials, click here: <a href=\"https://github.com/landlab/landlab/wiki/Tutorials\">https://github.com/landlab/landlab/wiki/Tutorials</a></small>\n",
+    "<hr>\n",
+    "\n",
+    "In this tutorial we will:\n",
+    "* Introduce species into a Landlab model.\n",
+    "* Set the zones in which the species operate.\n",
+    "* Evolve a landscape and the species over time.\n",
+    "* Explore SpeciesEvolver output data structures.\n",
+    "\n",
+    "The configuration of the model in this tutorial loosely follows the fault throw experiment in Lyons et al., in preperation for Earth Surface Dynamics.\n",
+    "\n",
+    "## Macroevolution processes built into species\n",
+    "\n",
+    "The evolution of the base species is driven by these processes:\n",
+    "* dispersal: geographic range expansion\n",
+    "* speciation: lineage splitting\n",
+    "* extinction: lineage termination"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Import modules and set the environment to show plots inline in this notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/njlyons/anaconda3/lib/python3.7/site-packages/xarray/core/merge.py:17: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version\n",
+      "  PANDAS_TYPES = (pd.Series, pd.DataFrame, pd.Panel)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from landlab.components import SpeciesEvolver\n",
+    "from landlab.components.species_evolution import ZoneSpeciesController, ZoneSpecies\n",
+    "from landlab.io import read_esri_ascii\n",
+    "from landlab.plot import imshow_grid\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import random\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prepare the grid and components of the model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we use code from the 'Introduction to SpeciesEvolver' tutorial. This code loads a model grid that has steady state topography and sets the top and bottom boundaries to open."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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URDwAfBU4ElggaX7yWgzclc63APsBJP+nAtvy7iX35NM4LyKW9fskoDHGmNFpczfU3pIWpPMnAS8HbgWuB05MwVYAV6bzq9I1yf8r6UPaVwEnpd1SBwAHAmvaktsYY8yOzK8OMjL7ACvTzqUnAJdFxNWSvg9cIukvge8A56fw5wMXStpINqI4CSAi1km6DPg+8AhwWkQ82qLcxhhjCijrvM8tJMVcfC5jjGmLbPMpN/Sbyvc/uI0xxlRiY2GMMaYSGwtjjDGV2FgYY4yppK+xkHRN/mV+xhhjdl4GjSw+B/yzpLMkPbEjeYwxxkwhA7fOStod+DOyt71eCPy65xcRH2lduhHx1lljjBmOqq2zVX/K+xXwM2BXYE9yxsIYY8zOQ19jIek44CNkr9tYGhE/70wqY4wxU8WgkcVZwGsjYl1XwhhjjJlO+hqLiHhxl4IYY4yZXvw/C2OMMZXYWBhjjKnExsIYY0wlbX78aD9J10u6VdI6Se9I7gslrZK0If3uldwl6VxJGyXdLGlpLq4VKfwGSSv6pWmMMaYd2hxZPAL8r4h4PtnnVE+TdDBwBnBdRBwIXJeuAY4n+wregcCpwCchMy7A+4AjyL7j/b6egTHGGNMNrRmLiPhxRNyYzh8i+6TqImA5sDIFWwm8Jp0vBz4fGd8i+1b3PsCxwKqI2BYR9wOryP5RbowxpiM6WbNILyQ8DFgNPDMifgyZQQGekYItAjbnbtuS3Pq5G2OM6Yg2v8ENgKQ9gC8C74yIf0/vHykNWuIWA9yL6ZxKNn1ljDGmYVodWaS31X4RuCgirkjOd6fpJdLv1uS+Bdgvd/ti4K4B7tsREedFxLJ+L8EyxhgzOm3uhhJwPnBr4Q21VwG9HU0rgCtz7n+QdkUdCTyYpqmuBY6RtFda2D4muRljjOmINqehXgS8EfiepO8mtz8FzgYuk3QycCfw2uR3DXACsBH4OfAmgIjYJukvgG+ncH8eEdtalNsYY0yBgd+zmFX8PQtjjBmOqu9Z+B/cxhhjKrGxMMYYU4mNhTHGmEpsLIwxxlRiY2GMMaYSGwtjjDGV2FgYY4ypxMbCGGNMJTYWxhhjKrGxMMYYU4mNhTHGmEpsLIwxxlRiY2GMMaaSNr9ncYGkrZJuybktlLRK0ob0u1dyl6RzJW2UdLOkpbl7VqTwGyStKEvLGGNMu7Q5svgccFzB7Qzguog4ELguXQMcDxyYjlOBT0JmXID3AUcAhwPv6xkYY4wx3dGasYiIrwPFjxQtB1am85XAa3Lun4+MbwEL0idXjwVWRcS2iLgfWMWOBsgYY0zLdL1m8cz0qVTS7zOS+yJgcy7cluTWz90YY0yHTMsCt0rcYoD7jhFIp0paK2lto5JNgCUqe2xj2qOrMueyPbt0bSzuTtNLpN+tyX0LsF8u3GLgrgHuOxAR50XEsn6fBDSTww3EdOP8mW6mJX+6NhZXAb0dTSuAK3Puf5B2RR0JPJimqa4FjpG0V1rYPia5GWMaYlNH36tfInWWlmmeNrfOXgz8K3CQpC2STgbOBl4haQPwinQNcA1wG7AR+AzwNoCI2Ab8BfDtdPx5cpsI02Lhp51Z1tMsy24ex/nYPPPbijgiXt/H62UlYQM4rU88FwAXjCpHk72ZTRFzpnfUq0xtP8ss6asLWWdJH2YwO1teTssC98zQMxhzIY220ulC/lGYRpmmhWnNMzM92FgYY4ypxMZiSOba0LPt0cVc0tW00NYIoJdnvfh35pFG1bPPQtluOv9sLIZgFgrItDCLupqVqZg25cxPTzaZf13+j6PtslcVf5PPOs6zFI3/uMx5Y1FWsYZVXhcFcC4xrQ1u3UZ2WuWfZdpcI2uaqro+6DmabCea0FWTep/zxqLIKJnZtZGYlgrVZK+kC+rIWqeiT3unoIuOS9O90i5oKv/qNK79OqFN5ksTcTXZ0d0pjEW+4E97QwDd98KaTquf/OM2Pk3JOCs93K7J62RWDGeRSRi4NtuWccpq03m4UxiLcZhkozItBmNUGfJxdtH41NFXWcWepY4EtL8poS1dtBlvvnw1kcaoo4s2qFuui9dN63qnMBbjNFST3tUzKYPRZOXrUn9V+ppUPjbdCLdRLtosZ71nb3Nhvo04q0YqXZSn4oivnxzFuts0O4WxGJc2et3j0vb2yabmS3txdkkdg5H3H3far+5aybRPf+U7B/0ayXFGmU0xrTqc9A61tju2c95YtN3IN9EADOq9TGL+tSkmOa3Tr8Gr6uGP8vyjlIE212+aLNtNMuxUSpNxNxFnnWmptpiGXXxz3lgYY4wZnzlvLMqG1cMO1ap6ceP0KKrWU8rcR01zEtMlxbnUSU4hTGpaLE9+qqBf3uZ/B8Uzaj7VvafpvBo0mhumTDexcaJOWSxOyw3Kr7apmh4shmmDmTEWko6TtF7SRklnDHt/3Xm/fvcWaWo6Y9B0Se+3mEbVGkpThaWpuKoayLppjVsR6jRWk2wQoN155zpltkoX49JUYzYtW0oHxdHWVGEb04N15JkJYyFpHvC3wPHAwcDrJR1c9/5iJSjzr7q/Tri6shQp6xHUbTSaWC8pnucr07D6qtqtUdUz6po2G8ZRaMNQDNNxGXRPlWxlZaluuqNsIhnUiWuKYUY9w4zYRolvmLLR5OaEHjNhLIDDgY0RcVtE/BK4BFhe9+ZxK1/v/mEa7jrb7saVqVh46kxpDZKnjoFqojFragqwzL+YB1VbCqtGdsUw4xjDOs/T9H2jxjlKHtfp7TdpDIct/4PiKXMbdrQ5zAxAVVxlYYYZxZSFHXdUNyvGYhGwOXe9Jbn1ZdBooMqvzr2D3HrXg3pMo6QxTM+rGH9epkHhy/zLGs5BaRfd++lj0HMOkiFPnQrQz7BWyV28zjccZc9QZw2iGKafLop66JcvZc806u6sYd376aCY9/3KbtG9bG1xGJn6hcv/9jMw/eQZNu1hym+V26jTzWXrhHXr0yAUUzL8HoSk1wLHRsQp6fqNwOER8fZcmFOBUwHmwW8PtCTGGGN24E64ISKWlfm19lnVhtkC7Je7XgzclQ8QEecB5wFIuudO+Blwb2cSNsPTmS2ZZ01emD2ZZ01esMxd0Ja8S/p5zMrIYj7wb2Tf7/4R8G3gv0fEugH3rO1nIaeVWZN51uSF2ZN51uQFy9wFk5B3JkYWEfGIpP8JXAvMAy4YZCiMMcY0y0wYC4CIuAa4ZtJyGGPMzsis7IYahfMmLcAIzJrMsyYvzJ7MsyYvWOYu6FzemVizMMYYM1nm8sjCGGNMQ8w5YzHuO6QalmU/SddLulXSOknvSO4LJa2StCH97pXcJencJPvNkpbm4lqRwm+QtKJluedJ+o6kq9P1AZJWp7QvlbRLct81XW9M/vvn4jgzua+XdGzL8i6QdLmkHyRdv3AGdPyuVCZukXSxpN2mTc+SLpC0VdItObfG9CrptyV9L91zrjTe39T7yPvXqVzcLOkfJC3I+ZXqrl8b0i9/mpY55/fHkkLS09P1ZHUcEXPmINsp9UPg2cAuwE3AwROUZx9gaTrfk2z778HAXwFnJPczgHPS+QnAlwEBRwKrk/tC4Lb0u1c636tFuU8H/g9wdbq+DDgpnX8K+KN0/jbgU+n8JODSdH5w0v2uwAEpT+a1KO9K4JR0vguwYJp1TPb2gduBJ+X0+4fTpmfgJcBS4JacW2N6BdYAL0z3fBk4vgV5jwHmp/NzcvKW6o4BbUi//Gla5uS+H9nuz03A06dBx61U3kkdSSnX5q7PBM6ctFw5ea4EXgGsB/ZJbvsA69P5p4HX58KvT/6vBz6dc98uXMMyLgauA44Grk6F7N5chXtMx6kwvzCdz0/hVNR7PlwL8j6FrOFVwX2addx7fc3CpLergWOnUc/A/mzf+Dai1+T3g5z7duGakrfg99+Ai9J5qe7o04YMqgdtyAxcDrwAuIPHjcVEdTzXpqGGfodUV6Spg8OA1cAzI+LHAOn3GSlYP/m7fK6PAe8Bfp2unwY8EBGPlKT9mFzJ/8EUvkt5nw3cA3xW2dTZ30nanSnWcUT8CPgwcCfwYzK93cB067lHU3pdlM6L7m3yZrLeNRVylbkPqgeNIunVwI8i4qaC10R1PNeMRdl83MS3e0naA/gi8M6I+PdBQUvcYoB7o0h6FbA1Im6oIdMgvy7zYT7ZMP6TEXEY2WteBq1VTVzmNM+/nGz6Y19gd7LX7/dLf+Iy12BYGTuVXdJZwCPART2nIeXqqg4+GTgL+LMy7z4ydCLzXDMWle+Q6hpJTyQzFBdFxBXJ+W5J+yT/fYCtyb2f/F0914uAV0u6g+w18EeTjTQWKHvlSjHtx+RK/k8FtnUob0+GLRGxOl1fTmY8plXHAC8Hbo+IeyLiV8AVwH9muvXcoym9bknnRffGSQu+rwLeEGk+ZgR576V//jTJc8g6ETelergYuFHSb4wgc7M6bnJ+c9IHWS/ztqTs3uLUIROUR8DngY8V3P+a7RcJ/yqdv5LtF7DWJPeFZPPye6XjdmBhy7IfxeML3H/P9gt7b0vnp7H9wutl6fwQtl88vI12F7i/ARyUzt+f9Du1OgaOANYBT05yrATePo16Zsc1i8b0SvaOtyN5fPH1hBbkPQ74PrB3IVyp7hjQhvTLn6ZlLvjdweNrFhPVcSuVd5IH2Y6BfyPb0XDWhGX5L2TDvpuB76bjBLL5z+uADem3l7Ei+yLgD4HvActycb0Z2JiON3Ug+1E8biyeTbarYmOqMLsm993S9cbk/+zc/Wel51jPmLtcash6KLA26fn/pgoz1ToGPgD8ALgFuDA1WlOlZ+BisjWVX5H1Uk9uUq/AsvT8PwQ+TmGTQkPybiSbz+/Vv09V6Y4+bUi//Gla5oL/HTxuLCaqY/+D2xhjTCVzbc3CGGNMC9hYGGOMqcTGwhhjTCU2FsYYYyqxsTDGGFOJjYUxHaDsDcS3S1qYrvdK10smLZsxdbCxMKYDImIz8Eng7OR0NnBeRGyanFTG1Mf/szCmI9KrX24ALgDeAhwWEb+crFTG1GN+dRBjTBNExK8kvRv4J+AYGwozS3gayphuOZ7s9Q7/adKCGDMMNhbGdISkQ8k+fnUk8K7e21uNmQVsLIzpgPTt40+SfdPkTrK3t354slIZUx8bC2O64S3AnRGxKl1/AniepJdOUCZjauPdUMYYYyrxyMIYY0wlNhbGGGMqsbEwxhhTiY2FMcaYSmwsjDHGVGJjYYwxphIbC2OMMZXYWBhjjKnk/wNXmALegebozQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Prepare the grid and components of the model.\n",
+    "\n",
+    "(mg, z) = read_esri_ascii('model_grid_steady_state_elevation.txt')\n",
+    "mg.at_node['topographic__elevation'] = z\n",
+    "mg.set_closed_boundaries_at_grid_edges(right_is_closed=True,\n",
+    "                                       top_is_closed=False,\n",
+    "                                       left_is_closed=True,\n",
+    "                                       bottom_is_closed=False)\n",
+    "\n",
+    "plt.figure()\n",
+    "imshow_grid(mg, z, colorbar_label='elevation (m)', shrink=0.4)\n",
+    "\n",
+    "# Set surface temperature at sea level.\n",
+    "\n",
+    "temperature_at_sea_level = 14.5\n",
+    "mg.at_node['land_surface_air__temperature'] = 0.006 * mg.at_node['topographic__elevation'] + temperature_at_sea_level\n",
+    "\n",
+    "# Plot temperature field.\n",
+    "\n",
+    "plt.figure()\n",
+    "imshow_grid(mg, 'land_surface_air__temperature',\n",
+    "            colorbar_label='surface air\\ntemperature (C$\\degree$)',\n",
+    "            shrink=0.4, cmap='RdBu')\n",
+    "\n",
+    "# Initialize SpeciesEvolver.\n",
+    "\n",
+    "se = SpeciesEvolver(mg)\n",
+    "\n",
+    "# Create a zone function and plot the initial zone extend.\n",
+    "\n",
+    "def zone_func(grid, **kwargs):\n",
+    "    \"\"\"Get a mask of the total zone extent.\"\"\"\n",
+    "    T = grid.at_node['land_surface_air__temperature']\n",
+    "    T_min = 15\n",
+    "    T_max = 25\n",
+    "    return np.all([T >= T_min, T <= T_max], 0)\n",
+    "\n",
+    "plt.figure()\n",
+    "imshow_grid(mg, zone_func(mg), plot_name='initial zone extent (white)',\n",
+    "            allow_colorbar=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Customize ZoneSpecies\n",
+    "\n",
+    "Default implementation of dispersal method:\n",
+    "```python\n",
+    "def _evaluate_dispersal(self, prior_zone, potential_zones):\n",
+    "    \"\"\"Determine the zones where the species will disperse.\n",
+    "\n",
+    "    The default implementation returns all of the potential zones.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    prior_zone : Zone\n",
+    "        A zone of the species in the prior time step.\n",
+    "    potential_zones : list of Zones\n",
+    "        The  zones where the species can potentially disperse.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    list of Zones\n",
+    "        The zones where the species will disperse.\n",
+    "    \"\"\"\n",
+    "    destination_zones = potential_zones\n",
+    "    return destination_zones\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class MySpecies(ZoneSpecies):\n",
+    "    def _evaluate_dispersal(self, prior_zone, potential_zones):\n",
+    "        # Create an empty list to fill.\n",
+    "        destination_zones = []\n",
+    "        \n",
+    "        for zone in potential_zones:\n",
+    "            if random.random() > 0.5:\n",
+    "                destination_zones.append(zone)\n",
+    "        print(22,destination_zones)\n",
+    "        return destination_zones"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create a species that exists in the zone"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>clade</th>\n",
+       "      <th>species_number</th>\n",
+       "      <th>time_appeared</th>\n",
+       "      <th>latest_time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  clade  species_number  time_appeared  latest_time\n",
+       "0     A               0              0            0"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sc = ZoneSpeciesController(se, zone_func, grid=mg)\n",
+    "sc.populate_each_zone(1, species_type=MySpecies)\n",
+    "se.species\n",
+    "#sc.zones"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "22 [<landlab.components.species_evolution.zone.Zone object at 0x1a1bda71d0>]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<landlab.components.species_evolution.zone.Zone at 0x1a1bda71d0>]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Update surface temperature at sea level.\n",
+    "temperature_at_sea_level = 18\n",
+    "mg.at_node['land_surface_air__temperature'] = 0.006 * mg.at_node['topographic__elevation'] + temperature_at_sea_level\n",
+    "\n",
+    "# Plot temperature field.\n",
+    "imshow_grid(mg, 'land_surface_air__temperature',\n",
+    "            colorbar_label='surface air\\ntemperature (C$\\degree$)',\n",
+    "            shrink=0.4, cmap='RdBu')\n",
+    "            \n",
+    "# Advance SpeciesEvolver by one unit of time. Time is dimensionless in this tutorial.\n",
+    "dt = 1\n",
+    "se.run_one_step(dt)\n",
+    "sc.zones"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model species evolution\n",
+    "\n",
+    "First, set model time step and uplift parameters.\n",
+    "\n",
+    "SpeciesEvolver runs every 10 timesteps of the other components."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dt = 1000\n",
+    "total_time = int(1e5)\n",
+    "nt = int(total_time // dt)\n",
+    "\n",
+    "dt_se = 10 * dt\n",
+    "\n",
+    "uplift_rate = 7e-5\n",
+    "uplift_per_step = uplift_rate * dt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run model. The ```run_one_step``` function of SpeciesEvolver takes the new zones of the time step.\n",
+    "\n",
+    "This code block may take a couple minutes to run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for time_i in range(nt):\n",
+    "    # Uniformly uplift.\n",
+    "    z[mg.core_nodes] += uplift_per_step\n",
+    "\n",
+    "    # Increment earth surface process components.\n",
+    "    fr.run_one_step()\n",
+    "    sp.run_one_step(dt)\n",
+    "    lf.run_one_step(dt)\n",
+    "\n",
+    "    elapsed_time = dt * (time_i + 1)\n",
+    "\n",
+    "    if elapsed_time % dt_se == 0:\n",
+    "        # Run SpeciesEvolver with updated zones.\n",
+    "        zones = Zone.get_zones_with_area_threshold(mg, min_stream_area)\n",
+    "        se.run_one_step(elapsed_time, zones)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot results\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.subplot(1, 2, 1)\n",
+    "imshow_grid(mg, 'topographic__elevation', colorbar_label='elevation (m)')\n",
+    "\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plot.imshow_zones(mg, zones)\n",
+    "\n",
+    "plot.number_of_species(se.record, se.species)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Click here for more <a href=\"https://github.com/landlab/landlab/wiki/Tutorials\">Landlab tutorials</a>"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/species_evolution/Introduction_to_SpeciesEvolver.ipynb b/species_evolution/Introduction_to_SpeciesEvolver.ipynb
new file mode 100644
index 0000000..a685eca
--- /dev/null
+++ b/species_evolution/Introduction_to_SpeciesEvolver.ipynb
@@ -0,0 +1,2023 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"http://landlab.github.io\"><img style=\"float: left\" src=\"https://raw.githubusercontent.com/landlab/tutorials/master/landlab_header.png\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Introduction to the Landlab SpeciesEvolver component\n",
+    "\n",
+    "<hr>\n",
+    "<small> For instructions on how to run an interactive iPython notebook, click here: <a href=\"https://github.com/landlab/tutorials/blob/master/README.md\">https://github.com/landlab/tutorials/blob/master/README.md</a></small><br>\n",
+    "<small>For more Landlab tutorials, click here: <a href=\"https://github.com/landlab/landlab/wiki/Tutorials\">https://github.com/landlab/landlab/wiki/Tutorials</a></small>\n",
+    "<hr>\n",
+    "\n",
+    "*After completing this tutorial, you will know how to use* SpeciesEvolver *with the default parameters and implementation.*\n",
+    "\n",
+    "## Tutorial example: simulate the response of zone-based species to climate change\n",
+    "\n",
+    "We will simulate species response to climate change using `ZoneSpecies`. This species type relies on `Zone` objects to evaluate evolutionary processes. The general workflow we will conduct is\n",
+    "\n",
+    "* Define a function that demarcates the combined extent of all zones. \n",
+    "* Populate each distinct zone with a species.\n",
+    "* Simulate a time step where surface temperature has changed and species respond to this change."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Import modules\n",
+    "\n",
+    "Individual and groups of species are managed by a `SpeciesController`.\n",
+    "\n",
+    "ZoneSpecies are managed by `ZoneSpeciesController`, so we must import this class along with the SpeciesEvolver component and the other modules used in this notebook"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/njlyons/anaconda3/lib/python3.7/site-packages/xarray/core/merge.py:17: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version\n",
+      "  PANDAS_TYPES = (pd.Series, pd.DataFrame, pd.Panel)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from landlab.components import SpeciesEvolver\n",
+    "from landlab.components.species_evolution import ZoneSpeciesController\n",
+    "from landlab.io import read_esri_ascii\n",
+    "from landlab.plot import imshow_grid\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Prepare the grid"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load and plot the initial topography. The top and bottom boundaries are set to open. The initial topography was previously created using a model built with Landlab. The model was run until topography reached steady state."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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3koQe7euNCL667nZ5fHXFi1FknLJtvPmc9XSaTMKlW+3RHY95dHmJJIpIkoQQ5YBilsF6Oo2pacRsg8APSEf70jYNvP4CQRB4ZW+P/bU1vvvoES/u7CCLIqos4+QcirMk150OhqqiyZ+f+gsCbo3FL6uIsshssUBTFERNQhZU8tM4qiwz930q7TaqLGMbBkaE8x+WSvTGY/RoIymyTGswwFAUprMZQgQbzBaL1SYez2ZMPI/NQg41pRMbW+h1NXxwNhOoSYP+B3WuOx1O63XulctMZzOSsRgZ26bW6zHyPFRZZrZYMJ7NeLi1tYoCiskklqYxaY1hGXBdb3Pd6dAaDHh+cxPXshhMJiu4rZROc1Kr8ZW7d3nryRNGnsdGOs399XX2oyjlqtkkH4/jmiaaLGNG93+vXMY1TZI7KRbjOcFsyaA15KrT4Vm1ykGhQMw0qbTbSKLIWiKBpeuMPA/9rMdePo8sSUznc0xNQ1+LUTQVJq0xg+mUjOOgyjKqLPNgfZ2EZWE6BvlpGGUtl0s0RcFUVZKxGGYshOdy8TjVbpe1ZBJRFBEEATXCo4vJJAKw9HzSu2k++tEzrtpt6r0ezcEAURSZLxb0J5MwATtdcFStUu12ue50+NLeHrZhMJnNQty9FOfo7Sp3y2Umsxm+tyC9m0EQQHE1prURjysVqt0uJ7Uav/7cc2Rcl1QsxtPra0RRRJHlMIKL62wsQpJNtdtFEASKG1livoH0qe9lHAdL15FtlUXXW+XCcvE4X71/n7/+5BN2CgWuWi1sXSdpKeS2M/hHAVa3yzIIkC0Vu2ijPOtwp1hkEHnya4kEpqYxmc2wBjMyexnmXY8gCNBkGVEUiek6xWQS09ERNZnxmYdrmiiyjHOYZvikjSAIq8S9LEmMZzMerK+zvZbH8+a4e0nGZ/1wXydclLhG47yFoaooskxM1xEiJ0tTlDAHkUrhmAZKMgFLmLTGdEcjPr68RJVl/vz99/nNhw/pjseMPI/F3GdtIxvO02hG5aROtdtFUxQyjoNjGByWSswXCxa+T8q2+fDsjIRlEbcsjE4IHZ/W6+wWCsQt63PUOLeRxS+tKAmd4jj0CP3xnG5rwFW7jSiKxE0z9FpEccWI0tdiUIHJbIauKCRiMVRDoZRK0ej3iVsWfhAQtywOo/zGdx894s1791Y4tzv3UZMG2Z0MiAJqXCOY+1w0WzT7fe6Wy8x9n1IqhZEwWHo+87ZP3LIQVBFbNjlYW6Pa7dLo93nz3j2+//gx65kMlq6zGdMoZJNossxkNiNhWSSTTogTGwbJWAw3emCeRYrLUFVihoG/XK4MniiKK+WbdhyCIFhBBLqigCAgaTL1ywYXrRaNXo8H6+uUUimuOx28xYI3Dg/54bNnFCOFPctk2NovsrZYIIkivfEYaxBj6fkrHP2tp09DjLtWIxGLkRFFWAbhGCNj1xoM0BUFTVEYD6ecNRqcN5s8v7nJ2PMwNQ05WjtREGgNh1S7XXaDPI4oUE6ncUxzhe23IojlJmICWEsmUWSZRq+HrqpkHQdNUbBsg3knhJEWvk8+k0BNGUiahD9ZMDzqMh3PWEsmkUSRq1YLVZZX+YTWYBDOY3Q9ArDTMfZlmbNmk3wqgZrQGdQH1Ho9vnL3Lt9//Jg3Dg9JxGJMWmMmsxn1Xo/NbBZdVVeG2ZvNGEwmXLRCBWy7GtVulzulEqPpFG/oYYo2clzDWCyYLRa8fucOP3z2jI1MhvV0Gk2WUacG096UarfLw+1tHl1coEVrLw8lTFNBEAQyrks5n2Z82mM89cjH4yiShCxJnDcaiNF8LuY+yYM0iqujZubkRi5q2kAQw8+f29jgqFoNc32iyHGtxr1yeaXkU/k4865HfxgaiotmE0WS+M5HH/G1559HiozLdaeDIsvEZzPigYviaFhRPsPSdYy4AcBssaDZ73NUrfLq/j7T+ZwfPnvGdi7HYDrlvNnkXrmMY5rYzi0M9f9GfmWNhaiIeN4cO28zqA44bTS4bLVIWNZKaYmCQD4ex3QM5l2Py3abSrvNvXKZsefBEpKxGA+3tvikUkFTFOKmydjzeGlnh7Tj8L2PPyYfj3PVapG0bUqzJHbCQi/EkC2V0XmPyWyGqeskLAvD0VGSBoIAzWdNJrMZndEIQRAobGaQJx4jz+P5zc0wGS3LoSEyTWRLYTnz8RYLTE3DDwIURyMzdtA0JcRhDYNRtxsm+SRphdVfdzoc1+u8f3LCm/fukXacMNFrGOzk8yRjMZ5dXzPPZCjZKlrGxLVDSO2wVOKq3WYrm8VbLHhufZ1mFPlsZjKkI69OVCVSBxkWgxn6QEON68yWU7QoSS1FcMCLOztcNJv4yyX7koQZYdGWpoUJ6SjS0VUljFAUBccwKKdSeBHx4MZY3RAFBEHgILpfIaKPioIQvo4MiyAIzFoTnlQqVDodXtzZ4bhWQxAE9gsFtLRJ46iBpWkYm0kUV2fendJ5HCqwi1YLXVFoDwb88Nkz3jg85LLdJu04pGybXDzOk0qFtONwXK9TikgLRiFGcblkOfcRNRlDU1frK4oi580myyBAkWWSlkUiYiJddzoIgrCChwRBIBWLhYoxgL18nsF0ymYxh5LU8ScLerU+l60WtW53lZi/aLWYzuc0+n3stoke19lcZPnR0dFqb7eHQwqJBNu6TDYdx58u0LIWlx9XuGg2cUyT2Xy+mlPHMOiORjimiVpR8T0fPWuR1mUkXWbWnuDETD48P+deuYypaTypVLhbLtMdjUKGnaYhx1ROT6tUOx1qvR6NXo9Ku73ao93RiO8+esRX7t7lg7OzFcvOmVjES3ECQnhKy5h0n7Y4aza5bDZ5cXsb1zS5bLUQBYFqt4trmtwpFqm028R0HTXzeRoLCILF53q+L5r8yhoLxdVJHqSZVIZMZzNiuk7atvnBkye8fnDAca3Gw60tihtZ1KSBP56T7tm0h0M0WaaQTqKmdOxxyOS48b5s0+RuqUQQBMiSRNw0aQ4GXLZaDKdTvPmc3NSlaMgIisTkasBePs/c92n0+2QAfc1muQi9kFIqxVW7HXqjiojpGOwHIaZ9Op2uEnnNwQC3Y2GUbXI9l/5kQtKyUFyNZFxn6S1Y1yQGnRGTiNUjiWLIVDIMfnx0xLNqlTcOD/n46opELBYar4SBHTGRmoMBreEQ8UqkpISw2jZ5Zt6cznCImjQozZJcttsc1+u8srfHOycnvLyzE8J1mkZs3SW2l8SfLJj3pvjj+QrmuFE0pWQyhO4yGSzbQNQk7K6O7KhIAzGEARUFRAEB0BUlZC65JuNGl4zr0h4MkESRg7U1CskkrmFgOgbPTq+47nSQRJG3nj7l9Tt36IxGLHwf3/cR7RCjb/b7YcJU00jFYiiqjCAJOLZFbC8R5ocaY2bNCVeRcrnudHBNkx8fH/P6wQFJ2ybtOLQGA7KOQzIW443DQ956+pSDYpHxdEo+kWDTlEncySDIItPqkP54Qmc45O2nT8k4DnfLZXIRCUNNh3M89/0Q9/+UoVtPp8nH46hJA8lUsIMAY6BjlGyCJUyrQ0RBIG5Z6IrCRxcXaIrCC59yPCRNQk2bCK0Rh6USY89b0YrXkknUpIGej+FPF0wu+6uI4Cenp6Qdh+8/fsxX7t7lh8+e4US5urPLGhvkUJMGWtpk8LhFq9qlPRyynsmQi8eZLxaokdEXBYF0xFATJIG1eBxLVUnZNv1Uiu1cjreePuUrh4fciRLoP3z2jFf39zmt15nO55QWKQpOhvReBiWuM70erBiEccuikEjQjZwwRZbZzGaJWQaCJNAdjcJ5VT7PBha3kcUvrQT+kvFFn/FwymmjwUWzyUWzyav7+yQjZst5s8nhoBxu8Mse9V6PuGmSTDpYmy6L8YLTeoiL3kQbIhCPWTT7Axa+z7Nqle5oxP31de6USmFYrGnhJnVU7P0UBAGL8Zzp8Ry7HOK5wXxJ+iDEs7vvjEimHRRHI1gs0UYy/ckEf7lEEsUwMlFVZDOk+k1mM3ZzOVrDIfKlgn2YZtQcMx149MZjjqpVnl5f8+r+PsOIllpIJDip11FlmVd2d3l6fU1M17Eag1WS+4WtLRzDwIgbKAkdURYYVgbUez321tbwpwsG0ykXUSj/XmQo3j895aWdHa47HdZ1mcBfomctZp0plVqLardLyrZRooRibzxmf22N6XyOaMgotopjmihxnfTIoT8eo2oKalInMwyppnHTRJRFjqtVrjodat0ur+7vU+v1VvBSXAiNfb3X47LV4vWDA4bTKc9F+RHLNhj3w8jiweYmoiBwr1zmqt3GXy7ZMBXsu2msksP4qs/kvE+11WHkeVy2WvQnE45rNV7Z3aXW61FIJFhLJtEVBds0GXoeJ/U6oiBwv1wmblkh9p80EESYVgaM2mMq7TY/Ojri1f19jCgJnrJt5osFsq2SOsjw5McnnEZ0aUWWeXp9vWJzidUh+lqM2G4SfzxnOfeZVkfUrtshhBkRH35td5fjWo3nNze5E0GniAJyTMUwtNVesXUdQ1VRLTWMXr0FXm3EuDuh2u1iqCq5eJz/65NPeP3OHXRFQY+M0CdXVxyWSgy6I5RaGJEaJYesIWNf6wCYJYfxZci402QZw3WwtuOo7SmKq60i+nq/T9ZxeOf4mIRloasqgiAwnE55dX+fH0dz5hgGqiSx9HyW8yVa1kRN6OTXUtiGwVmjwdz3OW82eXR5ySu7u6GzZymhQ6ZpuGsuiqt/fvomuDUWv7SyGM6YjLxVHcByuaQa4Z6qLK9qA4ajCdrQZDKbsVkMC+wmIw9zCaIqoSkKqixj6TqlyPPqVnuc1utcNJu8tLPDX37wAY5h4M3ndEcjrjsddskjOxp6FOoKnQnJnIs/WSBKIrKtM+tN8a5HrJeyKEmDaWXIoDXkutNBVxRSto0sSSEGn0ygJHRUVyd1kMEfzskZCqIqIcgCzav2KtcxjLxFRZa5G+Gz9X5/5dkXk8mwBqJeJ6br2IbBvaguRY0eKjmmImkSsVJUC1K06Z13V4azF9FBY7rOSzs7fHB2xq/fv8/xZZXcwCWlSEhGCD+lHYePzs95ZXeXd05OOCwWsXQ9jOoSOnJMxU3bKPEQ9jE1DdlSCBYBk9mMg7U1LlstdFVlK5dDkWWavR6KLPNcxHJyrVApx3QdVVGQo/u/Vy4jiiLpQgJBERk0uyGXX1UppJMs5/6K1BD4S0RZxOtMWM581LTBmh7WFvQnE8YRfVQQBJ5bX6c7HpN1XTaLOdSEjut5ZByH41qNeq+HJEnYenh/886EWqVNPVqjL+3thcZ2d5f76+s0ej00WcYdzAjmSxRJIqbrvP3sGa/s7mJGdSrT+Zz8JE5Zl/GTJl5rwrwzpVPvocoyGccB4J2jI968d49GFEElTJNsNo6a0AnmPrKrInVEZEla5cmCIKBoyoiqtNpPH19dkXdd3n76lC/fuUMqigjkKH8x9jzOm00SlhXWVigS1oZLsPCZdaZhrkgVmc7n6IqCpMvEdpOocR0tbTK+GlBKJtGiGo2bCEJTFE5qNRzDYDuXQ5YkXt3f50dHRysa8Mz3mS8WbMU1jLyN15isCvO8xYJSKoWpabx/ekpM19FVFXszjqVpyLaKljA+V51zC0P9EosgCDypVOhPJgymU17a2eG9kxP219awSyWKySSd4RCCgOxOBq86InaYRulMUByN5dzHjIqI5osFajx88GOGTiGRCIv8bJtX9/dXm7w9HHJvfZ16r0f2TGAx8NDSJmrKDD0ZARRbQzYVrJKLElMJljDvTmnXe1y0WlQ7HZKxGO+dnPBru7uc1uvsFQoIVZHlzEfLmJhrDovxHEEWmHdD9soN5dQxzRVMoysKhqFRiMc5q9dXCe5CIkFM17lstThYW0NVFI6rVfrjMTkvTlqTEVNhLieVj6PGdVofDdgvFBAEgUa/j6YoKJKEYxjsFQp8dHFBxnHCHEN3ir4WRnC1KGE7mc2QPpVonnemiLKIHFOx76RQbI3ajy5JOw5qKkySFsUc866H1u+jqDKt2oDrToeXd3f5yekpL+7soEUJcWU0p5BIMJnNVlj1YrkkYRgEy+AzbJ7BdErBD1BdHacX1l7ItopkyEwqA0bHXXrjMelCAgnynAEAACAASURBVFkUaQ0GzH2fn5ye8qX9fZKxGFnXZeH7aGmT4WV/BXuIgsB6RBVVYirBYomgSCsDrkZMuxtml63rJGMxJhEBYdKd8tHFBc3B4DOR270oVzT3fXqNAVrGpHPUojUIo8PBZMJwOl3ViyRiMe6vr3PVauEYBumUiRxTmXWnNM5bnNbrHEa0V8c0aQ0GBLMl/nyJKsvELYtiIsHbz57x2sHBqkix0m6jSBKtwYAXNjeZ+X5ItlhPoMY15n2P4VGXVm9AcT8fznEmLDIM5ksEKVyHaX3ErDGmNRzizefYhrEy8vl4PIQ2VZVN16U9Gq2uO5yGxIfWYIBrWeTP+igxFaNkI8cUprUR140GZ1Ge8qWdHWZRhwVjZJI6yDBrTphnvM9R29xGFr+0Yu+m0HMxJFHkutOh3uvR6PdRZZkgCBhMp6QyLhv3N1FcHcXWGKf7mEUHs+gwqQ3xGiFDYyuXI5uOoyR1ppdDjqs1Mo7Db77wPAt/ufLebpTo9x8/5sHGBgFw+bRFLsJk45aFUbSR91VGl32Gxx30fIxpZcDlef0z40w7Dq5l4ZgmX3vuORRJ+sz9+ZM5g8ctOq0+7eGQs0aDnOvy3ukpz21s8M0XXuCs0aA/mZBIhBGKqihIokiz32e3UMB2wgpsNx5DdlQMReG82URTwkQ6kkDmjXUUW2VSG3Lw2i7T6yHN6w6DyQRb1wkgjHpkmb9+/JjBZIIoinAGZUKW2b60xnA04bTRWCmhhe8z833k8ZzGu9c4JRc9Z5E+zGKWXQAGz9rMW2ENyVY2i1G0SbQt3rhzB0EQ8JdLPrq4YC2RYDObxXRCZZN1XV4/OODd42P219aIaRqLwQzZVimU0hzXauRcF1GTUBI6++U9BElAsTUGT1qcPL5aKd73Tk8ZeR7jKMfymy+8wNPra2RJYiefY+EvOfnogpN6nSeVCs9vbvKtF1/k8dUVWdclN3NJA/adNKXkBu4nTdymyfunpysG2rNqlXI6TWkzhxLXWVZDJfn6wQHDqBpfliQEYCeXw46ZaBkTPR8jo0q4nTjLuc+8NaUzHNIYDHj/9BRZFNm9t862V6Rb7dGudMjGFPSsRbLtEOt20WQZeyOOIAs4Qwd9zWZ8GlZoB0HAOycnfPnOHdYSCVzLYrlcUkgkqEYtXvLZZAirLgMUV0NxdYbP2sR2EyTjBRRHZ3TRY9qdsnGnyHLur4gf9af1MOLvdnn3+JjFcsnrN9cyTb7+4AFHtRpq0sCYz0OGYkRxj1sWjmHgmAbOc1nm/SnNx6EB6U8mZByHQiJBrddbVe5btoFkKGhpE/cgg2Krn6PGCb7wkYUgCK8B/wT4ClAAJsCHwP8B/M9BEPT+ruN/4cZCEAQJ+BFwFQTBtwVB2AL+EEgC7wC/FwTBTBAEDfifgJeAFvCPgiA4jc7x+8B/CPjAfxwEwb/4edf12mNGp11iJYeyEiairzSN1mCwYjX5kwXL+RJBFNCSBpIm408XzAcek4s+nesuu4UCetJAz4bfHwzH7BQLKAmNWWOyauFxw69/uL2NIkk8vb7GVFXulsusFVLIjoaa0FHiOsEyYHzRo1vtYY/n9HphoV/KtkO2TZRkf7CxwZNKBU2W2TtcR9Rl1ISOmjAIFkuMtRiiImJ1w2rm909PV0V+bpSIbw+HLBdL2sMhkiAw9jzWMxn0uI5kKWyKefqdIemMiZOx2VUULppN1pZLRF0CP6QWK5bK8FmbRiWEJ/YKBT65ukKVQ9hCjwzRRiZD1nWxXBMlqePVxpzXGtR7PY5qNR5ub/Ph+TkvbG7SGg5Zm8ZXCei0KuEcpFFsleV8ibXuImkS3uMFsb0kWtqkOMjhTxe0Gt0QvxcEColE2A5CERlGbSI+ODvj4fY2l60WjysV1pJJNnUZ0ZC5UyxyUq8znE4pej7O/TRW2SVYBhhFh/XJAq/vMfE8JrMZ/cmED87OSEZtY647nTDRnDLonDY4rtVWLS+Oq9VVYdrc95EkCSVpsFwsmTXD9jC2YVBMJsNamqjFynQ2A2C58Fn4Pq5lkXNdFElir1DgJ6enpGybZCwWMq/GbkjDfdKm2x+iyDLXnQ6iIPDo8pLnNzfpjccY1ypq0iC9F7LUlLjOYjRnOvJWFfOiKhHbSuBP5ixGszA3NZnQ6PdRJInRdBoW2Okqx5Uq153OKl+xlkyybPXwg4BsNoEc08JcirfAKrn0n7XwGiPsDRe9EEMQRWadCYt+CJU+qdf55OqKh9vbvHt8TG80Im6aZNNxZFvF6RvMO1MuWy3agwEvRpBnMhYjGYuFrXrKDkpcJ7WVwm6ZXNfbPLq4CFmFGxs8urhgMpuFzLmRg6RJLGdL1LH2ueq6L3JkIQjCnwIV4I+A/xKoAzqwD/w68EeCIPxBEAR//K87x/8fkcV/AnwMONHr/wr4b4Ig+ENBEP4HQiPw30d/O0EQ7AqC8I+j7/0jQRDuAv8YuAesAf9SEIT94OeszKwzZXg9YLYIqzq96K8oinRHYWFV3LLYk0RETUZUR6iOxqQ2ZHzRw2uN/6YGI2ehJg0GT1qkdzPMWhN6Vz164zEXzSYPt7d5/+QESRRDaqOqoisKp1GyM/ADgsWSwF9CAIIYcvCDIODo4prWYMDI81Ye7E2F9yyiqT6rVleMEjdpE9sTYLlkdNRdtV84azTCSlVFoZxOY8dMlISGcimjJnWyrrvq09MZDkn1Y0iGjLUVZzldoMY1JF1m3B6zVchx3WyzfLLEztqImowgCvSv+sx8n91CYVWP4pom/nRBpdPhfrkcKsCoXkPSFdSEzvoshGTOGg0MVeWFzU0+vLjgwcYGH19dhdW9CYvJeR/JkFFcDdU1UGwNrznCsEKK5WLohRDHaMJlu81RlLz1FqFHN+2GbT5qvR7Pb20hCWHdRSmZZDKbIWoSakLHaIaFYhnHQctbeK0JkqmgJU1ERUTLmAiyiOqpdC9rIRspIhp4iwWWrmOqKsuZjyyFeS1JFNFkeVVX8M2XHhL4S9SUEUIz3SmtkxAy6o3DJPcNDCWLIsXNHEpCR7FVEptJ2qMRhqOTkyT6kwlys8lmNrtiTWkZk3nPozcY0RoO6Y/HIRQ1nfJgfR3XsjiuVhlMpxRnSdJuBvsghaRJDI5CSu50NiOzk0F1NVRHY+ot8CfzsMBvaFDr9VZMLBEQlBC+HE6nZFwXVVFWrXP2CgW0jMlytkAyZOyNBEpMBUHALDmoCYP50GPWGrEYzlb5m5iu88LmJovlEjGqe7p57tS4HhoNS2XTz5KMxWgPh4iiyGY2i2uaWJrGYjRDisgf3e5wRaD46OICVZaRJCksOtQ0VEnCa4xZnPZw7n1+XcnDBPcXOrL4vSAImj/13pDQYX8H+K8FQfg7mx/+Qo2FIAgl4N8mtGT/qRCC1b8B/HvRV/5H4D8nNBa/E/0P8L8B/130/d8B/jAIAg84EQThGfAK8P3/J2O4arcZex7D6ZSx5yGLIuuZzMqLF1WR8UmXYLHEn8wxshaSLqNnLYx2SMVT4zqqrWHvpxiddGk3w9zCTZXwl+/c4eH2doihb2+jyTK5eBxDVTlvhrUUpqaRasQwN3xi2wnMcmg75YpExgkL6wbTKT85PcVbLMKma7bBaDBBU5QVG0YQBLSuiZY1MTYd8k2FxYUfep/n56vKdCcRQ8/HSBsKgwhWkEUxZAJtbIQ9gqR8SME8TLMYL9BSBok7GebdKWpPwV8uV/kFJaai6yrFpMmoPea82eSgWAwT+4sFth4ySw7W1sLWEa7G0lswa00YjUMo6fnNTQaTCcVkkuc3N3l0eclz6+tIokirEdajpB/5GCkTa8NFS5ro2RjznocSUxmddTmuVOmMRgwmE2RRRJEk8vE4uqsjiCFttNHvo8kyG5kMyyDAdPRQ+acMjILN6DhMcsuiSO2ojqXrBIslkioz702ZXAyotjqokkQQBHx8dcWD9XUGUe8sW9dRHI1Zd8pVu81wOg3bxYgibtTYcTn3UVwNKaYgqjJqQlx5vpORh6YofHB2xkWzGSq9kYGoS8iWguJobJSyYd3EeLxqL3Nar9MbjUjaNtmpw9pLZTIbKdy2xWg8pT0c8ujiImwfk0oR03U2MxkCwgp3UZUIlgFq0sDyFliETpWoy8yHM2a9KcOjLs1uf9UQUozYSJftdtiWxTHIT+Jk03FownsnJxyWSnSGQ6y2yeSij3M/w8LRERUReytBEATMOmEivv24iabImFGx6Ifn57yyt0ej10MSRfLxOPl4HEmLqMyHaXzPxx/Pw1qMbpcXNjd5dHGBY5phixzfJ69IqEmDVCmBres0o+JO2zC4Xy7z8eUltmFg6WGOKp9KhD06Pjf5YucsftpQCILg8Cn9HwRB+28xJp+RX3Rk8d8C/xlgR69TQDf4GxN8CRSj/4vABUAQBAtBEHrR94vADz51zk8fsxJBEP4p8E9vXitxDXcrwZ4oMpnPmcxm9MZjfnJ6ymm9TqPXw9JDqqat67iDGXrJxt5NoqdNEGAxmuOP5wiSyGzgMWtPIAhIpt1VN9TuaMTY88JQOipwWiyX7BUKpG2b3njMZjGHbKshO0QWERUJoxB67GraZFodItX6jDxvxaCZ+z6IAvFyHP9siaVpOK6FmjJCKKHv0T/q0B4O6U8m6IoSGsJ0muVyiTf0MCYLZs3xqifTw+1tVFnmuFbj/vo61VaHkq1iWgr2TpLFeMa8N6V71aWwlUHSZabXI/zJHDFjYaw7tB83uGq3KSWTJDIOwXxJrdkMu8KqKneKRUrrWRAFZp3pqj1JvdfjS3t7WJpGLpVYtbT4+PJyVe9gRS0h7MWCYOYj3BUxMjG0jIliayhxHVPTyLpu2AMpmvftXI6YpqHnrDBJGnnsneEQSw+L1dR0iFfPBx6t4ZDdXI6LVoutUh5zw2ExXiDIIsv5kuNKdQVnjTyPnOuGjQSj9iTzxQLZ1UAS2PSzyFGyV4y62d4tl3lareL0DOItC7fsY+8lWc6XAKgpA69SQRQEtnI5ar0e3nxOqm+TUST0vIVXl7m+DhssVjodntvYQFdVat0uOdclUYxHhAsFyRrBabAq4kvFYpiOTnGeojcek03H0bMWsi4zOO6EVdnjKZnn8mFdRS7GfOCxGM6QDBnHM1YFm0+vrymmUmyk05iOgV6IsWbKeI0xzX6fw1KJo6jH1eVFnVI5y7Q6ZDlfYm8lmPWmACzGcyaXAzRF5qhaw9I0nl1fc399HTmq+L/pLOAHAfnZjIIuoQoC875Hq9XDWyw4LJUYRrDYVjZLxnFIlsLKeDWuE/hL/PECdSKvCBiqorCdz4ftZVQ1zK0kwzzl5ycBy+UXOrIAQBCE/wj4LwjzFTfWMgC2f96xvzBjIQjCt4F6EAQ/FgThqzdv/y1fDX7OZ3/XMX/zRhD8c+CfR9cOBFFAUKQw2RqJGDU3283nw5bMioKdiqE4oSJS4+EGmg88xmc9ppUh7vNZlnMfrzlmdNylOxohS1IIa0Tnu2lup8hy2FNKklBEMaRezhIIkoBZdpAdDaMQQ1Ilpq0x09oQSZe5PK0BhEq8XObjqyvWo35RRpRA96IGe2LPwyjYSK6Ou5vEaBkIF4Q5iajxWjmbRk0bLEYzWo2wIlYQBJSoxfT99XXOGw0OikXa113kmIoc81h6CwjCIjg1ZaKnTdSkgdeahBCNKqFGsEsq42IUbYZP2mG7aFHE0LSQthoxU0bH3dArlkImUDLrkhKFEF9WRIZR/cJeoYATNQ6MpaxwLRI6qh3+FZUksqUy608pbeZYdL1VC5H76+uMPS/8TZGORzVSrB9dXKw6DTumSXpkk1Mk9JzF2laWpeej9sJ8i2ypGGtOaJBcne1SnsnI46RWYzKbUUwm8eZzips5tKwJy7By3p+ELdVv9sJkNiOIWsK4pomRMEIYKqJozrtT6kdhI0dL05AkCVEQwqS1E861EtcxMjFGx13iUVNKKYqgXMMgHrGW5mcLjDWb5XzJ6LhLvd8nFq2DIAhMBx7ZnQzLmc9iMAuhz5uGfPtJgkdNjIKNbCghPNSdMjzqUO/3aQ2HdIdDUrYd7qno3vTRDGU0QzYVOrMZ+XgcCOnWF60W+4UCtes2rmkiiALztMngSSvsQ5WxEFWJq2oYST2O6jNuWtWkbJvLVotS1I7d1DQW/Rnj2oj43Qy5zQyJZmzVzkMUBK7abRRZZi1bCvdpc8zwSZvOYMhlu82dYjGs3u506I3H3CuXw/vxZCYRg+rzkrCR4BffWAD/DLj386KIv01+kZHFl4F/RxCEbxEmUhzCSCMuCIIcRRclwqQLhBFDGbgUBEEGXKD9qfdv5NPH/Gtl3vPoP2tzWq+vaIWD6ZT75TKXrVbYqlrTUBwN2Qn/qlGRjtcc4XsL7Ltp9IzF6LxL92mL9jDEQ2+YMiPP48H6+qrD5g1V1TVNBtMphmqTOsigxFQUV0dLWwiSwLQ5YnzRZ3o9RLLC6uSrVot75TKL5XLVJVOTQ/zeaYfh9nWnw4aVYzGaoWcsFFdnWhlS3Mxx/V4XKTKM4/EUsS+i5WMkEmF088LmJh+cn69+eOagWOSq3WYvn2d43gspwRtxggAWgxksl8gxFS1pICjiqlZAS5mogwGiJjM66dIfT3h2fc16lNiudbsYth56b3GdfD658vL94RwtbyHIIuP+hNliwb1ymSeVCvfKZXRVBVGAICAIomTvOFLI1SH+ZIHihMymm5oRVZYpJsKGjYvBbNUrSRQEtrJZUrZNzNBR06EiVlwdfbFkeh0mhYftEVJFRo6pLL0Fkh7mceYfN0naNtVuOK9rG9mwziUZKv7FeEav0uOy3Q4rgqO6CFWSUOM6F2c1ynEj3FcJA685wmuOV5DiWaPB/XKZp9fXq15Y4lBCVCUmmkTrIixS608m3C+X+eTqinvr6yQta1XXoTghTVXPWsRmMx5H8/ikUmEzm8XZSRBbd1l64bwptoq9m8RrjZFtFSWm4rUneM0Ro9MeoiIRt6yVw/PB2RkPt7b44PyctUQibD+vyth3kmT2s8zaE66vmshREr7S6bCXz2Ntx9GSJrKpsPR8es0BRmMMgKXrPL664k70Oyrba3l6g9HqGTqt12kNBsQMI/wBLMfByFmIchiptrtDvKj/2GY2y9gLDZ2gSIyOurQGQzrDIXuFAqYTOky2YVBKpXhSqeBGv3+xlvybSO/zkYDwNzq/8HIEjP+/HPgLMxZBEPw+8PsAUWTxz4Ig+PcFQfhfgX+XkBH1HxBm5wH+OHr9/ejzvwiCIBAE4Y+B/0UQhD8gTHDvAW//vOsrroazm2RXFBlFraw/Oj9HVRQOSyUqkbeRnbgrCEO8E7JijEIIS5hFJ1T0cZ34Xgrz/+buzWJuW9e8rt/o+9l3X/+t5lvNPqcoKApLCjSgiVEv9ELkzojxEmNULgBjolE0xAsTLsFAAsSIJHqhidEQEU1FrcKqOqFO7bO71X/9/GY75ug7L56x5q4DnHPq4Nai9khW9lpzz3bMOd7nff7Pv1m4TNZSJNZxzPffv8fQdb7//r0odlsI4s3dHS+OjijWGbonVD1FUzF7NslNyO71ivQuIkxTdouUdRTxosX/71rstuM4aK3PzsDzcE46nHkm8/maUVELhNWzZd4QZjyZThkGwZ6yOUt7HDo65tBhGvX4pR/8gN9zdsZvvn/P7zk/Z+B5XLSpYY9nU9KrkO7zETQNmmug6Bqqru47iiopUU2NxbslR+dTFE1hvtjw4eGBF0dHdF0X1dE596asFluoG4KXMi9L8pyXx8e8ub/nsTnD9cSszrdtfnB5ycvjY97N5zyEIZO4i2dZBL6Lfeyjmjp1URG9WZEvEtIw42G7JW41CWGSMO11saYe2SLGNgziLENTVS4XC9ZxLDi4pmIfBOiuwe51xvJyxeGjCXrHomoLUr7NCD9foHdNVPjaW0pRKFYp5Vp4+Z1nQ3Zv15iGgWOanI5GbJOEu/Ua2zA46dqcnE7ZPoSggGppMg8IE3EJaGmfXdfFMU2+ur3F0HXuNxvG6w6q+bUe48XREW/nc75zesrnV1eyO9Y0NEuHRrqVj4Xl+eGhQGRtjsT5XUwZ5jhHHYooR1FVrIFDXdQYfdkYFduU8PPlfkgeZRlhK0BUW3dfXVWZ9ft0XJc0zVHfbrDGLsGzIbtlhNEWl4vZjHkYUnxV409zNFvHfdSjqWreXd4TpSlpUcisy3XQuxZGz+Lt373jB+38yjVNXMsSkohnYQ4d0nlM/HaNqqsM2gCq+81GYgJmM3GVdg0R2u0EfgKwD3zyeUxn42C1Dr/HwyEDz8May+/6mzv+sR9wfzz+LPB/KIryy8BeaNI0zb/9kx74O6Gz+NPA31AU5c8Bvw785fb2vwz89XaAvUQYUDRN85uKovxN4FOgBP7kT2JCAdR5TXK55c39vTCN0pTvnp7uzcyeTKdihz3xZJcVWNCI8ts/6WIPHYowZ/5/fsDs22TzmMXdmocwJHAcvrq52Rvw2YZB2dprTx+NZXep63z2+gMXsxnmoLUVaBrsiYfmGISvlug3O4y1xunxBGvmky8T9N1uz6sHQFEY/9OnlElBsUx48k8/Jb3dYQQm6X3E+tP53mXz4uCAP/zyJQqQFgWaY6BZorT9/W2EqmOa/L137/iZ01MGmsbT00M03yC625E+RPjnfayRR75KqNISRVWoy5rdhw35IuH4F8/RHJ1sHnFQCcvpy9tbHNNk8p0ZiqpgXotq3ezZeE/6nOY1t4uVKGc9YREVZbk/T65p8od+7juEix2OaeIcBTjHAf5Zn3KXU0a57Li3km2RFgVpnvPd01PKqpL5QdOgKAq/8uWXPDs8ZNzp8PzwkMFxn6aoKaMCmoYqq0hvdxz9wilqS6Ec/OyMfJsRfViQLxP+3m98xS5NeTqb8QefP5fYW9fFHYmldbaISa9DmqbBtSw+u7zE0DRORiOm3S7WxMV/1KebyLyr2GYCBSFWJyejEb/5/j1n4zHnR1MCx6FpGm7Xa8n0WCUc/uEzRpuM7adzft+LJ8znUogUReGzqyuO0yGd747RPJNO1+Pg9x5J4Xi3xDIMnh8e8ulX73h6cMDicsXkyRj1VEPPDPzjDlbfpkpLzJ5D9/dM6FRCbEBVCN+t+ermhtf395yMRl9viE5P8W0b6qa9xkr8gcfPOue8vr7l8eGM8585QbM1ylg2F/kyIdvlPHpySBnlvL26w7da2HfosPlCBIO/79GjPXTot4X0QO3jdgTq0rsWxTKlbHM8fub0dK93cfMOVVLQ/e4Y3TfgS+g/HuAc+Jhdi3NXR9EEJv7s6optEHBYDTB635zdh1zev/1ioSjKCSIVmCEtyV9qmuYvKIoyAP4b4Bx4C/zxpmlWLdnnLwD/ItIZ/ImmaX6tfa5/HfgP2qf+c03T/NUf89J/EfjbwG/wU7ZC/78Ui6Zp/g7wd9q/v0bYTH//fVLgX/0Rj/9PEUbVb/816wZqGHc6HLa7toHv88nJCZ9fXeFaYnE8ijp4loXr2thTgUhQFfJ1SrFJia5D0psdiqrgWha9uuYHl5d8cnIi0aLTDtorSRnrzrqYA4eZOiG82nIxm3G9WjH5QY3dtbEmHp2nA4zAxDvtYgQm1sbF6jsU24xqV6C29tuKolAmBeo2I7nZ4R6JHYYRmCh6gNWTYuZOPWbthfDVzQ0vjo4YHQ3oeAZmzyK9jVjtdhwcjdBcHeXNAz9zesqnl5cc9PucVBUHnTH9F2PpHnQV76iD1bMxfBPdcTD8Cv+kS3YfobsGeavQ/ciauZjNuFou6S1HWCOH4OmAKhUaZvxhSxRJPOik28Xo2+ieQW/SYXErEE9aFDKYvRijOTpm/2vlc7HLiN6sSG8j0qLg2cEBYZryxfW12Fu02QnFOv3aOE7TeHZ4yN1mIwyYp31cR8fsyvnTXLGdcA+FkVZlJWWcozsGzlGAci0Ua9MwcHoOTzngbrPhrGOjuzrFNiNZSZewjmMeT6f85ocPfHh4wLMs6i+XWGMPeyzdTr5M9pDVs4MDEYddqni2TZ1V+4wJ0zC4XCzoxDGPJx5m16LzyZjk/Uaoq4qCqWk8OzjgIQyp4gJ77IpuoKjJFynz7ZZnBwe8adXZHx4eeHQwJbmS4qZZGppjYAQW0YcN0fsN2X2MZmmsFuE+V8VviQJ5Wf5QhzXfbtE1Dee0i9GxMfsZ84c5jw9n3DwsOagbOt8ZEbQzIEWB3suRwJavV/uUv2DoUGyEJTeZ9bm6nO91Oq5l0R92UDQF3TfI7iKy+5jtLhaa7mxGUhRo7UDcfWMxMlU016CMC8YXY9KbCNXSCR730T2T1fdu98aMk24X2xUfrG/u+KkH3CXwp5qm+TVFUQLgVxVF+VvAnwD+l6Zp/ryiKH8G+DPIBvtfQFCVC+AXEAbpL7TF5T8Efh7Bwn5VUZT/vmma1Y963aZp/r2f/vN9ixXcZsfCPetgF74I2FY2VVwQ5/k+2D6wbbyxL51FV+YWhm9S7nJ2b1dkdzG7JCHKMtnNtjtaTVUxNY3JyQhFa51BVZVilaJ3TZyjDnog1NFxVaGrKu5JB2cWyBA1EMXrR2gqW8Rk85i7xYoPiwXPj454dXvLg+syCH0m6y7dFyPsmY/RsbEnPvH1lmKVsPwgTKNVFPG8XSA7joM/sFF0jTLMBW4JTDZfCY34y5ubfeiOa5qUu0Kss2c+mqWTrRKKMCPfpBidDGfsky1jrJFoH+qyxj3t4Kf53vTv8eGM7VdL/LxL52KIf9YjWyZ45z2MjoXzzmSbJARpSaVAvk737rCeLT5ZdVmLfkDLSO92BE8GGL6F91hmEk6YU2clqzfCo391e8txMeSoZ8mgWtP45PiYL29ueHF8vHfr5RW4j3o4s4Cmaug8H5LeR6iGhn/WI32IiF5LQdqlKaejEV/c3JAWBX1bw+7anAUW60UIgPd0IarysgAAIABJREFUgGkJ0+bpbLZ3MT2fTLhZrRiUJf1NKrqVDxuSD6KtOB4M6B52aapm/x0XoxEnXQtz7LJbRpi6znQ6kNlMXFCsU+JtytVqxfOjIwmhOjri7FgKgO6ZaLZO9G7BJox4dDBlvtpwcXCACvtF/Phkgu4aJDchZVLgn/XZvVoR30eEcUyU58RZRtb+xpM855OTE8Ikoeu6nE8mexjM9EwxMAzM/eKcXIYCnQ0dmloKsGbrBE+HVHlJ+MWCcLHD0DS8sY/Rsyh3OeNxjzqt9iSBtCg4HA9xz7qouuSqeGc9jK6FvfTobwU5Wb2/22eJD84HmF2ht3eej0CBKi2xpx55mJHdRyzu1lKsDw9ZR9FeJ/NNHT+tzqJpmhvgpv17qCjKDxCW578M/JH2bn8V2WT/6fb2v9Y0TQP8X4qi9BRFOWjv+7eaplkCtAXnnwf+6x/x0v9ryxz9H/hhGGr5k97zt7ZYOFMf1dQo44LwywXhOuJ+I86yz4+OuF2v2VkW06bBjUVVrRoa2oEui8h5H6vv0PxGTbcQkdZiE6IpCp9fX4tdSFpS55VcxDc35KMRB5pC3Xr7Kz2LvqWjGirFJhOrgYFDvk2JLjdkD7Ewo+5jlncbFmHI88ND2QG2w1vbNFEdnaZqCB7JLimdR6IHuAxJWlbKpNvF96R9fwhDtEud4KWFe97F7Nmsv3fHJo758uaGl8fHAHxxfc3L42MewpAzptQnHbJVQrZOiN9tUEyVztMhumNgjzzKuKCMc6J3a5KrkCTL0DWZbRgDGxNwDnwUXUVRQNEUVFNF0VXyqqIbiKV6eh/xEIYi7ssyXt3dCfxkmgzXAZ5lYc088k2Kf9YT3F9TMXo2+SJmkvQJF7uvRVxljWLI3402wTDNczTf4HA0IV+lNEVFU9UoCjgHAVVaorsiHDSCrwuS9npNEsnn0hQFRVcJXgyp068XAkUBVDg8m5Auk/3OW1NVnh4ciNmgLvBTvkjYJgknp1NQwDnqUIYZ5o2+32SgKhgdi2AiZIQkTBmYGsV1yMObBzlXsxllVaGqqnw2WxeeoMKe5TR5IoLRw0cTioVQVs2xw7EzYX6zxFvFuH2BCMthjv9EzBX1e41u0T53UZAVBV/e3GC2LsG9ieiArpdL5m3c6jjsoNkawbPhnnJrLh2Wlyu8eYw1ETuS4MmA7E1MUzf0T/vkr+eoloZm6gQXA8qoYPflcs/k8m2bLM1RL7dUUUHnOyPsaSCiSU1FMVWym4jAcfbnr04rqrQk36RYQ1cYfQcBhi/d0+6rlQRkzWZ4XYfO0KfaFfvz9k0dTf1DqM5IUZT/+7f8+y+1jM1/4FAU5Rz4fcAvA9O2kNA0zY2iKJP2bntpQXt8lBD8qNt/1PFR4/Znf+tb53eSOvs7fTRNIxTY9xuavMZzbYZ1vU8zu5jNsD0L+yiQjqJlQxmeiaLJAqfZOk3dUOey0JhLm+Wl7Ib7nieeOGXNsKzRDw/56vaWtCiYxl2GloZ30kV3TRQF7KkvXYVvEl2H5IuE/F5sxT8KoJ4eHOD0HDYP4b5d7vR9acVXCYqqoBoq+TYlvYvIi5LDRxPm78WRVfcMrLGLdilfq6KA/6hPfLVlt425XCx40e5OXx4d8UnLnHlxdMSHq3uMjonyVKHYZCRXIZ1PRiR3O1AU7KFDcNYjXcSy0+tYGB+2RJf3KIZ0SMvv31Ml5Z4eXIQZ0es18X1EkkmUp5dXmD2bvu/TedKn+nLxNfzQcbCmLrpriitp3wFFoQgzwi8XaK5Ber0jjTLezOcy3FRVzL7AOZMnY37z11/tFx1FUTAHNvZBS48OLLyjLrv3K5yDAHvkUewyVENDdwxUQ9hI2a/doCmK6D40YZhZI0/Ea+tMFvYXI6LXsgi9+62GfE/6mJfG/rtSTE0IFEcBRsckm8dkdxGv7+54dnjIarcjfoiwRi6qrWHpOp1j+d0YPZv+QY84z/HHPtu77b4Tq7OS3ssZhi9daufFiDqTwKLo9Yqb1Yqiqji3NKyJizHX6T7qY409dN9E0RTssYdqaFgjl7qoqaKc3ZdzbtdrnrcW8ps4ptzme3h01uvRcV0s16TYZFhjb6+naGowb3X8C8ntdg87Mnj2hIWVtAy0bB6LGPTJgDqvSNYJ99stL46O+PLmhq7nMQi9VrMRobkG4WcLNM9g9VY2wB/P3+s7mYEkixjvULRL/nEX1VBJF1KkrImLeqtKAmHHEXbVqTANv8EFh6b6IUb/Q9M0P/+THqYoig/8t8C/0zTNVvnRBez/lbTg67fZPPpJ7+lHHd/aYlGEOdki4faVhKUkeU5WFHv44tnhIY4mF7Thm6iGRpUU5KGKqqvs3q0o1imaY6BoCnXeyMC33UVmZUlT1NgHPjRgXOX7Bd7veaTXO1RNlUCYoYvhmxI1qakYgYl/3sfsO9jbjO33EkZBgH/UETHgnSiy39zfc6FpRPM1h5q6Z29ki0R2am2gzjAp+f4Xb7nQDvcOrkq7Wy22meRKrFb7uNCPO7K+bfPy+Fg6jKMjsocEzdvRFBVRluFsMsq4oC5qMYrzTfJtRpXIbYou+pIqEUGb5Ysth9GR/9ZVjf9Eokntmx0ARsdC0VW8mS9w30f1s6Kg2Tq6Y2AEkt2huQblLie9j6jikvUHsTeJs4ynsxnbOObw0URmHIGY0026XdZRxOVySVnXIuzq21SZFK/4dot/0qMua+LbkOQuRDN1ovcbzL5Neh/xbj7nyWzG3WZD/VZwft01Cc76FMOc3bs1xSqhTit8y9rrIEzLQFFVgpdDcS0ua4JnA8LPFhgdkyqtiNuMjIuDA/zA2Q+tFRU0WxhQVVrKHGWXE96HHJ1NUA0Rl14cHPD67o6Tcoh56e7hU3vkohoauw/iRmAbBkezEdd3C44tjfHFmPCdkBSMni1JiC9Eh1FXNWWYsbsM2aUpzw4O0FyD9aXE3xpDm05VcTYe8+r2dk9r7SwdziYehmcSXwoVvP9iTL5KCJ4N9x5fmqUT34REb9cSVtS1qAuxgy+2GesokjlEC0U1TUNZ16zmWwYHPWhA8wzuX82xW/bYs4MDtNYm/X67ZdaXUChVU6nykjIpSW52xG/WrEKZq2m2zny+JghTgpMu1ugbTsqrfjrqrKIoBlIo/qumaf679uY7RVEO2q7iAPFwgh8tIbjka9jq4+1/5x/yWn+4aZpf+jHvpQOcNk3z/R91n29tsfi4s5s9mbC7DsXeehPSDTyeqbIjuVAPqHOhoRZbMVzsPB2iGhrFOhX1ciZFIStKmqbZ72iiNKXYZjhHAflCQmI+7nQu1AO2cUw/KfBmPvkqxTvpUkQZ3lH3h6Gk6x0Hj8Y0ZYM1cmiqhtHJgPg+QlfV/cJldGTg+1EPYQ0cSTO7DimjgqcHB1wvl0yqim5VEzwbojkG6XxDehny9GCGMZDO6MXR0f5zDDyPl223cVpVHAUm1a4gzjLuX81xLQuqhqao8M56Mmy+i0gisVA5Pp3sL3yja+Gf9/ZGhx+LDIqCYqhUu4KmAbul42bziP6dy8N2y+16jR1FjJcdjL6NPRHoTzNF7a45BubIwX5t8OruDkvXOXw0ETihEQgRRSFrh+VnoxGbOGb+boF7a2F3RWvhTH2sniNzma1AeXpgklyFpLc7qMXZ1Rw5nHVsVouQ+MNWhq2uuPEm7zfc34tj66u7O563XeVJOeJAV7GmHm5LEkgfYuzDACOwqfMYa+wyyISqq3sm7mkXRZVkx3Inflv2zKfYZWR3EcPnY1Dkt3zAmOhW4LdVFKF/cY/vSOek6ipmzxGDvJGLtYrQfANtLpsMo+9g3EX4F5IlYXTk9XbvN2TziGKdEacZx6cTjIFN/iDw2i5JqHYF/lGHXp5zt17jWRbnB5Kk+LFjcw4Cef9ToaWWu5y6qImvtzRVQ/J+Q+eTMeUuxxq66E5ridKxOHw0oQxzYfC1LgRVVTE6HWBPPZKrkN3lds+8u5jNcPuuzNQ0jeOTCdEqRnu7ps6/tjWxWpv74vNKLHwOfdwwoXcxFLr0N2n30bSkmt/u+iQtxF8GftA0zX/xW/7XRwnBn+cflBb8W4qi/A1kwL1pC8r/DPxniqL02/v9c/wwxPTx+FcURfnPgf8J+FVgjujfniJGgmfAn/px7/lbWyzEtCzBe9TbU+TMpU2xEZ7+xcEBb+7u+M7FOel1SDaP6XwyomkQSw5TAwVWK5kLfBxuXxwc4HUdelYXc2CjGCrrXYRv23T6HheqPO/TgwMs16QpG7HzcA2KbYoR2ILvdmyyh5jOiyFoCqqu0VQ1yVVIsRC//qdtdsSHhwf0L+Q9JbcqRZiLuO92x/Zyw9ViwfMXZ9i7HcEkwJ6JPqHc5bIQVBW9Z0NUS6ef13u8X1Ol88nDkOet2jV+iLhaLnl8OMOaeXuvIqMj7CjvkWD75l1E9nqO5gr92OjaoCn7wWaxzYjersjuY9EYZBm9QYCiyHeTLRKKTbZ/H6ejkTClAhPnwN+/XrZICL9aors6dVHzdj7nyXTKOoqYzjzcl939MFizNFFGt53K6fFEuPyBKQtmR8KjiignutqQ3kZiTHi53Q91s7LEM01mMw/7rEtd1m3AlIaqqaRhxHoRil3F7S3PDw8B8cR6dXdHWVUcpiX2zBMfr6EUxmInViqrd4L7Z2XJhaahmvIdaLYumwHHgKYR9frIxTvroRoa+SYR/6q7aG+QGJx1QVNEDxRYVFkJTUNyFTJ8PqZOSpnh6Cqare1ZYGbfwRn7FKHMhMy+FA53JxTlYpNJ19RCeUbfpoqKvf38uNMBFeyZv4ds67ImuBiSb1KCR32qFnJtmobkOiR4McLs2pRxvhcDJjehEEGSkl2Y7Dv+KE05OZ1SFzW6L12I6Ri8vboTKvrIQfdMNg+hOOcmJf1nI/Fw69oomkL8bkt6HZKsEqI0xe5KFo3r2uiB6De+SbuPhuan7Sz+EPCvAb+hKMr32tv+faRI/E1FUf5N4D1fM0T/R4Q2+xVCnf03QAbTiqL8J8Dfbe/3H//DhtVN0/y7bUH5Y+1zfrQo/wHwF39c1/Hx+NYWi+C8jz3yiD5sKMOcYp2S7XK8A59PTp9RRjkvTUMGl56B/7SP1lLpNFPDnviiU/AMimXKu2vBpVHgzftbJt0uw4GN2bE5+8VHhF8s8Z4OMJYJL1sr58ezKZorO0VFAe9YLLcVFPyzPvbIFbbGIhax3heCNY86Hb77c0+xxi7Ruw3O1sQ+9DEHbnvfHddv7qnqmqquuXhyTPc7Y9yzLtHrFeFnC6rzkuDxgP7Pzuh+UrH81RseblcsdjtORyNGQYDXd3FPOpx3LB6+nO/DgYqqospL4vcbvPMe6c0O+zCg83RA9hCT3uywph79oQjIFr9+Q3DSpfvJGCOwMNuL0DvryeKzzZi+nOKf97GHLvFNCE1Dsc2F/fX4GM0zyOcJTssaa6qazecPZPMY1dRIbyIuP9zz8vc+ZvV2yeF3ZZE2OhbZMmH7/TkP25CO4/D7v3tBvE749Kv3PJnNCOOY2ZMJqqnT+DXJ3Y7gvE/n8YBslZJvU8owI7nacfX2jsd/9ELEeYHJ+J86w+yIs+nmsznRWzGPPBuN+O7JCd7TPvkq4er13d7JdhPH+MsUq++SLRPCV0vSmx27dUT3sMv4OzOSD1veXN4yznN6rbmfNfKwjzK80x7ecQfnQKC6IhT7chQFa+zywjjm9fUtz7o27llHYCxNpckqyqhg+s88okoLLv/2a6aPxwQXA9zDDu5hR4rjmxVVUqJZGnFbNNP7CGvsksxjirLki5sbnkyn9MYdmWWpYq3e8zze3N/zVJtx979/ycmzA2gadM/E7Fh0ng4xPIsqK2Rw71t0/+iIfJ2Ko3DHIny9JH63IVpKXswyDHk0nfL7P7mAukHfSs6Idy6GgstfvhLBnWHs5wzOYcDUNVh/OkdzdeLXa+pDmU2ppia5NAcB8U2IexexuF5hdCx6Pzdt35+EmX1jR8PfP7P48XeXxflHDSj+2X/I/RvgT/6I5/orwF/5bbzmCvgv2z8/9fGtLRbOoI/u7ARrD0yKMMeJcoyORXoXSQG4F5hlamo4RwFmx8bq2TKYfbcmvYtY3m9IC0lgsw99yQRYanh9FxTZScuAcSiw0IctV6sVj2dTPjwsOLOn++G55ugYgYXmGJSxQAD5JqVKS+J3Wx7CkJPTKcbQxh57YuCnS/SlvHZGvkxZtOrj1dWa7ijg5upBqLkHAaqpkt5GchIU0F2DdFGgByb9JGAVRVi2yWlvynaxE/uK1j7rfrPh/GDK0+mUt3Phzj98OWd0MW531xJyb888rJFLsU6xpx6arbH7coVqadgT8QAqwoz4ckNyvSN4OSK9DjF+xqJoZxBllLNabMX0T1MxOhbW2MNrVfMf2WLZXUSUinX78ckEc+Ti3segKASP+xiBRRnnaJ6BFiptdrfDdrHjyWwmMN50yvZK4BDVEqGY7sgQOnq/JpvH5IuEJMvRNE3otS9HFGEuv4m+Q1PVaK4hnZauY/gmbksLzlcJg0DOraqqjE+HwgjTFJqqpqnFnr4zDnCPO2LQeB2KRUjPk925JrOzj/TtKin2hSy53OIcd6jLimpX8P5Ovpvl/YYyLnCOAvHt6tmYbSdVpcVe2FkmBU1V484CoqJGnQXYY4/kNkRzJeu8aRqu39zTdV1e39+LHqTrUucVmmui6BqDgx5X7+55Opvx9v6eJzMxIizCnHy9wTvpCjQUiG+WPfYIzvtCWOjYaIZFPF/L0Ns3sbY5Rydj1p+J35ruy8bKCj2BLNudeuc7Ixl0q9Jd+08HmB0RYroTEUoGL+X6U3XpvMNXC3THILnc8nC74uDZjN3ltrU5sfDPehjeN5xnUf+usPv4Rz6+tcWiSCN279fCZirlS1RUhexBRFIfbuacH8lCvrsNUV+vKXc5qqEJa+a0h2rpaI7O3Zs53oGPcxSQziOOmwm31wvGuwLN1vAfid9OGRcYXQt9o6IHJmf2lJvbBYqmELwYUt4Uomo+6ULdkG9S0tsdTdOwXu84OBdxlnveRdFUkqudpJuNegKBLCJWb5dMnowxejb+UhbrI0MTR1tDwx66MEW8o3xTrBLeb7h/Jal5jw+ljTcHDtWuQPdNVENl9HyMf+1wc7/k+HTCY2PGh/sHzg4nJFehDFI9E/9RnyLM0SwNo2djjzyZWZga/uOvL+JsGVPuCjovRwJLbSRCtYwL6rwivd4xeTymigpurh44NCe4x5294Z0RWPiP+1hDgbzSN3NUW5MFtW+16lyJqdUdWXwGA9llGz2b0cWY9HrHk9mU93P5HIqmkNyEWAMZbBqBiXvcpcoqKYCLhPzVXJhCIw974stQP8yoMmFxZfMYXVUpdrn8trKSKhYr8Y/F6aga4J310B2DxtLxjrtUUSGQaGATX225un7g9GjCYr5m1plQxjmlawiEkwkle/duLTDiMhXPrk3G1e0D50dTojBhcjEhvQzbvHQdw5fZgeFbQg3WRKhmdh3Z+ZY1/qkM96usZPd2TXYf7aGaoe/z1Z1APZeLBY8dC/9pXwriMqbcZBydiSfUx89q/EBDtaTAqrpGnZXYB/7eHsY77qKqJnm8I11E1GUts5qzLqqtk16FXxf1FtJyjgK0Q30fP5zcQP6Q7CE1ANXQ9iaJqqWhB+YejsuWMXVWsX2zIcoyZk8m2FNvP5v66Nig2d/g8tdA/VN0Fr8bj29tsajLSmwq3qzEMmInduMfeeQnRxNubhc8efSIfk8Ee+6JqKppIA9T8kWMarWRlobWMoxs6qSUBaOqSO8inFlAchMSv92wCnccn06o4hLnJODI0lANlXKbkd5GNGW9p4XaIxdFV7j+pXeMH49QNBXdlRmBoigourLvKvJNxu7ViuHzMWbPFs3GxMWe+pRxweLNgu5OFnH/XIJnim1GU9eYQ5fBpEv8IUN3DayRgx5YuI+6VGmJe9hHcw0pHgu5+O2OxUFRCZvmZEL8boMzC3DGHlbfaXeOYhanGrLjq9ICferTIAuH7hv7hVn3THTPJFsJ9t75RASNWdOgqyrhbSgGhorSzosUVF3dY+K6qrK6XOM96hE8G1IlonsowpykZVqtVgJDNUUleSEqLD6fc3Y44fL2AXcpUEb/tE9T1ahWH7NjiRbgQYa8mqrKguIaeEdd6rISR9Y3K1RDhabh+HQigsqW1aMo7Au9qig4jjChknmEPRR7dWvkYg0kKKrcyutors7siaj9m6pBM6Vj8U965JtE5gldi+3nC+Zf3pPkuXRXAwe9Y+KedDB7Ftk8JrF1dM/YP/ZjAS/DXK6BqXR8H11mmwbRAulSUIw7jde37Uxg4PDYMrh5WHLezgeMjo37qIc1cojfb7l7dc+T6ZSrpdiLWLpO9lmOaRkUmwy9YxFWK8yeQ2WURFdbsoVAisl1KN3nKuX2fsnxkxlPNJV393MOilI2bB0TFIGE67zCPg7Qv0pJb3aiuzE16UYPPKhprxf5rRiBmDeqlkb6xYN8vq6N2RMCSXbXZqV/g95Q/wgzi991x7e2WBiujXsQoKhim1ElYj/hpUJJvHsz5+hsQrFOsaY+7lGA2bGwBi7Rhw1UDcHFkIdf+sD4YkL8foPuGVRJyeLdktmTCUbX2nvgfOxC9FhD71i45z2U9kd9870r7FuT/ssR7klPFvJdTtTSDYfPx2InctKVguTLomr2bfTbnSzEkXQ9umdg9hw0R8c56WB2bYKLIet3KzRHLLfrrATPFDjtw5b0KuRuvuL4ZMLqfoPetdADWSSrVGivTVWzvFzh2zIAtEaOQE4Lm9urBQfnY5LbEEVT9udJ0TXSeSRdjamiu6ImVnQVpy1iumuAIrt46oa6kGyHIspJbyMe3i6YPhpTbnOMnoV31BFB2yoh/HKJoqv7bkrVRdzoHndxZ4Gcp3VKXVakNztGB33StezCFUWhTkqGz2UR1zWN8fFQXuO428I9AkOYiN+TPfOINsLNz5ZJyy6qiN6tyB5iilVKnOd0hj7+s55sHIqK4PmQ8MslH+4feHw44365hs8kf1wzNayhi3MQyHBdydADE10VivZHtX96HZLeR7LQdQRLV1RZAN3jAEVTuHk7F3qxZ+CedLAGLk3Pwew5lKmIzOqypghzog8bghfDfZGu0tanapcRvl1BA8nNjvQy3KfdPT6cyfdu6axvN5ycTtlcbloL+w7B4z7ZWiJRp08mFJuME3uMHphojvz+VdtAd3T01k7D8E3SRUy2iqXb3OSUYU6W5nvb949mjpqmSZ790iH5sMU+CtBMHXvii85GVfEe96hagWRd1lRRQXwXoTm6UHafDDF7NvbYZftFIjYlmooz8WgaWj+winybfvNsqN8lnYWiKL+IeE/91vCjv/aTHvetLRaqaqI7ecvBl4FWcr+j3OUsP6xkse873H7virGl7znhxS7DHktaXnwbEnwimdC6Z5BchVgTT9S9nimsksOWJ6+AOXHR17FYQgcm9khUyPpvaHSe9Ck2Gakd7XUERmBSbAUHt8c+1sCB1goj32RErzf0XwrLQxkJpz56u0G1dVwvIDjvU2WStaGpqlw47zdUWSkdkWfiHnfQPYPFQorduCfQhdGRRck/7bH7sCZ6s957FFVxgWYFmD2b9FYsKBaXK46mPrvXS7yzPk3V7LUEzmFAGeUCP60T/MMRuiusF9019jMJ3TNxJj7J3Y5ilbJ5u8LQdfTAwjnqUOcVZt8hfYhIrrdorsHqsznDizFmX4Jt4vfbNnbVAlXZi3A7n4xI7yOcsdvi9hbBxZAqK6l2QslUdZWmbASiay1Xdu/FzVTRhZ6qaxrBxQCzZWPVRYV31m9txmPyzx8IXgwxOzZVVlJshEKtKAqaoqA5OkePp614zxbnYFNsRYptRpWV6IG5V243dYN73MHoWfL86wSzZxPfhGRzsTXXHIP19YaD8zHz9wsmhopiaML80VW8k67QUD2TMs5FjFbWpJsU91hmGbrtS2e3lMyS+HIr38dJgLbQiS4z8qzACyTL3DUlk8Qcu8QftuiBhXEQSDjVixFVUpDe7KTwdSzsNqAKEGhTVzE8E023MPwS/6SH2RXL8GKTUn/6QOe8j9mzSK523NwsODuW0Kzb6wWHjyZQNzS16HkUQzQVqqnhnnSlQ+/aBM+GmAOH6M2azssR9cf44jYOtv9iTPTVCvcoEFgxsDBcg/hW228WvpGj+el1Fr8Th6Iofx14AnwP+Oh30iCmhj/2+NYWiyLZsXu/JltJS66oCsl1yPZyQ/+ghzX1ZdCmaaS3O6qkEDWqPsDqO2RL8WsKHvcp05JimxE8G1JsMwbPx+y+WtKUNcHTAfFNSNNA9HqN69qiBDY0NFtHjWQB0lqGhixClsAghiYCuLcbmhp0zxDorMWSg09GVHEh+cVhRnoXETwbygzBM/dttWoITOOcdnFmPmbHQrN0klVI/GFDU9b74igce4vsPhJYwjVQdemMjK4l7K2JLKaKJsNsYxPTO+sT/uABvWtRl42k0lmSlxBdbmVo+xCLd1SdY/itIt63qFLxIirClHydCmT3ZkP/2YjdV0uKjcxevJOuLO5JgaJr7D59oP98JEl3PZsiFIuK7EHibtUWcgieDAV6yeS3X1c1iia78uTtjvg++nqhfTJm93aFf95HNVSqpCC6iyjWKWWY03s+Il9JV/Fx5lBlFdlKWGD9ZyPR5hga8eWG6O2GeJeya+meq/sN44sJwScB+UNMpNB2hCbJ/Y7kOiS7j5k8GbN6u2TYwpRm18HsOtSZDGntkYuqqTQNbD8VkoHuGUwMjcWbBf28Ek2BruwXQRQwPBOv7VDj60rMGDcpVrfX5sA3mF2bbJGw+mxOXpYUbT6EOxJXAkVVZPYWmFhj2dSktzuMjtV6acXUZY05crh6fSczs2WCPfNlZjH1qcsKdxYTfb2UAAAgAElEQVSgaJIMWSYFxTZtKdMpne+M95Tn+w8PTId9EbgqCpO84uGDdJPRhy3eaRcAQ5dzpWoqznEHwzWlkKhCNInfb2XIbQzkd/FMNguqoZLchCiqdFbBoz7+aQ/DDn7k+vHTH/+Agvsf1+PngU9adtVPdXxri0VdiSgsnUdU7byijAoGz4XPbw9cmgZ0TcM772KNPVnIA5OmbmTHdxSg6CrpfYR73MGZ+NQjl/h2JyKw9nyXu4z43ZbOJ2Lz7H9k6exy4ith/OTLVEzfpgJN7N6HZMsE/+mgVesWe5W0PfUoNqkMYA/FfHDz2bxVxQqNM99kWIMc3TOxJ35boGT4J7bYKelDjGqorL5a0jQN/cBC0RS8485euPSRLWa3rBLvxBfICPbW4JYuXYr+xMQaOqJX6FgYrqi0m1KEjcGjPtkyxuxY2N0xzrTCsHrYrkOyuya528mO+U4KoYjuJJejLkQ4la+Efx+/20qxTIr9565a4zfb0vGOOiJufIhlR9d2Oun1jvQ22hfkpqzpvRyRL5L9Aq21mdPecQf3SIbq5sCRYuiblGFG+hCJTqCqiW+2ZPcxwQspbu5RIFYeuob/pE/96YP4Nbk64ydj4vcbOs9HEhylKHv1vtkK79KbHfbEY2hpbL5aUixT7MOA4HEfd+Zj+Ba792viq5B8HtNtF9Zim5He7BhfjFm/WVEnpeh3NlmrrdFwRjJT0gyNbBHjtMwnw/GJH5ZEV1tho20zOqc9FF2ljHKu39yjaIrM2mY+7llXrFumAfkyJrgYoChtPOrtjuT9hu0q4vBsIov9ozHrT+cYrSWKHphUcYk9dDG9Lu6BdD5N0+DMpMvJ1wnFOmX2RDoK5yAAVWYJ5sph9XZJZ9qh950J3lkXs2dJ+qKpo1oaqq7t52feSZcwW+5tdT6y3ZL7Hel9RFM1VGlF8LQNPVLAsmbf3ILzU4ryfgeP7yO26Dc/7QO/tcXCcG3cQ8F6y7ig2OWEnz1QVzVm9+s8Zt3QiN9JS94EFgpycTuzAMM1SOZiy+2f9jACi/h6i6rJD9oaiY0HioJqyVDOHrsCNbTmf1rrOWQfihmabhti2tRSP8uokAUzkAVYNcRkTeALsdVWWyXzR28pPZAhaF1U1HlJvknovBwSvZJhvqqrMsh7iMluI/ovRzQ1RG+EHWY8HRA86gt7q8WUy12Gomti/qdrZKuE5HZH/H5L5+UIo2Ox+rVbwaZtnSqXUCJFEzhFszTcmVA4i7jA7tZ4vUcUxYJktyJbxeTblGweEbyQ5yt2mTCc+jbBWR/DM1n9vVtZ7HsWZt+Gvi3wHFCnkvjmHgTonkUZZaT3O+IPW1RLY/1uhT/296FRiqpgTz2cqU8xdCXGVtOwp/5+4bMHLnVWEb5eyuzJUCWPvKVh1k0jFh5PxcdIc/S9Atk76RK+WuIcBYSvUunEph56x6LcZfIb8k2MwCa5C4mvttIdPh9iDYWWqqsq9mGAMxPmlWbJec03GU1REbwYYvhyrpJL8evSHIN+uzkwBw66bUhM771Y6fvTKU25kuK7TdFdg6YjKvuPny+5DfdBSOGXCw7PxGNsamp7koSi0Nq3CINP1VSS+0h28nHJ6ELsQmZPJjy8XTC+mGAN5drS3faPZ0FLeDA6FsGjAXVRkd7LrGvzeonlmnRejvbstqaq5dxoWhscpmD1HPJlQvB8KH5vdYNm6Vg9G2fqE76Vz9tUNaqmUkQ58fWW9HYnIU1fLXFPhC1ljzxUXaMo1t/YetPwuwOGAkbAp4qi/Ao/7Dr7L/2kB35ri0XT1BJoc7ml2KTQwPAXjsSNVlcJ366FMvqHTtBMjfDVkmKbkQ1i7JEnlhr2GEW7xT/pka1iwrcrwq+WuMcdrLHb5i5Iu94dTUSp+mSAf9LD6rvkYSrDwa5g903VUMQ5Vj+gezGSBeQ6pKkbgsd9zK4jGLqlMfz5I8I3ol3wT3sS/eqbOGNRzbrjPqY5Jtq8I3g0IPqwkWL0W+YE1sjFO+sSvVkTPBPuf/4Qk9xH+GcG/kkXzbJBUSijnLqs2zlERfoQUSUloz94TFPV2BN53fDzhdheFDGqruIfDQnOpVhUWUn38Am63qUoHsiya9JFKM6/b1bQNIx/8ZQ6r2SRPfDp/9wBast4KkIJCep8Zyy8fEsTWuvQo85LjK7dFkyLbJOye7sivd2Rr1Lc0w6ubzP8A0fYQ/le6hbqS+52hJ8vcI4DnMBm++lcnGSrBsMzWydgV2i0aUFdNXiHQkE1zD6GbxHf7djdL+l+d4pm6fLdtFqK3eWW6cspiqqy/vW7ds4lEIu428qQ2Rp59L87RVEVVj+4F1tv30R3dDa/eU9dDHFnAVZfbEn8sx40DeHrFfbEY/JHzskWMeEXC9yTDuGXSxleqwqD706F3FDWqKqD6ZWYfTF/tDpdDGOA0lNRnsp5VnWhYifXO+K7iOBxj7PTR+SblOWv3lAmogaf/MFTxj93Rh5F7N6uia+3Yhd/2hEI9mrL+gcPTJ5Pid/JMDzfpFh9RzY5ms3u+p70ISJbJlgDZ78JUTSFyT95DLUws3RP8iiqpCS9Dpn8gSN2r1ZQy5zJ7DvEN6EMvF8tSedSuNxZgDsLqJISdyZEgo8GoFVWobsm/Z+d0dQNm88fiK+3cn5Pv9EF53cLDPUf/aM+8FtbLFTVRDMFKgouhsSXWzEks8RJNjjvkS4E+5ZIRgt35pNvUhmQmh10vYflZyTrBfk2I7neEjwdSADQOqWMcpq6JjjvUxc15S4nuRMhoGppshD1HcFsdxn+owHOyEPTXKL7G7JlQvCo3yqaQTOsvbV6EWbCLtEFo3VnAcl9hO6a+NMZhtGjKFYi/KobovcbgWtalk+VldI9RGIf7UwlqyKxdfJFTNJ2CFa721R1DWvgYngC39hjj3IngUDWwBEq6HVI8HxIcrUleDoUGKupsLwB2plGcr/D9Daoqk26uyOZR1IAolzeSyJWIMldiO4Z6K6J09I3AZK7Hf7TAcnVFjMQM0JnamHYPpWRfC1UbCHAppIFofNiiB6IviO+3KBZGmZPwnWiq43AXs+H4iFlaQTPBnuoqi4qcUU971PlFbu3gqvXuVA4lc6O5CGSFMFHAxRV2e98Jeo1xT/t4kx96bBsnfjtGu+0i3fYkeG+HWDYNVorbPyYwR68GLJ7vUK1dZlF3bS/HVMjOOtRxDm7t+s210P8xJKbUPQtE1/EnO33Ht/tCM56OP0eut5BVQ3s4ZxsIx2P0RtRJDuiyy3ZSuJW05sdZVTQezEEVUUxVPKHZB+4FLwYUca5aF8AFAgeD8hWCcHFEFVXiV6t6b0cSaf8ckT8bkPnxRDVbGd2qlCGaVmJ4ZdLqqzc29xkdxGjf+JYhIhFtadOq5Yu0OaLIdH1Vgbb0y6aqZHMIzRbWGaarYvxYFwQPO6TbzJhC5qa2Ke4Bs5YnIyj6610wEcdNFND1/1vdM2pfxd0Fk3T/G+KokyBP9De9CtN09z/uMd8PNT/797W7+xRVxlVKpYXVk/S2USBrOGNZ+iuKXnTtnDbrb6opp1ZQJmWVFVK01Tk6UYu0tudsD5aCEJ3DdyjDnVRY/UEN/Uf9cUt1dLRLB1FFR63amvY02AP2yiKKtYNQwd3JvTXuqxpmhJ/cI5hyX11xxC82ezjTAP01jJdVW3pnNINu/drGb4/HZCvRfimuwbewVCetxDlsdV3cCYe1tAleDokX6USoLTLMVyBduqiEuilrCl2OcHjAflWqKjFLid4OsAeuUL1nO+E/bSJUVULy55JoTL6qGpLn21nKWZXxHv+SZcizETV68n8w+71MVyXMspJ5xF1XuE/HgiEMBcaq2EMUVUxnrOHLkqr5NVsHbWFhZyJhz31Rclt65RRTnIbUsUlwdMhzkQYbrproDsGwVMRUiZzUbubXTFm9M/7sliZkmuiqkZ7Tk2csYd/LDGf9tjDOeqgmkICsCc+Zkesw4PnQ4owI1sn1KXMWVRVFsxim4ndyNOBQJSmkCGcqU/n6UAo3lmJ5U328w63pSEnbSCUNXIlUOpAoM34UjLPo8sNRRpRVRG63sE/nGD1HJzOEYbRxXB8vOMOwXmf4PEA/+lAHHU7lmSl2MLMs4YOwScjkpuQ8O2KIsoxXBv/rIfuGgTnov9o6gbVFkFc8LiP7ht0XgxFR9HavZflag/BlWHevqaO0bXxDjs4xx2aRoLA0vuI3Zsl8eWW4PkQs2PhjH2asqGMCupSzDzLXY7/aEA6j6BpMMwOztTHmw2w+jaG46IbUsAN36RMS0yvizVw0FyDbJnI9amY39yC07KhPv75x/VQFOWPA7+C+EP9ceCXFUX5Y7+dx35rO4sqk4vOOxaGje6buAcBhmdR1wnZKpHbPbEiqGcyy7B6HqBiGH3KUhbjMsqFuz1yKaMCZ+IJ53yZEJz3MZwAe1KiapIF4Ux9DLNLWWz35nDlLsM/76ObPrrew59AXWcYRh9rEKPZBqCg611AwZ3m0ECVFDRBLSKt056YxaFQFCuyVbKHRIJHIsT7yEgynADDs3GmHmUsz2V2XOqiIny7JnjUJ11E0kl1xGQNBXSjQ6k84M58rIGLeicdUpUWaJZAC0bHksjQZSIakGqH615QBVFbLGwse8ru4QPOxJM5kdehLCKsvtPCMimGa6LrXeLNB9KFdFn5WuwisoUUxmKXUXobdL2PdyAeSUUsUbNllO+ZW6ohKmKzY6PZkqHgzCTkyBq5aIZGuohFxXu7k8WyVaM3ZY3hjAhOZQe9e7fCGjgSKWv0cacFdblAdxzqKmf3fk2+lNhdvSUDaC3Wb09lZrJ7u6bKSuyhh3qmoRkJyW1IuogpQum0dm9XBBdD0lthtzlTH903yRYxyqmGMxhQ5ZUY9RWVCCoHzv43u/n8geCpZEfE1yHB4z51UREt3+H0MhznHMPYousdsmxOXcvv4KN+I/6wwTntYgYyK0gfYoKLoQz/j0QN70wD6rxCUXS83imlvybb/j/cvXusrPma1/V57/e617pf9qX36T59BAUGZiKj8ZKMSExQiRr/UDQa/hATTIwRhIiKUdCEBGNiQiJhTAxCVJREA4wIkhkZmAGGYU537/tae6+91qq16v7e7/7xvLtOz2HO6T5Mn/HMvMlK9661quqtqrd+v+d5vrcN2UI8rzRHCiPZRASDa+qW9Fa6JP+ws4pRFYJHgldoriFFQM9G98yOOWigOQbOYY86XYrGZ+qJQWGnakcRLNKauJg9m7ZqhCSyb+CPHgAq6oGFrvdRFJ1CXWIOHdy9Aaoq2hXDk3Wg2OaYva8Qs/jVM4b6A8Bvft9NKIoyBf4v4H/+ojt+x81CUZT/E/h32ra9+IpO8lf0MFwXI0gxPBfVyLHHbpdHoaPrQ4xAVL/ekcxz9f0CXQ8oy7VoNLoW1T8bEF9tJJ95LxAfIlvH8D0B+rIKe3yMNnGJ7q8EEPQsdD0g2y4k9c6RLqTOKsosxDT3UFWLuk5o2xp/eoamuTRNRpK8xDCGmOaEdq9EUTU5P/ce2z9CCRR0PaCutzIGuNrsWEj++bCzO0hRjTXe6JTKzdF9C8PqUZWheGQd+L9oISjDDHd0gOWAYYzRRh51HaOqDtrxijJNKdaZcPqP+12X4qNZ7xWwwmn3vK+h670uXlLBn5wCCm1bYhgDTLNF09+hKBrWMMNwfKpqS7HNCM4H6I6D2cuo0rLDRWoZJdKiqiaGOaIsVxiuJQyqqsY96u30BW3TymbZEzNDowNZDddAtfTdXPu9HiQ4H2B3qmoCBdPcI16/xT8Xc0AzsLAsMIwh9ijFMIY0WoY1WEouRFfd50spPHYA/yaXYmKRyMKsKmiah2aHeMc9osu1bOh9WfD8RyOyhehv3P0AM7CoqjWmuY89ijHMPvakpIq7nPBpD1A7IoYvneWjIeldhO4Y1EVNXb7DPj3FMMbUdUyW3JDeRZSbnDLutDSOgXsUdBu6sOKcAx9tkwuI78m41d0fdEWARV2lpPcx+aKjlacl2UxGeu6Bj2ZLt5Rcb8VFocmxJ16HHRhEl2ussUPTJRdaAyEvvKdM58uU4PGIbJlQF7WYUvpd7ozZR9cD1AMbRVFp92qKMEfX+7RtRVkuKbOQvFljOC5u7wzT3e42D90xMXoVVl8os4Yx5Ks8fpA7is8d6reNnRZ8yQnTd+ss/hTwlxRF+XHgv2rbtvwHP79f+UPXh5iDGNs+pNJj1DNNFhxjKJX9WKHsLVFVB0XRaduYPL8luZNF2N9TsO1jmrLG2fOxgj663se2VTTNpa4ztAMbRTG7DWiAN2lRjVsMY4Sm+eiOgXsQSGeRlPhnAyxnStuWFMUd8fUG76hE14cy8vrcbYYxxfM/7B7bxxnMURQV09yjKO7Ea2ee4Oz7OFOZX+uOgWH7qIZUTIqiYLiuLMzGBEXRMQcZ1iCgLnNhPS1j7MEI09yjLNedwFBF14Ouy2nJ8hBn36dtWky3R7vfYHlDTDcAFAxjQBw/pd//IXTdoyiW6Lq/Y5sYxoCmkc3Y8z6kqjYoBwaGMaBtG6xRiOuf0rY1eXNLcRPinw/Ey2uZUue3ONMYz/sQ09yjqta0B9LuW0MH3XApNvd4xz1hEq1SoUF7ZucvZKKoGk1ZY/Zt6qLqRmIF1tDFDvYxjAF5fk2xyfAOB5iBRbHNML01rvsQdSAdaVWFZPexMIruYgFeBzbFNt9Zm+SrFP9MVOLFOhNmj9vHGm6J3qzRfdnM7IlLlZR4Rz3MnkW+ki7PHR6hKDqWNUVVLfL8mioupFPKK+y+T9NkXaiWhdV3iK+3BA+GZPcx/vkQd3hAXYdk2Tts+xjbPUQ7nhMra+ypS3oX01StYGW2gaYgupjAwho4GI5PE4iRouEEaJrbdbMJTSnV/vtux/AtskWMaqj4p4OdRYdQuRVUTSc4G5ItYxGYdjTiOqtQNRPDGJGW73CP5PNz9v2OUWhgev3dd7rI1pTqBsPsY1lHaCMf1biiqrZU1YrkLhSLm7rBPayg36KqFmn8Ft30cYIj4BrDGEFfCqOv7Oi8t34VHH+hy8B4n9H9ryD25194fMfNom3bP6soyv8B/MfAz3bKv+Zzv/9j3+m+PwiHZU1RhyamOUXX++i6h66LqVlVbbHtY9q2Iby9wBq6JLNwlzHhnw/Iwjt03ccfPuR9SqEsfi1t29A0WbeQ94jjp9KtGH20kYOu96iqLZZ9QBS/wT0YUkYxhtWjaUqKfE56H0lUqq6iGcmO9pevUtq2xT/Wug4iAlo872tomkuWvSMNryjWMpZSFLC9A6pxQZ1XOH4Pf9wDQNcHKIqOcmhgWRPBEqYGut4DR8BpfW+FYQxp25Jo/gZ3NCHbih2+P3ogs++JBHS1bYVhDDCMAarqUFXb7rWG5NsNifEaxzmnLDek6WvyVQKAM07Jww12b9x1H7IANE2Jrvfxe0/kvdNc8uxWKuzxHqDQNjPc8ZSmKVAUUT63bYFpTVBONXR9gKbZOHsp0ds1TWeWV8UFVi8QbUzVYHsTrHHWkQcKFE3djfUc10NRVKoywZn6WNYhltWiWe9kdAOoqkGyvSC9j/EfCs23qYSv758OyBYxigKGa3YYmCvUYF3FcGUTqYsK/0z+1rIPUI8s0sUa0+tjuBWKuqLKKvA1THNCVYWoqkXT5PhHeyiKjmzOQ5omJziTQkNRNJQTg7YpO+NMwUmy7Ip0uUGd2JjmFEVRUY512rbchQ/VhZgW2qMezX6N6fnd9VChqg663qIoBtH2BbZ7gDsdo+hrDM8kervufM5kc1A0FUU1sPwe6rmGqmkYxhjTnFDXKdp0jmYuZAxp9HZjWNPcp3ZT4uimoxtbWGNX9DuqRRbd0zYNyU2Ioii4hzWg4DgPUPs24fIZ2fs8+/tYcIMul0ZREuLrLd5RjeXr+ANh65nmFMP41kb0yz7a9leFkWDbtv+Boii/E8nTUJBs8D/3Ze77RZhFCcSABQR8brP4QT9c95yyDNlu/w6KYpCFd3iDcxTFJIlf4XqPaNuSdBZTZRXFKhUXy6QgervBHjuU/hrP+4A8vyPPb0hmG2jB2U/JlzEoCv4epPchih7jjCI872uk6RuycI4djPFGp6iqiWU1gEK0ek06k3FBuZXxWDKL8M/6lEkpdiMTl3wdApeUUYwzOOy6ogGK0uk2ph62c4Sq2jRNhjc9RlUNdH1A0ySU5Zq2bajrkKapKMutCKKcE+o6p65jqcjKDVW5oakainVKW99RrDNZSOuMPL/FMIYYxhhFUTGMPnl+T1kuiBe3eOMDqioi32QY/kxYaJqLZR1T+5dEl2taWnnM0SOaJhcsaPWKpmpwhwcYxpA0fYNhDNF0B2+vj2EMURQddWp0G4JHVW1pmow0eocbnOK6jyjLLZrmYpgj/DNlt0D7R9LNeIOH3RhsjDq0Kf0ViTnHGY2xrENat+jYQzZ+8FHX4d1jGEM8/yNUVe9Ghhm2v49qLgDk3AxNRmfmBP0g7s5xgznM8fwnlNaWpkkxjAGa9q3CQzfXmOZUxnx73m7z1PZdFEU+5zR9Q5lvsN1DPO9D6jrqPtOiK1Q0sT5vJerXNCdAi3pkoWkeTZMSXs1py4ZEmaFOLSxrH1U1aNsWVV1gj6VYqXMZz/rTExTFIF5e7txhFUV0N+ldhHa8wLIO0CYBTZPjncjiqBk61rjA3e91nbu8n3It+JTlkqYpMc2pdAOqRVVtsKxDqmpDVW3k/d4DaNE0F+3Y6QqSDdGlZFFk87hzJQb1TANeUZXhDh/yToQsEr5YoBgqZSg4Rb5I5HWcazRNRlkudtfXV3W0/KrpLGjb9n9B4ly/p+O7YRa/DfhjSJzfb2zbNvkHP71f+SNJ3nQV/4B48Zb0LqbOXpItE4LzIdBK9T+SiM3o9ZrggxHB4xH2sAc0xPcz2raWL2YrVgnh6xXFNpfK/nzA+vWFqE97NpZ1SBj+AtGlgM/ZOBGW1PkQw3GJrudoto6z5wvDpGdx/zeuGHxDfHDyZUo+TwRwdgy2r5bCGw9C1q/v8U+HMk5SDUxjSNNkbGevyJepgLy6itmTPI5kFgnFdyUdSPTuZ/CPxzTNPln2jujtWvyw8noHkEcXK/yHI7zjHkWYsX76KW3d4J8n0FxhDhxMc0xZrmhbcVud/Z3PxEzvfMDm5YzmvEDVTaJ3C9Ep+Cabz+bonkGWvhW76mJOscnIl6lkSpTX5KsUe+wyPPwNFMWCzewpmqWTrxK8Q8GVsviO7csFqq7CmcS1JjeSqd02LeldRP/sAZZ1gG3vU5ZrynKLYUy7je8aTfPwp0fo+oCqWhPdXYlLrqbijg5pmpzV0zfYEw9veoim+cTr1xRhjrc/RFVNTHMiryHMMVyXeP1GMJuBjEfUA4f17S9IMTE+oyxXLN98SltJpoRp7bOe/9zutaWb2c7PyB+fo6om8d0t6SwmctY7ZXZd1ESXK1AUvOMe4esVzoHgUP7ZACfYp4giwou3O+aXe9pHN2SBLoo5eXZD9HaNfzqgd34ACNbUNIV0l+Ez6W6A5d+9wezbZPcxzmFAU1cUxT1FHKNqKl7/A8pyjqIYjM7PKYoZm7ev5HPrOl/d8CmzkOjNGmtoo3uWUHeTEsOTxLzpr/+QLLyjykqsvsPm8kpox4ZBU1Vk84Tt0zlN2eA/Ggoz0THIlJjwYoXhmcQXa/JFgu4a5HPJGQ9fiVNwcrUVWvttuBMM2pMZ3t7hV7fg/IB7QymK8pNt2/6ooigh70cl3a+QbKXeFz3Gd9ta/wDwL7Vt+81f5nn+/3LI2MKgrmPswUAW+ssVwbkwkhRFpS5FLKZ7kq3cdLnRVSqjoPQ2whq6ZIu4m82a2FOP8OWS4LFQVdNZxN4Pn2L7+1RViGEMcPZLyQt4vaRtkPwK2h1z6b2tSFPWNFklPv+lOGHWcUn4ain6hs7wTlVtyqigSgva5q5rqwVCKrc5xUY0H+K/lBNertAsfeeBVCbd7Nnok6VXpJ2VRXSxFn+eKKeMC+qsFuCxaSlDCSkS7npGvkw4mB5SliuiKxHYmQMHe+wSvl6RrzOhS+qa0DT9iOhytZtNu0cBxTZD069pGhEAVmkpHlxL2Xid4ZQ4/gzLOka3dcLLteAMgUVeXe3stYMH4teV3UtGehnlZPOEKi1J+7doI4eiMEiS1xTZCi94gKZ5tG1LeHOLqqu4kwxVFXA1ncWd1fodmqVThLlYTvRXqLbVObnmRNVCtAbHKvHNgmKToRkaxSbr7FJaqmpNfP+ObJHgn/ZpmpymKeTzuF1h+CamVUvWStVg9mzSu4i2bjtn1IY4foo1EiZQ+Gq1o9eKUFEXuxldLF3eU7brtCTK30nl3bZEr5ZMf/hUwqbyO4JDlbbtzDTjUt6rMMI/mpIkr6nrENs+wfXOMcwVebzGfzBk8bPvpICaeLRVQzRbCL35bEDTZPJ+Gir+VL53mqXvvmeG2SO8uUWzxNIlfL3CGgtNWrONXZGShXek9xKIVYZFx9CzyIpb7LGHf9YnMVTCzxaEzxYEXxvTlDXW0MHs2RTrDGvPI/x0jnMSUCcl4acLVFNFMUVX40x9VEuDFrEIKWpU9as0Emx/oDuLtm1/tPvvP7Ah1ndEwdu2/cd+tW4UAGW5Il0tqauYpqmoSzFeq8uaZDGnLNcYllxA8eWGJpffRxcrsqVUPlVSUMXCi48u15RxsVvgq6QQFfK+3z3fhvj+HVUV7XQWqqlThbnkL6cp2TzBDCxRi4ditueeSTBOdhcRfrbAPe/jHvUIHkmFb3gmVbmlSuULXkY5+SqVcYsmtEOrc9ZVVIUWAS3DF8udiC16LdVXXaek85h0FnXiQ7vL+yjFR6jzoSrjguwuokoKWSRfLzvwVqVtZEOR92RF27RYI0fyHnQVRTFkgSwqFFUhulzLRlw2pHcxZRpRl+WO/RK+7miqrikjpvuQqtqg210Y0TwAACAASURBVOIeW0Y56Z2cc1M3okPIZINIb0PMvi2/vw2FKdQ/QFVt4vgZ0FBssh3QblkHgkstEvLthrJcYQ0D7LFDOgvlNb1dUyel2La3rYxPArEcSWcRmq3TNAWqIQFI4eVKaNXBGEUxUBS9857KSW5DymJFmSaks26RTgSzUjSFIpRiI18kklFi6dR1SNu25OsEwxPPrmIp90/vIuyJh7PnsX2xoIwKzJFL1Z0HHV5RFzV1VkvHfCkbe9vW1FUqeRpJQTaP0RyDus6I725I70PKck3TlGSbhWiCVOVzRVRNchuS3EoQlm70qCqp5jVLcJAyi3bK/zqvKLKNbJKXK8pQGFbhqxVtC+HLpdCefVM+y5k4GUSvl7JRLN6/Z7mICgML57RHsUiJ32wow2LHmgs/m1OnJUVasnw6x/9gSPDxBOesT+/DMdmNnFeVlJg9C7d/gj85+2rZUG3nR9f9/KAeHfb8hbf9Usf3TZSnKMqpoih/RVGUTxVF+aaiKL+3u32kKMpPKIryvPvvsLtdURTlv1EU5YWiKD+vKMpv/Nxj/a7u758rivK7vszzt624TVZZSZWmHatGFjlV12R2XsaSwd1KzkIVibNruRVBX9UF7BieSXIdSnZxp98QQzmZkRqOT5WmJLOIKk1FNGaJyrhOK5TO76atGqJLSeR7XxUbPYvw0wXbb87xHvSpkwpr7OIe9fCme0Dn25+UZPOEMi6owlw2s0Jopsm7LelNuBO1KapCk1eimH25RNFEaFfXCXVWidbi1Wpnx25OXMLPFkIVHToitjI0mrQifLnCmnjyXlYrdKMno5ShI885j8UC5ViEUe/xBXvs4R33hStf1BK4032RNMPAmYpn07deV05yt8II7B0RAUXBmfqEr5biJ3TfLURFLZ9PXHbPv9ixkoRCuSBdLqnKkCouqMoteT4jXl5iOA7FJiO8WFGsU1RV2Dz2xGP7cknbtNRZJfkVUUFVrYjerqizjto5TyiTTLCuuJBkQkUhvp9RVWthDK3TTkQmr/f9Z18lpdBXoxz3MJDX9noloUhpKQLLLEJRVKKLtdCAfRP7ICB8vqDcZrRN042YxHW4KcU6RWqEVkKPdJUmrwhfrXbUXUXRRah2IFbd0YVsIsntlnTWYQEogmVl1W6Bl42+JrkJxXZkLJqVuo6oiqQTP0aU+ZamkO9GnVUk3fWoqJIPHr5aiQV93yJ8vsA5ErtzteuU6qQketnZl2Ry7u+NLsPXK4mGrWrKqt5Zl4cXK8JXK/KoEPv/omDwcIjRt3daKGffxxiKNsUaOtj9CYYxwnFO0TTnK1vvBLNodz8/wMc3Pv8PRYCb3/Rl7vj9VHBXwL/ftu3XgR8Bfo+iKB8Dvw/4y23bPgH+cvdvgH8WeNL9/G7gvwPZXIA/BPww8FuAP/R+g/luR1muyOaJgHed0V3T8bZ11yC6FaqxdzYg+GCM93DA9tOF2HTbuiwOSSlGgr6ojZOrLVWY0+Q1zmlPkrwcg6pMpIoMM5KbkOQulEAgXaXJa4ptDm2LvecRPBrhHAVsP52T3oRUSUFRVXiP5SKXXGlxO41mM8LL1c6bKXy57KotySxo8gpnKo+ZXG3FA+liRV1U1GlF9HyJfeDjnfUxbGG5uAcB/tkAe+oSPl/KwvnJHPfhQGJe52LRkXXjCvvAk7HG6xVNXVMWkpdhBJYsOi+W2PseVVygqCpZfEMaX5PMwi5hzRIfo2eyoCuKQnS1koW0EtsMzdIpVhnhaxmfNU0ugPLIBQWcA1/OdZtLrkXViIjtoHv+A7G2Di9WVOWGsliJHUsmlXydV3I9LFOausCeemT3CUWYU2YR8fWWOq+ps1L+20WCgoitAOKrDXVeiyo5KSVEJ6+oc8ERmqrZzf3bqhFNjyomlk1Zg6pQJ6VYlNTy+3yV0uSVjA5fr9AcA7nmpVsMX8nn3RQ1TSbme2VUSOzvSY/w6UIM+daZXPOhdITZLKLOa7FpV1URvqkOTZN2JoUZdSaxrdHrFeZQuhpNc1FVcWQ2ena3wMtnFL6QTqCpGsLLNWWUkd5Hu/e3Lip0z5ICamCTXG+7kZfknFRbGRWGz5Z4Z/2dLoYW/JO+WOEc9wifLaUD7V5TUzXij/VsQZ3VlFWFYnbLVgvp2y3O1GX28o7xgzHpVUj0atUVhdKRiDODjzPuY5oTsduJn3fd51d0dGOo9z8/aIeiKL+/wyt+vaIo2+4nBGbA//5lHuP7tlm0bXvTtu3f7v4/BD4FjoHfAfx492c/Dvzz3f//DuB/aOX4aWCgKMoh8M8AP9G27bJt2xXwE8Bv+xInIHzvvKaMC/lidlVSuZURRrHNRFimKjSltNr5vVSUO8FTKAu9EVjYBz7bTxd4Dwcymhi7FOsUw5TkNnvsEb5ckN7IKCG9icQUrfuSi59R0wW4BKw/nVOuM+qmIZ+JCyeKQlu3JLch0cWKfCHdhO4aOIc+4bOFYBmeSV3UO5M697RP+NkcGmjyepeAV6wysUXohG0oyGhmm8uY6JlU7XUsdurFKpOMDNugyWqiFyvClwKq6oYrY6nLNcUqBVXBPvAJny6okpL4ai0V6TaTajPMdg64TSkVdpUI0BheSKX5/jMJXy1ldKealOWC6GpBvhaFeLHJd0FD4dMFxTbb+U7VaUX0aiUdlKKgGz3KuKAIc/KFdFJVVlLFufhUJTITb/JKuq9EXHrTm5AmExffOu26nTDv2GmeWLpkFcU2J3y9lOfOauK3G7EzGQs9tm2azg67pNhk8n50nWTTqZCT6xDdFSWxe9IjfLGUgCLPIHq7Jl0u0VwDzTPESymTar3tbLCbqgFVwXvQJ/x03pnwmUSvl2R3McnlFu/BAP98IBb3riUd0rvFtyxd8orkOsQau+SLhLalY1ytiG+2XcKf5GtUcUEdiyli9Eos3tO7WMKm9n1xRnBt2qbB8MTfzD2WAqHOK5qywehbhJ/Occ/7QmseOHI/25cRcXdezklA+Mkca0+u8fBihbPvY01cwos1ZV2TzxLRU7QtxsDi5uKe6dkYY2DjnAQEXxvR5BXZQjg5wfkAf3oi2gwt6ASnBuly+ZWsdfLB8wO9WbRt+192eMV/3bZtr/sJ2rYdt237+7/MY/yK2H0oivIA+A3A3wD227a9AdlQFEXZ6/7sGHj7ubtddbd9p9u/6yGzbsEPjMBCtw3aspZFW1Uo1tkuCCa52pBdR5RNQ+AZuy96XVSUm1xER/NYKsP3F0RXbcssXxHLA9/cRUDahwG6b5IUm29V0alUc/mdRHTmZUmxzBg+GGHvi6smCNUvvtpSZ+UOE4nfbsjnEkRfhgXFMhUfq0RA4qaUirjcSEXWFg2qLaZp9kTOMVq8we4PcKZlt4g3WHseVZizfDrH7dkStLTJZBxTVNg9H/+RZFWXWUx2L7Pf5N1WRmifLQg+GmNNPOl60kre31VKORQDwnwuyW1lVOzsKsIXS1mMikYIA0/GuAc+0fW8M3zUiC5WGD3x7AIJAQLZDMttQfxqJfbpfXsnuIO2s7S2dwCqbhsUnaFhmZQ7N9LkVlT876Ng60w0B01eo9kaZVR0rDdRxrvHAeEzca99D7S+x4uKTUZWJfgHU+y9Encti6V73kcNczRbo04r0hvxd6JtZcxTNoKXdWOq952deyg4pKKrhJ/Od0FHbdXI9fFmg6JCWdc7A8sqqYhfruh9LBbwiq529jVQxDGarcviO/VwT/sy+rpY79Tfmi3LQZ1WxIsYs+/gPR6y/WRO7+tjwVlWmWyUeSWmgR+KiWBTV+KY3FmnN2XdXas58cWa7Doi+HgiJp09GzOwMH2PpsmJ36zJF6mMV8OCuqioQino8kWK/sSkzis0RaGsZVPJFynFMqWtWw6OxqT3CdbExX882tm/eMc9CeGyJ8Iw277C8Y8ASFbXlHHxRcvIlz/abhP/AT/atv393WTmCWB/7va/9kX3/b5vFoqi+Ain999r23arvM/B/CX+9Je4rf0ut3/78/xuZHwFgOFL1vL9T78V187AxD3rEz5d4D7oS2DMxJM5etl0M/1aFt/7RCys8xrV0ndZAtsrSXfTHIPo5ZLggzHucY8iW8licSdh8vZhIBXfcUDZNDh73s74zj8fyIK8zTHvYm4u7nn4Iw+w93xh9dxFtA1ktxG9D8c7OwvvrI85sIkv14SfzPEe9QlfLAg+GKPqgo94jwYsn85RgOGHkx02IpTaSADaLm61rVuCRyPqvCaMZNYrOhMRdi2e3jP+cIq97++Uvaqu4nZOqlVcEL8S8LpOS+kiDI3sJkRRFcqwEHqwb+7YZqqhSe7zXHCh/D7ZOca+H0uU3bjCHMhzhi9lUzECE+/hQDbEbU70ckXv4ylN2UgQU+e2W+Zb0lt5jiavSO/EKLBtxICuTkvsPdmAw2dLoVGaGqqm7EgOUsnXZHeLnZr4vf23e9Zj+4lEqxqBhWpKyFDRGSTWVYKiqlRxiXveJ/xsgfegj6LJZmQEEvpTTUW9nV6HsrCWNcn1lnye4HaLnHfUk7FcXhHfRqQ3kQjfNFVyGj4a03845P75PZMPpqxfL7Fts/PNKtFtnToX5ll0uRbgfSPaHiOwWP/creRDXG9xDgJhZ3k93EOx4ghfShfRlDVVUqFogg0Uy5TkckPw8QT/XDLlhbBQSac1sCnXmXS4RY3uG5R1TVt119d7072mkayMM/lOVHFBFZcUq5TF03sG50PKZUbwwUhME00N48bi7ZsZR6mor9/M7vnab3qIfeRTLFNJ5tNV8WzzLXTTI75/hzMedkXVWwxPUiCr6KvbLNrvUcGtKMqfBP454K5t23+ou20E/BkkH/sC+Jfbtl0psmj+ceC3Awnwb7yf2nQY7h/sHvY/b9v2x/kuh6Io/zbwe4ETJFr1R4C/DvxTX3TO39fNQlEUA9ko/se2bf/X7uaZoiiHXVdxCLz3KbkCTj939xPgurv9n/i22//qtz9X27Z/AvgT3fO2iirmZfaex+Jnr/HO+xh9m8Gv25eM53chqqGy/6PnNHWLNXbwoj7ZXczN1RzFUFlfbzj9xx/uzNfysmT9bM7woynW1CN+K/P74Tf2AcQG+d2W8JM59pGAt2mek1yH8nvflAXbMdB9gya32DsYEj1fUSwzmrJmfbVm/GRK7+sT7JGL7gnFNl8k2FMP3dvDmrhEL1es360p1znOSUCxypi9vGP/0RRrKs6wweORLKJd5KZqasx+6lLcWk2N4OEQzW7wzsQNNnqx4tXfeIWmaRw+nO6yMcKXC+q0Yu8fPeN96PXkN59QbDLSWUj8es39L8wYnA7YXkvHETweitngPJEozUwWkmKdsn25Yvj1CdZUxjtGTywm2rbFHntojkH8drPbcFc/c0Pw8UQunqcz9h5PcY4C7Km7s3SvswpnNCC+WWANHbyTPuldRHSx4u6n3uCd9SmWKZGxof9kjOYY2Acei79+hfdoQFM25JF0H2mc40QFwcMRui+Aq+Gb9B6Ndpkbq5+5wf9gSH6X4D4cUK7E7p4HYA0cRr9uX8ZdhZATVp/d0gJuJOmD2V2MveehaJLbvfyb1/iPh0Lr7PLi67TCPeqhOwaat6BJKy5+6hVlXXN4OCZ6tuTmai551YDXd3DO+mIq2bOwR66MtTrNjeRXyLWouTpVXJK83WIfSsSwamoUsWBf3okIBW/+0kvckx51XDD75g3TJ3uUm5zBP3IgmNHbDcGDIc5gQnsiluDFNkPvWTgnAW//6iuapmH6eIqiv/cSE0uU95nd+SIhmycUS0nOs/Y89v7hQ7K7mNn9in64R/h00Y0kMya9Hs+vrnm8v88HH5+SvN5gjhzMsYM5lDAkZzQmur0jn8+piwpz4ODsB6SzkOXP34rwdfHVSse+RyPBPwX8t/zi7Ov3eO4fURTl93X//g/5xXjuDyN47g9/Ds/9IaSA/luKovz5blz/nY7fi9iT/3Tbtv+koigfAf/plznh7ycbSgH+e+DTb7MG+fPAe0bT7+Jb4MqfB/71jhX1I8CmG1f9ReDHFEUZdu3Tj3W3fddDVaUybYoa77zP9tM55TZHNdTdKEkxNPJNRhlmhJ8tKFaZRLA2DW3RUFYVTS0hSvldQl3XBMc9Afw+m+8W4rZpd4yRvLO8Xjy/p8lrqqYhvRKzPBlv6Gw/m6MoisylHZ2LdzPyecLs9T2DkwH5LCa+WBO93VBGOXVRk7wT8RVNizl0cE569CYB89sV2a2MyKqmYfVmxfabc8JnC6I3G/Ius0PVxHjO3vPI7mQkgQJ1Ue+6q7ZqqJuG/cMR5sjBnnpCAX2zxRw7goFsBMQvwxzN1rHHHu5Jj2Av4PblHcGejE/S24j4aoOiq7jHAb3HQ7JZxPLZnN5jcXttilpyLTpgt9hmFNtujFbVslE8m5PkgjXkdwnTB2PMsUPwZITZk7zz6EKq5qbOUS1NgO5YgGDnIMA767P9TBg43rFoj8ptTrnJ8R4PWT6bc//8Dq8Dyqu6pi1rmkqo1OGrJWUk2gBFVzH6Nv4HQ5ZP5xgDi/CzOebYxZl6VGlJ9HaN7gmbi7al3GT0Hw4pq4oqKth+MiedRdCxzuq0wn88ZPH0nmwWE34yJ3q9IrkWMZne5WqjKhw83uPwcMzl1R1Xb+44PJkIg27iYh8KfmD2ZaN4Hy3cFDXh86UQMw59wk/m5LMY+8jHfzKSTiawoIXkVhhuZSzaEPekx/3Le2av7pk8nGCO5L23Rg5VJH+TLxKS5b2Y+vnmbuyruQaDoz5107B5sybrwHpr5BA8GGIG1o4lp5oqum9iThwWn91JtkfRUNXS6amWxvXrO4q65uXtLefTKes4pgoLnOMA59gneDTcjZ4MY4C7F0giY1js6LnvHajDl8vdePMrOdqWtmx2P1/85+1fA74dNPmVwHOztm0zAEVRrLZtPwM+/BKv8PvaWfxW4F8D/p6iKD/X3fYfAX8E+LOKovxbwBvEVx3EzOq3Ay+QVuvfBGjbdqkoyh8Gfqb7u/+sbdsvRKbi2R3WyEUxVLKrkLZjdaSzSEDsuiZ9s8E76Un1l1fMb1ds05Sz6YSrt3ecnO6huyZFJjP3qmmo4pLwzYrxk6lQ/F4uZQ7rmd2FalJMMvTrkOvXd0yHfdJVir3J0Uyd+GJNuRKAuYoKlm9XnE0mXM8WnJ7tSwrZcU9yhbsLP3m3pepyENqyxn84lECivGKYVry7mNG0LSdne5hjRxbgQMzqzKGzC6qP3207bKMkn8e7NLfo9Zp8FrNYbKjbVkRmxwHhqyU0UIUF6XWEfz4kud5SLFNiXel8egKh1poadVTy7mLGcO7TeziUEczDgeg98prbTy7YezzF7LoJs9OGhJdrdOdbl6LRs3CPJbI0OAy4eX1PcZdwc7Pg0Q8/kphVRZHI3Fi0IMm7LaqukVyHomuJC3TPkMV6m+/0AmWU4+x5eCc9jMCkDAtGH07JbyNmr++ZlmMpEroFtk5K3LOejLFi6RLrTMZSvZM+8xdzJk8kIxsger3C3vNls/JNvJP+Lj97fD5i9uqe8f5gN2pCVSSLZJUyfDDi7uU9e4+nJJcb3PM+iqpQrFPiy003/hNQv65rHjw4kGp6YIsD7p6H0bPQbAHKg/PpbkGs4pLtp3NZWE8D7p/fc/KjD7BHLqqloWgq+TqVUeAm2+Ep2XVEVpYcPdzDGNjojticNFUjxJF5QvBoRBnlRB2jq1iLB9f2kzn3qw17kwHuaU9wiUjEnv6DQTd6C9k+W1CFhQQylSV5WbK5mHEwHlI1DdtP5rhnPQLH4fnNDR8cHOAMHfyjgHKdEzwZ7XAQ3e/y46stZZQJwJ4JFpTehCiGSvJ2i3vS231mX8nx94+hJoqi/Ozn/v0nuunHdzt+JfDcK0VRBsD/BvyEoigrZILzhcf3bbNo2/Yn+aXxBoB/+pf4+xb4Pd/hsf4k8Ce/l+dPboXWp7ty8RSVgGZoCqt3ayZPphIiE1jURY194GOEKVVd83p2x8PDfdAU6qxEDyy8xwP0O5O3b2bYhsHyxRzHNNEHUrkAEpqUV7uErr3RgIubGR98dCqzWFO+RHGeY1yFGAMLz7J4NZvx6GAfY2ChGhJe5Bz4Mp7pC5fePhKWSPDxZJf5YE0kx/nAkAbRHDtie103Qhd2jJ1lhqIqqIZG/GZNk1aoto6iKRiBif9wgDlyaD9p2b6boTtCBqjCgvX1hqDndml6Eh5lTVw5l48mYjmx72NPPNzzHntVw81qhXlrkMY55shGNVTSd1uaphE/rKNvJZSVYSEJhI9GO8BR7/IO2rqh2hbYhsGbqzvOziQ6NHy+3C2yiiYxofaeL8wmQxPA9y6S7s3Wd11TdhsJ/uJLTnRbtzt6r2pqTE2Nd5d3KMD9xQLLMHAHzk7ZjmqiaArhp3OMoc3dK1nYzaGNNbB3xIMyLERz0jHWsnlMfp8IltDpJKqoIFcVqej9b1m9vMfNrANfssINVfCdWcxmHRHnOXGe83A6xeg2CdXWxcvJVbEGDvlGxH7uYY86K0Wf0ll4LJ7e0zvsUTfNLgEver3CPe5RhnnHYhNspykb4jynaVuatKKOC7a3Ed6DAXVS0NbtLuWQVub228/mAPgPB+iewaE3wdrzcA79nSOB2Rd8STN0vKOeYHW3MebIwdMU6rSimKe8ur7laDSCpiV9G7KJY+qmYR6GWLouG99RgHMQSHoiEL0R52TvtBUW2kzGee+V8MnLhYx4u3P5qo4WqJpftFnM27b9oa/o4X9ZeO4v+mXb/gvd//4niqL8FaAP/IUvcxK/ZsOPimWyc5B1jgMUTeH++T3jh2MZM3X8bUVVxMp6YDM6G+IYBs9vb4mTjMlxQPjZAr1nyeLZjXoMXWf0WIDZpqjRfalQd6yqrXyhs7LkbDIhnkXkZcn0yR7mwKI/Cbi8vOW02iOvKk4nE+43W7wsx7UtrH0P+0DmyHUuDKwmKynykvw+JrsOsY8CzJ5F20K8SXAsU6q2a2n1nZNA0sA0ZVftaqaGEVikb0OavCZ6vcJ/OBQl703IahVSNwL2p1chs/sVe5MB/odjrKHdqX9LkrcbqljOpQyLzipDw+qCjo4tjYt3Mx4c76O5YpiYzWImh0OMkU25ykjfhQRPxigKlJuc+GqDakkWhO6ItYp72hftwWuFZRTRZDL+K9KSoMunrrOSOi6JL1Y0RUDbtOieIWylJ+PdXLqsa/y+WInna6Eyt12+tRGYHTOr5qgY07aI9sCRjcYc2gLiptVObLe8XFJ115HmGqIivw6l0v26vK74rYzh6lTOsahr6kYwjPD5Aue4R75MJVyrb+M9GqK5BteXdzzae0B8uUbVBXey9j36wNDqU6wznt/eclqW9A/6oAqm1vtwQpkIBbrcSm6F4Zm4xz103xSx5mXNzcU9B0djFFWl6DYINIVynZO+3eI+kPe9qXImT6b05gmXV3ecFhOSJMMcis348umcya+TTPGmrDF7FvXYYfl0Lmy7pt2pyt8XOKquiqbDEINIdWxRHuXQgDmwdxRp2pbjbMTL21u+8ficcJvwdrHgw6Mjen0PxVDRPKNjX2lElyux3F+lOAc+tOzGd03dEj1f0pYNwccTdN+irVvRgHxFR9u21M0vmw31fcFzP38oivLHgT/Ttu3/27bt//O9nNyv2c1C9y3CpwuMwBRGSdXNP7sWfvNmTbHKJGs6KcluJAqybluapsHUhU65uF2jzhR82+bi/p6Hh/vY+57EqY6/lVrW5KIYDz4aS5tr9knfbjFGNp/93Rc82NujWKQoCmLvUZaE65hXsxm/4Rsf4O55tEWDcyohSXanpSjjgvh6S3aXoCkKdy/vGR8Mdl2G0bMYnA/55Ode8bCesoxjDh9MMToldls1aI5BGYquIr+Lqcqa5GKDfegJf79qMAY2A99jHoaURcXNbMWD432cUxFZGb6IFZ0uCS51t9w/v2d4PCBfpDvxmu6aZHVEVddsNzHWRqrK4GtjXv7sa46rCck2Y/Sh5DubgY01ddl+Osc+8EXIWDVoIGym65D1MuR0b8Kr61t82+bo4R72gbdbgOxD6braFlElv9nS+3hCucko1hnLiyWTJ1NhGnVgvojgdHTfYPvpHHPikF1FtHVDWdU4xwHmyMbwrG9hU1WDd9yjrRuMq5B3FzOK+5RymksO+sWa3sdTjMAmXwu24532sfel8lUuVMyFxuXVHZMgQPNMrIlD8nZLGeYYPRvqlrKuWX06x93zyLqCx38I5kio1crlhofNHu+WS6Isw7dtetNgR59WdJWqSwBUbAPd0VFU6VA1W8O4tbh6c8dHH4oeoVzn6K7B6umc4YcToXA3oi2y9lyKZcrp3oSLGykAAPJFSl6WO5+pphThnubo+Ps+16/vmPQC5tsQdxXj2hbmxME+7AKiogIjEKNNa+QKq2yVkbzdkr4LCaOEOM85n07ZrIXi3LatdDlFjW5p5DOhcQcPhqi6tmMHapZO1vmfGYGEMrVlzeab99LRKZJA6T8afaVrTl3Xv9yHeI/n/hH+fjz331UU5X9CAO5Nt6H8ReC/+JxI+ceAL9JM/G3gDyqK8jXgzyEbx89+wX2AX8ObhWZpcqGvM1RLp7hPGU/7vLu84/h8b1dJKppKXVSols798zsMXRegOIpwNx6WYfB6NkPTND7Y3yeKU7StTu/rE3GH7ebihm9JbGTVdNkULUbPpFjnPNjb49nNDY/qPdR1jGdbTHs9nl1f87WjI9J1yuDDCcbAwgxsFF3F7FlUmah7VU3F3nMFANzaXL254+RsjyrMMfoyijgdj3l+e0vPcVi960ZNH01QFIX4zUbS/QaWVPpVRW/i4J70MXxTmEfPlhgjmwf1Hi9ubnh8cACqjKkUVSF8JeaJmiUK5jIsGB4PuH59x34yok5KdN8gu0vYhDFN25IWhWyYPRPNNTg+mkiFejARq45ZjNdp6Ok/pQAAIABJREFUOOwDn/nze45/63lnZJiT3Qlwv/d4Sls3PKimUs2XDdGLFXVU0vvGRLKaz3osn97Tti3jj/YwAovo9YrZizv2H+8JS6bzvgJEFfzZAmvqCqj69B7Xtnh5O2MSBCjPFtgjB3vPxWnF1qXYZJ1eoxRSQNuCAooq2oSsLLEXCdbYEf+pebKjS6uagmpq6NcGp6bQUctNtvtcFk+F3FAlJU3bEpz20QOJWn1/zsU6I3mzJbwTRtPhcEiwF6D3xfpD9y3Su4j0OpTHqRqyRUJys8U97GGPXMp1RrnJhXjxLkR3DdIwQ11o5GVJ9ELU7/a+AMbp2y35NmcVRdIVZRXp25DbxYqTB7JxCJAv7+ny2Ryv7zDyPF7dzvjoGw/QfOkUjb69C6WK3qzJl5Kx7psaZl80Od5ZH6Nv4XTn2eQ1f/OTZ3xwcEDdNFze3+NaFt5KRriDaTc+m8nYucpKskWCooD/cCidTdl0pIQRy6f3OJ6Fcxx8peK577WzUBTlTyNdwURRlCuE1fR9x3M7au2Pd0yq3wn8UUVRzjpHje96/JrdLMqtsHhUS0NzdFDZzb3rpEQ/72ONJaAmudwQ30TkVcVss6FuGnzbptoUO0DNNoQrPjwc4Jz2dnTU8GJF8HCEZkpAfZ3XwvcPc1AUzIGFFzg8qvd4en3Nh0dHrKOYF7e3fHh0hB84rFYhPJ3jDB3so4Dg0VAM2N6syboozOg6JCsEACwqEQuG84jxh1PyWczL21seHxzQAoNHo908GwX8R0Ou/+9XBCcCmJa1GPuJIV+xE4a1ZUPTNNRtyzKK2FNFG4CWC8Wyy7t29n10RydfpOxHQy6vZpwkIzTXINmmvFss+ODwkLZtMScORk/GFuXY4bSacPFuxnkxJcpkpFGFuSxiVUX0bIl96BM8GuGeCn3T6NuEn87Zpik3qxUPmj02ScL+SKzJq6ykbRr8kYfm6ph961t+TO9HRY4h/P8wxxra2PsSsbp8Osef+gRjnzdvZtRNw95jAayNviW57EObpqhIZxHJ2y3ryxVxLguuYohArI7kc1F0tXM9nRN0s3G1Y1DlqxRzJEXK3/u5lxxkA/YsXVToWYY5i3lzf8/RaCS02bM+Zt/auQ0ohoo5dlDvI25WK3zHoaf3OtNKwQ7KTUZyscE575Hdi56lWGUYvtVllgjb7HRvQtEx5RzP4vryjv3xEPvQwznpdc7IFu55H903cdYu+X3C89tbbMPgwfE+xtDGfyA6izTqgPem4fLqjiTPeXJwIOFepobWJepZI2fnSBC+XIpb7MWK4IFc85qto2YaqqXjnFjEr9dUdU3ZjfCGvs/JeCTd39jFPevTNi2apVEnFfl9QrFICT4a05YNxTYjvlijB5aMA5sG+8AXgPsrxCyA72mzaNv2X/0Ov/q+47nd8QHwEaLp+OTL3OHX7GZh9Cz8D4Y8/8kX7A/6XN7Jl9AcCRfb6Ns4Bz7bFwv0wMKtW4y1jqXrrDsgbb2NeLi3R5RluL7N8GyEObQxBw6apUlFcxXinw4Ib0LoLD5UU6NYJOTzFO/hgDTOud9uqZuGrCx5eXvLR8fHBK6Dve+xP3ZoshprT5xWjY7V4XQq8CoqMAY2VVyKu+Y85eVsxtFwKNYg24wHe3u4Qxdrz8Xo27JJWjrugU8yiwhO+uKfsz+QzSKvZK7/4XgnePv5v/oJ016PpmlQoFvoJRo03+aEz5f4j4eYA4cyEpV2EmUcD4c8v73laDjk9d0dJ6MRztjF7s5FGDo6xTanqRrOyykvb2ecT6c7OvD7bin4cNyJAE00W9x7m6LCnLrsjWysNwYvbm95cniIc+RTLITBE15tWIQhD75+gu6b5POEbCafY3YXY01dAaefLWTsoauSHzH1eXcxY2/QZxwEhGnK8mJJb+ihqH2cqY85EMGYveejWjpVUtIzekQ34W7ju1mtOD3bx+jbFBsB8Y2eJZjR5XrHSoperggX0e49RhWdxSQIeH57y5ODA5xDH++hqMbfW3aHny12oz7T0BkHYsRXbnKSWczg66JDeU9zXjwVtXt2HQmhQFGgwxCqpmG9DBn6Psoy2+FL9nGAexRgjRyskUsZF5RbYcJtVxGbJKFpGvYHAxRNJb3aYk0cFFVB9y28hwOMvoV1afDi5oa8qti8vMOzbdw9D/VDEaZGF8KasqYe4TNhzDUdJhhfbcmuQ5JlgmnoWPsejw8O+PTqiq+fnDD2fezjAN/VpVPpnJMVTcEc2qyfzhl+fSIFW5QTvlhS3KfoScn87ZK9R1JcaY4ubLSv6Gjb9tsB7h/IQ1GUPwr8i8BLRAD4h9u2XX+Z+/6a3SzyZUp+nzD0PJ5d33AyGtF7PMQ7kwszerEkebuRReS92kQFyzB4uL/Pz19e8uTwkJPHBwIm94Xmmd5GYvXxdIkemCRXW/JOkBW+WBK+3cj4pfvJ31zx0dkJj08P0d6pfPPtWz4+OeFyPme22fCkPsB7JApW3TfQfUuM+y7XhC8WdOsDxTxhcx9iGQabROa5iqKQvtmyDEOWcYyzWuG8MXEtC9ey8I8Cqq+NsScCmB/aGtl1RJSmRDchw4+mYuK3zohfr5kEAZ+9e8fpZMLxg32sicvqb93Sti3z7Za+O6bc5iJ666ifziyiXKV83Tf56Z//jI+Oj5k8mmCNHdJ3Efm9pLGZjUX4yZzlessw8HlydkS0TXj6C9cYmsaTgwPh6PuSfZ1chygq+A+FlmkEoltwlxkPplN+4c0bvlaWvFks0FWVJwcHeLaMmpqiJr2RgJx8WVJ3nlzpdcjm3Qb9F0yCxyOsiStAqrrP5nrD89tbTsZjDn/oBLMvtuThxZrtyyU0DfZBgBlYTH7Lcef1BZNatDSbJGG0SkjXKYamofdMcS9OKnofjWknIrD0HvRRdJX/j703eXItTc/7fsCZz8HBDCSQyPnmnepWdXUVu7uaTdKiwrLlaaGFV/bK/4oUjvDWjrAXDm8VXtk7RWgI0iIpNpstVlXfqr5jziMyMeMAOPPgxXcSRUq0KFElmV3RX8SNuBV1E3kTF+d7v+99n+f3BHd3APj9JZ4X8L7f53Gnw+1shuW6bCQZ9hNROAtSEamkEPRXZLko4+HQcdjtIhUKWHOBANc7Ft61+DxU5hWMno39qI7RsUWIUT7stpwSzq3D2+MbHndFCNDk3ZBo7mM/aRAtQhbHY6y9KkpZRR+VqE48FoMF725veZx2MDVBTLafNUj8GOf1iOl8geN59Op13t3e8vHermgV1k3UsoZsyOgti9XljOkbsbGXtqtCMDFxiWa+OMAMdRanM95+/p5aqUSSppwPBgwcB3sywdZ1anaJ8octwTOLU8GIalWRLZXV5UzIgfsrCqoAetq6DgWof9Zb54d/Wyvj3+1m8f/jOgN+M8uy0b/rF35ni0U08xmfj7kajTjsdjGbgni6OBKxmEF/hbEn3LGJFwvlS5ighillw+Bxt8vbmxtKuk5NKkAqkszcS0eQWAswOBlSLpnrfi1pRu1ZC/sBvObGOIsVo5mDqaqoskyWZRz1+zzb2qJimtzOZtTeRZiqkHDqXQtJlcRwOgP3Yo7vh0yXy7UGfbNeFwavJGG8XHI5GvHJ/j5hHFNtlYmXIaXHNWI3pihLZPlmmcXCOBSnwnyXJSmL4wlGt4RS0bg5vhDXdEWhqEl4Nwum8wWT5ZKDzQ6FIhhdG6Us5LmSKq2LaDQLSNKUyXJJZVKiIBWZ3E7Xjt8/v07u7tmoVDi5v+dJt4uuKHhhiKVJLE4mFJUiwUAgV+JViHezwL2YUygW0DoW6UyczB9uGEEssO5FR1p7J7IwYTyeEycJipozj9wINwxpqRJJKGYGi6MJ4+USx/NI85/9QdcfrwRkcHU8QeuUcoVVHa+/xNwqY/TstdprP93gqN9nv93mYjjko+3Haw9GQRZoCdlQSCs6qhtz0BZtycNOB4A4ScRAd6uNXNYwd8pr74SgxxYoalKenZ2izgwKhQJvr6/ZrNepTTyCqk7khCRBQhgJMm5RKYoWVi4xXl3MWN4vWfo+44VQv4VRxP3NjL3ehpAtx6mYj9WNNa5/fjRmslyyyN+nKEk4vrvjWXVXKArLGqXHdZR7jej8nvf9Pk83N4VgwxNIf/txXaiQPMFEi2KBmwdhyFyeTHEHq7XgQq8bdP0ajuetW1A7TTGHK23aKDmXyz2fMx8tiGIhMkn8GPfSQS4pUICL2wFPPz1AtlWs3arAw5RUZOtbvln8+w+4/2Os/x347wqFwkGWZX+/UCjsAJ0sy37+V33hd7ZYAMiSxF67jVE1oCg05lbFZDKaUzHNNZRveTzFmYoHaOn7tCuV9UnhbjYjTlPaikShAM5USFPvxlM6tSp6tyRS72KhZJJNVWQPzANmqxWO53E5GrHXbvPu5obnW1voqip6zrrO7lYbtWmSRgnmltjov8kgQEDhrhfoqiJyAZYu725viZMELwzXhcferojeeFUnXoZoLQtTldeb5+LNGGexYun7xEmCJssi0vRUxHYqVR1NUYTb1nUxruf4UcT1ZMJhp7Nu3ZEhQIszf02plQyZxVgMQCVJEma5ecDS96lOPPzrBfYHTeFVGanEZylH/T6Pu12siom5W8EKYjFTcATUsP60KRz4USL63WXBBGrGCXPXJUlTHne72CWTslJEa5sCoRKnuJdzPMcXkL00JQwipm9GyMUifhjins9F8mFZw35cp3hSXL9mGEWYsbpmYxmbNlqnxOTdkPrTFuE8wL8VHpmiJq19BkqxyG6zyVG/z5NuV0iMA4EPWbzzsZ81kFSZ5fGEoiFjl0wOOx3e3txQKBR41utRrZbQu4KwqlZ01LLG8nKO3jIpyEUB8pv5pMuQxI1IUzFkl4pF0ZKaCeSLls/X7v+VFlBBLlJUJWpPmhTej9AVkcf97lagM676Q1qrMpIhC/R+zk5S6waVxw2MiUk8C3A8j3c3NzzZ3FwfsggTYicgcSN0VSVNhUejOvVZTV3qz1oi9Oh0glLWxLOXJERjQUVWShrGVpl4EQolVbWMWtMpGwY/e/+eFzs71C2LUkeo1AqyRBYL/4u5XyWNU8aLBdE8EKieyQJzpXExHLJZq6F3LCRdWUcUhE6wvrV/W+tX5GbxvwIpggX194EFAsn0w7/qC7+zxSKcipOTbQgsgReE3Ewm6MsldcsSKpiasT4RaVOD6jxgcj/n3c0NnVqNJE2pl0oCMVHVSPyEkmVwenvHwWYHc6eM0bVzQJwmQHJejLVbFbeEmUV96iNJEq8uL/lgexs/iug16sjFIoP5nG6xSOWjNkVNRrU1YjvKI1dT7Ed14RswFeFUdkKWRx47zSZvrq95e3PDs16P4XyOdalht22MrTJGxxaboaUS+xHhxCUKxU0kjGOhhIkipCuH8XJJ4axI+YMmu80mJV3n7c0NhqpyPhjwZHOT0UIM11tZ9o356XxGkGcpGJs2iiz/uVuJTDT1WQUBF9cDerWa8ES4EWmQoOdFKcsRCYs3I8ovWnk+xhQ/DIWyZRWS+DGr0ymTiUOtVGK6XHI5HPK012Ph+xiaRn23idG1xelblbD2q8hlDfVURst9M3utFmeTCb16HW3DWsML/b64VXSq1XUbrm7bbNXrZJmIl3Uv5vhhSDBYEQxWqE0TxVLx7pbrE/d0KQ4bh50OhqqSuDFeGDK8HLNx2BaAu3zQO3o7IEnFDS9KEp5vbVE2DIyeTbwMCSc+G397j9AJcPsLEaQF+PdLVudzFo6LLEmc3ouBvCJJrBYeJaWIrqprUUZ1p4ZSFh4iQWItYj9uCNlo10YyFPzb5bpoPev1MJsWFAoC43+9EAeJmi6Gw25ElH+GkjTlejJBleV1DsfsYspgPmfhC+y+VCxydXkvZjllTcy4zudobZMsSQnjGGe0QOubgsSLOKFLhQInt3dsejUUWSbNMrIso1Q2Uao64dQnHHlEcSJUY2WNeBWuFYE7cRNVljnu90VXYeObQpHGKatrh2AsJMnf1voVull8lmXZp4VC4UuAHFSo/tt84Xe2WKg1na2wyb98c8SjjQ0ux2Oe5CfZglTIN9WScPEaMmmoUtRkyk5It17n1eUlL3Z2aHZqqHWDgizh3cwYzRzCWKTNFZUiwdjFu1sKKmxZoKrjZbDGF0R+zNLzhMwSeNTZQG0atDcsrNsFXiAIqnrHQqvqlHaqIpd6liNBFgILUlC+CeMZLxZEScKL7W0qlkXTtikWixi9kkBoSEWR9V0VCAR9o0T1eRPp7Zh6vcxy4YmWSSJURc2y6PNLqswyf9DDOBYPs+exu9lGqWrrTUexVUp7VdS6jnvlcH88oF4XruDZaoVxp+KFIUvfR5Ek/CjC8mOmpxPGiwXOwyk+SdaFN5x4wiMxdfGjCO9mgVLRkTRJKIBmS07u7xnO53ywtcXK9+nWapT3aig1HdlQRNiQL+B7RUVC0iTkaxVFknifn/grm5U1hbgoFbEOqii5f8G7cjjM+/eKLOcmRpXS4zrStczl5b34N6zuEkw8lLJGeb9GeBTTsG2Wnse721s263V6WQ0vDKmXShTlIsujCVJJQa3o2DWLX74/ZxUE601m6DhwCrPVimantuZdhSOXqG6IecRGiSyF4G1Ifzplt9UiyzJeX1/ztNdjtlphG8Z6M8/ilCxlDcLUasbac/OA43AWK0Z5O+rhFr2Zt68eilSyiojyQbiuqry/veX51hZRHKPIMsWcICBLEnu9DfzVN7ePVqUiTJk5/Xc+W2LHKTfDMTtbbdS2yAGhIFpZ1oGAIGo3Cu9ub9lqNNbtzebCpngvcd+fEEYR2zsbyLaKWtEoP28SjFx24ibHd2LO9rjbRa8bIjPkSJg0Y1fcLLMkW/tWvo31lzi4/6auqFAoSORO70Kh0ELcNP7K9Z0tFrKtofgxu63WWrK68Dx0RaH8ooVWN1ArOs77EcuzGf7Ew2xbjJfLtfknyzKMLTvvuYuUtpKj059OmbsuxROJKIzRShpqVReehouZUI8sXRzPY+G6mJomnNFpiusHFB0JtWVSfd7EdgVORHgeNBI/EhLNmwXBYIW3CvCCAFmSKBaLBHFMxTS5Hn+jjpENRSTt5XLHxcWU4H5JMFhh7VXRGyZq3aD8oiUelvc+++02b29ueNTpMF0sUQY60/mCpm0jSxJvrq95sb3N3HW57A8pzwz2fveRmFHI3/AnlYpOvVnh/EoovF5dXaHKMhfDIbutFk3bxqoIVVTtoI41NYWyynV5cyOGq67rMzl26BxuUCgU8MO8vx8mrE6nyJZKxTIZLRY83dykYllstGqoDQMjJ6YuzqZCZtpfYj9uQB5lW5CLpLkGPs0y4nnI8mImCmnNQDIVim4sihWQpinDxQJVlrGXEWm8XOcm1EslrsZjhkcDqq2ywJRXdZqHTbyrBaPFgkedDu9ubmiUSvhRhFQU4U/TmxnlhmgxKXWDrUZDpAb6/lrps1ZD9WzcqzmSpRDNAvH73Fgajj3COBbueE20/572ery6vOTDnR0hM41jzodD/CiiYppYnRKSJotMeS/CaJWE81mTKNsWQRxzO5lgqCqthvA5RPOA6f2cNMtoZIKommbZ+u9aMU2MqoHeE+KPLMuoPm+SBDGr995aUbjwPK4nE/YsAWHUVXV9QAiWoYBCPqmjlNQcxpcQTn28KGKr0eDdzQ1pllE2DDRZZnw/YzCbiaJeAKWi5bfuYA0BjeI4b3EKtP3yeIrWNvHvRILl4m6B3RFqsm9rZVn2bZjy/mOs/wVhxmsXCoX/Efhv+QZx/m9c39liIRy9GW/zOUHVsoRstq7nqWRqHis5YzFYMHddGlmGIkncTafr00w0DxicCLOXpesMHYcgiridzZisVpiqSs9sQpYRLnyQCsimghlpFAC5WGTp+6RZxtlgwHS1orkqs+lVqbxoYe1WRWxr3RASTfcbiabWNDHzm8UvPn+PG4a4QcBOs0mSppwNBoyXS8qGwaGtotZ00jhFtXXiaojzZoSSzwEefAP+/ZI091E8bGyH3S435/dCaZK3oT7Y3qZimrQqFbSSaGVQKBB7wigYjFZiiFjWUGs6nUWVle/zZHOTV1dXfLizw0algr1dEf6MsibyBgoFSjsVuITH3S5vrq+pWBbdapU0SJDzgrh0XLIsYzCakQ0zjFwgcDYYYKgqVk0A8LIcFOhezInK4nZInouxPBE3wclSOMrPh0MsR6Pj+tjNEvazBtE8WLeS5q7LwvPYajQol8y1nPPmYoCuKJwPhzzpdjFLuvgs2TrhzCMYuMxWKw47Ha4nEx53u8xdFz8UkbmziynT/LOSrCLUmk7rcRv/ZrH29Zze3/O01xNiASdYZ4o0n7Uxt4SCKQliKELrcQvJVFgeT0Ur78HZnMs3oyShU62u4X9qTRdRwWGCe+Ug6WIeMTwbMV0uRdssl3UvHRcttEQR3G/yxZ+9E++9Iq+L7mixQCoW0UyBEkmjRGSTOwHRLEAqFmmVy5R0ka3TbQrfz/LaWfuBtKZBqaqjVMRtPEszQsdf41pqkYkbhjzZ3OSP370jyzL6sxlXoxHPej2iOMZSBQBxdTpjOl4wXS7FnCgvmNEyxHN8ho7DQWmbaOIzGM3Y3G9j7lS+dZ/Fr8LNIsuyf1goFD5H+DkKwN/LsuzNv83XfmeLRZZlHN3drU/0Dzhw2dbQagbhzMO9FvGTsqkQnSQc9/u0q1Wa5TL92YyyIZAZet73bpRKXA6H7LRabFQqa/WRXNGQbU2gKY4nTBZCPrjIf81WKw46HbpV4XHY7rbQ2qZILntQI1kqsRuuQ2qMjZIA6S1FeM3+xgZ+GDJ3XX55dcVHOzvYuo6p6xi2jrFVznHdUwpKEe/KYRUEmMuQNIgJpx7244aY09wLdVMUxhx2u7y+uuLx5iZlw+Criwueb22hSBJRkiBL4oGUS0qu+w9YvB8jGTIFpcjwaEDrcRvd0vji9JSNanX9nodxLOSeUUoSJDhnoqddtSwKCAVQkqbiRFsuo1Q1rs/vqZdKom1EF0NVeXd7S71U4u3NDT9+/Ji72Uy0eKY2dVkSw/eyyt3JgEf/6RNiX/TXlbLGhlbDjyJGjkOvVqNeFd/H3C6j2OI2WHncQDoWZkXHdQnjmNPbO7bDJkbVwFBV3lxf86zXo1gsYj/LE99slSxNsQ5E71ttmWzm7bV/8eYNT3s9ioXCephvbJeRLAXvyiFLM4q68FfIxSJvb244vbtj6VXYVyXuh1M299vr0KQ0zz23nzQEWuPtmJPbOxaet27rpbnK7eF9zTIB+lNsjcXplCxNiWaitZmlGRXbWh+okjRFLhYxTV00JfJTtx+GREmCUdbXs6aqaWKqqrj5XcyQSyqrkynTqTh0LXyfpefRrdUI41go8ZKUhStozyvfp4QtcrazDH/gUn6aF+7jCZPlktlKiDFWvk+Sm/KO7+74YHubLMso5gFTD3kcmuML6CAwmM/F0L7TEfL0bpfltSNad5vttShDrer/H7vHX2+/+Zs8s8gd2w9rAPyff/7//duQvL+zxeJmOuXs/p7PHj/G1PITRE7ZDOdCyTM5GbG984ikpNDYrKEpCu9vb7kej4nimLkr4k+TNKVimrw8P+fFzg6GqhLGsVB9hMkarFaQCuLEWyhgahplw8DxPMqmyaONDUq6jtoUrQ/v0sHvC1ntftsiWoZMXt4hmQrBYIX9pCHCk64cpJJCoSCQ4Lqq8qzX44uzMxqlEs96PZRiUTy4l/O1akfrlLCDhNefH3Ow2aHyYUuk6e1WyZIU92zONH8oDzY2eHl+zn/5/e8TJQm/ODvj4709DFXMvfo3IxpzH6Wqs3g7RrIU/P6SYClEA9alwCd8tLtLfzoliMQg9OHUG4491KaJ43nMXJer0Yi56/Jkc5NPDw7QVRXZVglHgjH18vycF9vbrIKA6Uq4mquWxW8+eUJrq0HncGOtzordCP9uSTDymK5WePdLlLK4Td1djbgej2naNmEcc3J/jyxJNKpa3rv3SdyI0m6VQgH2JybbC8Ggen19jRdFHGad9fzFC0MuR+I0bhsG5ZqF/UFT4MbLohUjmzLe7ZIfP3nCeLHgD16/5vnWlpD5FgrEy5CbiwHFQuEvFP7dVovDbhd7V/gw5hc3xMd9muUy9q440CRhgn+/Yn40RpElmrZNSdexDYPx+TlxmrL0fdww5G42YwuB4wgdn3gR0D8b0m6LwhYtQo6ubxnM58xdlw93djA1jXdXN3QWVdqPWmL477qMHIe72Yw319d8tLODKstCJbexsU7sKz2po81MWn5M/2zIdZLw1cUFh50OaZKSeDFBHOMGAVGS8LOfvUJXFDRF4XCvRzjzKe3XKEhFtBOVnWqXyeUUu2zyJ798S5oKtdPS87Dyz6UzXdKs6ZSfNtE3hLQ5yzK2NIUwjnl1dcXjbhfHddEVhaZt53ORKsaGncfwfjsrg7/RxQL4nH+dVPvw3xlw8Fe9wHe2WFQMg2e9Hv3plMNuF23DFK5UJ8C/W5LFKW4oshCU/EOTRgk7YRM3EAM6TZZZLTze9/sYqkqUJLy9vsZQVXRVxVRVLF1nXxL472jmc9+fcDebscxvFW4YcrCxwdx1uZvN2Imb6JZGfzrFjyIONju5vDVAMpVvAIEldS2dTZOMQqGAOvwm2euDrS2+vrhAKhYJWy2Kr4uoTYOl71NeBEiWQhwlYuMOY5xX4nX1poRaMwjuV8jLIlneHouShIXvM3KcdUsjzU+bm/tt1JohcCBBwvhuJk59QYAXhtxMp3QRn7pFPsy/n82QikVURaF+2BSbe26+OxsM6NZqfH15yQdbW6LdQBfNFAqsB6SJGwR8fXnJx3t73IzHlA2DglxYK3zIxFB62p+hKQpBFDF6N6S+UyMNEsqGQbtc5tXVFc+3trANg4HjUAsrpHEiwn4uHKHmKRRYDpcMHYc/wkodAAAgAElEQVSL4VDQhSUJx3VZ+T5hHPP+9paNapWtnTZplApfQY52cd6NIc2EcGKzRBaJNubzrS2+vrzk0Ua+cU/ETTPLMl5dXfHx3h5xknCYD2PVhilaW3HMTrNJaVO4+EXORMDqZMokV14N58IA6oahkAhHEW9vbvje3h6tcpksT+kjTVn2haKN3FiqVHQO2m1UWWayXBLGMZfDIc+2tmhs1lCrOqmfcLCxwdeXl8Rpykc7O7hBwGa9jqlp+FFE/dkm8SIkHHtMrqfUt2o0SiWSRKjewjgmSlOUWPiXdppN3t7csNtq0a3VUDUxm0qjlCzJ0JoCKpilGU1NMLSe9XrcTibr25Kp65jVnMRQM4R3w42InIDBaIZtGHRrNXRFWc/QHnU6tMpl1LqOVjdRbHUduvVtrG+JOvsfbGVZtv/v+xrf2WJhGwZDx0FTFEaOgzExIMmYn00xaybD/gQvFFykaL7COZtS3qtSWUbst9sUCwW+PDujbJqoksTXFxd8vLeHpWk4nsdmrcZ0tWJ3sy1uLEUxBMyyjEcbwqC102xyN5utT+rT5ZKKaZKkKQPHoVevEwYRWZKxOBpTVCQCP0QbuWg14dy1tit4gxXjqwkDx2HkOLh5pkGcptQsi2KhwM1kwpYqCl3shKRBwshxCOKY0/t7evU60tkMSZMIRi5pnKIpCo1cSTVeLFj6PpauE+dpZCPH4XmvR2UoMiaUni0G6VOdeq72Gi+XvLm+xtQ03CDAUFXiJKFh21iaRpwkuBcO8Sqi/biNf73gSbeL43l8sLXFV5eXfG9nB7lYRK0byJJEGEU4nsfXFxd8sr9PzbKoWRb38znFsyKNYYnyhxIU86yHOOZuNsOPIgbzOaWxQRhEHPf76HmRv51MeNrr0avXMbaEGXNxNMGf++j5TMfuCNxHp1pls17n5fk5H25v867f53s7O9iGQcUUvpCiXEQpayRBLGCVclHgJEoqWttiMhH/TkPHIUkS0feXJAqFAp1qlX/8i1/w8e4uVcsSUu6GidowRHyoXCTMC+b96ZBazUZ6UCgpEr3dNv3LIXpeIIMoopB/XovF4rq1d3Fxxy4dCi2TNMsIoojV1MW/n7Fx2MbYtKkGwbqgfS9vbSbLiIIsDhJR7uf5eG+PqmUJiW/NoFzVoQhpkBBOPPrnQ2arFSVdx/E8LoZDzodD/s5HH2FYGhfXA9I05f3tLS+2t2nYNqWtMkpFJ5r7GN2SCOUau4Qzn2gWECwD/FCg5B9Mk0mWiVsaHWRLEUqzswlZkiFp8jogabvRWKvCDjoddvJgMaWik4YJy/MZpb1vUTqLwOD/TV95gul/D+xnWfYPfm3KQ5xwx4sFfhSx1xYnwXDsMXAcylHEXa6oyOKUcOIxmM/RxxpyWaVdqJBmGYamMXQcXl9d8fH+PoVCgW6thqGq1DYqpHeZkHUqkhgq1wPqAwvZUqktSmy2GpRNE1mS+PzkhO/v7+OFIbZh0KvXeXN9Ta9ep3xbIwtTBlcT2o9a6JslvHtBXLUP68RLkY2RpCnPej3uZjMMVWXl+3x5dsb39vbEDMWLWPk+k4mDoWkUCyI3fL/dptoqY+5XiZfCN3AznTJyHNHz9oTCZua6/OLsjO/v71MxTUqaxsx1RZ+/IjIAFFslDWJSPxFGsDgmiCKcXPV1nIsDju8ETrximlRMk41Vle2/dSAGuFMfNYqEHj9JGMznGKrKdkUXJkpN48vTUz45OKBWKtEoiSChsmlid23Umo6cp+yVHlVRhhr1icVkJeSZkiGjZBmmpvH15SUvtrepl0osPI/d3Q56yySY+mRZxsr30ftLwaSq62wW2lTvLabLJR/t7PDl2Rmf7O9TsSykQkFsxm6E3LaEnPpihncu/CpeGKKcT2hoEs1uTfwbFIvczWaMFwtaZRHp+vbmhiRJWPo+vXod2VAo6hLR3GcwEGbNIIq4zoGMsiX4YGrDwNwTcMXWPB96ezFRGDNZLDjIDylBFHE/nwtn/MyjoBYpb4rv/bCRBv0Vs9UKVZZZeB5JkpBmGVfjMY82O6zOZmuj5cMtM0nETVVZCCWV1jTxbhdMb2bIxSKT/GfUFIWyKQqUIsuMx6LleTka8dHOjjB9qirGIsToiix1xdZYXTss3k+YOcv13GPp+9RKJUxNI80yTu/vaZfLXI/HPNLVdTJeOPPp34wYOA6z1QpT09Zts6op/BlKRUO2VBbHk3XMwLe2/obPLP7c+t/4xpT3D/i1KQ+KxSKyJLGRn4bSvGeqSNIaDS49uF77E6arFeqduOp2qlXKhsHdbMYXp6f8xqNHTBYLNqpV3CBg45FQmVT9WORhbwhonOqbWHsJZBntRRmtY8EAqkuL7+/v82fHxzzZ3Fw/gEEkcNTepRi+bW23kS1VmMDuV4JzFKcoZZ1WuYxcLNLYrGHrOm4YIhWLfLS7yxcnJ/zXPxChXN1ajbc3N/QaDU7u7ni+tUUYx1gHVbS6iXe3EL31YpEn3S5uzrCauy5hFBEnCTfjMaoss1Wvi0JR03DejCm/aJGGCfOjvzjEf2hVfH9vj1qpxFXuWG+Xy5j57aK+U8O7cQhGHje3Q2RJQlcU0ixbby5JECMXiyxcV+RgZ9laINAslzGUhwS9HIjnxazO50ynC8b5/MVUha+iWCiIbI5YEF27tRr1dgUKol+/PJrgTMUGD4LWqtZNJEPh5kTE1CZ5ayHJMuar1Xo4/7RQEKqrDIqyhNLQKScJw/mcimEIim+UMpjPuZ8JamrFNDFUFccTORAf5AXM0DTOb+7Z9hsoNR1L0zjq9wljYeo7HwwoLwzqY5uq0sI+qOV54CITgomPUtWQi0IibGra+mCyyIfMh7KEdVDFXBpsh421QvB+Pl//veKcvbS/sSGS6WYep4PBWhL6UPxtw6BqWTRXNhuKxPByzMBxmOe0gsF8ThDH6+KSZhnT1YqT+3uxcVsWm/U6elWHYiHns1lCxVfWsJ/U0aYm9bm4WXhhyD97+VIc+LIMW9fZ39igZdsoNSHxpSCCk9pN8XwuXHfd4jQ1gTIfHg1o7jUoahLhxENrm+tgrG9jPdzCfgXWr015/+qK8odNkeV1cQiiaK0TN1R1PeB68Cu8yecRSs5wWnjeug3Sq9dFC6JtoW9Ya5KqYqtC+pdl6E0L+cH0pAkMwsJxuZ1MmK1WfHJwwM+PjrB0ncvhUCikajWO7+7o1QU3Z3Um5J6bB22sXZE3kaUZWteik5qkYSoYV57Hq6ur9Yzg7fU1H+T6961mk69zEOJ0uaRXrxMMXRHaU9YEOXYsHlZ94uGpKuPFgs9PTnja67HXbhMnidDi6xLL/oLpakU5EyqgyuM68qnQzEdxzMVwSBBFDB2Hl+fnfHpwwMhxhLpG09Z5EqOv7xnM58xWK6ZLcXqM8xbNKp+XGKow9D0U03c5n2vkOFi6zqbvi7wJRUJvmZQe1VBGGtWRxdLz+YPXr5EkibJhYOab4MMts1S3MPeqKLaK3i3hOh5uEHB/O6HhhNgfSMSu+N6vr66YP3CQ4pijfp+6ZQlERZKQxTlb6v2YcS679cJQeGHuVihVjaZtr1sy48WCVqVC2TB41OnwT37xizXPS0AaTews43I0wtQ0aqUSNcvC0oUZUq3rqBWR2U1B+IiWR1Nu78fIkkSrUaFZLhOn6fpg8vc++4yKaYp0wnnAYDAlzSWys9WKqmXxy8tL2pUKSZLQq9exNA3ZVpFShf2kzf/1J3/Cp48eYes6hqqui39rV3hMWjsNKiOThe8zmM95eX6+Fkz8xqNHSEWBUolikffx0BrWlvIaVdIYCBmz1hAKQcmQCe5XYh4TiDyYPzs54QePHtEql6mYAocSLyNcR4giGqUWasNgJ2hSNgzqts3L83M+2tkhydEj9sgUdAEnYPF2jLld/tb2m1+VNhS/NuX962v9UH75Jd/b2xMn57xlcnJ3x8SyaK7K7H26S8U0KRaLOK7LzXSKoWliiK0oXI3HJGnKdrOJ2bQwemUoFNCbVp7I5bJ4NxbmosO6iDKVi2hNk9iNaOzWeZ73XO9nM9Is4+uLC35weCjCa8pCPfW+3+dx1uFuNmOzXhfqKrn4TRpc28J5Jaitw8WChefxYnt7rdRZeB4poFZ0KoHBk81Nvjo/53v5APX+eMDGYVsMA/N5w9LzucmRDUma8snBAa1ymU61iqLKRGHMYuZwm8uI3fM59vMG+obwgcjXwnOiyAKr8fOjI370+DH1Ukm010wTq2mhtS20ukF1GWKMdMzhjE61ysBxeHl2RpQkvM6LXdk0Oex2qVoWPz8+5keHh+JWoaqi378rktzUqo6kK0KcUCgQDFzmrsvjbpeX5+c86/X45eUln+zv07BtGqUSSAUWb0cUX7RIvIhioYAXRTSKRbRu7gUZu4wWCz7Y3mbl+ziexxenp3x6cMBmrUbZNKkc1FFyGqzRs1GOBejOzdVbg/mc/aTNwvMYzGakaUrFslAlCaOsU44iPt7b4+fHxzRtm+/v7wsiKqBIEl9dXvI7z58jS9I6zU+p6SJhzhHSZYDb+zF3M0GXVmWZJEm4HA7F77OM6/FYbP6mwvBoyDxXNgVRhCxJaLLMRrXKFycnfHJwgON5WLpOGqUUCjBdrdbQSU1RMDUNuyTCotSajtG1SYOY5f2SUT5PU2R5fbOxNA1FktY49S9PT7E0DV1VhVdG0zB1HalQoPC+QPFFkdiNGb8bEiUJ725u1hkmaV70d5pNrJrJ1aVQtt1Op/zoxx8It78hRAv1kUi8fGgjfrS7SxBF9EcTtk0Fo1vi/nS4bmF9G+tX6Gbxa1Pev7qMTZvOSObj/X0+Pznh04ODNdJ5p9EgShK6WyKdzqyZDC+ctS7+z46Pedbr8S+Pj/lvfvADapZFtWRRVIss3o6w9ip5WlzA6nzGbLKgCkQtn0SX1w+zWs3T2a5ETzfNtfAvtrepWhaGorBaeIK3E0W87/fpVKuUWiWMTVtEVw5XIifA1jC2bbSVx+NOZ40oL+a3jJKu44ch11cD6qWS4EAlwjti5xTdNBRZyIOLEaPFYm1Cc3Ktfq9eXzuLF0t3LXN93uvRbFcx96soJeGYzZIUrWWyKbWpjV10VeXLs7P1LU2RJJRiEUkT2I3lyQS9UxLZDVHK6e0ds+WSj3Z315uLH0WUcn9GnCSkeWskiCK6tRp7vQ0RWlPRkXWZxcmEcOJj7lYwdytsacKf8KzX48vTUz599IiqZdEolVCrOjcXA9rlMuHMZ3YxZeg4hJEA/gX9FVrDQKlo4v31fVawDt65mUyQikUUSaKXk1QLxQy1qtPaaghPQ6HA61x906vXKek6zXKZs8GAm7H4TGiKgm0aOHmrbbfVwtZ1wiRhvFgwmM+J4pi3uVmx6li0nTI1TV4n20mmwuhoSK1UYug4XI/HomDLMlKxyJ8eHfFZXrStiujX17drFAsFHNcliGPhrB6LW0mcy1I/2NqiXamgVDW8GyFTTdOU436fkeNQtSzalQq9sCZMdqcTZFuj3CujShJSsYgfRWzWavzL42M+PRBqzMlyyVcXF3x6cCBYbXmLcbNeJ4xjOo/agiBb1gnGQno9Xi5FimQYrmGCpVx8sRgv15SB51tbIkNk5uFdOKRJytV4zGa9jheGPN/a4ouTE/6r3/gNWjUhS/bvxK0lcaNvbb/Jsowojr+11/sPtX5tyvtL1up2IRhKeTsqThJ+/OQJm7VafjISKIDVyQxzt8yeXOT6XJxWKqbJH715w8d7e+y1Wix8n+P+HYeZSP66fXlLY+xRKBaY3c0FQVbTCMaeyA7YtFmdTHl3dMpGRQwkC4UCtmHwG48e8c9fveLHT57wp/M5H+/trRUk6zZAScV5O+bi+p7drQ2C+xVBoYC1V6XXFJnI0u2S5WwlYHqqyj/+8kt+9PgxzXIZRZbxIzHsftbr0S6X2WjVkMsa1k4Fa7dKb7jCuxbhPf3plKebm/yjzz9HlWWuRiOO+n12Wy2e93o0WlXGwzmJF8OLJmkoMqJvJ5M1JPGhF/+XDflWZzNuzwbIb4s0OzUq32/z4W6ZJzOfyfUUI2+D/aPPP+fv/ehHnN3f89XFBb/17JmQPNYqGNs20Szg4qdn65nRA168m6QodTHAVKcyJV3nxc4Of/z2Lc97PeE2DgKuRiPCOObFRy3Mkk4ymzF3XWESlIssj6YErjChnQ4GXI3HXA2H/PDwUAQFFQpcjEZsvB/TPxvS3W8hWQpZkrL5wSatmU+9VOJuNuOrC4F7/7OTEz57/BhFltlpNtFtHW/h40cRn+zv84evX/PDw0MUSeLlxQU/ePSInWaT3/rhh4BgnGktk8X7CZP+jJHj0CqXGTgOLz55xKMkIU4S5qsVf/z2Lb/17Bl/9/vfZxUE7G13KB3WWLwZCzqrrlExTSbLJT97/55nvR4/e/+eXqOBbRhYug5SgTe/OAWgVioRxDH1UokX29vr96D8vAlpyuxoTLFYpAjobYsDexNZkrgajWhXKvzx27cA/OTpU373xQtxKzNNSltlinJRtITmPuHUR+/aUACtabLT2GVzJYyVq74oNL/x6NH6vWqVy0R5cb2bzSj/8p6z+3uCOKZsGARRxP/9p3/KbLXiB48esddu0yzbyLbKzfk9/dmMnUZjnRr4bawMfiWKBUCWZW+Bt/+uX/edLRYL1+W436dQKKyvsNvNJpKpcHs2oBM0yFKLye2UpiaR+KJF5eXRpetsA89bEzn9KKI/mBIlQsOPVOA+17r3ZzN2pzr+cEk4C1CqOhuLCu/7fZ5vbtKwbaZ5b/vHT57wR69f85NnzzgfDHja6wk9+oPzNkoIVyFLzyNYBhQk4aPJ4pR4EaL37DxLQUaWJI7u7vjR48f87P17fnh4yMhxAAiiiPe3twzncwqFAluGTJYjLIxNe93jreby288eP+ZfvHlD2TT50ePHbDUa2KaJt/C5nkxo74n3L41SSr0yjVzX/yLX358PBpze37MKAjq1GsViEVMqQ0EM1ItFkeuwPBbJZrdnwlNx1O9z3O/z4ydPvhkqpym3k4kIcdJ15Jl40FdBQG3iIVsKUW5Cu74c0PXrFKQi1XKJSc73ipNkbcgczOcsPA/bMEQkreOiyrKAQmYZk5lDvV7mdjJhr7vBQbtNxTRpl8v89N07fnh4KHwyzaaIufU8kWXthNzcjegFTWIvWs9d/uDVK6I45sdPnuAGAc9bLap1G2OzROG+wIf5e/bDw0N++vYtiizzk6dPqZdKNGwbrSH+ngWpSFGWMLdsikqRwiW8u71lu9kkmvlYlsF2s8n729tvWjWtljgUNQyyOCWNEm6mU+qWJVqMskzZMPjjt2/58ZMnKLJMvVSiZBkkofgcXo/HdGo1ojimpOvCQd3v02s0KK0i9LaFvVXh5efv6dZqTM5WbO632ayJG8zS99c/W5Km60JRflQjnodIVTGXuLsd02pUIMe2OK9FJo+xbYto1ZbJi+3tv/B6v/38OUf9Pp8cHNCt1bibCjJAo1zm68tLmrbNYDZbGyP32m3UpoFsa3T8BmGS8Cb3enxbK80ywl+RYvHXXd/ZYlEyDA67Xf7hH/4hP37yhI1qFUvTuLkdMl4uUWWZtq0yWS4pT8T8oVsTLu6mbdOr1/mDV6+olUosfZ/7+VzwaXLa50O+QMU0hfmrWUWpaoSzgOHRAF1V0W2Nnl8TJ5mdDXpBHUvXWXoeP3n2jD96/Zq/9eKFkJ/GMZYmTn5pmAoare8LI9Vc3Ezkksr4fkZlIZhDtWctsjBlL2nxh/kJ+Kdv3/LZkyd8cXrKhzs76xnE5kYDSZPw+ysWS1f4JnLcgTSTmY0Xa/bPhzs71EslqpbFbLUSJkXXJZz4qPWA2ZsRqqFQsoTxsaTrXOWu94pl8bjbpWnb2A0RqpRl0OzV+ZM/e8VWo0FxVqTbrqMrCpejEbV8cBzG8TecozSlXalQK5UAcc3PQPhCcpBhuVcmSRLe9/tIxSJeGNKuVFjlt4go19nfTCac3N2x02yyWauRxqIYTVcrHnU6a4+BrqrMXBfZVjF9nZscGPnwvn5vb4/Nep3ADVkGAYEbrj0zVctCVxRUWeary0vcIOC3nj1bI8grpolSVpm+G2H3yqx8MS/qT6eEccxnT54wd11Kuax69n68JgJLeu4pmQt10HaOqO80ariuj+O63M9mJGkqJKZpymS1oqlu4A9WGNtl6mG4Ju8WCgUmyyU/PDwkyg2BrVoFtWmQ+DFNt8xgPufL09P1Z+LV1RXPej3SNBW4kjhFqWhr9d1uq0UaJCg1nWpkrSGUXo4LeXN9zQfb2+gDjdv+mM1uA88Rz9Xmfpss+WbGejeeUg8EBaB52CROEvrTKdf5/DDM22h306lwZpfLSMUiL8/O2Go0+Nn796Lw2jamrgt5ckmEi2VRwuaqSgF4dXX1re03WZYR/LpY/GquNMsYOg5R/sHabjbXCqKHh5cMFr7PcCz6xNs7G1i5vn28WPDbz58zmM+pl0q8PD8nzTI+3t2lYdtkScpothS4C02jFZaJZgFKTRcY536fxqKEG4iUuMSLae43kS4mjBxnncl9P5+zUa2u1UQN26Y8gf5stkY3zFyXimki34lN8n1fxMSmcYJsq1SkMh8ud1h6Hp/lt5bf+eADurUavXodu2yi92ym70XrqFUu4/dXyG7E+HqKpihcjEac5CytLJdgjhwBflv4vsA81HUSL0bRZZE9vdmmpOuMcwdwkqb4YchkuaRVFkE2zuuRiFWt6dRtm1dXV7QqFWRJwgtDBvM57UqFTw8O+Bdv3vA/dLuUdF3gOe7uCOMYdXsbwxVgxsvRSKQFGjKypVIqm/QC4VnZbbUwLFFwH3IQgihaCwrETcmgIBWpN8UG8+72Fi9HUCx9sfFGUx+zrLMbNbF1nV9eXRE+nLDztubS8/ByUOCDh6BQKKArCook8dnjx2KwbttC6bRbZXwyYuQ4qJrCTrOJpiisApEw6HgeH+XF3SjrfPX+nMedDtqGQMGkfoLRs2n6MUd3d0Ip5Pq87/dR8yFykopQqaXvc7CxgX+z4G4wodNp4IbhmiigSBJm7qH5aGdHGO12yrl82KAyEcFMxUKBrUYDgJHj8Co3gbYrFR4pEkVVWt+si4UCw/6EMEno7bbRJvL6hv6QCBgnCW4O9rM0jbnrsvQ8xlcTWrpMQS6gtU2MxYqjuzs61SrT0wmd/BDXqlTYajT457/8Jb/74YcC+GgYlA1jLa396du3/OTZM2qlEnXLEuFPNZPxuyHV3doamxPm6qxva2VZRvgtvt7fxFX8q//Ir+ZSJWmt8lFlmdlqxc1E+CluJxMcz2N5Iz60b66viZKENE4pWeKD9/HeHlKxyO+9fMnQcdioVJgsFpwNBjl63OPs/p7Tuzu6tRpIBa4u7/Fvl+gVnWa+MX51eYmhqlzeD4mmYvO3DUPwiqKIpm0LXk5+KikUCkKBksdsDh2HX15eosryuiX2YFCKxr4g4sYpK9/ndDBYQ+Sux+O15l3SZGbvRxi2vg74WSxdgqHLYD5HylPeLF0XiOck4fXVFVejEeeDAXvtNlXLQtJlvEuH23tx6vXy3ILTuzu+Oj9f5xs8bNbv+33uZjNObu8Ixx7dapX9jQ1O7+6YrVYokkS7UuHnR0f80evX/ObTpziex11+St5ptQTg0DRxlz792QypIG5z0cRndDSkaIjvt9tq8fXlJe7Sp2KaPO52+eHh4RroV7MsNFkmTlL8mwVZxlrlE+UtwPuckzR0HIqGTCH3ajyA+oIo4qvLy7UCzY8ibMNgs17nF+fnTJdLpqsVuqJwM5mw9P118VhezgX+3HFYrgR998/foh6kqeVaiSRIWLiuuHVMPMbvhtwd3+PfrUgRuRF+Li/WZJkvz86+cStvbPBie5vNWo3zm3vu53OWMzHQfXhv31xfi6yS/GeNkgT3Ys7t2YBw6KKVReuuXirxR69f87P379ev+6zXY7/dRm0I/pabI1+mqxWv8+coCwU65sFjctjpcNjpYOs6C89julxyO53y6uqKnWaT+laN2AkYvxuS+gkVW4g/3l5f8+72ljCK6FSrmLlPJc1hibP8ey5zefLDTCqMY97d3nKbK8XSWLzvw9MRwxOhCrvPYZTf1kqzDD+K1r++i+s7WywWvs90ueQ3nz7l//n6a+5nM/706Ij9dpvdVoswjjnu9/n68pKdZlP0cisaRVUS2PFGg/2NDX73o4/4J198wR+8esWHOzs86/XEvAJxmvDCEFWWWbk+t9Mpi6WLM15i59df1/d5dXWFIkmEXsTcdXl5fi4e7ty09PPjYw42NkQwfSKc0Zv1OvsbG/zi7IydVguzLE5QvUaDV5eX4lQ2WXE1HjO4m4r+exTx+199xe988AEf7+1xsLGBrev4c3ECHQ5nYrPKoXgPJqrL4ZAwb5d8vLfH7718SRjH/PTdO55sblI1TcqmgXctMhsuhgLtsAoCbF3noNNZM450RWG2WjGczynpOq+uroRCKhMIZz+KWAXCkT53XY7v7uhPJvz28+c0ymXh2LUs0nxmcTedEuVzhTQVsa1RknByd8/NZEI09cmAID/xP2j3l77/F9oWr6+vmbkuJ3d3zFcuruNxPhxyMRzi5Q70L05OhOGrViH1ROHbajT4aHeX3/ngA37/q6+wdR0vDMXPkBsjDVWlbBj8ybt33M9m/N7Ll3y0u8tWo0FJ1zm+u+Po9pabyYS567IKAt73+yIzJU0JcvDfbLXi8mbA/WSG43moskwaJNxOp1xPhIlw8ec2uqN+X7jjq1V+/6uv+O3nz6mVSnhhiKFpqLLMYD4XMEvDYLfV4ouTE5I0/Qt/xwJweivwLt7MI16ELHyfq/GYz548YbJYcHp/v+Z9SbpMQRK4k26txsHGBr84P6dZLq8FHetEvVz5JOeok/50im0YvDw/Zw3qwIcAAA/2SURBVK/dxjYEc+z6csD1ZII7WqHWdLq1GiXD4Ho85t3tLYscyLnbavGfvHjBP/vyS/706IggirBNk4ONDT7e3+cnz57xB7/8JbauC7k0cHEtQqveXF+T5c9tmmV4QfCX7Bx/vZVmGX4Yrn99F9evTBuqUCj8F8D/DEjA/5Fl2f/0b/rztq5zsLHBxXDIbz57xj/78kv+808/FcllxSJvrq/XgLGKaYoT0ukQgPPhkL1Wi4ppMl2tSJJE+Adsm3alsh6UqoqCGwTMcxnkdLnkfDhch69ULYunvR5uEHA2GKDmG6mpafzTL7/k7376Kc1yGUvT+Om7d/zk6VNGjsPBxgZKvvmvgoCx4yDpMqu5u2Y4eWEoTobzObPlkk6tJvrLuTrmfjajadsCL6Lr3IzHxEnC2f09H+/tYRuCw7RRrfIy92N08yjZOE35p19+yd/5/vdxPBEKtHA9Jsslk+WS/nTK9/b21k7yMIpoVyrrwvrJo0foioIkSSK/w/dxg4CL4ZDL0YhVEBBGEeX8hhXnRdNQFK5HI37/5Uv+s08+YSOPOi0UCoS5+EDO4X5n9/e82N4GwHFd7qZTVnmGuixJbDUaaIpCu1Lh916+5Hc/+ojBfL5Gi+y0muthrKmq/PHbt/zO8+dUTRNn6VJYwsVoxE6ziaGqIswnf183a/9ve+caG8d13fHfIZfLXZJLcvmQREmERJWGYlmqKEVIbTV+xPW7QowA/WC3KNKmSYCmKdoUbWHDQNF+y+uDEbS1YxQu0sLNo25aB4IdwQgM5SFXLinXpiiS0uq1XIkUueSSy93lUnzcfrh3RytV5IriUktS5wcMdubO7Mx/zs7smbnn3nPDpKamuDQ2Rq3LF3VPSwvlZWUcOXGCJ/fvZ2tDA7XBoO3ENzvLueFhkpkM+1yfis7RURpragi4joN+n22scHHYjqG9rbmZUDDIZZdwcjCRoCkUIlBR4cXWjDGkp6c9bTNzc/REo7bJqbN3LtV3bTBIuLqa9pYWftXX5+WrMq4j4GAiYWMKdQGmxm1z7uzMDL/q6eG3OjpoCYdpCoVsh77Lw2xjA3PZWU4NDFATCDA1PU0ilSJdV8eVXPqWpiZm3Qh8WxoaCPj9JDMZemMxDrS3A9bGNYEAvvJyroyPE3JVfdWhINubm/GXl3tv2ADnh4ftG+L8PDs3b6Zt40ab8NJlBEhmMkxftSMJtoTD+MrLiY6MeFVuoUCAiYxt3JAuorMw8/NFdT6rkTXxZuF6HP4D8DSwC3heRHYt9p0rExPeYDlHurp4dO9eGkMhL22G3+fj/f5+6qqquDo7S9+lS0y7nq2x0VHG0/bVfX5+npnpacZSKZv/JpWiZ2DADozknmZ7YzFm5+fZFA5z4uxZKly+naDfT0NNDcf6+ryUCPFkksPHjvFYRwfNtbVsqK1lNJUi6570czf45bExLsbjXsbTxOC419Y/19s5kU7TXFdnHZHPNhl9cPdu3unsJJG2zWp7YzFGJiepCQTojERo27iR2qoqhicmSE1NeR3JBuJx/OXltNTX075pE491dHCkq4uE+7OZmLLO4oNIhO2upVBu0J6cPXKOtcLno8wFnCfSaUTEG8+80uez+atSKU65WEDO7rPz87zT2cmDu3fbcROqqykrK7PpL+bm8Pt8jE5O2jTh6TTReJzk1BSzc3NUuyf+UwMD9MViNhV5TQ1NtbWeExsaHycyOEhZWRnnhq4wkcnQd+kSR3t6vOaxfp+P3ljMs8lEJkNFeTkh1yQz1yw512R52gXl/a7X/9zcHIl0mr5Ll4hPTnp9RDbV13tvYzNzczSFQhzr7/euhR0uhX1lRQXHz5yxzXlFGHdvPJtdv43k1BTd0Sjj6TSjk5ME/X52bNzIo3v3cqSri3KntayszIunXBwZITk15fUT2dXa6jVzvjA8zIfnz9MSDtt407DtYb+5vp5PbNnCtKtSCdfUMJZO0xuLEaioYGh4jDNDQ4gI/336NPe1trK1qYk5Y4iOjNAXi1EmQjaT8a6HeecUU+6t/6MLF6j0+YgM2XE5Euk0PQMDDIyOMjSSoEyE+upqjp85w1gqxejkJBXl5Rw+down9u2j0Y0Fksvf5vf5bCLM+XnvmMYYaquqqHcPhMPuvrmcSHjxxGIwbwyZbNab1iNr5c3iU0DEGHMOQER+ADwLnFroCyejUULBIDWBAJlJ29JnxtVlBvx+RlzCsYlMhjHXGarGdRgaHBtjczjMeDpNbVUVM1ev2k9XT5prRx7w+9mzbRupbJZTAwPUBoM2fbRrqVFXVUWwspJ0NktdMOilkp5x1SVVlZX0DAywrbmZSp+Pdz/8kMf37fM6SZ2IRPjMnj001dZyYWSEy+5Jtr2lhWP9/fz6tm2EgkHiySQTmQzzxvCTn/+cQw8+yAbXFv1iPE6oqopf9vbS0dZmRzpLJokMDdHuOp/lEsqdHhyksqKCCp+9LDLuzyiRSnlVEONuNLKh8XHPCUTjcS+VRWZy0nuiTmezJNJphhIJb2yM1qYmRISjJ09y4J57OHL8OIcOHmSDG0zq8f37eevoUT73yCNMZDKUu97tgYoKAn4/hzs77dgWO3dSHQhwxVWjtTY1cV9rK93RKJ9qb2d0cpKBeJytjY00hkI8uncvb7//PocOHqTHxYCa6+oIV1cz5dKO5FrtDI6P24yxLjVJrjprZnraPkXn3uqSdhS+rY2NRIaGPJvVBoOe3bNu+NvpmRkS7qFgaHycuqoqdm7ZQl8kAkB1IECvc3IT6TQjSZsvLHv1KvHJSdLZLBfjtllpKBDgve5ufvehh0ik014wf8Zl/U24pIblZWUk0ml2+f2kslkG4nGyMzO8dfQozz78MIlUynujBeiORmnftInBRILWpiYSqRQzLgAfGRwkNjpKR1sbE66zZks4zC9OneJAe7uXVDKeTFJVaQfCqg4EmLl61Wuw0BuLsbG+nr3bt/Nedzef2bOHyakpRpJJW8Xa0EDn2bOMJJPcu3Wr19gi5dK1zM7Pe9obQyGCfj/d0SjbN2zgwvAw4ZoajDFkXaLCRCrFUCJBQyjEL3t7+fS993rnFgoEmMwULzfU3OwsE+73Wa9IrsftakZEfgd4yhjzRbf8+9iEWF/N2+bLwJcByuGTW0qiVFGUtUYUuowxB5azDxH5KdCUVxQ3xjy1PGWri7XyZnGzkdWv83LGmNeA1wBEZCQKaWC1uPomVo8WUD2FWE16VpMWWJ96ti1XxHpzDDdjrTiLGNCat7wVuLzQxsaYZhHpXO7TQrFYTVpA9RRiNelZTVpA9dzNrIkAN/A/wD0i0uZyrz8H/KTEmhRFUe4a1sSbhTFmVkS+ChzBNp193RjTU2JZiqIodw1rwlkAGGPeBt5ewldeWyktt8Fq0gKqpxCrSc9q0gKq565lTbSGUhRFUUrLWolZKIqiKCVk3TkLEXlKRPpFJCIiL6zgcVpF5D0R6RWRHhH5M1feICLvisgZ9xl25SIi33G6PhaR/Xn7+rzb/oyIfH4ZmspF5EMROeyW20TkuNvvD3MDs4tIpVuOuPXb8/bxoivvF5Enl6GlXkTeFJE+Z6MHSmybr7nf6aSIfF9EAnfSPiLyuogMi8jJvLKi2UNEPiki3e473xGRmzU3X0zLt9xv9bGI/KeI1Bc654XutYXsuhQ9eev+UkSMiDTdCdsoi2CMWTcTNvh9FtgB+IGPgF0rdKwWYL+bDwGnsalIvgm84MpfAL7h5p8B3sH2GbkfOO7KG4Bz7jPs5sO3qekvgH8DDrvlHwHPuflXgT92818BXnXzzwE/dPO7nM0qgTZny/Lb1PI94Itu3g/Ul8o2wBbgPBDMs8sf3En7AA8B+4GTeWVFswfwAfCA+847wNNL1PIE4HPz38jTctNzZpF7bSG7LkWPK2/FNmq5CDTdCdvotMjvVGoBRT0Ze0EcyVt+EXjxDh37LeBxoB9ocWUtQL+b/y7wfN72/W7988B388qv224Jx98K/Ax4FDjsbox43h+AZxt3Az7g5n1uO7nRXvnbLVFLLfbPWW4oL5VttgAD7o/E5+zz5J22D7Cd6/+gi2IPt64vr/y67W5Fyw3rPge8cbN7KHfOLHCvLXbdLVUP8CawF7jANWex4rbR6ebTequGyv0p5Ii5shXFVVPsA44DG40xgwDuc0MBbcXS/DLw10BuyLFGYNwYkxu+K3+/3jHd+gm3fbG07ABGgH8WWy32TyJSTYlsY4y5BHwbiAKD2PPtonT2yVEse2xx88XS9QXsE/jtaFnsurtlROSzwCVjzEc3rCq1be5a1puzKJgWpOgHFKkB/gP4c2NMcrFNb1JmFilfioZDwLAxpusWjreiWhw+bLXCK8aYfdjUK4vFj1ZUj4sFPIutRtkMVGMzGC+075W2TyGWevyi6RKRl4BZ4I1SaRGRKuAl4G9utvpO61Es681ZLCktyHIRkQqso3jDGPNjV3xFRFrc+hZguIC2Ymj+TeCzInIB+AG2KuploF5Ecn1p8vfrHdOtrwPGiqQlt/+YMea4W34T6zxKYRuAx4DzxpgRY8wM8GPgIKWzT45i2SPm5pelywWFDwG/Z1ydzW1oibOwXW+VX8M69o/cNb0VOCEim25DT1Fso7DuYhY+bGCrjWtBt/tW6FgC/Avw8g3l3+L6oOU33fxvc31g7gNX3oCt3w+76TzQsAxdj3AtwP3vXB9o/Iqb/xOuD+D+yM3fx/XBzHPcfoD7F8BON/+3zi4lsQ3wG0APUOWO8T3gT++0ffj/MYui2QObEud+rgVxn1milqewKf+bb9jupufMIvfaQnZdip4b1l3gWsxixW2j0wK/UakFFP2EbGuJ09iWGi+t4HE+jX2d/Rj4Xzc9g62z/Rlwxn3mLljBDuB0FugGDuTt6wtAxE1/uExdj3DNWezAtgSJuBu40pUH3HLErd+R9/2XnMZ+ltFqBOgAOp19/svdwCWzDfB3QB9wEvhX9+d3x+wDfB8bL5nBPu3+UTHtARxw53YW+HtuaFxwC1oi2Dr/3LX8aqFzZoF7bSG7LkXPDesvcM1ZrKhtdFp40h7ciqIoSkHWW8xCURRFWQHUWSiKoigFUWehKIqiFESdhaIoilIQdRaKoihKQdRZKOsasdmBz4tIg1sOu+VtpdamKGsJdRbKusYYMwC8AnzdFX0deM0Yc7F0qhRl7aH9LJR1j0vL0gW8DnwJ2GeMuVpaVYqytlgzY3Aryu1ijJkRkb8Cfgo8oY5CUZaOVkMpdwtPY1NK7C61EEVZi6izUNY9ItKBHZjqfuBruUyviqLcOuoslHWNG2/5Fex4I1Fsptdvl1aVoqw91Fko650vAVFjzLtu+R+BT4jIwyXUpChrDm0NpSiKohRE3ywURVGUgqizUBRFUQqizkJRFEUpiDoLRVEUpSDqLBRFUZSCqLNQFEVRCqLOQlEURSmIOgtFURSlIP8H0pudv5vAIjAAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "(mg, z) = read_esri_ascii('model_grid_steady_state_elevation.txt')\n",
+    "mg.at_node['topographic__elevation'] = z\n",
+    "mg.set_closed_boundaries_at_grid_edges(right_is_closed=True,\n",
+    "                                       top_is_closed=False,\n",
+    "                                       left_is_closed=True,\n",
+    "                                       bottom_is_closed=False)\n",
+    "\n",
+    "imshow_grid(mg, 'topographic__elevation', colorbar_label='elevation (m)', shrink=0.4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Create and plot a grid field of the air temperature at the land surface\n",
+    "\n",
+    "We use a lapse rate of 5&deg;C / km."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "9.59590309985 25.9999927129\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lapse = -0.006\n",
+    "\n",
+    "# Set surface temperature at sea level.\n",
+    "temperature_at_sea_level = 26\n",
+    "mg.at_node['land_surface_air__temperature'] = lapse * mg.at_node['topographic__elevation'] + temperature_at_sea_level\n",
+    "print(mg.at_node['land_surface_air__temperature'].min(), mg.at_node['land_surface_air__temperature'].max())\n",
+    "\n",
+    "# Plot temperature field.\n",
+    "imshow_grid(mg, 'land_surface_air__temperature',\n",
+    "            colorbar_label='surface air\\ntemperature (C$\\degree$)',\n",
+    "            shrink=0.4, cmap='bwr')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Initialize SpeciesEvolver\n",
+    "\n",
+    "This component requires only one parameter, a model grid, for the default functionality."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "se = SpeciesEvolver(mg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "View the `record` of the component instance. Time and other model variables are stored in the record. The first and second columns are the row number and model time, respectively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   time\n",
+       "0     0"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "se.record"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create a zone function\n",
+    "\n",
+    "Zones are portions of a model grid. A `ZoneSpecies` object exists within a zone. `Zone` objects are explained in more detail later in this tutorial.\n",
+    "\n",
+    "The zone function must return a numpy array with the length of the grid's `number_of_nodes`. True values indicate where zones should be created. \n",
+    "\n",
+    "Here, we indicate zones should be created where the air temperature is between 15&deg;C and 25&deg;C, inclusive. The temperature of all nodes is within this interval, therefore all elements of the mask are `True`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def zone_func(grid, **kwargs):\n",
+    "    \"\"\"Get a mask of the total zone extent.\"\"\"\n",
+    "    T = grid.at_node['land_surface_air__temperature']\n",
+    "    T_max = 25\n",
+    "    T_min = 15\n",
+    "    zone_extent_mask = np.all([T >= T_min, T <= T_max], 0)\n",
+    "    return zone_extent_mask"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`True` values are shaded white in the plot below"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "imshow_grid(mg, zone_func(mg), allow_colorbar=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a species that exists in the zone\n",
+    "\n",
+    "The initial zone is populated by instantiating a species with the zone as its parameter. The species must then be introduced to SpeciesEvolver in order for the species to be tracked and to evolve over time.\n",
+    "\n",
+    "The type of species that will be simulated is evolved by these processes:\n",
+    "* dispersal: geographic range expansion\n",
+    "* speciation: lineage splitting\n",
+    "* extinction: lineage termination\n",
+    "\n",
+    "Also below, we print all the species that exist in SpeciesEvolver. Only the one species exists; the species we introduced. Further below, the `clade` and `species_number` identifier elements will be explained."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sc = ZoneSpeciesController(se, zone_func, minimum_area=50000, grid=mg)\n",
+    "\n",
+    "# Plot zones.\n",
+    "for zone in sc.zones:\n",
+    "    plt.figure()\n",
+    "    imshow_grid(mg, zone.mask, allow_colorbar=False, plot_name='zone in white')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>clade</th>\n",
+       "      <th>species_number</th>\n",
+       "      <th>time_appeared</th>\n",
+       "      <th>latest_time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>B</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  clade  species_number  time_appeared  latest_time\n",
+       "0     A               0              0            0\n",
+       "1     B               0              0            0"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sc.populate_each_zone(1)\n",
+    "\n",
+    "# Print the species data frame.\n",
+    "se.species"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Simulate the effect of changing zone definition on species\n",
+    "\n",
+    "< about climate elevation gradient >\n",
+    "\n",
+    "For example, ecosystems are zonated by elevation.\n",
+    "\n",
+    "If temperature decreased by 4&deg;C at sea level (the elevation at the upper and lower grid bounderies), then the 800 m zone maximum would lower by about 409 m assuming a lapse rate of 9.8&deg;C / 1000 m.\n",
+    "\n",
+    "Next, we set the zones of species are limited to areas with an elevation less than 400. Two distinct areas of the mask are now seperated by the higher elevations in the center of the grid."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "15.5959030999 31.9999927129\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Update surface temperature at sea level.\n",
+    "temperature_at_sea_level = 32\n",
+    "mg.at_node['land_surface_air__temperature'] = lapse * mg.at_node['topographic__elevation'] + temperature_at_sea_level\n",
+    "print(mg.at_node['land_surface_air__temperature'].min(), mg.at_node['land_surface_air__temperature'].max())\n",
+    "# Plot temperature field.\n",
+    "imshow_grid(mg, 'land_surface_air__temperature',\n",
+    "            colorbar_label='surface air\\ntemperature (C$\\degree$)',\n",
+    "            shrink=0.4, cmap='RdBu')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "bbb 2\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Advance SpeciesEvolver by one unit of time. Time is dimensionless in this tutorial.\n",
+    "dt = 1\n",
+    "se.run_one_step(dt)\n",
+    "\n",
+    "# Plot zones.\n",
+    "for zone in sc.zones:\n",
+    "    plt.figure()\n",
+    "    imshow_grid(mg, zone.mask, allow_colorbar=False, plot_name='zone in white')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<landlab.components.species_evolution.zone.Zone at 0x1a24b75ed0>]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sc.zones"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "21.5959030999 37.9999927129\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Update surface temperature at sea level.\n",
+    "temperature_at_sea_level = 38\n",
+    "mg.at_node['land_surface_air__temperature'] = lapse * mg.at_node['topographic__elevation'] + temperature_at_sea_level\n",
+    "print(mg.at_node['land_surface_air__temperature'].min(), mg.at_node['land_surface_air__temperature'].max())\n",
+    "# Plot temperature field.\n",
+    "imshow_grid(mg, 'land_surface_air__temperature',\n",
+    "            colorbar_label='surface air\\ntemperature (C$\\degree$)',\n",
+    "            shrink=0.4, cmap='RdBu')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "imshow_grid(mg, zone_func(mg), allow_colorbar=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Increment SpeciesEvolver using the `run_one_step` method. The only parameter of this method is the time step duration. Here we use 1000 to signify 1000 yrs. This method evaluates and updates the zones and species of SpeciesEvolver.\n",
+    "\n",
+    "The `run_one_step` method determined that two distinct zones now exist."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "bbb 2\n",
+      "bbb 2\n",
+      "bbb 2\n",
+      "bbb 2\n",
+      "bbb 2\n",
+      "bbb 2\n",
+      "bbb 2\n",
+      "bbb 2\n",
+      "bbb 2\n",
+      "bbb 2\n",
+      "bbb 2\n",
+      "bbb 2\n",
+      "bbb 2\n",
+      "bbb 2\n",
+      "bbb 2\n",
+      "bbb 2\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>clade</th>\n",
+       "      <th>species_number</th>\n",
+       "      <th>time_appeared</th>\n",
+       "      <th>latest_time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>A</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>A</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>A</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>A</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6</td>\n",
+       "      <td>A</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7</td>\n",
+       "      <td>A</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>8</td>\n",
+       "      <td>A</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>9</td>\n",
+       "      <td>A</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>10</td>\n",
+       "      <td>B</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>11</td>\n",
+       "      <td>B</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>12</td>\n",
+       "      <td>B</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>13</td>\n",
+       "      <td>B</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>14</td>\n",
+       "      <td>B</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>15</td>\n",
+       "      <td>B</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>16</td>\n",
+       "      <td>B</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>17</td>\n",
+       "      <td>B</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>18</td>\n",
+       "      <td>B</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>19</td>\n",
+       "      <td>B</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>20</td>\n",
+       "      <td>B</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   clade  species_number  time_appeared  latest_time\n",
+       "0      A               0              0            1\n",
+       "1      A               1              2            2\n",
+       "2      A               2              2            2\n",
+       "3      A               3              2            2\n",
+       "4      A               4              2            2\n",
+       "5      A               5              2            2\n",
+       "6      A               6              2            2\n",
+       "7      A               7              2            2\n",
+       "8      A               8              2            2\n",
+       "9      A               9              2            2\n",
+       "10     B               0              0            0\n",
+       "11     B               1              1            1\n",
+       "12     B               2              2            2\n",
+       "13     B               3              2            2\n",
+       "14     B               4              2            2\n",
+       "15     B               5              2            2\n",
+       "16     B               6              2            2\n",
+       "17     B               7              2            2\n",
+       "18     B               8              2            2\n",
+       "19     B               9              2            2\n",
+       "20     B              10              2            2"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAACtCAYAAABBYlMUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAATGElEQVR4nO3dfbBcdX3H8feHBILylMQIxiRA0FQNnREoAtYHHNBAkCF0ijVoJWKUmYpWtNaC6YiPHfAJpRYwJWh4EEjBFobBYgYhSmcEExEkYMyFkAdAAk14ECwQ+PaP873h3M3uPcnNnt27N5/XzM6e8/v9zu73/vbufvec39nzU0RgZmY2mJ26HYCZmQ1/ThZmZlbJycLMzCo5WZiZWSUnCzMzq+RkYWZmlZwszGog6fOSLq75OW6V9NEWdftK+qOkUXXGYDuO0d0OwGwkioh/6fLzrwF271+XdCtweUTUmsBs5PKehZmZVXKysBFH0vvzEEz/7bn8Zo2kvSRdKukxSasl/bOknbLuw5Juk/RNSRslrZI0s/S4e0laIOkRSQ9J+mqrwzySvijp8lzeX1JImiNpjaTHJc1rsd1USU+UYrpY0vpS/eWSzihtsp+k/5H0tKSfSprQ8JyjJX0NeAfwveyP72WbN0paLGmDpBWS/mbovW4jnZOFjTgRcXVE7B4RuwOvBR4ArszqfwX2Ag4AjgROAU4tbX44sAKYAHwdWCBJWbcQ2AS8HjgYmAE0HTNo4e3AG4CjgS9IelOT2FcBT+XjQ/Eh/8dS23cCS0qbfCDj3xvYBfhsk8ecB/wC+ET2yyck7QYsBn6U254MXCDpwG34e2wH4mRhI1Z+O/8RcGtEfD/3At4PnBURT0fEg8C3gA+VNlsdEf8eES9SJIeJwD6S9gFmAmdExDMRsR44D5i9DSF9KSL+FBF3AXcBb27RbglwpKTX5Po1uT4V2DO37feDiPh9RPwJWAQctJWxHA88GBE/iIhNEfFr4FrgpG34e2wH4gFuG8m+BuwB/H2uT6D49r261GY1MKm0/of+hYh4NncqdgfGAzsDj7y8o8FOwNptiOcPpeVnKQ1AN1gCnACsA34O3EqR0P4P+EVEvDSEx2y0H3C4pCdKZaOBy7Zye9vBOFnYiCRpNsWhlbdExAtZ/DjwAsUH5b1Zti/w0FY85FrgOWBCRGxqc7iNlgDfoEgWS4DbgIsoksWSQbYbTOPlpdcCSyLiPUMN0nYsPgxlI46kgynGJk6MiMf6y/PQ0iLga5L2kLQf8Bng8qrHjIhHgJ8C35K0p6SdJL1O0pHtjj8iVgJ/Av4W+HlEPAU8Cvw1Q08Wj1KM0/S7AfgzSR+StHPe3tJsHMUMnCxsZJoFjANuK50R9ZOs+yTwDMWg920UYxqXbOXjnkJxGOteYCPFWMLEdgZesgT43/y9RP+6gDuH+HjfBU7Ks7zOj4inKQboZwMPUxzOOhcYs31h20glT35kZmZVvGdhZmaVnCzMzKySk4WZmVVysjAzs0pOFmZmVmlE/ihvlBQj8g8zM6vR8/B4RLy6Wd2I/EwdDbymspWZmZWtGXgpnAFG5GGo57sdgJlZj1lTUT8ik4WZmbWXk4WZmVVysjAzs0pOFmZmVsnJwszMKjlZmJlZJScLMzOr5GRhZmaVnCzMzKySk4WZmVVysjAzs0pOFmZmVsnJwszMKjlZmJlZJScLMzOrVHuykDRK0p2Sbsj1qZJul7RS0tWSdsnyMbnel/X7lx7jrCxfIemYumM2M7OBOrFn8SngvtL6ucB5ETEN2AjMzfK5wMaIeD1wXrZD0nRgNnAgcCxwgaRRHYjbzMxSrclC0mTgvcDFuS7gKOCabLIQODGXZ+U6WX90tp8FXBURz0XEKqAPOKzOuM3MbKC69yy+A3wOeCnXXwU8ERGbcn0dMCmXJwFrAbL+yWy/ubzJNmZm1gG1JQtJxwPrI2JZubhJ06ioG2yb8vOdJmmppKXbHKyZmQ1qdI2P/TbgBEnHAbsCe1LsaYyVNDr3HiYDD2f7dcAUYJ2k0cBewIZSeb/yNptFxHxgPoCkLZKJmZkNXW17FhFxVkRMjoj9KQaofxYRHwRuAU7KZnOA63L5+lwn638WEZHls/NsqanANOCOuuI2M7Mt1bln0co/AVdJ+ipwJ7AgyxcAl0nqo9ijmA0QEcslLQLuBTYBp0fEi50P28xsx6Xiy/vIIin27XYQZmY9ZE1xtywiDm1W719wm5lZJScLMzOr5GRhZmaVnCzMzKySk4WZmVVysjAzs0pOFmZmVsnJwszMKjlZmJlZJScLMzOr5GRhZmaVnCzMzKySk4WZmVWqc6a8XSXdIekuScslfSnLp0q6XdJKSVdL2iXLx+R6X9bvX3qss7J8haRj6orZzMyaq3PP4jngqIh4M3AQcKykI4BzgfMiYhqwEZib7ecCGyPi9cB52Q5J0ynmtjgQOBa4QNKoGuM2M7MGdc6UFxHxx1zdOW8BHAVck+ULgRNzeVauk/VHS1KWXxURz0XEKqAPOKyuuM3MbEu1jllIGiXpN8B6YDFwP/BEzr8Nxfzak3J5ErAWIOufBF5VLm+yjZmZdUCtySIiXoyIg4DJFHsDb2rWLO/Voq5V+QCSTpO0VNLSocZrZmbNdeRsqIh4ArgVOAIYK6l/7u/JwMO5vA6YApD1e1HMxb25vMk25eeYHxGHtpoS0MzMhq7Os6FeLWlsLr8CeDdwH3ALcFI2mwNcl8vX5zpZ/7MoJgi/HpidZ0tNBaYBd9QVt5mZbWl0dZMhmwgszDOXdgIWRcQNku4FrpL0VeBOYEG2XwBcJqmPYo9iNkBELJe0CLgX2AScHhEv1hi3mZk1UPHlfWSRFPt2Owgzsx6yprhb1upQvn/BbWZmlZwszMyskpOFmZlVcrIwM7NKLZOFpBvLF/MbiVaPwMF9M7M6DLZn8UPgp5LmSdq5Q/G0TTkRrI7YfGusb7w3M7MtDXrqrKTdgC9QXO31MuCl/rqI+Hbt0Q2RpNjWU4L3U7OripiZ7Ri299TZF4BngDHAHg23nlFOBM2WnSjMzAbX8hfcko4Fvk1xuY1DIuLZjkVlZmbDymCX+5gHvC8ilncqmLqsjhh076Kx3szMBmqZLCLiHZ0MpA79CWCwwWsnCjOzajvE7yycDMzMts8OkSzMzGz77PDJwnsdZmbV6pz8aIqkWyTdJ2m5pE9l+XhJiyWtzPtxWS5J50vqk3S3pENKjzUn26+UNKfVc5Y5CZiZtU+dexabgH+IiDdRTKd6uqTpwJnAzRExDbg51wFmUsyCNw04DbgQiuQCnA0cTjGP99n9CcbMzDqjtmQREY9ExK9z+WmKKVUnAbOAhdlsIXBiLs8CLo3CLynm6p4IHAMsjogNEbERWEzxi3IzM+uQjoxZ5AUJDwZuB/aJiEegSCjA3tlsErC2tNm6LGtVbmZmHVLnHNwASNoduBY4IyKeUuuxhGYVMUh54/OcRnH4yszM2qzWPYu8Wu21wBUR8eMsfjQPL5H367N8HTCltPlk4OFBygeIiPkRcWiri2CZmdnQ1Xk2lIAFwH0NV6i9Hug/o2kOcF2p/JQ8K+oI4Mk8THUTMEPSuBzYnpFlZmbWIXUehnob8CHgt5J+k2WfB84BFkmaS3FV3Pdl3Y3AcUAf8CxwKkBEbJD0FeBX2e7LEbGhxrjNzKzBoPNZ9CpJsW+3gzAz6yHbO5+FmZmZk4WZmVVzsjAzs0pOFmZmVsnJwszMKjlZmJlZJScLMzOr5GRhZmaVnCzMzKySk4WZmVVysjAzs0pOFmZmVsnJwszMKtU5n8UlktZLuqdUNl7SYkkr835clkvS+ZL6JN0t6ZDSNnOy/UpJc5o9l5mZ1avOPYsfAsc2lJ0J3BwR04Cbcx1gJjAtb6cBF0KRXICzgcOBw4Cz+xOMmZl1Tm3JIiJ+DjROUjQLWJjLC4ETS+WXRuGXwNiccvUYYHFEbIiIjcBitkxAZmZWs06PWeyTU6WS93tn+SRgbanduixrVW5mZh1U57Sq20JNymKQ8i0fQDqN4hCWmZm1Waf3LB7Nw0vk/fosXwdMKbWbDDw8SPkWImJ+RBzaakpAMzMbuk4ni+uB/jOa5gDXlcpPybOijgCezMNUNwEzJI3Lge0ZWWZmZh1U22EoSVcC7wImSFpHcVbTOcAiSXMp5gd/Xza/ETgO6AOeBU4FiIgNkr4C/CrbfTkiGgfNzcysZopoOgTQ0yTFvt0Owsysh6wp7pa1OpTvX3CbmVklJwszM6vkZGFmZpWcLMzMrJKThZmZVXKyMDOzSk4WZmZWycnCzMwqOVmYmVklJwszM6vkZGFmZpWcLMzMrJKThZmZVeqZZCHpWEkrJPVJOrPb8ZiZ7Uh6IllIGgX8GzATmA6cLGl6d6MyM9tx9ESyAA4D+iLigYh4HrgKmNXlmMzMdhi9kiwmAWtL6+uyzMzMOqBXkoWalA2Y4k/SaZKWSlo6qkNBmZmNFFWzi9Y2B3ebrQOmlNYnAw+XG0TEfGA+gKTH1sAzwOMdi7A9JtBbMfdavNB7MfdavOCYO6GuePdrVdETc3BLGg38HjgaeAj4FfCBiFg+yDZLW80lO1z1Wsy9Fi/0Xsy9Fi845k7oRrw9sWcREZskfQK4CRgFXDJYojAzs/bqiWQBEBE3Ajd2Ow4zsx1RrwxwD8X8bgcwBL0Wc6/FC70Xc6/FC465Ezoeb0+MWZiZWXeN5D0LMzNrkxGXLIbTNaQkTZF0i6T7JC2X9KksHy9psaSVeT8uyyXp/Iz9bkmHlB5rTrZfKWlOzXGPknSnpBtyfaqk2/O5r5a0S5aPyfW+rN+/9BhnZfkKScfUHO9YSddI+l329Vt7oI8/nf8T90i6UtKuw62fJV0iab2ke0plbetXSX8h6be5zfmSmv2eanvj/Ub+X9wt6T8ljS3VNe27Vp8hrV6fdsdcqvuspJA0Ide728cRMWJuFGdK3Q8cAOwC3AVM72I8E4FDcnkPitN/pwNfB87M8jOBc3P5OOAnFD9CPAK4PcvHAw/k/bhcHldj3J8BfgTckOuLgNm5fBHwd7n8ceCiXJ4NXJ3L07PvxwBT8zUZVWO8C4GP5vIuwNjh3McUVx9YBbyi1L8fHm79DLwTOAS4p1TWtn4F7gDemtv8BJhZQ7wzgNG5fG4p3qZ9xyCfIa1en3bHnOVTKM7+XA1MGA59XMubt1u37JSbSutnAWd1O65SPNcB7wFWABOzbCKwIpe/D5xcar8i608Gvl8qH9CuzTFOBm4GjgJuyH+yx0tvuM19nP/Mb83l0dlOjf1ebldDvHtSfPCqoXw493H/5WvGZ7/dABwzHPsZ2J+BH75t6des+12pfEC7dsXbUPdXwBW53LTvaPEZMtj7oI6YgWuANwMP8nKy6Gofj7TDUMP2GlJ56OBg4HZgn4h4BCDv985mreLv5N/1HeBzwEu5/irgiYjY1OS5N8eV9U9m+07GewDwGPADFYfOLpa0G8O4jyPiIeCbwBrgEYp+W8bw7ud+7erXSbncWF6nj1B8u6Yirmblg70P2krSCcBDEXFXQ1VX+3ikJYvKa0h1g6TdgWuBMyLiqcGaNimLQcrbStLxwPqIWLYVMQ1W18nXYTTFbvyFEXEwxWVeBhur6nrMeZx/FsXhj9cCu1Fcfr/V83c95q2wrTF2NHZJ84BNwBX9RdsYV6feg68E5gFfaFbdIoaOxDzSkkXlNaQ6TdLOFIniioj4cRY/Kmli1k8E1md5q/g79Xe9DThB0oMUl4E/imJPY6yKS640PvfmuLJ+L2BDB+Ptj2FdRNye69dQJI/h2scA7wZWRcRjEfEC8GPgLxne/dyvXf26Lpcby9suB3yPBz4YeTxmCPE+TuvXp51eR/El4q58H04Gfi3pNUOIub193M7jm92+UXzLfCA7u39w6sAuxiPgUuA7DeXfYOAg4ddz+b0MHMC6I8vHUxyXH5e3VcD4mmN/Fy8PcP8HAwf2Pp7LpzNw4HVRLh/IwMHDB6h3gPsXwBty+YvZv8O2j4HDgeXAKzOOhcAnh2M/s+WYRdv6leIab0fw8uDrcTXEeyxwL/DqhnZN+45BPkNavT7tjrmh7kFeHrPoah/X8ubt5o3ijIHfU5zRMK/LsbydYrfvbuA3eTuO4vjnzcDKvO9/YUUxI+D9wG+BQ0uP9RGgL2+ndiD2d/FysjiA4qyKvnzDjMnyXXO9L+sPKG0/L/+OFWznWS5bEetBwNLs5//KN8yw7mPgS8DvgHuAy/JDa1j1M3AlxZjKCxTfUue2s1+BQ/Pvvx/4Hg0nKbQp3j6K4/n977+LqvqOFp8hrV6fdsfcUP8gLyeLrvaxf8FtZmaVRtqYhZmZ1cDJwszMKjlZmJlZJScLMzOr5GRhZmaVnCzMOkDFFYhXSRqf6+Nyfb9ux2a2NZwszDogItYCFwLnZNE5wPyIWN29qMy2nn9nYdYheemXZcAlwMeAgyPi+e5GZbZ1Rlc3MbN2iIgXJP0j8N/ADCcK6yU+DGXWWTMpLu/w590OxGxbOFmYdYikgygmvzoC+HT/1VvNeoGThVkH5NzHF1LMabKG4uqt3+xuVGZbz8nCrDM+BqyJiMW5fgHwRklHdjEms63ms6HMzKyS9yzMzKySk4WZmVVysjAzs0pOFmZmVsnJwszMKjlZmJlZJScLMzOr5GRhZmaV/h8E9iO2UIdz7QAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAACtCAYAAABBYlMUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAATcklEQVR4nO3dfZBddX3H8feHhAeVhyRGMCYhBE3V0BmBIsT6gAMKARlCp1iDViJGmaloRWstMR3xsQM+YakFTAENDwIp2MJQLGYQonRGMBFBAsYshDwAEmjCg2CBwLd/nO+Sszf37tms99y7d/N5zezcc76/37n3u7/dvd8953fuOYoIzMzMBrNTtxMwM7ORz8XCzMwquViYmVklFwszM6vkYmFmZpVcLMzMrJKLhVkNJH1O0oU1v8Ytkj7Som1fSb+XNKbOHGzHMbbbCZiNRhHxT11+/XXA7v3rkm4BLouIWguYjV7eszAzs0ouFjbqSHpfHoLp/3o2/7NG0l6SLpH0qKS1kv5R0k7Z9iFJt0r6hqTNktZIOqb0vHtJukjSw5IelPSVVod5JH1B0mW5vJ+kkDRP0jpJj0la2GK76ZIeL+V0oaSNpfbLJJ1e2mSapP+R9JSkH0ua2PCaYyV9FXg78J0cj+9knzdIWippk6RVkv5q+KNuo52LhY06EXFVROweEbsDrwHuB67I5n8B9gL2Bw4HTgZOKW1+GLAKmAh8DbhIkrJtMbAFeB1wEHAU0HTOoIW3Aa8HjgQ+L+mNTXJfAzyZzw/Fm/zvS33fASwrbfL+zH9vYBfgM02ecyHwM+DjOS4fl/QKYCnwg9z2JOA8SQdsx/djOxAXCxu18r/zHwC3RMR3cy/gfcCCiHgqIh4Avgl8sLTZ2oj4t4h4gaI4TAL2kbQPcAxwekQ8HREbgXOAuduR0hcj4g8RcSdwJ/CmFv2WAYdLenWuX53r04E9c9t+34uI30bEH4AlwIFDzOU44IGI+F5EbImIXwLXACdux/djOxBPcNto9lVgD+Bvc30ixX/fa0t91gKTS+u/61+IiGdyp2J3YAKwM/Dw1h0NdgLWb0c+vystP0NpArrBMuB4YAPwU+AWioL2f8DPIuLFYTxno2nAYZIeL8XGApcOcXvbwbhY2KgkaS7FoZU3R8TzGX4MeJ7ijfKejO0LPDiEp1wPPAtMjIgtbU630TLg6xTFYhlwK3ABRbFYNsh2g2m8vPR6YFlEvHu4SdqOxYehbNSRdBDF3MQJEfFofzwPLS0BvippD0nTgE8Dl1U9Z0Q8DPwY+KakPSXtJOm1kg5vd/4RsRr4A/DXwE8j4kngEeAvGX6xeIRinqbf9cCfSPqgpJ3z683N5lHMwMXCRqc5wHjg1tIZUT/Ktk8AT1NMet9KMadx8RCf92SKw1j3AJsp5hImtTPxkmXA/+bnJfrXBdwxzOf7Z+DEPMvr3Ih4imKCfi7wEMXhrLOBXf+4tG20km9+ZGZmVbxnYWZmlVwszMyskouFmZlVcrEwM7NKLhZmZlZpVH4ob4wUo/IbMzOr0XPwWES8qlnbqHxPHQu8urKXmZmVrRt4KZwBRuVhqOe6nYCZWY9ZV9E+KouFmZm1l4uFmZlVcrEwM7NKLhZmZlbJxcLMzCq5WJiZWSUXCzMzq+RiYWZmlVwszMyskouFmZlVcrEwM7NKLhZmZlbJxcLMzCq5WJiZWSUXCzMzq1R7sZA0RtIdkq7P9emSbpO0WtJVknbJ+K653pft+5WeY0HGV0k6uu6czcxsoE7sWXwSuLe0fjZwTkTMADYD8zM+H9gcEa8Dzsl+SJoJzAUOAGYD50ka04G8zcws1VosJE0B3gNcmOsCjgCuzi6LgRNyeU6uk+1HZv85wJUR8WxErAH6gEPrzNvMzAaqe8/i28BngRdz/ZXA4xGxJdc3AJNzeTKwHiDbn8j+L8WbbGNmZh1QW7GQdBywMSJWlMNNukZF22DblF/vVEnLJS3f7mTNzGxQY2t87rcCx0s6FtgN2JNiT2OcpLG59zAFeCj7bwCmAhskjQX2AjaV4v3K27wkIhYBiwAkbVNMzMxs+Grbs4iIBRExJSL2o5ig/klEfAC4GTgxu80Drs3l63KdbP9JRETG5+bZUtOBGcDtdeVtZmbbqnPPopV/AK6U9BXgDuCijF8EXCqpj2KPYi5ARKyUtAS4B9gCnBYRL3Q+bTOzHZeKf95HF0mxb7eTMDPrIeuKhxURcUizdn+C28zMKrlYmJlZJRcLMzOr5GJhZmaVXCzMzKySi4WZmVVysTAzs0ouFmZmVsnFwszMKrlYmJlZJRcLMzOr5GJhZmaVXCzMzKxSnXfK203S7ZLulLRS0hczPl3SbZJWS7pK0i4Z3zXX+7J9v9JzLcj4KklH15WzmZk1V+eexbPAERHxJuBAYLakWcDZwDkRMQPYDMzP/vOBzRHxOuCc7IekmRT3tjgAmA2cJ2lMjXmbmVmDOu+UFxHx+1zdOb8COAK4OuOLgRNyeU6uk+1HSlLGr4yIZyNiDdAHHFpX3mZmtq1a5ywkjZH0K2AjsBS4D3g8778Nxf21J+fyZGA9QLY/AbyyHG+yjZmZdUCtxSIiXoiIA4EpFHsDb2zWLR/Voq1VfABJp0paLmn5cPM1M7PmOnI2VEQ8DtwCzALGSeq/9/cU4KFc3gBMBcj2vSjuxf1SvMk25ddYFBGHtLoloJmZDV+dZ0O9StK4XH4Z8C7gXuBm4MTsNg+4Npevy3Wy/SdR3CD8OmBuni01HZgB3F5X3mZmtq2x1V2GbRKwOM9c2glYEhHXS7oHuFLSV4A7gIuy/0XApZL6KPYo5gJExEpJS4B7gC3AaRHxQo15m5lZAxX/vI8ukmLfbidhZtZD1hUPK1odyvcnuM3MrJKLhZmZVXKxMDOzSi4WZiPM2op5xKp2szq0LBaSbihfzM/MOmeoBcOFwzplsD2L7wM/lrRQ0s4dysdsh9b/5j9NzS5cwIA2FwrrpJafs4iIJZL+C/g8sFzSpcCLpfZvdSA/s1FjbcSgRaBqW9i2iAz3+cy2V9WH8p4HngZ2BfagVCzMbGjKewCtCkbjXkLj+jSJtRFN+7lgWCe0LBaSZgPforjcxsER8UzHsjIzsxFlsD2LhcB7I2Jlp5Ix62VDnUPY3rmG/j2H/r2Lcry8t1Fu996GtdtgcxZv72QiZr2u8c28VR9oXjDKb/BVh5fKBaS8TWPMrF3qvJCg2Q6nsRi0Kg6Nb+hV7eV4q4LkImF18ofyzNqo8bBQv6rTXf1GbyOd9yzM2qjqTb8dRcGFxbqhzpsfTZV0s6R7Ja2U9MmMT5C0VNLqfByfcUk6V1KfpLskHVx6rnnZf7Wkea1e02wk85u89bI6D0NtAf4uIt5IcTvV0yTNBM4AboqIGcBNuQ5wDMVd8GYApwLnQ1FcgDOBwyju431mf4ExM7POqK1YRMTDEfHLXH6K4paqk4E5wOLsthg4IZfnAJdE4ecU9+qeBBwNLI2ITRGxGVgKzK4rbzMz21ZHJrjzgoQHAbcB+0TEw1AUFGDv7DYZWF/abEPGWsXNzKxDap/glrQ7cA1wekQ8qdbHbZs1xCDxxtc5leLwlZmZtVmtexZ5tdprgMsj4ocZfiQPL5GPGzO+AZha2nwK8NAg8QEiYlFEHNLq/rFmZjZ8dZ4NJeAi4N6GK9ReB/Sf0TQPuLYUPznPipoFPJGHqW4EjpI0Pie2j8qYmZl1SJ2Hod4KfBD4taRfZexzwFnAEknzgXXAe7PtBuBYoA94BjgFICI2Sfoy8Ivs96WI2FRj3mZm1kAxCm+gIin27XYSZmY9ZF3xsKLVoXxf7sPMzCq5WJiZWSUXCzMzq+RiYWZmlVwszMyskouFmZlVcrEwM7NKLhZmZlbJxcLMzCq5WJiZWSUXCzMzq+RiYWZmlVwszMysUp33s7hY0kZJd5diEyQtlbQ6H8dnXJLOldQn6S5JB5e2mZf9V0ua1+y1zMysXnXuWXwfmN0QOwO4KSJmADflOsAxwIz8OhU4H4riApwJHAYcCpzZX2DMzKxzaisWEfFToPEmRXOAxbm8GDihFL8kCj8HxuUtV48GlkbEpojYDCxl2wJkZmY16/ScxT55q1Tyce+MTwbWl/ptyFiruJmZdVCdt1XdHmoSi0Hi2z6BdCrFISwzM2uzTu9ZPJKHl8jHjRnfAEwt9ZsCPDRIfBsRsSgiDml1S0AzMxu+TheL64D+M5rmAdeW4ifnWVGzgCfyMNWNwFGSxufE9lEZMzOzDqrtMJSkK4B3AhMlbaA4q+ksYImk+RT3B39vdr8BOBboA54BTgGIiE2Svgz8Ivt9KSIaJ83NzKxmimg6BdDTJMW+3U7CzKyHrCseVrQ6lO9PcJuZWSUXCzMzq+RiYWZmlVwszMyskouFmZlVcrEwM7NKLhZmZlbJxcLMzCq5WJiZWSUXCzMzq+RiYWZmlVwszMyskouFmZlV6pliIWm2pFWS+iSd0e18zMx2JD1RLCSNAf4VOAaYCZwkaWZ3szIz23H0RLEADgX6IuL+iHgOuBKY0+WczMx2GL1SLCYD60vrGzJmZmYd0CvFQk1iA27xJ+lUScslLR/ToaTMzEaLqruL1nYP7jbbAEwtrU8BHip3iIhFwCIASY+ug6eBxzqWYXtMpLdy7rV8ofdy7rV8wTl3Ql35TmvV0BP34JY0FvgtcCTwIPAL4P0RsXKQbZa3upfsSNVrOfdavtB7OfdavuCcO6Eb+fbEnkVEbJH0ceBGYAxw8WCFwszM2qsnigVARNwA3NDtPMzMdkS9MsE9HIu6ncAw9FrOvZYv9F7OvZYvOOdO6Hi+PTFnYWZm3TWa9yzMzKxNRl2xGEnXkJI0VdLNku6VtFLSJzM+QdJSSavzcXzGJenczP0uSQeXnmte9l8taV7NeY+RdIek63N9uqTb8rWvkrRLxnfN9b5s36/0HAsyvkrS0TXnO07S1ZJ+k2P9lh4Y40/l78Tdkq6QtNtIG2dJF0vaKOnuUqxt4yrpzyT9Orc5V1Kzz1P9sfl+PX8v7pL0H5LGldqajl2r95BWP59251xq+4ykkDQx17s7xhExar4ozpS6D9gf2AW4E5jZxXwmAQfn8h4Up//OBL4GnJHxM4Czc/lY4EcUH0KcBdyW8QnA/fk4PpfH15j3p4EfANfn+hJgbi5fAPxNLn8MuCCX5wJX5fLMHPtdgen5MxlTY76LgY/k8i7AuJE8xhRXH1gDvKw0vh8aaeMMvAM4GLi7FGvbuAK3A2/JbX4EHFNDvkcBY3P57FK+TceOQd5DWv182p1zxqdSnP25Fpg4Esa4lj/ebn3loNxYWl8ALOh2XqV8rgXeDawCJmVsErAql78LnFTqvyrbTwK+W4oP6NfmHKcANwFHANfnL9ljpT+4l8Y4f5nfkstjs58ax73cr4Z896R441VDfCSPcf/laybkuF0PHD0SxxnYj4Fvvm0Z12z7TSk+oF+78m1o+wvg8lxuOna0eA8Z7O+gjpyBq4E3AQ+wtVh0dYxH22GoEXsNqTx0cBBwG7BPRDwMkI97Z7dW+Xfy+/o28FngxVx/JfB4RGxp8tov5ZXtT2T/Tua7P/Ao8D0Vh84ulPQKRvAYR8SDwDeAdcDDFOO2gpE9zv3aNa6Tc7kxXqcPU/x3TUVezeKD/R20laTjgQcj4s6Gpq6O8WgrFpXXkOoGSbsD1wCnR8STg3VtEotB4m0l6ThgY0SsGEJOg7V18ucwlmI3/vyIOIjiMi+DzVV1Pec8zj+H4vDHa4BXUFx+v9Xrdz3nIdjeHDuau6SFwBbg8v7QdubVqb/BlwMLgc83a26RQ0dyHm3FovIaUp0maWeKQnF5RPwww49ImpTtk4CNGW+Vf6e+r7cCx0t6gOIy8EdQ7GmMU3HJlcbXfimvbN8L2NTBfPtz2BARt+X61RTFY6SOMcC7gDUR8WhEPA/8EPhzRvY492vXuG7I5cZ42+WE73HAByKPxwwj38do/fNpp9dS/BNxZ/4dTgF+KenVw8i5vWPczuOb3f6i+C/z/hzs/smpA7qYj4BLgG83xL/OwEnCr+Xyexg4gXV7xidQHJcfn19rgAk15/5Otk5w/zsDJ/Y+lsunMXDidUkuH8DAycP7qXeC+2fA63P5Czm+I3aMgcOAlcDLM4/FwCdG4jiz7ZxF28aV4hpvs9g6+XpsDfnOBu4BXtXQr+nYMch7SKufT7tzbmh7gK1zFl0d41r+eLv5RXHGwG8pzmhY2OVc3kax23cX8Kv8Opbi+OdNwOp87P/BiuKOgPcBvwYOKT3Xh4G+/DqlA7m/k63FYn+Ksyr68g9m14zvlut92b5/afuF+X2s4o88y2UIuR4ILM9x/s/8gxnRYwx8EfgNcDdwab5pjahxBq6gmFN5nuK/1PntHFfgkPz+7wO+Q8NJCm3Kt4/ieH7/398FVWNHi/eQVj+fdufc0P4AW4tFV8fYn+A2M7NKo23OwszMauBiYWZmlVwszMyskouFmZlVcrEwM7NKLhZmHaDiCsRrJE3I9fG5Pq3buZkNhYuFWQdExHrgfOCsDJ0FLIqItd3Lymzo/DkLsw7JS7+sAC4GPgocFBHPdTcrs6EZW93FzNohIp6X9PfAfwNHuVBYL/FhKLPOOobi8g5/2u1EzLaHi4VZh0g6kOLmV7OAT/VfvdWsF7hYmHVA3vv4fIp7mqyjuHrrN7qbldnQuViYdcZHgXURsTTXzwPeIOnwLuZkNmQ+G8rMzCp5z8LMzCq5WJiZWSUXCzMzq+RiYWZmlVwszMyskouFmZlVcrEwM7NKLhZmZlbp/wEkW1xQfgFmUwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Advance SpeciesEvolver by one unit of time. Time is dimensionless in this tutorial.\n",
+    "dt = 1\n",
+    "se.run_one_step(dt)\n",
+    "\n",
+    "# Plot zones.\n",
+    "for zone in sc.zones:\n",
+    "    plt.figure()\n",
+    "    imshow_grid(mg, zone.mask, allow_colorbar=False, plot_name='zone in white')\n",
+    "\n",
+    "se.species"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   time\n",
+      "0     0\n",
+      "1     1\n",
+      "2     2\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23a30490>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a24c0bb90>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23ab5f90>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23ab5750>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23ab5fd0>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23ab5450>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23ab50d0>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23ab5510>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23ab5650>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a24407d10>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a24015b50>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a2400d510>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a2400d050>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a2400de90>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a2400d8d0>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a2400d410>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a2400d1d0>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a2400d810>]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(se.record)\n",
+    "se.run_one_step(dt)\n",
+    "\n",
+    "sc._species"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Three species have existed in this model:\n",
+    "\n",
+    "- A.0: The initial species we introduced.\n",
+    "- A.1 and A.2: The child species of A.0. These species were created when the zone of the first time step split into two in the second time step.\n",
+    "\n",
+    "All species belong to clade 'A' because the child species (A.1 and A.2) were spawned from the same parent species (A.0)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "OrderedDict([('clade',\n",
+       "              ['A',\n",
+       "               'B',\n",
+       "               'B',\n",
+       "               'B',\n",
+       "               'B',\n",
+       "               'A',\n",
+       "               'B',\n",
+       "               'A',\n",
+       "               'A',\n",
+       "               'B',\n",
+       "               'A',\n",
+       "               'B',\n",
+       "               'B',\n",
+       "               'B',\n",
+       "               'A',\n",
+       "               'B',\n",
+       "               'A',\n",
+       "               'B',\n",
+       "               'A',\n",
+       "               'A',\n",
+       "               'A']),\n",
+       "             ('species_number',\n",
+       "              [0,\n",
+       "               0,\n",
+       "               1,\n",
+       "               8,\n",
+       "               10,\n",
+       "               6,\n",
+       "               5,\n",
+       "               1,\n",
+       "               7,\n",
+       "               7,\n",
+       "               8,\n",
+       "               2,\n",
+       "               4,\n",
+       "               9,\n",
+       "               9,\n",
+       "               6,\n",
+       "               4,\n",
+       "               3,\n",
+       "               3,\n",
+       "               2,\n",
+       "               5]),\n",
+       "             ('time_appeared',\n",
+       "              [0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]),\n",
+       "             ('latest_time',\n",
+       "              [1, 0, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]),\n",
+       "             ('object',\n",
+       "              [<landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a24daf2d0>,\n",
+       "               <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x8225c1ed0>,\n",
+       "               <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a24daf250>,\n",
+       "               <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a2400d410>,\n",
+       "               <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a2400d810>,\n",
+       "               <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23ab5450>,\n",
+       "               <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a2400d050>,\n",
+       "               <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23a30490>,\n",
+       "               <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23ab50d0>,\n",
+       "               <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a2400d8d0>,\n",
+       "               <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23ab5510>,\n",
+       "               <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a24407d10>,\n",
+       "               <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a2400d510>,\n",
+       "               <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a2400d1d0>,\n",
+       "               <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23ab5650>,\n",
+       "               <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a2400de90>,\n",
+       "               <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23ab5750>,\n",
+       "               <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a24015b50>,\n",
+       "               <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23ab5f90>,\n",
+       "               <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a24c0bb90>,\n",
+       "               <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23ab5fd0>])])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "se._delegate._species"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Species identifiers\n",
+    "\n",
+    "Species identifiers are automatically generated. They are stored as tuples in species objects. The first element of the identifier tuple is the clade name, and the second element is the species number. For example, the first species of clade A is 0, so the identifier is (A, 0) and the tenth species of this clade is (A, 9).\n",
+    "\n",
+    "### Clade name\n",
+    "\n",
+    "A clade is a group of species that share a common ancestor. Species may share multiple common ancestors, so clades can be defined differently in the same tree. The clade(s) of this example tree can be defined in multiple ways:\n",
+    "\n",
+    "<img src=\"images/clade_definitions.png\" style=\"width: 400px;\"/>\n",
+    "\n",
+    "In SpeciesEvolver, clades are defined by the root node for the purpose of assigning species identifiers. The species at the root node is:\n",
+    "* the least recent common ancestor of all species in the tree, and\n",
+    "* typically introduced to a SpeciesEvolver model using the ```introduce_species``` function.\n",
+    "\n",
+    "The clades of the first 26 species are named alphabetically from A to Z. The alphabet is repeated for subsequent species following this schema:\n",
+    "\n",
+    "<center>clade sequence</center> | <center>clade name</center>\n",
+    "--- | ---\n",
+    "1–26 | A–Z\n",
+    "27–52 | AA–AZ\n",
+    "53–78 | BA–BZ\n",
+    "... | ...\n",
+    "703–728 | AAA–AAZ\n",
+    "729–754 | ABA–ABZ\n",
+    "... | ...\n",
+    "\n",
+    "### Species number\n",
+    "\n",
+    "Species are numbered sequently by clade beginning with 0.\n",
+    "\n",
+    "In the example tree below, species A.0 produced 4 child species. Only one species, B.0 exists in clade B because child species were not produced by B.0.\n",
+    "\n",
+    "<img src=\"images/species_identifier.png\" style=\"width: 250px;\"/>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.59590309985 19.9999927129\n",
+      "bbb 0\n",
+      "bbb 0\n",
+      "bbb 0\n",
+      "bbb 0\n",
+      "bbb 0\n",
+      "bbb 0\n",
+      "bbb 0\n",
+      "bbb 0\n",
+      "bbb 0\n",
+      "bbb 0\n",
+      "bbb 0\n",
+      "bbb 0\n",
+      "bbb 0\n",
+      "bbb 0\n",
+      "bbb 0\n",
+      "bbb 0\n",
+      "bbb 0\n",
+      "bbb 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "temperature_at_sea_level = 20\n",
+    "mg.at_node['land_surface_air__temperature'] = -0.006 * mg.at_node['topographic__elevation'] + temperature_at_sea_level\n",
+    "print(mg.at_node['land_surface_air__temperature'].min(), mg.at_node['land_surface_air__temperature'].max())\n",
+    "se.run_one_step(dt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for zone in sc.zones:\n",
+    "    plt.figure()\n",
+    "    imshow_grid(mg, zone.mask, allow_colorbar=False, plot_name='zone in white')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### About Zones\n",
+    "\n",
+    "Zones are portions of a model grid. A Species object exists throughout a zone.\n",
+    "\n",
+    "At each timestep, the spatial intersection of the zones at the prior time and current time are identified and typed. For example, a zone in the prior time intersects two zones in the current time. This zone path is typed one-to-many.\n",
+    "\n",
+    "Macroevolutionary processes are carried out by the intersection of zones over time. The connectivity of zones in SpeciesEvolver are called 'paths', and the relationship of zones is described by the path type. Not all combinations of none, one, and many are considered at the time of this tutorial. All none-to-n and some many-to-n are not considered. \n",
+    "\n",
+    "<img style=\"float: left;\" src=\"images/zone__one_to_none.png\"> &nbsp; zone<sub>*t*</sub>: a zone in time, *t*.<br>\n",
+    "\n",
+    "<img style=\"float: left;\" src=\"images/zone__later_time.png\"> &nbsp; zone<sub>*t*+1</sub>: a zone in the time following *t*.\n",
+    "\n",
+    "<center>path type</center> | <center>graphical<br>represention</center> | <center>zone temporal connectivity</center> | <center>macroevolution implications</center>\n",
+    "--- | --- | --- | ---\n",
+    "one-to-none | ![](images/zone__one_to_none.png) | zone<sub>*t*</sub> does not intersect a zone in *t*+1. | The species in the zone of the earlier time will go extinct. \n",
+    "one-to-one | ![](images/zone__one_to_one.png) | zone<sub>*t*</sub> intersects zone<sub>*t*+1</sub>. | The species in the zone of the earlier time will relocate to the zone of the later time.\n",
+    "one-to-many | ![](images/zone__one_to_many.png) | zone<sub>*t*</sub> intersects multiple zones in *t*+1. | The species in the zone of the earlier time disperses across the multiple zones in the later time. The species in the zones of the later time are geographical disconnected, thus speciation occurs.\n",
+    "many-to-one | ![](images/zone__many_to_one.png) | Multiple zones in *t* intersect zone<sub>*t*+1</sub>. | Species density increases locally.\n",
+    "many-to-many | ![](images/zone__many_to_many.png) | Multiple zones in *t* intersect multiple zones in *t*+1. | Species distribution shifts. Speciation occurs where the zone of a species at *t* intersects more than one zone at *t*+1.\n",
+    "\n",
+    "The rules of macroevolution are programmed into species. Behind the scenes, zone paths are the conditions evaluated by the macroevolution rules. The outcome of the rules given the zone paths are described in the zone table above. For example, a one-to-many path:\n",
+    "\n",
+    "![](images/zone_path__one_to_many.png)\n",
+    "\n",
+    "The base species class \n",
+    "\n",
+    "Users may develop species with different behaviors and macroevolution rules.\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "Populate SpeciesEvolver zones with species.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>clade</th>\n",
+       "      <th>species_number</th>\n",
+       "      <th>time_appeared</th>\n",
+       "      <th>latest_time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>A</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>A</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>A</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>A</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6</td>\n",
+       "      <td>A</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7</td>\n",
+       "      <td>A</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>8</td>\n",
+       "      <td>A</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>9</td>\n",
+       "      <td>A</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>10</td>\n",
+       "      <td>B</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>11</td>\n",
+       "      <td>B</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>12</td>\n",
+       "      <td>B</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>13</td>\n",
+       "      <td>B</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>14</td>\n",
+       "      <td>B</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>15</td>\n",
+       "      <td>B</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>16</td>\n",
+       "      <td>B</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>17</td>\n",
+       "      <td>B</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>18</td>\n",
+       "      <td>B</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>19</td>\n",
+       "      <td>B</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>20</td>\n",
+       "      <td>B</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   clade  species_number  time_appeared  latest_time\n",
+       "0      A               0              0            1\n",
+       "1      A               1              2            3\n",
+       "2      A               2              2            3\n",
+       "3      A               3              2            3\n",
+       "4      A               4              2            3\n",
+       "5      A               5              2            3\n",
+       "6      A               6              2            3\n",
+       "7      A               7              2            3\n",
+       "8      A               8              2            3\n",
+       "9      A               9              2            3\n",
+       "10     B               0              0            0\n",
+       "11     B               1              1            1\n",
+       "12     B               2              2            3\n",
+       "13     B               3              2            3\n",
+       "14     B               4              2            3\n",
+       "15     B               5              2            3\n",
+       "16     B               6              2            3\n",
+       "17     B               7              2            3\n",
+       "18     B               8              2            3\n",
+       "19     B               9              2            3\n",
+       "20     B              10              2            3"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "se.species"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temperature_at_sea_level = 20\n",
+    "mg.at_node['land_surface_air__temperature'] = 0.006 * mg.at_node['topographic__elevation'] + temperature_at_sea_level\n",
+    "se.run_one_step(dt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>clade</th>\n",
+       "      <th>species_number</th>\n",
+       "      <th>time_appeared</th>\n",
+       "      <th>latest_time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>A</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>A</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>A</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>A</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6</td>\n",
+       "      <td>A</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7</td>\n",
+       "      <td>A</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>8</td>\n",
+       "      <td>A</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>9</td>\n",
+       "      <td>A</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>10</td>\n",
+       "      <td>B</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>11</td>\n",
+       "      <td>B</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>12</td>\n",
+       "      <td>B</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>13</td>\n",
+       "      <td>B</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>14</td>\n",
+       "      <td>B</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>15</td>\n",
+       "      <td>B</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>16</td>\n",
+       "      <td>B</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>17</td>\n",
+       "      <td>B</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>18</td>\n",
+       "      <td>B</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>19</td>\n",
+       "      <td>B</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>20</td>\n",
+       "      <td>B</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   clade  species_number  time_appeared  latest_time\n",
+       "0      A               0              0            1\n",
+       "1      A               1              2            3\n",
+       "2      A               2              2            3\n",
+       "3      A               3              2            3\n",
+       "4      A               4              2            3\n",
+       "5      A               5              2            3\n",
+       "6      A               6              2            3\n",
+       "7      A               7              2            3\n",
+       "8      A               8              2            3\n",
+       "9      A               9              2            3\n",
+       "10     B               0              0            0\n",
+       "11     B               1              1            1\n",
+       "12     B               2              2            3\n",
+       "13     B               3              2            3\n",
+       "14     B               4              2            3\n",
+       "15     B               5              2            3\n",
+       "16     B               6              2            3\n",
+       "17     B               7              2            3\n",
+       "18     B               8              2            3\n",
+       "19     B               9              2            3\n",
+       "20     B              10              2            3"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "se.species"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "16.0000072871 32.4040969001\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>clade</th>\n",
+       "      <th>species_number</th>\n",
+       "      <th>time_appeared</th>\n",
+       "      <th>latest_time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>A</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>A</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>A</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>A</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6</td>\n",
+       "      <td>A</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7</td>\n",
+       "      <td>A</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>8</td>\n",
+       "      <td>A</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>9</td>\n",
+       "      <td>A</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>10</td>\n",
+       "      <td>B</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>11</td>\n",
+       "      <td>B</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>12</td>\n",
+       "      <td>B</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>13</td>\n",
+       "      <td>B</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>14</td>\n",
+       "      <td>B</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>15</td>\n",
+       "      <td>B</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>16</td>\n",
+       "      <td>B</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>17</td>\n",
+       "      <td>B</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>18</td>\n",
+       "      <td>B</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>19</td>\n",
+       "      <td>B</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>20</td>\n",
+       "      <td>B</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   clade  species_number  time_appeared  latest_time\n",
+       "0      A               0              0            1\n",
+       "1      A               1              2            3\n",
+       "2      A               2              2            3\n",
+       "3      A               3              2            3\n",
+       "4      A               4              2            3\n",
+       "5      A               5              2            3\n",
+       "6      A               6              2            3\n",
+       "7      A               7              2            3\n",
+       "8      A               8              2            3\n",
+       "9      A               9              2            3\n",
+       "10     B               0              0            0\n",
+       "11     B               1              1            1\n",
+       "12     B               2              2            3\n",
+       "13     B               3              2            3\n",
+       "14     B               4              2            3\n",
+       "15     B               5              2            3\n",
+       "16     B               6              2            3\n",
+       "17     B               7              2            3\n",
+       "18     B               8              2            3\n",
+       "19     B               9              2            3\n",
+       "20     B              10              2            3"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temperature_at_sea_level = 16\n",
+    "mg.at_node['land_surface_air__temperature'] = 0.006 * mg.at_node['topographic__elevation'] + temperature_at_sea_level\n",
+    "se.run_one_step(dt)\n",
+    "print(mg.at_node['land_surface_air__temperature'].min(), mg.at_node['land_surface_air__temperature'].max())\n",
+    "se.species\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   time\n",
+       "0     0\n",
+       "1     1\n",
+       "2     2\n",
+       "3     3\n",
+       "4     4\n",
+       "5     5\n",
+       "6     6"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "se.record"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Click here for more <a href=\"https://github.com/landlab/landlab/wiki/Tutorials\">Landlab tutorials</a>"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/species_evolution/Modeling stream-based species.ipynb b/species_evolution/Modeling stream-based species.ipynb
new file mode 100644
index 0000000..a8cc2ba
--- /dev/null
+++ b/species_evolution/Modeling stream-based species.ipynb	
@@ -0,0 +1,714 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"http://landlab.github.io\"><img style=\"float: left\" src=\"https://raw.githubusercontent.com/landlab/tutorials/master/landlab_header.png\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling stream-based species\n",
+    "\n",
+    "<hr>\n",
+    "<small> For instructions on how to run an interactive iPython notebook, click here: <a href=\"https://github.com/landlab/tutorials/blob/master/README.md\">https://github.com/landlab/tutorials/blob/master/README.md</a></small><br>\n",
+    "<small>For more Landlab tutorials, click here: <a href=\"https://github.com/landlab/landlab/wiki/Tutorials\">https://github.com/landlab/landlab/wiki/Tutorials</a></small>\n",
+    "<hr>\n",
+    "\n",
+    "In this tutorial we will:\n",
+    "* Introduce species into a Landlab model.\n",
+    "* Set the zones in which the species operate.\n",
+    "* Evolve a landscape and the species over time.\n",
+    "* Explore SpeciesEvolver output data structures.\n",
+    "\n",
+    "The configuration of the model in this tutorial loosely follows the fault throw experiment in Lyons et al., in preperation for Earth Surface Dynamics.\n",
+    "\n",
+    "## Macroevolution processes built into species\n",
+    "\n",
+    "The evolution of the base species is driven by these processes:\n",
+    "* dispersal: geographic range expansion\n",
+    "* speciation: lineage splitting\n",
+    "* extinction: lineage termination"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Import modules."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/njlyons/anaconda3/lib/python3.7/site-packages/xarray/core/merge.py:17: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version\n",
+      "  PANDAS_TYPES = (pd.Series, pd.DataFrame, pd.Panel)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from copy import deepcopy\n",
+    "from landlab.components import (SpeciesEvolver, FastscapeEroder, FlowRouter,\n",
+    "                                LinearDiffuser)\n",
+    "from landlab.components.species_evolution import Species, StreamZoneManager\n",
+    "from landlab.io import read_esri_ascii\n",
+    "from landlab.plot import imshow_grid\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prepare the grid and components of the model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load a model grid that has steady state topography. The top and bottom boundaries are set to open."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "(mg, z) = read_esri_ascii('model_grid_steady_state_elevation.txt')\n",
+    "mg.at_node['topographic__elevation'] = z\n",
+    "mg.set_closed_boundaries_at_grid_edges(right_is_closed=True,\n",
+    "                                       top_is_closed=False,\n",
+    "                                       left_is_closed=True,\n",
+    "                                       bottom_is_closed=False)\n",
+    "\n",
+    "imshow_grid(mg, z, colorbar_label='elevation (m)', shrink=0.4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Initialize the components that will be run alongside SpeciesEvolver. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fr = FlowRouter(mg)\n",
+    "sp = FastscapeEroder(mg, K_sp=5e-5, m_sp=0.5, n_sp=1.0)\n",
+    "lf = LinearDiffuser(mg, linear_diffusivity=1e-3)\n",
+    "\n",
+    "fr.run_one_step()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Initialize SpeciesEvolver.\n",
+    "\n",
+    "SpeciesEvolver requires only one parameter, a model grid, for the default functionality.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'StreamZoneManager' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-5-b69f353104b5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mszm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mStreamZoneManager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_drainage_area\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# Instaniate SpeciesEvolver and get initial zones.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSpeciesEvolver\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzone_managers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mszm\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mzones\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzones_at_time\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'StreamZoneManager' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "szm = StreamZoneManager(mg, min_drainage_area=50000)\n",
+    "\n",
+    "# Instaniate SpeciesEvolver and get initial zones.\n",
+    "se = SpeciesEvolver(mg, zone_managers=[szm])\n",
+    "zones = se.zones_at_time(0)\n",
+    "\n",
+    "stream_mask = mg.at_node['drainage_area'] > 50000\n",
+    "imshow_grid(mg, stream_mask, allow_colorbar=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Populate each zone with a species."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for zone in zones:\n",
+    "    species = Species(zone)\n",
+    "    se.introduce_species(species)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'zones' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-6-8b28561dbdc8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mzones\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'zones' is not defined"
+     ]
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initiate a fault"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the faults. A block in the center of the grid uplifts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAACdCAYAAABFGFS5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAPy0lEQVR4nO3de4xc5XnH8e8vXjC5YptbXNsY01htTKWAa4EpvSBIbeMgTKUgmUaNS9xYamhF0kuKayk0lz8giQJCDRAr0BhEAdfQGrmk1DJU7T8x2OFqwPGCsb2AMMiGtEECDE//OM/as+uZPWY958zM7u8jjeac531n59l3Z+bZc5nzKiIwMzMbyYc6nYCZmXU/FwszMyvlYmFmZqVcLMzMrJSLhZmZlXKxMDOzUn2dTqAKE6QYk7+YmVmF3oHXI+KkZm1j8jO1D/hkp5MwM+sxu2FXq7YxWSze6XQCdtR2+cuiPWmm1OkUbJR2l7T7mIWZmZVysTAzs1IuFmZmVsrFwszMSrlYmJlZKRcLMzMr5WJhZmalXCzMzKyUi4WZmZVysTAzs1IuFmZmVsrFwszMSrlYmJlZKRcLMzMr5WJhZmalKi8WkiZIekzShlyfJWmzpB2S7pF0bMYn5np/tp/W8DNWZny7pIVV52xmZkPVsWVxFfBsw/p1wPURMRvYDyzP+HJgf0R8Crg++yFpDrAUOANYBNwkaUINeZuZWaq0WEiaDnwO+HGuC7gAWJdd1gCX5vKSXCfbL8z+S4C7I+LtiNgJ9ANnV5m3mZkNVfWWxQ3A14H3c/0E4I2IOJDrA8C0XJ4G7AHI9jez/8F4k8eYmVkNKisWki4G9kbE1sZwk65R0jbSYxqfb4WkLZK2fOBkzcxsRH0V/uzzgEskLQaOAz5BsaUxSVJfbj1MB17O/gPADGBAUh9wPLCvIT6o8TEHRcRqYDWApMOKiZmZjV5lWxYRsTIipkfEaRQHqB+KiC8ADwOfz27LgPW5fH+uk+0PRURkfGmeLTULmA08UlXeZmZ2uCq3LFr5O+BuSd8BHgNuzfitwB2S+im2KJYCRMQ2SWuBZ4ADwJUR8V79aZuZjV8q/nkfWyTFqZ1Owo7KrjH4uhwPZqrZIUbrBbuLu60RMa9Zu7/BbWZmpVwszMyslIuFmZmVcrEwM7NSLhZmZlbKxcLMzEq5WJiZWSkXCzMzK+ViYWZmpVwszMyslIuFmZmVcrEwM7NSLhZmZlaqypnyjpP0iKQnJG2T9M2Mz5K0WdIOSfdIOjbjE3O9P9tPa/hZKzO+XdLCqnI2M7PmqtyyeBu4ICI+A5wJLJI0H7gOuD4iZgP7geXZfzmwPyI+BVyf/ZA0h2JuizOARcBNkiZUmLeZmQ1T5Ux5ERH/l6vH5C2AC4B1GV8DXJrLS3KdbL9QkjJ+d0S8HRE7gX7g7KryNjOzw1V6zELSBEmPA3uBjcDzwBs5/zYU82tPy+VpwB6AbH8TOKEx3uQxZmZWg0qLRUS8FxFnAtMptgY+3axb3jebYitGiA8haYWkLZK2jDZfMzNrrpazoSLiDeC/gPnAJEmDc39PB17O5QFgBkC2H08xF/fBeJPHND7H6oiY12pKQDMzG70qz4Y6SdKkXP4w8FngWeBh4PPZbRmwPpfvz3Wy/aEoJgi/H1iaZ0vNAmYDj1SVt5mZHa6vvMuoTQXW5JlLHwLWRsQGSc8Ad0v6DvAYcGv2vxW4Q1I/xRbFUoCI2CZpLfAMcAC4MiLeqzBvMzMbRsU/72OLpDi100nYUdk1Bl+X48FMNTvEaL1gd3G3tdWufH+D28zMSrlYmJlZKRcLMzMr5WJhZmalWhYLSQ80XszPzMzGr5G2LH4C/KekVZKOqSkfMzPrQi2/ZxERayX9O/ANYIukO4D3G9p/UEN+ZmbWBcq+lPcu8CtgIvBxGoqFmZmNHy2LhaRFwA8oLrcxNyLeqi0rMzPrKiNtWawCLouIbXUlY2Zm3WmkYxa/V2ciZmbWvfw9CzMzK+ViYWZmpVwszMysVJWTH82Q9LCkZyVtk3RVxqdI2ihpR95Pzrgk3SipX9KTkuY2/Kxl2X+HpGWtntPMzKpR5ZbFAeCvI+LTFNOpXilpDnA1sCkiZgObch3gIopZ8GYDK4CboSguwDXAORTzeF8zWGDMzKwelRWLiHglIn6ey/9LMaXqNGAJsCa7rQEuzeUlwO1R+BnFXN1TgYXAxojYFxH7gY3AoqryNjOzw9VyzCIvSHgWsBk4JSJegaKgACdnt2nAnoaHDWSsVdzMzGpS5RzcAEj6GHAv8NWI+KVaT7vYrCFGiA9/nhUUu6/MzKzNKt2yyKvV3gvcGRH3ZfjV3L1E3u/N+AAwo+Hh04GXR4gPERGrI2Jeq/ljzcxs9Ko8G0rArcCzw65Qez8weEbTMmB9Q/yLeVbUfODN3E31ILBA0uQ8sL0gY2ZmVpMqd0OdB/wJ8JSkxzP298C1wFpJy4HdwGXZ9gCwGOgH3gKuAIiIfZK+DTya/b4VEfsqzNvMzIZRxGG7/3uepDi100nYUdk1Bl+X48HM1sckrcvtLu62ttqV729wm5lZKRcLMzMr5WJhZmalXCzMzKyUi4WZmZVysTAzs1IuFmZmVsrFwszMSrlYmJlZKRcLMzMr5WJhZmalXCzMzKyUi4WZmZWqcj6L2yTtlfR0Q2yKpI2SduT95IxL0o2S+iU9KWluw2OWZf8dkpY1ey4zM6tWlVsWPwEWDYtdDWyKiNnAplwHuAiYnbcVwM1QFBfgGuAc4GzgmsECY2Zm9amsWETEfwPDJylaAqzJ5TXApQ3x26PwM2BSTrm6ENgYEfsiYj+wkcMLkJmZVazuYxan5FSp5P3JGZ8G7GnoN5CxVnEzM6tRldOqfhDNpteKEeKH/wBpBcUuLDMza7O6tyxezd1L5P3ejA8AMxr6TQdeHiF+mIhYHRHzWk0JaGZmo1d3sbgfGDyjaRmwviH+xTwraj7wZu6mehBYIGlyHthekDEzM6tRZbuhJN0FnA+cKGmA4qyma4G1kpZTzA9+WXZ/AFgM9ANvAVcARMQ+Sd8GHs1+34qI4QfNzcysYopoegigp0mKUzudhB2VXWPwdTkezFSzw4zWC3YXd1tb7cr3N7jNzKyUi4WZmZVysTAzs1IuFmZmVsrFwszMSrlYmJlZKRcLMzMr5WJhZmalXCzMzKyUi4WZmZVysTAzs1IuFmZmVsrFwszMSvVMsZC0SNJ2Sf2Sru50PmZm40lPFAtJE4AfAhcBc4DLJc3pbFZmZuNHTxQL4GygPyJeiIh3gLuBJR3Oycxs3OiVYjEN2NOwPpAxMzOrQWXTqrZZs+m3hkylJmkFsAJgQh0ZWaU845pZvU7l4Gx5TfVKsRgAZjSsTwdebuwQEauB1QCSXtsNvwJery3D9jiR3sq51/KF3su51/IF51yHqvKd2aqhJ+bgltQH/AK4EHgJeBT444jYNsJjtrSaS7Zb9VrOvZYv9F7OvZYvOOc6dCLfntiyiIgDkv4CeJBiL9NtIxUKMzNrr54oFgAR8QDwQKfzMDMbj3rlbKjRWN3pBEah13LutXyh93LutXzBOdeh9nx74piFmZl11ljesjAzszYZc8Wim64hJWmGpIclPStpm6SrMj5F0kZJO/J+csYl6cbM/UlJcxt+1rLsv0PSsorzniDpMUkbcn2WpM353PdIOjbjE3O9P9tPa/gZKzO+XdLCivOdJGmdpOdyrM/tgTH+Wr4mnpZ0l6Tjum2cJd0maa+kpxtibRtXSb8t6al8zI3S0X25pkW+38vXxZOS/lXSpIa2pmPX6jOk1d+n3Tk3tP2NpJB0Yq53dowjYszcKM6Ueh44HTgWeAKY08F8pgJzc/njFKf/zgG+C1yd8auB63J5MfBTii8hzgc2Z3wK8ELeT87lyRXm/VfAPwMbcn0tsDSXbwH+PJe/AtySy0uBe3J5To79RGBW/k0mVJjvGuDPcvlYYFI3jzHF1Qd2Ah9uGN8/7bZxBn4fmAs83RBr27gCjwDn5mN+ClxUQb4LgL5cvq4h36ZjxwifIa3+Pu3OOeMzKM7+3AWc2A1jXMmbt1O3HJQHG9ZXAis7nVdDPuuBPwS2A1MzNhXYnss/Ai5v6L892y8HftQQH9KvzTlOBzYBFwAb8kX2esMb7uAY54v53Fzuy34aPu6N/SrI9xMUH7waFu/mMR68fM2UHLcNwMJuHGfgNIZ++LZlXLPtuYb4kH7tyndY2x8Bd+Zy07GjxWfISO+DKnIG1gGfAV7kULHo6BiPtd1QXXsNqdx1cBawGTglIl4ByPuTs1ur/Ov8vW4Avg68n+snAG9ExIEmz30wr2x/M/vXme/pwGvAP6nYdfZjSR+li8c4Il4Cvk9xdYVXKMZtK909zoPaNa7Tcnl4vEpfovjvmpK8msVHeh+0laRLgJci4olhTR0d47FWLEqvIdUJkj4G3At8NSJ+OVLXJrEYId5Wki4G9kbE1iPIaaS2Ov8OfRSb8TdHxFkUl3kZ6VhVx3PO/fxLKHZ//BrwUYrL77d6/o7nfAQ+aI615i5pFXAAuHMw9AHzqus9+BFgFfCNZs0tcqgl57FWLEqvIVU3ScdQFIo7I+K+DL8qaWq2TwX2ZrxV/nX9XucBl0h6keIy8BdQbGlMUnHJleHPfTCvbD8e2FdjvoM5DETE5lxfR1E8unWMAT4L7IyI1yLiXeA+4Hfo7nEe1K5xHcjl4fG2ywO+FwNfiNwfM4p8X6f136edfp3in4gn8n04Hfi5pE+OIuf2jnE79292+kbxX+YLOdiDB6fO6GA+Am4HbhgW/x5DDxJ+N5c/x9ADWI9kfArFfvnJedsJTKk49/M5dID7Xxh6YO8ruXwlQw+8rs3lMxh68PAFqj3A/T/Ab+TyP+T4du0YA+cA24CPZB5rgL/sxnHm8GMWbRtXimu8zefQwdfFFeS7CHgGOGlYv6ZjxwifIa3+Pu3OeVjbixw6ZtHRMa7kzdvJG8UZA7+gOKNhVYdz+V2Kzb4ngcfztphi/+cmYEfeD/5hRTEj4PPAU8C8hp/1JaA/b1fUkPv5HCoWp1OcVdGfb5iJGT8u1/uz/fSGx6/K32M7R3mWyxHkeiawJcf53/IN09VjDHwTeA54GrgjP7S6apyBuyiOqbxL8V/q8naOKzAvf//ngX9k2EkKbcq3n2J//uD775aysaPFZ0irv0+7cx7W/iKHikVHx9jf4DYzs1Jj7ZiFmZlVwMXCzMxKuViYmVkpFwszMyvlYmFmZqVcLMxqoOIKxDslTcn1ybk+s9O5mR0JFwuzGkTEHuBm4NoMXQusjohdncvK7Mj5exZmNclLv2wFbgO+DJwVEe90NiuzI9NX3sXM2iEi3pX0t8B/AAtcKKyXeDeUWb0uori8w291OhGzD8LFwqwmks6kmPxqPvC1wau3mvUCFwuzGuTcxzdTzGmym+Lqrd/vbFZmR87FwqweXwZ2R8TGXL8J+E1Jf9DBnMyOmM+GMjOzUt6yMDOzUi4WZmZWysXCzMxKuViYmVkpFwszMyvlYmFmZqVcLMzMrJSLhZmZlfp/dQv2e8OC4u0AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_of_fault_1 = mg.x_of_node > 5000\n",
+    "x_of_fault_2 = mg.x_of_node < 10000\n",
+    "core_mask = mg.node_is_core()\n",
+    "upthown_block_mask = np.all([core_mask, x_of_fault_1, x_of_fault_2], 0)\n",
+    "\n",
+    "imshow_grid(mg, upthown_block_mask, allow_colorbar=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Uplift the nodes of the upthrown block by 50 m."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "z[upthown_block_mask] += 100\n",
+    "imshow_grid(mg, z, colorbar_label='elevation (m)', shrink=0.4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model species evolution\n",
+    "\n",
+    "First, set model time step and uplift parameters.\n",
+    "\n",
+    "SpeciesEvolver runs every 10 timesteps of the other components."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dt = 1000\n",
+    "total_time = int(5e5)\n",
+    "nt = int(total_time // dt)\n",
+    "\n",
+    "dt_se = 10 * dt\n",
+    "\n",
+    "uplift_rate = 5e-3\n",
+    "uplift_per_step = uplift_rate * dt\n",
+    "\n",
+    "mg0 = deepcopy(mg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run model. The ```run_one_step``` function of SpeciesEvolver takes the new zones of the time step.\n",
+    "\n",
+    "This code block may take a few minutes to run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for time_i in range(nt):\n",
+    "    # Uniformly uplift.\n",
+    "    z[mg.core_nodes] += uplift_per_step\n",
+    "\n",
+    "    # Increment earth surface process components.\n",
+    "    fr.run_one_step()\n",
+    "    sp.run_one_step(dt)\n",
+    "    lf.run_one_step(dt)\n",
+    "\n",
+    "    elapsed_time = dt * (time_i + 1)\n",
+    "\n",
+    "    if elapsed_time % dt_se == 0:\n",
+    "        # Increment SpeciesEvolver.\n",
+    "        se.run_one_step(dt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "OrderedDict([('time',\n",
+       "              [0,\n",
+       "               1000,\n",
+       "               2000,\n",
+       "               3000,\n",
+       "               4000,\n",
+       "               5000,\n",
+       "               6000,\n",
+       "               7000,\n",
+       "               8000,\n",
+       "               9000,\n",
+       "               10000,\n",
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+       "               12000,\n",
+       "               13000,\n",
+       "               14000,\n",
+       "               15000,\n",
+       "               16000,\n",
+       "               17000,\n",
+       "               18000,\n",
+       "               19000,\n",
+       "               20000,\n",
+       "               21000,\n",
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+       "               28000,\n",
+       "               29000,\n",
+       "               30000,\n",
+       "               31000,\n",
+       "               32000,\n",
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+       "               37000,\n",
+       "               38000,\n",
+       "               39000,\n",
+       "               40000,\n",
+       "               41000,\n",
+       "               42000,\n",
+       "               43000,\n",
+       "               44000,\n",
+       "               45000,\n",
+       "               46000,\n",
+       "               47000,\n",
+       "               48000,\n",
+       "               49000,\n",
+       "               50000]),\n",
+       "             ('number_of_captures',\n",
+       "              [nan,\n",
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+       "               0,\n",
+       "               0]),\n",
+       "             ('area_captured_max',\n",
+       "              [nan,\n",
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+       "               0,\n",
+       "               0,\n",
+       "               187500.0,\n",
+       "               0,\n",
+       "               230000.0,\n",
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+       "               0,\n",
+       "               0,\n",
+       "               0,\n",
+       "               0,\n",
+       "               0,\n",
+       "               0,\n",
+       "               0,\n",
+       "               0,\n",
+       "               0,\n",
+       "               0,\n",
+       "               0,\n",
+       "               0,\n",
+       "               0,\n",
+       "               0]),\n",
+       "             ('area_captured_sum',\n",
+       "              [nan,\n",
+       "               0,\n",
+       "               0,\n",
+       "               0,\n",
+       "               0,\n",
+       "               235000.0,\n",
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+       "               282500.0,\n",
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+       "               0,\n",
+       "               0,\n",
+       "               0,\n",
+       "               0,\n",
+       "               0,\n",
+       "               0,\n",
+       "               0,\n",
+       "               0,\n",
+       "               0,\n",
+       "               0,\n",
+       "               0,\n",
+       "               0])])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "se.record"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "imshow_grid(mg, stream_mask, allow_colorbar=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "imshow_grid(mg, z, colorbar_label='elevation (m)', shrink=0.4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot difference of pre and post fault topography."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "A_c = 50000\n",
+    "\n",
+    "stream_change = np.zeros(mg.number_of_nodes)\n",
+    "\n",
+    "a0 = mg0.at_node['drainage_area']\n",
+    "stream_change[a0 > A_c] = 1\n",
+    "\n",
+    "a1 = mg.at_node['drainage_area']\n",
+    "stream_change[a1 > A_c] = 2\n",
+    "\n",
+    "stream_change[np.all([a0 > A_c, a1 > A_c], 0)] = 3\n",
+    "\n",
+    "cm = plt.cm.get_cmap('jet', 4)\n",
+    "imshow_grid(mg, stream_change, cmap=cm,\n",
+    "            allow_colorbar=False, plot_name='stream change')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot results\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.subplot(1, 2, 1)\n",
+    "imshow_grid(mg, 'topographic__elevation', colorbar_label='elevation (m)')\n",
+    "\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plot.imshow_zones(mg, zones)\n",
+    "\n",
+    "plot.number_of_species(se.record, se.species)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from landlab.components.species_macroevolution import plot\n",
+    "\n",
+    "plot.tree(se.species)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Click here for more <a href=\"https://github.com/landlab/landlab/wiki/Tutorials\">Landlab tutorials</a>"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/species_evolution/SpeciesEvolver_tutorial.ipynb b/species_evolution/SpeciesEvolver_tutorial.ipynb
deleted file mode 100644
index 75f7a7d..0000000
--- a/species_evolution/SpeciesEvolver_tutorial.ipynb
+++ /dev/null
@@ -1,1587 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a href=\"http://landlab.github.io\"><img style=\"float: left\" src=\"https://raw.githubusercontent.com/landlab/tutorials/master/landlab_header.png\"></a>\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Using the Landlab SpeciesEvolver component\n",
-    "\n",
-    "<hr>\n",
-    "<small> For instructions on how to run an interactive iPython notebook, click here: <a href=\"https://github.com/landlab/tutorials/blob/master/README.md\">https://github.com/landlab/tutorials/blob/master/README.md</a></small><br>\n",
-    "<small>For more Landlab tutorials, click here: <a href=\"https://github.com/landlab/landlab/wiki/Tutorials\">https://github.com/landlab/landlab/wiki/Tutorials</a></small>\n",
-    "<hr>\n",
-    "\n",
-    "In this tutorial we will:\n",
-    "* Introduce species into a Landlab model.\n",
-    "* Set the zones in which the species operate.\n",
-    "* Evolve a landscape and the species over time.\n",
-    "* Explore SpeciesEvolver output data structures.\n",
-    "\n",
-    "The configuration of the model in this tutorial loosely follows the fault throw experiment in Lyons et al., in preperation for Earth Surface Dynamics.\n",
-    "\n",
-    "## Macroevolution processes built into species\n",
-    "\n",
-    "The evolution of the base species is driven by these processes:\n",
-    "* dispersal: geographic range expansion\n",
-    "* speciation: lineage splitting\n",
-    "* extinction: lineage termination"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Import modules and set the environment to show plots inline in this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from landlab import CLOSED_BOUNDARY, FIXED_VALUE_BOUNDARY\n",
-    "from landlab.components import (SpeciesEvolver, FastscapeEroder, FlowRouter,\n",
-    "                                LinearDiffuser)\n",
-    "from landlab.components.species_macroevolution import plot, Species, Zone\n",
-    "from landlab.io import read_esri_ascii\n",
-    "from landlab.plot import imshow_grid\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Prepare the grid and components of the model."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Load a model grid that has steady state topography. The top and bottom boundaries are set to open."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "(mg, z) = read_esri_ascii('model_grid_steady_state_elevation.txt')\n",
-    "mg.at_node['topographic__elevation'] = z\n",
-    "mg.set_status_at_node_on_edges(right=CLOSED_BOUNDARY, top=FIXED_VALUE_BOUNDARY,\n",
-    "                               left=CLOSED_BOUNDARY,\n",
-    "                               bottom=FIXED_VALUE_BOUNDARY)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Initialize components. SpeciesEvolver requires only one parameter, a model grid."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fr = FlowRouter(mg)\n",
-    "sp = FastscapeEroder(mg, K_sp=10e-5, m_sp=0.5, n_sp=1.0)\n",
-    "lf = LinearDiffuser(mg, linear_diffusivity=0.0001)\n",
-    "se = SpeciesEvolver(mg)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Prepare SpeciesEvolver objects\n",
-    "\n",
-    "Species and zones are the two object types central to SpeciesEvolver.\n",
-    "\n",
-    "### Zones\n",
-    "\n",
-    "Zones are portions of a model grid. A Species object exists throughout a zone.\n",
-    "\n",
-    "At each timestep, the spatial intersection of the zones at the prior time and current time are identified and typed. For example, a zone in the prior time intersects two zones in the current time. This zone path is typed one-to-many.\n",
-    "\n",
-    "Macroevolutionary processes are carried out by the intersection of zones over time. The connectivity of zones in SpeciesEvolver are called 'paths', and the relationship of zones is described by the path type. Not all combinations of none, one, and many are considered at the time of this tutorial. All none-to-n and some many-to-n are not considered. \n",
-    "\n",
-    "<img style=\"float: left;\" src=\"images/zone__one_to_none.png\"> &nbsp; zone<sub>*t*</sub>: a zone in time, *t*.<br>\n",
-    "\n",
-    "<img style=\"float: left;\" src=\"images/zone__later_time.png\"> &nbsp; zone<sub>*t*+1</sub>: a zone in the time following *t*.\n",
-    "\n",
-    "<center>path type</center> | <center>graphical<br>represention</center> | <center>zone temporal connectivity</center> | <center>macroevolution implications</center>\n",
-    "--- | --- | --- | ---\n",
-    "one-to-none | ![](images/zone__one_to_none.png) | zone<sub>*t*</sub> does not intersect a zone in *t*+1. | The species in the zone of the earlier time will go extinct. \n",
-    "one-to-one | ![](images/zone__one_to_one.png) | zone<sub>*t*</sub> intersects zone<sub>*t*+1</sub>. | The species in the zone of the earlier time will relocate to the zone of the later time.\n",
-    "one-to-many | ![](images/zone__one_to_many.png) | zone<sub>*t*</sub> intersects multiple zones in *t*+1. | The species in the zone of the earlier time disperses across the multiple zones in the later time. The species in the zones of the later time are geographical disconnected, thus speciation occurs.\n",
-    "many-to-one | ![](images/zone__many_to_one.png) | Multiple zones in *t* intersect zone<sub>*t*+1</sub>. | Species density increases locally.\n",
-    "many-to-many | ![](images/zone__many_to_many.png) | Multiple zones in *t* intersect multiple zones in *t*+1. | Species distribution shifts. Speciation occurs where the zone of a species at *t* intersects more than one zone at *t*+1.\n",
-    "\n",
-    "### Species\n",
-    "\n",
-    "The model domain is populated with species by the user.\n",
-    "\n",
-    "The rules of macroevolution are programmed into species. Behind the scenes, zone paths are the conditions evaluated by the macroevolution rules. The outcome of the rules given the zone paths are described in the zone table above. For example, a one-to-many path:\n",
-    "\n",
-    "![](images/zone_path__one_to_many.png)\n",
-    "\n",
-    "The base species class \n",
-    "\n",
-    "Users may develop species with different behaviors and macroevolution rules.\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "Populate SpeciesEvolver zones with species.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fr.run_one_step()\n",
-    "\n",
-    "min_stream_area = 1e6\n",
-    "zones = Zone.get_zones_with_area_threshold(mg, min_stream_area)\n",
-    "for zone in zones:\n",
-    "    new_species = Species(0, zone)\n",
-    "    se.introduce_species(new_species, 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initiate a fault"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Define the fault."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "grid_width = mg.number_of_node_columns * mg.dx\n",
-    "x_of_fault = grid_width * 0.5\n",
-    "core_mask = mg.node_is_core()\n",
-    "upthown_nodes = mg.x_of_node >= x_of_fault\n",
-    "upthown_block_mask = np.all([core_mask, upthown_nodes], 0)\n",
-    "\n",
-    "imshow_grid(mg, 'topographic__elevation')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Uplift the nodes of the upthrown block by 20 m."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "z[upthown_block_mask] += 20"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Model species evolution\n",
-    "\n",
-    "First, set model time step and uplift parameters.\n",
-    "\n",
-    "SpeciesEvolver runs every 10 timesteps of the other components."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dt = 1000\n",
-    "total_time = int(1e5)\n",
-    "nt = int(total_time // dt)\n",
-    "\n",
-    "dt_se = 10 * dt\n",
-    "\n",
-    "uplift_rate = 7e-5\n",
-    "uplift_per_step = uplift_rate * dt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Run model. The ```run_one_step``` function of SpeciesEvolver takes the new zones of the time step.\n",
-    "\n",
-    "This code block may take a couple minutes to run."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for time_i in range(nt):\n",
-    "    # Uniformly uplift.\n",
-    "    z[mg.core_nodes] += uplift_per_step\n",
-    "\n",
-    "    # Increment earth surface process components.\n",
-    "    fr.run_one_step()\n",
-    "    sp.run_one_step(dt)\n",
-    "    lf.run_one_step(dt)\n",
-    "\n",
-    "    elapsed_time = dt * (time_i + 1)\n",
-    "\n",
-    "    if elapsed_time % dt_se == 0:\n",
-    "        # Run SpeciesEvolver with updated zones.\n",
-    "        zones = Zone.get_zones_with_area_threshold(mg, min_stream_area)\n",
-    "        se.run_one_step(elapsed_time, zones)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plot results\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.subplot(1, 2, 1)\n",
-    "imshow_grid(mg, 'topographic__elevation', colorbar_label='elevation (m)')\n",
-    "\n",
-    "plt.subplot(1, 2, 2)\n",
-    "plot.imshow_zones(mg, zones)\n",
-    "\n",
-    "plot.number_of_species(se.record, se.species)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Species identifiers and phylogenetic trees\n",
-    "\n",
-    "Species identifiers are automatically generated. They are stored as tuples in species objects. The first element of the identifier tuple is the clade name, and the second element is the species number. For example, the first species of clade A is 0, so the identifier is (A, 0) and the tenth species of this clade is (A, 9).\n",
-    "\n",
-    "### Clade name\n",
-    "\n",
-    "A clade is a group of species that share a common ancestor. Species may share multiple common ancestors, so clades can be defined differently in the same tree. The clade(s) of this example tree can be defined in multiple ways:\n",
-    "\n",
-    "![](images/clade_definitions.png)\n",
-    "\n",
-    "In SpeciesEvolver, clades are defined by the root node for the purpose of assigning species identifiers. The species at the root node is:\n",
-    "* the least recent common ancestor of all species in the tree, and\n",
-    "* typically introduced to a SpeciesEvolver model using the ```introduce_species``` function.\n",
-    "\n",
-    "The clades of the first 26 species are named alphabetically from A to Z. The alphabet is repeated for subsequent species following this schema:\n",
-    "\n",
-    "<center>clade sequence</center> | <center>clade name</center>\n",
-    "--- | ---\n",
-    "1–26 | A–Z\n",
-    "27–52 | AA–AZ\n",
-    "53–78 | BA–BZ\n",
-    "... | ...\n",
-    "703–728 | AAA–AAZ\n",
-    "729–754 | ABA–ABZ\n",
-    "... | ...\n",
-    "\n",
-    "### Species number\n",
-    "\n",
-    "Species are numbered sequently by clade beginning with 0.\n",
-    "\n",
-    "In the example tree below, species A0 produced 4 child species. Only one species, B0 exists in clade B because child species were not produced by B0.\n",
-    "\n",
-    "![](images/species_number.png)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "E ---------\n",
-      "  children number  subclades  x_max  x_min      y\n",
-      "0      NaN      4        2.0  100.0   20.0  0.500\n",
-      "1      NaN      3        2.0  100.0   20.0  1.000\n",
-      "2      NaN      1        0.0  100.0   10.0  1.500\n",
-      "3      NaN      2        0.0   10.0   10.0  0.750\n",
-      "4      NaN      0       -1.0    0.0    0.0  1.125\n",
-      "D ---------\n",
-      "  children number  subclades  x_max  x_min    y\n",
-      "0      NaN      0       -1.0  100.0    0.0  2.0\n",
-      "C ---------\n",
-      "  children number  subclades  x_max  x_min    y\n",
-      "0      NaN      0       -1.0  100.0    0.0  2.5\n",
-      "B ---------\n",
-      "  children number  subclades  x_max  x_min     y\n",
-      "0      NaN      4        0.0  100.0   10.0  3.00\n",
-      "1      NaN      3        0.0  100.0   10.0  3.50\n",
-      "2      NaN      2        0.0  100.0   10.0  4.00\n",
-      "3      NaN      1        0.0  100.0   10.0  4.50\n",
-      "4      NaN      0       -1.0    0.0    0.0  3.75\n",
-      "A ---------\n",
-      "  children number  subclades  x_max  x_min    y\n",
-      "0      NaN      0       -1.0  100.0    0.0  5.0\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from landlab.components.species_macroevolution import plot\n",
-    "\n",
-    "plot.tree(se.species)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## DataFrames\n",
-    "\n",
-    "Data are stored in Pandas DataFrames."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>clade</th>\n",
-       "      <th>species</th>\n",
-       "      <th>time_appeared</th>\n",
-       "      <th>latest_time</th>\n",
-       "      <th>subtype</th>\n",
-       "      <th>object</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>A</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>100000</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x1c19ad9860&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>B</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x1c19abfd30&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>C</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>100000</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x1c19ad9f28&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>D</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>100000</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x1c19ad9438&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>E</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x1c19ad96a0&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>B</td>\n",
-       "      <td>2</td>\n",
-       "      <td>10000</td>\n",
-       "      <td>100000</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x1c25163828&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>B</td>\n",
-       "      <td>3</td>\n",
-       "      <td>10000</td>\n",
-       "      <td>100000</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x1c25163048&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>B</td>\n",
-       "      <td>1</td>\n",
-       "      <td>10000</td>\n",
-       "      <td>100000</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x1c25163710&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>B</td>\n",
-       "      <td>4</td>\n",
-       "      <td>10000</td>\n",
-       "      <td>100000</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x1c25163940&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>E</td>\n",
-       "      <td>2</td>\n",
-       "      <td>10000</td>\n",
-       "      <td>10000</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x1c25163978&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>E</td>\n",
-       "      <td>1</td>\n",
-       "      <td>10000</td>\n",
-       "      <td>100000</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x1c25163fd0&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>E</td>\n",
-       "      <td>3</td>\n",
-       "      <td>20000</td>\n",
-       "      <td>100000</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x1c2517dd30&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>E</td>\n",
-       "      <td>4</td>\n",
-       "      <td>20000</td>\n",
-       "      <td>100000</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Species at 0x1c2517d240&gt;</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   clade species time_appeared latest_time subtype                     object\n",
-       "0      A       0             0      100000    base  <Species at 0x1c19ad9860>\n",
-       "1      B       0             0           0    base  <Species at 0x1c19abfd30>\n",
-       "2      C       0             0      100000    base  <Species at 0x1c19ad9f28>\n",
-       "3      D       0             0      100000    base  <Species at 0x1c19ad9438>\n",
-       "4      E       0             0           0    base  <Species at 0x1c19ad96a0>\n",
-       "5      B       2         10000      100000    base  <Species at 0x1c25163828>\n",
-       "6      B       3         10000      100000    base  <Species at 0x1c25163048>\n",
-       "7      B       1         10000      100000    base  <Species at 0x1c25163710>\n",
-       "8      B       4         10000      100000    base  <Species at 0x1c25163940>\n",
-       "9      E       2         10000       10000    base  <Species at 0x1c25163978>\n",
-       "10     E       1         10000      100000    base  <Species at 0x1c25163fd0>\n",
-       "11     E       3         20000      100000    base  <Species at 0x1c2517dd30>\n",
-       "12     E       4         20000      100000    base  <Species at 0x1c2517d240>"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "se.species"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>time_appeared</th>\n",
-       "      <th>latest_time</th>\n",
-       "      <th>subtype</th>\n",
-       "      <th>object</th>\n",
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-       "      <th>0</th>\n",
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-       "      <td>30000</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Zone at 0x1c19ad9978&gt;</td>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0</td>\n",
-       "      <td>100000</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Zone at 0x1c19ad9a58&gt;</td>\n",
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-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0</td>\n",
-       "      <td>100000</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Zone at 0x1c19ad9b38&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0</td>\n",
-       "      <td>100000</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Zone at 0x1c19ad9c18&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0</td>\n",
-       "      <td>100000</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Zone at 0x1c19ad9cf8&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>10000</td>\n",
-       "      <td>100000</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Zone at 0x1c2512f208&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>10000</td>\n",
-       "      <td>10000</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Zone at 0x1c2512f438&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>10000</td>\n",
-       "      <td>30000</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Zone at 0x1c2512f668&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>10000</td>\n",
-       "      <td>50000</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Zone at 0x1c2512f080&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>10000</td>\n",
-       "      <td>10000</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Zone at 0x1c2512f898&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>10000</td>\n",
-       "      <td>20000</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Zone at 0x1c2512f550&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>10000</td>\n",
-       "      <td>20000</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Zone at 0x1c2512f780&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>20000</td>\n",
-       "      <td>30000</td>\n",
-       "      <td>base</td>\n",
-       "      <td>&lt;Zone at 0x1c2515de80&gt;</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   time_appeared latest_time subtype                  object\n",
-       "0              0       30000    base  <Zone at 0x1c19ad9978>\n",
-       "1              0      100000    base  <Zone at 0x1c19ad9a58>\n",
-       "2              0      100000    base  <Zone at 0x1c19ad9b38>\n",
-       "3              0      100000    base  <Zone at 0x1c19ad9c18>\n",
-       "4              0      100000    base  <Zone at 0x1c19ad9cf8>\n",
-       "5          10000      100000    base  <Zone at 0x1c2512f208>\n",
-       "6          10000       10000    base  <Zone at 0x1c2512f438>\n",
-       "7          10000       30000    base  <Zone at 0x1c2512f668>\n",
-       "8          10000       50000    base  <Zone at 0x1c2512f080>\n",
-       "9          10000       10000    base  <Zone at 0x1c2512f898>\n",
-       "10         10000       20000    base  <Zone at 0x1c2512f550>\n",
-       "11         10000       20000    base  <Zone at 0x1c2512f780>\n",
-       "12         20000       30000    base  <Zone at 0x1c2515de80>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "se.zones"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>time</th>\n",
-       "      <th>origin</th>\n",
-       "      <th>destinations</th>\n",
-       "      <th>path_type</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>10000</td>\n",
-       "      <td>&lt;Zone at 0x1c19ad9978&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c19ad9978&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>10000</td>\n",
-       "      <td>&lt;Zone at 0x1c19ad9a58&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c2512f208&gt;, &lt;Zone at 0x1c19ad9a58...</td>\n",
-       "      <td>one-to-many</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>10000</td>\n",
-       "      <td>&lt;Zone at 0x1c19ad9b38&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c19ad9b38&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>10000</td>\n",
-       "      <td>&lt;Zone at 0x1c19ad9c18&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c19ad9c18&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>10000</td>\n",
-       "      <td>&lt;Zone at 0x1c19ad9cf8&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c2512f668&gt;, &lt;Zone at 0x1c19ad9cf8&gt;]</td>\n",
-       "      <td>one-to-many</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>10000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[&lt;Zone at 0x1c2512f080&gt;]</td>\n",
-       "      <td>one-to-many</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>10000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[&lt;Zone at 0x1c2512f208&gt;]</td>\n",
-       "      <td>one-to-many</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>10000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[&lt;Zone at 0x1c19ad9a58&gt;]</td>\n",
-       "      <td>one-to-many</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>10000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[&lt;Zone at 0x1c2512f438&gt;]</td>\n",
-       "      <td>one-to-many</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>10000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[&lt;Zone at 0x1c2512f550&gt;]</td>\n",
-       "      <td>one-to-many</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>10000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[&lt;Zone at 0x1c2512f668&gt;]</td>\n",
-       "      <td>one-to-many</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>10000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[&lt;Zone at 0x1c2512f780&gt;]</td>\n",
-       "      <td>one-to-many</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>10000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[&lt;Zone at 0x1c2512f898&gt;]</td>\n",
-       "      <td>one-to-many</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>10000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[&lt;Zone at 0x1c19ad9cf8&gt;]</td>\n",
-       "      <td>one-to-many</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>20000</td>\n",
-       "      <td>&lt;Zone at 0x1c19ad9978&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c19ad9978&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>20000</td>\n",
-       "      <td>&lt;Zone at 0x1c19ad9a58&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c19ad9a58&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>20000</td>\n",
-       "      <td>&lt;Zone at 0x1c19ad9b38&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c19ad9b38&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>20000</td>\n",
-       "      <td>&lt;Zone at 0x1c19ad9c18&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c19ad9c18&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>20000</td>\n",
-       "      <td>&lt;Zone at 0x1c19ad9cf8&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c19ad9cf8&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>20000</td>\n",
-       "      <td>&lt;Zone at 0x1c2512f208&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c2512f208&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>20000</td>\n",
-       "      <td>&lt;Zone at 0x1c2512f438&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c2512f550&gt;]</td>\n",
-       "      <td>many-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>20000</td>\n",
-       "      <td>&lt;Zone at 0x1c2512f668&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c2512f668&gt;, &lt;Zone at 0x1c2515de80&gt;]</td>\n",
-       "      <td>one-to-many</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>20000</td>\n",
-       "      <td>&lt;Zone at 0x1c2512f080&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c2512f080&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>20000</td>\n",
-       "      <td>&lt;Zone at 0x1c2512f898&gt;</td>\n",
-       "      <td>[]</td>\n",
-       "      <td>one-to-none</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>20000</td>\n",
-       "      <td>&lt;Zone at 0x1c2512f550&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c2512f550&gt;]</td>\n",
-       "      <td>many-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>20000</td>\n",
-       "      <td>&lt;Zone at 0x1c2512f780&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c2512f780&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>20000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[&lt;Zone at 0x1c2512f668&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>20000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[&lt;Zone at 0x1c2515de80&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>30000</td>\n",
-       "      <td>&lt;Zone at 0x1c19ad9978&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c19ad9978&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>30000</td>\n",
-       "      <td>&lt;Zone at 0x1c19ad9a58&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c19ad9a58&gt;]</td>\n",
-       "      <td>many-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50</th>\n",
-       "      <td>50000</td>\n",
-       "      <td>&lt;Zone at 0x1c19ad9c18&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c19ad9c18&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>51</th>\n",
-       "      <td>50000</td>\n",
-       "      <td>&lt;Zone at 0x1c19ad9cf8&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c19ad9cf8&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>52</th>\n",
-       "      <td>50000</td>\n",
-       "      <td>&lt;Zone at 0x1c2512f208&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c2512f208&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>53</th>\n",
-       "      <td>50000</td>\n",
-       "      <td>&lt;Zone at 0x1c2512f080&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c2512f080&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>54</th>\n",
-       "      <td>60000</td>\n",
-       "      <td>&lt;Zone at 0x1c19ad9a58&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c19ad9a58&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>55</th>\n",
-       "      <td>60000</td>\n",
-       "      <td>&lt;Zone at 0x1c19ad9b38&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c19ad9b38&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>56</th>\n",
-       "      <td>60000</td>\n",
-       "      <td>&lt;Zone at 0x1c19ad9c18&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c19ad9c18&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>57</th>\n",
-       "      <td>60000</td>\n",
-       "      <td>&lt;Zone at 0x1c19ad9cf8&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c19ad9cf8&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>58</th>\n",
-       "      <td>60000</td>\n",
-       "      <td>&lt;Zone at 0x1c2512f208&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c2512f208&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>59</th>\n",
-       "      <td>60000</td>\n",
-       "      <td>&lt;Zone at 0x1c2512f080&gt;</td>\n",
-       "      <td>[]</td>\n",
-       "      <td>one-to-none</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>60</th>\n",
-       "      <td>70000</td>\n",
-       "      <td>&lt;Zone at 0x1c19ad9a58&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c19ad9a58&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>61</th>\n",
-       "      <td>70000</td>\n",
-       "      <td>&lt;Zone at 0x1c19ad9b38&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c19ad9b38&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>62</th>\n",
-       "      <td>70000</td>\n",
-       "      <td>&lt;Zone at 0x1c19ad9c18&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c19ad9c18&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>63</th>\n",
-       "      <td>70000</td>\n",
-       "      <td>&lt;Zone at 0x1c19ad9cf8&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c19ad9cf8&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>64</th>\n",
-       "      <td>70000</td>\n",
-       "      <td>&lt;Zone at 0x1c2512f208&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c2512f208&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>65</th>\n",
-       "      <td>80000</td>\n",
-       "      <td>&lt;Zone at 0x1c19ad9a58&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c19ad9a58&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>66</th>\n",
-       "      <td>80000</td>\n",
-       "      <td>&lt;Zone at 0x1c19ad9b38&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c19ad9b38&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>67</th>\n",
-       "      <td>80000</td>\n",
-       "      <td>&lt;Zone at 0x1c19ad9c18&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c19ad9c18&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>68</th>\n",
-       "      <td>80000</td>\n",
-       "      <td>&lt;Zone at 0x1c19ad9cf8&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c19ad9cf8&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>69</th>\n",
-       "      <td>80000</td>\n",
-       "      <td>&lt;Zone at 0x1c2512f208&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c2512f208&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>70</th>\n",
-       "      <td>90000</td>\n",
-       "      <td>&lt;Zone at 0x1c19ad9a58&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c19ad9a58&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>71</th>\n",
-       "      <td>90000</td>\n",
-       "      <td>&lt;Zone at 0x1c19ad9b38&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c19ad9b38&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>72</th>\n",
-       "      <td>90000</td>\n",
-       "      <td>&lt;Zone at 0x1c19ad9c18&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c19ad9c18&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>73</th>\n",
-       "      <td>90000</td>\n",
-       "      <td>&lt;Zone at 0x1c19ad9cf8&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c19ad9cf8&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>74</th>\n",
-       "      <td>90000</td>\n",
-       "      <td>&lt;Zone at 0x1c2512f208&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c2512f208&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75</th>\n",
-       "      <td>100000</td>\n",
-       "      <td>&lt;Zone at 0x1c19ad9a58&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c19ad9a58&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>76</th>\n",
-       "      <td>100000</td>\n",
-       "      <td>&lt;Zone at 0x1c19ad9b38&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c19ad9b38&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>77</th>\n",
-       "      <td>100000</td>\n",
-       "      <td>&lt;Zone at 0x1c19ad9c18&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c19ad9c18&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>78</th>\n",
-       "      <td>100000</td>\n",
-       "      <td>&lt;Zone at 0x1c19ad9cf8&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c19ad9cf8&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>79</th>\n",
-       "      <td>100000</td>\n",
-       "      <td>&lt;Zone at 0x1c2512f208&gt;</td>\n",
-       "      <td>[&lt;Zone at 0x1c2512f208&gt;]</td>\n",
-       "      <td>one-to-one</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>80 rows × 4 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      time                  origin  \\\n",
-       "0    10000  <Zone at 0x1c19ad9978>   \n",
-       "1    10000  <Zone at 0x1c19ad9a58>   \n",
-       "2    10000  <Zone at 0x1c19ad9b38>   \n",
-       "3    10000  <Zone at 0x1c19ad9c18>   \n",
-       "4    10000  <Zone at 0x1c19ad9cf8>   \n",
-       "5    10000                     NaN   \n",
-       "6    10000                     NaN   \n",
-       "7    10000                     NaN   \n",
-       "8    10000                     NaN   \n",
-       "9    10000                     NaN   \n",
-       "10   10000                     NaN   \n",
-       "11   10000                     NaN   \n",
-       "12   10000                     NaN   \n",
-       "13   10000                     NaN   \n",
-       "14   20000  <Zone at 0x1c19ad9978>   \n",
-       "15   20000  <Zone at 0x1c19ad9a58>   \n",
-       "16   20000  <Zone at 0x1c19ad9b38>   \n",
-       "17   20000  <Zone at 0x1c19ad9c18>   \n",
-       "18   20000  <Zone at 0x1c19ad9cf8>   \n",
-       "19   20000  <Zone at 0x1c2512f208>   \n",
-       "20   20000  <Zone at 0x1c2512f438>   \n",
-       "21   20000  <Zone at 0x1c2512f668>   \n",
-       "22   20000  <Zone at 0x1c2512f080>   \n",
-       "23   20000  <Zone at 0x1c2512f898>   \n",
-       "24   20000  <Zone at 0x1c2512f550>   \n",
-       "25   20000  <Zone at 0x1c2512f780>   \n",
-       "26   20000                     NaN   \n",
-       "27   20000                     NaN   \n",
-       "28   30000  <Zone at 0x1c19ad9978>   \n",
-       "29   30000  <Zone at 0x1c19ad9a58>   \n",
-       "..     ...                     ...   \n",
-       "50   50000  <Zone at 0x1c19ad9c18>   \n",
-       "51   50000  <Zone at 0x1c19ad9cf8>   \n",
-       "52   50000  <Zone at 0x1c2512f208>   \n",
-       "53   50000  <Zone at 0x1c2512f080>   \n",
-       "54   60000  <Zone at 0x1c19ad9a58>   \n",
-       "55   60000  <Zone at 0x1c19ad9b38>   \n",
-       "56   60000  <Zone at 0x1c19ad9c18>   \n",
-       "57   60000  <Zone at 0x1c19ad9cf8>   \n",
-       "58   60000  <Zone at 0x1c2512f208>   \n",
-       "59   60000  <Zone at 0x1c2512f080>   \n",
-       "60   70000  <Zone at 0x1c19ad9a58>   \n",
-       "61   70000  <Zone at 0x1c19ad9b38>   \n",
-       "62   70000  <Zone at 0x1c19ad9c18>   \n",
-       "63   70000  <Zone at 0x1c19ad9cf8>   \n",
-       "64   70000  <Zone at 0x1c2512f208>   \n",
-       "65   80000  <Zone at 0x1c19ad9a58>   \n",
-       "66   80000  <Zone at 0x1c19ad9b38>   \n",
-       "67   80000  <Zone at 0x1c19ad9c18>   \n",
-       "68   80000  <Zone at 0x1c19ad9cf8>   \n",
-       "69   80000  <Zone at 0x1c2512f208>   \n",
-       "70   90000  <Zone at 0x1c19ad9a58>   \n",
-       "71   90000  <Zone at 0x1c19ad9b38>   \n",
-       "72   90000  <Zone at 0x1c19ad9c18>   \n",
-       "73   90000  <Zone at 0x1c19ad9cf8>   \n",
-       "74   90000  <Zone at 0x1c2512f208>   \n",
-       "75  100000  <Zone at 0x1c19ad9a58>   \n",
-       "76  100000  <Zone at 0x1c19ad9b38>   \n",
-       "77  100000  <Zone at 0x1c19ad9c18>   \n",
-       "78  100000  <Zone at 0x1c19ad9cf8>   \n",
-       "79  100000  <Zone at 0x1c2512f208>   \n",
-       "\n",
-       "                                         destinations    path_type  \n",
-       "0                            [<Zone at 0x1c19ad9978>]   one-to-one  \n",
-       "1   [<Zone at 0x1c2512f208>, <Zone at 0x1c19ad9a58...  one-to-many  \n",
-       "2                            [<Zone at 0x1c19ad9b38>]   one-to-one  \n",
-       "3                            [<Zone at 0x1c19ad9c18>]   one-to-one  \n",
-       "4    [<Zone at 0x1c2512f668>, <Zone at 0x1c19ad9cf8>]  one-to-many  \n",
-       "5                            [<Zone at 0x1c2512f080>]  one-to-many  \n",
-       "6                            [<Zone at 0x1c2512f208>]  one-to-many  \n",
-       "7                            [<Zone at 0x1c19ad9a58>]  one-to-many  \n",
-       "8                            [<Zone at 0x1c2512f438>]  one-to-many  \n",
-       "9                            [<Zone at 0x1c2512f550>]  one-to-many  \n",
-       "10                           [<Zone at 0x1c2512f668>]  one-to-many  \n",
-       "11                           [<Zone at 0x1c2512f780>]  one-to-many  \n",
-       "12                           [<Zone at 0x1c2512f898>]  one-to-many  \n",
-       "13                           [<Zone at 0x1c19ad9cf8>]  one-to-many  \n",
-       "14                           [<Zone at 0x1c19ad9978>]   one-to-one  \n",
-       "15                           [<Zone at 0x1c19ad9a58>]   one-to-one  \n",
-       "16                           [<Zone at 0x1c19ad9b38>]   one-to-one  \n",
-       "17                           [<Zone at 0x1c19ad9c18>]   one-to-one  \n",
-       "18                           [<Zone at 0x1c19ad9cf8>]   one-to-one  \n",
-       "19                           [<Zone at 0x1c2512f208>]   one-to-one  \n",
-       "20                           [<Zone at 0x1c2512f550>]  many-to-one  \n",
-       "21   [<Zone at 0x1c2512f668>, <Zone at 0x1c2515de80>]  one-to-many  \n",
-       "22                           [<Zone at 0x1c2512f080>]   one-to-one  \n",
-       "23                                                 []  one-to-none  \n",
-       "24                           [<Zone at 0x1c2512f550>]  many-to-one  \n",
-       "25                           [<Zone at 0x1c2512f780>]   one-to-one  \n",
-       "26                           [<Zone at 0x1c2512f668>]   one-to-one  \n",
-       "27                           [<Zone at 0x1c2515de80>]   one-to-one  \n",
-       "28                           [<Zone at 0x1c19ad9978>]   one-to-one  \n",
-       "29                           [<Zone at 0x1c19ad9a58>]  many-to-one  \n",
-       "..                                                ...          ...  \n",
-       "50                           [<Zone at 0x1c19ad9c18>]   one-to-one  \n",
-       "51                           [<Zone at 0x1c19ad9cf8>]   one-to-one  \n",
-       "52                           [<Zone at 0x1c2512f208>]   one-to-one  \n",
-       "53                           [<Zone at 0x1c2512f080>]   one-to-one  \n",
-       "54                           [<Zone at 0x1c19ad9a58>]   one-to-one  \n",
-       "55                           [<Zone at 0x1c19ad9b38>]   one-to-one  \n",
-       "56                           [<Zone at 0x1c19ad9c18>]   one-to-one  \n",
-       "57                           [<Zone at 0x1c19ad9cf8>]   one-to-one  \n",
-       "58                           [<Zone at 0x1c2512f208>]   one-to-one  \n",
-       "59                                                 []  one-to-none  \n",
-       "60                           [<Zone at 0x1c19ad9a58>]   one-to-one  \n",
-       "61                           [<Zone at 0x1c19ad9b38>]   one-to-one  \n",
-       "62                           [<Zone at 0x1c19ad9c18>]   one-to-one  \n",
-       "63                           [<Zone at 0x1c19ad9cf8>]   one-to-one  \n",
-       "64                           [<Zone at 0x1c2512f208>]   one-to-one  \n",
-       "65                           [<Zone at 0x1c19ad9a58>]   one-to-one  \n",
-       "66                           [<Zone at 0x1c19ad9b38>]   one-to-one  \n",
-       "67                           [<Zone at 0x1c19ad9c18>]   one-to-one  \n",
-       "68                           [<Zone at 0x1c19ad9cf8>]   one-to-one  \n",
-       "69                           [<Zone at 0x1c2512f208>]   one-to-one  \n",
-       "70                           [<Zone at 0x1c19ad9a58>]   one-to-one  \n",
-       "71                           [<Zone at 0x1c19ad9b38>]   one-to-one  \n",
-       "72                           [<Zone at 0x1c19ad9c18>]   one-to-one  \n",
-       "73                           [<Zone at 0x1c19ad9cf8>]   one-to-one  \n",
-       "74                           [<Zone at 0x1c2512f208>]   one-to-one  \n",
-       "75                           [<Zone at 0x1c19ad9a58>]   one-to-one  \n",
-       "76                           [<Zone at 0x1c19ad9b38>]   one-to-one  \n",
-       "77                           [<Zone at 0x1c19ad9c18>]   one-to-one  \n",
-       "78                           [<Zone at 0x1c19ad9cf8>]   one-to-one  \n",
-       "79                           [<Zone at 0x1c2512f208>]   one-to-one  \n",
-       "\n",
-       "[80 rows x 4 columns]"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "se.zone_paths"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Record DataFrames\n",
-    "\n",
-    "Instances of SpeciesEvolver, species, and zones each have DataFrames called ```record```. Rows are data stored at each time the model is ran.\n",
-    "\n",
-    "Examine the SpeciesEvolver record."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>time</th>\n",
-       "      <th>number_of_captures</th>\n",
-       "      <th>sum_of_area_captured</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>10000.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>240000.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>20000.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>80000.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>30000.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>40000.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>50000.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>60000.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>70000.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>80000.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>90000.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>100000.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        time  number_of_captures  sum_of_area_captured\n",
-       "0        0.0                 NaN                   NaN\n",
-       "1    10000.0                 6.0              240000.0\n",
-       "2    20000.0                 2.0               80000.0\n",
-       "3    30000.0                 0.0                   0.0\n",
-       "4    40000.0                 0.0                   0.0\n",
-       "5    50000.0                 0.0                   0.0\n",
-       "6    60000.0                 0.0                   0.0\n",
-       "7    70000.0                 0.0                   0.0\n",
-       "8    80000.0                 0.0                   0.0\n",
-       "9    90000.0                 0.0                   0.0\n",
-       "10  100000.0                 0.0                   0.0"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "se.record"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Examine the record of a species."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'be' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-15-10af7c02b047>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspecies\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecord\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'be' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "s = be.species.iloc[0].object\n",
-    "s.record"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Examine the record of a zone."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "z = be.zones.iloc[0].object\n",
-    "z.record"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Click here for more <a href=\"https://github.com/landlab/landlab/wiki/Tutorials\">Landlab tutorials</a>"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/species_evolution/images/clade_definitions.png b/species_evolution/images/clade_definitions.png
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diff --git a/species_evolution/model_grid_steady_state_elevation.txt b/species_evolution/model_grid_steady_state_elevation.txt
index 5ced41f..10c609c 100644
--- a/species_evolution/model_grid_steady_state_elevation.txt
+++ b/species_evolution/model_grid_steady_state_elevation.txt
@@ -1,105 +1,105 @@
-ncols 20
+ncols 300
 nrows 100
 xllcorner 0.0
 yllcorner 0.0
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diff --git a/species_evolution/model_grid_steady_state_elevation_low_relief.txt b/species_evolution/model_grid_steady_state_elevation_low_relief.txt
new file mode 100644
index 0000000..9dc2673
--- /dev/null
+++ b/species_evolution/model_grid_steady_state_elevation_low_relief.txt
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diff --git a/species_evolution/untitled.txt b/species_evolution/untitled.txt
new file mode 100644
index 0000000..e69de29

From d75416cc67f83f02f8ffe08c987be04327b2cce6 Mon Sep 17 00:00:00 2001
From: nathanlyons <nathan.j.lyons@gmail.com>
Date: Thu, 7 Nov 2019 11:14:10 -0500
Subject: [PATCH 12/15] plot zones by color

---
 species_evolution/Customize_ZoneSpecies.ipynb |   52 +-
 .../Introduction_to_SpeciesEvolver.ipynb      | 1911 ++++++++---------
 .../Modeling stream-based species.ipynb       |    2 +-
 species_evolution/untitled.txt                |    0
 4 files changed, 876 insertions(+), 1089 deletions(-)
 delete mode 100644 species_evolution/untitled.txt

diff --git a/species_evolution/Customize_ZoneSpecies.ipynb b/species_evolution/Customize_ZoneSpecies.ipynb
index 90963a8..01f509d 100644
--- a/species_evolution/Customize_ZoneSpecies.ipynb
+++ b/species_evolution/Customize_ZoneSpecies.ipynb
@@ -28,10 +28,35 @@
     "\n",
     "## Macroevolution processes built into species\n",
     "\n",
-    "The evolution of the base species is driven by these processes:\n",
-    "* dispersal: geographic range expansion\n",
-    "* speciation: lineage splitting\n",
-    "* extinction: lineage termination"
+    "The evolution of the base species is driven by three processes.\n",
+    "\n",
+    "### Dispersal: geographic range expansion\n",
+    "\n",
+    "ZoneSpecies has two dispersal methods.\n",
+    "\n",
+    "`_init_dispersal`: this method determines where species can redistribute given the conditions of the zone_func. Consider that `_init_dispersal` figures out where species can go, barring any conflicting conditions with other species. This method is more complicated, not designed to be modified as often\n",
+    "\n",
+    "`_evaluate_dispersal`: the default of this method is that the dispersal of the species determined by `_init_dispersal` is \n",
+    "\n",
+    "### Speciation: lineage splitting\n",
+    "\n",
+    "### Extinction: lineage termination\n",
+    "\n",
+    "\n",
+    "### Evaluation order\n",
+    "\n",
+    "The potential dispersal of all species are first determined. Then the dispersal, speciation, and extinction of each species is evaluated in sequence.\n",
+    "\n",
+    "These processes are run:\n",
+    "```python\n",
+    "for a_species in all_species:\n",
+    "    a_species._init_dispersal()\n",
+    " \n",
+    "for a_species in all_species:\n",
+    "    a_species._evaluate_dispersal()\n",
+    "    a_species._evaluate_speciation() \n",
+    "    a_species._evaluate_extinction() \n",
+    "```"
    ]
   },
   {
@@ -45,16 +70,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/njlyons/anaconda3/lib/python3.7/site-packages/xarray/core/merge.py:17: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version\n",
-      "  PANDAS_TYPES = (pd.Series, pd.DataFrame, pd.Panel)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from landlab.components import SpeciesEvolver\n",
     "from landlab.components.species_evolution import ZoneSpeciesController, ZoneSpecies\n",
@@ -83,12 +99,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -100,7 +116,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -112,7 +128,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
diff --git a/species_evolution/Introduction_to_SpeciesEvolver.ipynb b/species_evolution/Introduction_to_SpeciesEvolver.ipynb
index a685eca..a362d8a 100644
--- a/species_evolution/Introduction_to_SpeciesEvolver.ipynb
+++ b/species_evolution/Introduction_to_SpeciesEvolver.ipynb
@@ -15,19 +15,44 @@
     "# Introduction to the Landlab SpeciesEvolver component\n",
     "\n",
     "<hr>\n",
-    "<small> For instructions on how to run an interactive iPython notebook, click here: <a href=\"https://github.com/landlab/tutorials/blob/master/README.md\">https://github.com/landlab/tutorials/blob/master/README.md</a></small><br>\n",
+    "<small> For instructions on how to run an interactive Jupyter notebook, click here: <a href=\"https://github.com/landlab/tutorials/blob/master/README.md\">https://github.com/landlab/tutorials/blob/master/README.md</a></small><br>\n",
     "<small>For more Landlab tutorials, click here: <a href=\"https://github.com/landlab/landlab/wiki/Tutorials\">https://github.com/landlab/landlab/wiki/Tutorials</a></small>\n",
     "<hr>\n",
     "\n",
-    "*After completing this tutorial, you will know how to use* SpeciesEvolver *with the default parameters and implementation.*\n",
+    "This tutorial provides an introduction to SpeciesEvolver. We will build and run a model that exemplifies SpeciesEvolver functionality. You will have the opportunity to\n",
+    "* Populate a model grid with species. \n",
+    "* View the SpeciesEvolver record that stores model variables over time.\n",
+    "* See how species are assigned identifiers.\n",
+    "* Plot some model output."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example model: climate change impacts on species distribution and evolution\n",
     "\n",
-    "## Tutorial example: simulate the response of zone-based species to climate change\n",
+    "Here we explore the large spatiotemporal scale response of zone-based species to climate change.\n",
     "\n",
-    "We will simulate species response to climate change using `ZoneSpecies`. This species type relies on `Zone` objects to evaluate evolutionary processes. The general workflow we will conduct is\n",
+    "Ecosystems are zonated by elevation.\n",
     "\n",
-    "* Define a function that demarcates the combined extent of all zones. \n",
-    "* Populate each distinct zone with a species.\n",
-    "* Simulate a time step where surface temperature has changed and species respond to this change."
+    "In this model we\n",
+    "\n",
+    "* Set surface air temperture across a model grid. Temperature will be a function of elevation.\n",
+    "* Populate species onto the grid. These species exist within a fixed elevation interval.\n",
+    "* Simulate a time step where surface temperature has changed and species respond to this change.\n",
+    "\n",
+    "The model progresses in terms of time steps with unprescribed duration.\n",
+    "\n",
+    "Use `ZoneSpecies`. This species type relies on `Zone` objects to evaluate evolutionary processes.\n",
+    "\n",
+    "\n",
+    "The type of species that will be simulated is evolved by these processes:\n",
+    "* dispersal: geographic range expansion\n",
+    "* speciation: lineage splitting\n",
+    "* extinction: lineage termination\n",
+    "\n",
+    "First we need to create a grid and populate it with the initial species."
    ]
   },
   {
@@ -36,28 +61,19 @@
    "source": [
     "### Import modules\n",
     "\n",
-    "Individual and groups of species are managed by a `SpeciesController`.\n",
+    "Individual and multiple species are managed by a `SpeciesController`.\n",
     "\n",
-    "ZoneSpecies are managed by `ZoneSpeciesController`, so we must import this class along with the SpeciesEvolver component and the other modules used in this notebook"
+    "ZoneSpecies are managed by `ZoneController`, so we must import this class along with the SpeciesEvolver component and the other modules used in this notebook."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/njlyons/anaconda3/lib/python3.7/site-packages/xarray/core/merge.py:17: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version\n",
-      "  PANDAS_TYPES = (pd.Series, pd.DataFrame, pd.Panel)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from landlab.components import SpeciesEvolver\n",
-    "from landlab.components.species_evolution import ZoneSpeciesController\n",
+    "from landlab.components.species_evolution import ZoneController\n",
     "from landlab.io import read_esri_ascii\n",
     "from landlab.plot import imshow_grid\n",
     "import matplotlib.pyplot as plt\n",
@@ -68,14 +84,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Prepare the grid"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Load and plot the initial topography. The top and bottom boundaries are set to open. The initial topography was previously created using a model built with Landlab. The model was run until topography reached steady state."
+    "### Prepare the grid\n",
+    "\n",
+    "In this tutorial, the topography of the model grid is loaded from a file. The topography was previously developed using a model built with Landlab. An initial surface was uplifted, stream erosion was conducted using the FastscapeEroder component, and hillslope diffusion was conducted using the LinearDiffuser component. The model was run until topography reached steady state."
    ]
   },
   {
@@ -99,13 +110,17 @@
     }
    ],
    "source": [
+    "# Create a model grid and set a topographic elevation field.\n",
     "(mg, z) = read_esri_ascii('model_grid_steady_state_elevation.txt')\n",
     "mg.at_node['topographic__elevation'] = z\n",
+    "\n",
+    "# Set the top and bottom grid boundaries to open. \n",
     "mg.set_closed_boundaries_at_grid_edges(right_is_closed=True,\n",
     "                                       top_is_closed=False,\n",
     "                                       left_is_closed=True,\n",
     "                                       bottom_is_closed=False)\n",
     "\n",
+    "# Plot the elevation field.\n",
     "imshow_grid(mg, 'topographic__elevation', colorbar_label='elevation (m)', shrink=0.4)"
    ]
   },
@@ -113,9 +128,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Create and plot a grid field of the air temperature at the land surface\n",
+    "### Create a grid field of air temperature at the land surface\n",
+    "\n",
+    "We set a surface air temperature at base level (upper and lower boundaries). We use a lapse rate to create a synthetic surface air temperature field at all nodes of the grid. The lapse rate, *L* is modeled as the decrease of temperature, *T* with increasing elevation, *z* as\n",
+    "\n",
+    "\\begin{align*}\n",
+    "L = -\\frac{\\delta t}{\\delta z}\n",
+    "\\end{align*}\n",
     "\n",
-    "We use a lapse rate of 5&deg;C / km."
+    "We use a lapse rate of -6&deg;C / km."
    ]
   },
   {
@@ -123,13 +144,6 @@
    "execution_count": 3,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "9.59590309985 25.9999927129\n"
-     ]
-    },
     {
      "data": {
       "image/png": 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\n",
@@ -144,12 +158,13 @@
     }
    ],
    "source": [
-    "lapse = -0.006\n",
+    "# Set a rate of temperature decrease per meter of elevation increase.\n",
+    "lapse_rate = -0.006\n",
     "\n",
-    "# Set surface temperature at sea level.\n",
-    "temperature_at_sea_level = 26\n",
-    "mg.at_node['land_surface_air__temperature'] = lapse * mg.at_node['topographic__elevation'] + temperature_at_sea_level\n",
-    "print(mg.at_node['land_surface_air__temperature'].min(), mg.at_node['land_surface_air__temperature'].max())\n",
+    "# Create a surface temperature field.\n",
+    "temperature_at_base_level = 26\n",
+    "T = lapse_rate * z + temperature_at_base_level\n",
+    "mg.add_field('node', 'land_surface_air__temperature', T)\n",
     "\n",
     "# Plot temperature field.\n",
     "imshow_grid(mg, 'land_surface_air__temperature',\n",
@@ -157,6 +172,68 @@
     "            shrink=0.4, cmap='bwr')"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create a zone function\n",
+    "\n",
+    "Zones are portions of a model grid. A `ZoneSpecies` object exists within a zone. `Zone` objects are explained in more detail later in this tutorial.\n",
+    "\n",
+    "The general workflow we will conduct is\n",
+    "\n",
+    "* Define a function that demarcates the combined extent of all zones. \n",
+    "* Populate each distinct zone with a species.\n",
+    "\n",
+    "The zone function must return a numpy array with the length of the grid's `number_of_nodes`. True values indicate where zones should be created. \n",
+    "\n",
+    "Here, we indicate zones should be created where the air temperature is between 15&deg;C and 25&deg;C, inclusive. The temperature of all nodes is within this interval, therefore all elements of the mask are `True`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def zone_func(grid):\n",
+    "    \"\"\"Get a mask of the total zone extent.\"\"\"\n",
+    "    T = grid.at_node['land_surface_air__temperature']\n",
+    "    T_max = 25\n",
+    "    T_min = 15\n",
+    "    zone_extent_mask = np.all([T >= T_min, T <= T_max], 0)\n",
+    "    return zone_extent_mask"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`True` values are shaded white in the plot below"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "imshow_grid(mg, zone_func(mg), allow_colorbar=False, plot_name='zone extent in white')"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -168,7 +245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -184,7 +261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -209,10 +286,12 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>time</th>\n",
+       "      <th>species_count</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
+       "      <th>0</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
@@ -221,11 +300,11 @@
        "</div>"
       ],
       "text/plain": [
-       "   time\n",
-       "0     0"
+       "   time  species_count\n",
+       "0     0              0"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -238,45 +317,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Create a zone function\n",
-    "\n",
-    "Zones are portions of a model grid. A `ZoneSpecies` object exists within a zone. `Zone` objects are explained in more detail later in this tutorial.\n",
+    "### Instantiate a SpeciesController\n",
     "\n",
-    "The zone function must return a numpy array with the length of the grid's `number_of_nodes`. True values indicate where zones should be created. \n",
+    "Next, we instantiate a ZoneController.\n",
     "\n",
-    "Here, we indicate zones should be created where the air temperature is between 15&deg;C and 25&deg;C, inclusive. The temperature of all nodes is within this interval, therefore all elements of the mask are `True`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def zone_func(grid, **kwargs):\n",
-    "    \"\"\"Get a mask of the total zone extent.\"\"\"\n",
-    "    T = grid.at_node['land_surface_air__temperature']\n",
-    "    T_max = 25\n",
-    "    T_min = 15\n",
-    "    zone_extent_mask = np.all([T >= T_min, T <= T_max], 0)\n",
-    "    return zone_extent_mask"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`True` values are shaded white in the plot below"
+    "Two distinct areas of the mask are now seperated by the higher elevations in the center of the grid."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -288,62 +343,28 @@
     }
    ],
    "source": [
-    "imshow_grid(mg, zone_func(mg), allow_colorbar=False)"
+    "sc = ZoneController(mg, zone_func, minimum_area=50000)\n",
+    "\n",
+    "# Plot zones.\n",
+    "zone_map = np.zeros(mg.number_of_nodes)\n",
+    "for i, zone in enumerate(sc.zones):\n",
+    "    zone_map[zone.mask] = i + 1\n",
+    "    \n",
+    "cmap = plt.cm.tab20\n",
+    "cmap.set_under('w')\n",
+    "title = 'zone count = {}'.format(len(sc.zones))\n",
+    "imshow_grid(mg, zone_map, cmap=cmap, allow_colorbar=False, plot_name=title, vmin=1)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Create a species that exists in the zone\n",
-    "\n",
-    "The initial zone is populated by instantiating a species with the zone as its parameter. The species must then be introduced to SpeciesEvolver in order for the species to be tracked and to evolve over time.\n",
+    "### Seed zones with species\n",
     "\n",
-    "The type of species that will be simulated is evolved by these processes:\n",
-    "* dispersal: geographic range expansion\n",
-    "* speciation: lineage splitting\n",
-    "* extinction: lineage termination\n",
-    "\n",
-    "Also below, we print all the species that exist in SpeciesEvolver. Only the one species exists; the species we introduced. Further below, the `clade` and `species_number` identifier elements will be explained."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sc = ZoneSpeciesController(se, zone_func, minimum_area=50000, grid=mg)\n",
+    "One species is populated to each of the two initial zones using the ZoneController method, `populate_zones_uniformly`. This method creates the species and introduces it to SpeciesEvolver.\n",
     "\n",
-    "# Plot zones.\n",
-    "for zone in sc.zones:\n",
-    "    plt.figure()\n",
-    "    imshow_grid(mg, zone.mask, allow_colorbar=False, plot_name='zone in white')"
+    "The SpeciesEvolver attribute, `species` provides data about all of the introduced species. The data is presented in a Pandas DataFrame. The `clade` and `species_number` identifier elements will be explained later in the tutorial."
    ]
   },
   {
@@ -373,21 +394,21 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>clade</th>\n",
-       "      <th>species_number</th>\n",
+       "      <th>number</th>\n",
        "      <th>time_appeared</th>\n",
        "      <th>latest_time</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>0</td>\n",
+       "      <th>0</th>\n",
        "      <td>A</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>1</td>\n",
+       "      <th>1</th>\n",
        "      <td>B</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -398,9 +419,9 @@
        "</div>"
       ],
       "text/plain": [
-       "  clade  species_number  time_appeared  latest_time\n",
-       "0     A               0              0            0\n",
-       "1     B               0              0            0"
+       "  clade  number  time_appeared  latest_time\n",
+       "0     A       0              0            0\n",
+       "1     B       0              0            0"
       ]
      },
      "execution_count": 9,
@@ -409,9 +430,11 @@
     }
    ],
    "source": [
-    "sc.populate_each_zone(1)\n",
+    "species = sc.populate_zones_uniformly(1)\n",
     "\n",
-    "# Print the species data frame.\n",
+    "se.introduce_species(species)\n",
+    "\n",
+    "# Display the species data frame.\n",
     "se.species"
    ]
   },
@@ -419,15 +442,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Simulate the effect of changing zone definition on species\n",
-    "\n",
-    "< about climate elevation gradient >\n",
+    "### Simulate a temperature increase and its impact on species distribution\n",
     "\n",
-    "For example, ecosystems are zonated by elevation.\n",
+    "The temperature at base level is increased by 6&deg;C.\n",
     "\n",
-    "If temperature decreased by 4&deg;C at sea level (the elevation at the upper and lower grid bounderies), then the 800 m zone maximum would lower by about 409 m assuming a lapse rate of 9.8&deg;C / 1000 m.\n",
-    "\n",
-    "Next, we set the zones of species are limited to areas with an elevation less than 400. Two distinct areas of the mask are now seperated by the higher elevations in the center of the grid."
+    "< FIX COLOR MAP >"
    ]
   },
   {
@@ -436,15 +455,62 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "15.5959030999 31.9999927129\n"
-     ]
-    },
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>time</th>\n",
+       "      <th>species_count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   time  species_count\n",
+       "0     0              2"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "se.record"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -456,28 +522,21 @@
     }
    ],
    "source": [
-    "# Update surface temperature at sea level.\n",
-    "temperature_at_sea_level = 32\n",
-    "mg.at_node['land_surface_air__temperature'] = lapse * mg.at_node['topographic__elevation'] + temperature_at_sea_level\n",
-    "print(mg.at_node['land_surface_air__temperature'].min(), mg.at_node['land_surface_air__temperature'].max())\n",
+    "# Update the surface temperature field.\n",
+    "temperature_at_base_level = 32\n",
+    "mg.at_node['land_surface_air__temperature'] = lapse_rate * z + temperature_at_base_level\n",
+    "\n",
     "# Plot temperature field.\n",
     "imshow_grid(mg, 'land_surface_air__temperature',\n",
     "            colorbar_label='surface air\\ntemperature (C$\\degree$)',\n",
-    "            shrink=0.4, cmap='RdBu')"
+    "            shrink=0.4, cmap='bwr')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "bbb 2\n"
-     ]
-    },
     {
      "data": {
       "image/png": 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\n",
@@ -492,8 +551,9 @@
     }
    ],
    "source": [
-    "# Advance SpeciesEvolver by one unit of time. Time is dimensionless in this tutorial.\n",
+    "# Advance SpeciesEvolver by a time step. The duration of a time step is not prescribed in this tutorial.\n",
     "dt = 1\n",
+    "sc.run_one_step(dt)\n",
     "se.run_one_step(dt)\n",
     "\n",
     "# Plot zones.\n",
@@ -502,29 +562,18 @@
     "    imshow_grid(mg, zone.mask, allow_colorbar=False, plot_name='zone in white')"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Simulate a second temperature increase\n",
+    "\n",
+    "The temperature at base level is increased again by 6&deg;C."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<landlab.components.species_evolution.zone.Zone at 0x1a24b75ed0>]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sc.zones"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -536,7 +585,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -548,14 +597,16 @@
     }
    ],
    "source": [
-    "# Update surface temperature at sea level.\n",
-    "temperature_at_sea_level = 38\n",
-    "mg.at_node['land_surface_air__temperature'] = lapse * mg.at_node['topographic__elevation'] + temperature_at_sea_level\n",
+    "# Update the surface temperature field.\n",
+    "temperature_at_base_level = 38\n",
+    "mg.at_node['land_surface_air__temperature'] = lapse_rate * z + temperature_at_base_level\n",
+    "\n",
     "print(mg.at_node['land_surface_air__temperature'].min(), mg.at_node['land_surface_air__temperature'].max())\n",
+    "\n",
     "# Plot temperature field.\n",
     "imshow_grid(mg, 'land_surface_air__temperature',\n",
     "            colorbar_label='surface air\\ntemperature (C$\\degree$)',\n",
-    "            shrink=0.4, cmap='RdBu')"
+    "            shrink=0.4, cmap='bwr')"
    ]
   },
   {
@@ -563,6 +614,13 @@
    "execution_count": 14,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "n_zones 1\n"
+     ]
+    },
     {
      "data": {
       "image/png": "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\n",
@@ -577,45 +635,123 @@
     }
    ],
    "source": [
-    "imshow_grid(mg, zone_func(mg), allow_colorbar=False)"
+    "imshow_grid(mg, zone_func(mg), allow_colorbar=False)\n",
+    "print('n_zones', len(sc.zones))"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>time</th>\n",
+       "      <th>species_count</th>\n",
+       "      <th>speciation_count</th>\n",
+       "      <th>extinction_count</th>\n",
+       "      <th>pseudoextinction_count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   time  species_count  speciation_count  extinction_count  \\\n",
+       "0     0              2               NaN               NaN   \n",
+       "1     1              2               0.0               0.0   \n",
+       "\n",
+       "   pseudoextinction_count  \n",
+       "0                     NaN  \n",
+       "1                     0.0  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "se.record"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "Increment SpeciesEvolver using the `run_one_step` method. The only parameter of this method is the time step duration. Here we use 1000 to signify 1000 yrs. This method evaluates and updates the zones and species of SpeciesEvolver.\n",
+    "# Advance SpeciesEvolver by one unit of time. Time is dimensionless in this tutorial.\n",
+    "dt = 1\n",
+    "sc.run_one_step(dt)\n",
+    "se.run_one_step(dt)\n",
     "\n",
-    "The `run_one_step` method determined that two distinct zones now exist."
+    "# Plot zones.\n",
+    "zone_map = np.zeros(mg.number_of_nodes)\n",
+    "for i, zone in enumerate(sc.zones):\n",
+    "    zone_map[zone.mask] = i + 1\n",
+    "    \n",
+    "cmap = plt.cm.tab20\n",
+    "cmap.set_under('w')\n",
+    "title = 'zone count = {}'.format(len(sc.zones))\n",
+    "imshow_grid(mg, zone_map, cmap=cmap, allow_colorbar=False, plot_name=title, vmin=1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "bbb 2\n",
-      "bbb 2\n",
-      "bbb 2\n",
-      "bbb 2\n",
-      "bbb 2\n",
-      "bbb 2\n",
-      "bbb 2\n",
-      "bbb 2\n",
-      "bbb 2\n",
-      "bbb 2\n",
-      "bbb 2\n",
-      "bbb 2\n",
-      "bbb 2\n",
-      "bbb 2\n",
-      "bbb 2\n",
-      "bbb 2\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -638,231 +774,360 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>clade</th>\n",
-       "      <th>species_number</th>\n",
+       "      <th>number</th>\n",
        "      <th>time_appeared</th>\n",
        "      <th>latest_time</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>0</td>\n",
+       "      <th>0</th>\n",
        "      <td>A</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>1</td>\n",
+       "      <th>1</th>\n",
        "      <td>A</td>\n",
        "      <td>1</td>\n",
        "      <td>2</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>2</td>\n",
+       "      <th>2</th>\n",
        "      <td>A</td>\n",
        "      <td>2</td>\n",
        "      <td>2</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>3</td>\n",
+       "      <th>3</th>\n",
        "      <td>A</td>\n",
        "      <td>3</td>\n",
        "      <td>2</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>4</td>\n",
+       "      <th>4</th>\n",
        "      <td>A</td>\n",
        "      <td>4</td>\n",
        "      <td>2</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>5</td>\n",
+       "      <th>5</th>\n",
        "      <td>A</td>\n",
        "      <td>5</td>\n",
        "      <td>2</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>6</td>\n",
+       "      <th>6</th>\n",
        "      <td>A</td>\n",
        "      <td>6</td>\n",
        "      <td>2</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>7</td>\n",
+       "      <th>7</th>\n",
        "      <td>A</td>\n",
        "      <td>7</td>\n",
        "      <td>2</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>8</td>\n",
+       "      <th>8</th>\n",
        "      <td>A</td>\n",
        "      <td>8</td>\n",
        "      <td>2</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>9</td>\n",
+       "      <th>9</th>\n",
        "      <td>A</td>\n",
        "      <td>9</td>\n",
        "      <td>2</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>10</td>\n",
+       "      <th>10</th>\n",
        "      <td>B</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>11</td>\n",
+       "      <th>11</th>\n",
        "      <td>B</td>\n",
        "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>12</td>\n",
+       "      <th>12</th>\n",
        "      <td>B</td>\n",
        "      <td>2</td>\n",
        "      <td>2</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>13</td>\n",
+       "      <th>13</th>\n",
        "      <td>B</td>\n",
        "      <td>3</td>\n",
        "      <td>2</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>14</td>\n",
+       "      <th>14</th>\n",
        "      <td>B</td>\n",
        "      <td>4</td>\n",
        "      <td>2</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>15</td>\n",
+       "      <th>15</th>\n",
        "      <td>B</td>\n",
        "      <td>5</td>\n",
        "      <td>2</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>16</td>\n",
+       "      <th>16</th>\n",
        "      <td>B</td>\n",
        "      <td>6</td>\n",
        "      <td>2</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>17</td>\n",
+       "      <th>17</th>\n",
        "      <td>B</td>\n",
        "      <td>7</td>\n",
        "      <td>2</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>18</td>\n",
+       "      <th>18</th>\n",
        "      <td>B</td>\n",
        "      <td>8</td>\n",
        "      <td>2</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>19</td>\n",
+       "      <th>19</th>\n",
        "      <td>B</td>\n",
        "      <td>9</td>\n",
        "      <td>2</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <td>20</td>\n",
-       "      <td>B</td>\n",
-       "      <td>10</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "   clade  species_number  time_appeared  latest_time\n",
-       "0      A               0              0            1\n",
-       "1      A               1              2            2\n",
-       "2      A               2              2            2\n",
-       "3      A               3              2            2\n",
-       "4      A               4              2            2\n",
-       "5      A               5              2            2\n",
-       "6      A               6              2            2\n",
-       "7      A               7              2            2\n",
-       "8      A               8              2            2\n",
-       "9      A               9              2            2\n",
-       "10     B               0              0            0\n",
-       "11     B               1              1            1\n",
-       "12     B               2              2            2\n",
-       "13     B               3              2            2\n",
-       "14     B               4              2            2\n",
-       "15     B               5              2            2\n",
-       "16     B               6              2            2\n",
-       "17     B               7              2            2\n",
-       "18     B               8              2            2\n",
-       "19     B               9              2            2\n",
-       "20     B              10              2            2"
+       "   clade  number  time_appeared  latest_time\n",
+       "0      A       0              0            1\n",
+       "1      A       1              2            2\n",
+       "2      A       2              2            2\n",
+       "3      A       3              2            2\n",
+       "4      A       4              2            2\n",
+       "5      A       5              2            2\n",
+       "6      A       6              2            2\n",
+       "7      A       7              2            2\n",
+       "8      A       8              2            2\n",
+       "9      A       9              2            2\n",
+       "10     B       0              0            1\n",
+       "11     B       1              2            2\n",
+       "12     B       2              2            2\n",
+       "13     B       3              2            2\n",
+       "14     B       4              2            2\n",
+       "15     B       5              2            2\n",
+       "16     B       6              2            2\n",
+       "17     B       7              2            2\n",
+       "18     B       8              2            2\n",
+       "19     B       9              2            2"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
+    }
+   ],
+   "source": [
+    "se.species"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Three species have existed in this model:\n",
+    "\n",
+    "- A.0: The initial species we introduced.\n",
+    "- A.1 and A.2: The child species of A.0. These species were created when the zone of the first time step split into two in the second time step.\n",
+    "\n",
+    "All species belong to clade 'A' because the child species (A.1 and A.2) were spawned from the same parent species (A.0).\n",
+    "\n",
+    "\n",
+    "**Profile map**\n",
+    "```\n",
+    "              N\n",
+    "     ___________________\n",
+    "    │    P1             │\n",
+    "    │     │             │\n",
+    " W  │ P2──┼───────      │  E\n",
+    "    │     │             │\n",
+    "    │                   │\n",
+    "     ‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾\n",
+    "              S\n",
+    "\n",
+    "\n",
+    "```\n",
+    "**T0**: Profile 1\n",
+    "```\n",
+    "            ╱╲\n",
+    "           ╱  ╲\n",
+    "   N     a╱    ╲*     S     ─┐\n",
+    "        a╱      ╲*           │ Species temperature range\n",
+    "   aaaaa╱        ╲*****      │\n",
+    "   ‾‾‾‾‾          ‾‾‾‾‾     ─┘ \n",
+    "                             \n",
+    "\n",
+    "```\n",
+    "**T1**: Profile 1\n",
+    "```    \n",
+    "            *a\n",
+    "          *a╱╲*a            ─┐\n",
+    "         *a╱  ╲*a            │ Species temperature range\n",
+    "   N    *a╱    ╲*a    S      │\n",
+    "       *a╱      ╲*a         ─┘\n",
+    "        ╱        ╲\n",
+    "   ‾‾‾‾‾          ‾‾‾‾‾\n",
+    "\n",
+    "\n",
+    "```\n",
+    "**T3**: Profile 1 (left) and 2 (right)\n",
+    "```                  \n",
+    "                                                             ─┐\n",
+    "                                                              │ Species temperature range\n",
+    "            b+                     +b                c@       │ \n",
+    "          b+╱╲b+                 +b╱╲+b            c@╱╲c@    ─┘\n",
+    "           ╱  ╲                   ╱  ╲      ╱╲      ╱  ╲\n",
+    "   N      ╱    ╲      S      W   ╱    ╲    ╱  ╲    ╱    ╲   E\n",
+    "         ╱      ╲               ╱      ╲  ╱    ╲  ╱      ╲\n",
+    "        ╱        ╲             ╱        ╲╱      ╲╱        ╲\n",
+    "   ‾‾‾‾‾          ‾‾‾‾‾    \n",
+    "\n",
+    "\n",
+    "```\n",
+    "**Phylogeny**\n",
+    "```\n",
+    "\n",
+    "         ┌─ b\n",
+    "a ───────┤\n",
+    "         └─ c\n",
+    "         ┌─ +\n",
+    "* ───────┤\n",
+    "         └─ @\n",
+    " __________\n",
+    "T0   T1   T2\n",
+    "\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>time</th>\n",
+       "      <th>species_count</th>\n",
+       "      <th>speciation_count</th>\n",
+       "      <th>extinction_count</th>\n",
+       "      <th>pseudoextinction_count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>18</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "   time  species_count  speciation_count  extinction_count  \\\n",
+       "0     0              2               NaN               NaN   \n",
+       "1     1              2               0.0               0.0   \n",
+       "2     2             18              18.0               2.0   \n",
+       "\n",
+       "   pseudoextinction_count  \n",
+       "0                     NaN  \n",
+       "1                     0.0  \n",
+       "2                     2.0  "
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "se.record"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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8EACr1YrBYEAphXbcHPof//gHZ599NpdeeqnzNTNmzCA1NRWASy+9lDfffLPLvwshROd4fBphUwv272dXdXWbz39ZWYnJkUwa1dps3Pjtt7x09Girr5kQHs6zI0e2ecxly5aRnZ3Nrl32NUrr1q1j//79bN26Fa01l112GRs3biQhIYEDBw7wz3/+k5UrV5KRkcE//vEPNm3axJo1a/jNb37D+++/D8Du3bv58ssvqampYeLEicyZM4fs7Ow2j7tv3z5eeeUV5zuBpUuXEhMTg9VqZebMmezevZs777yTFStW8Nlnn9GvX792f5bfffcd69evx8/PjwceeIDzzjuPVatWUV5eTmZmJueffz5hYWHO/fPz84mOjibIcReklJQUamtrOffcc1m+fDkA2dnZnHnmmW2eMz09nWXLlnHvvfe2G58Qovu8KoG3p2Xybm97V6xbt45169YxceJEAKqrq9m/fz8JCQkMGzaMcePGAfYEN3PmTJRSjBs3joKCAucxLr/8ckJCQggJCWHGjBls3bqVTZs2tXncxMREzjrrLOfr3377bVauXInFYqGoqIhvvvmGtLS0Tn0fV199NX5+fs7vac2aNTz9tL11e319PYWFhYwZM8a5f1FRES2bjT333HOdOueAAQM42sZ/pEII1/OqBH66kTJA0pYtHGzlRseJQUFscCTG7tJac//993PLLc07AxQUFDhHpwAGg8H52GAwNKs1t5w211iGaOu4LUfCTz/9NNu2bSM6Opp58+a1OYe66Xla7tP0mFpr3n33Xc4444w2v++QkJB252qnpKQ0u6DZUn19PSEhIac9hhDCdXyqBu6Oe2QajUaqqqqcj2fPns2qVauodpRyjhw5wrFjxzp1zA8++ID6+npKS0vZsGEDGRkZHT5uZWUlYWFhREZGUlxczL///e82Y42LiyMnJwebzcbq1avbjGf27Nk899xzzlr2zp07T9ln1KhRzd5FtOYnP/kJX3zxBR999JFz29q1a9mzZw9gL9s01sOFEO7nVSPw9jTeWu3BvDwKTSYSgoJYmpzcrVuuxcbGMmXKFFJTU7noootYvnw5OTk5nH322YD9QuHf//53ZzmiIzIzM5kzZw6FhYU89NBDDBo0iEGDBnXouOPHj2fixImkpKSQnJzMlClTnM/Nnz+fiy66iPj4eD777DOWLVvGJZdcwtChQ0lNTXX+59DSQw89xIIFC0hLS0NrTVJSEh9++GGzfcLCwhg+fDgHDhxgxIgRrR4nJCSEDz/8kAULFrBgwQICAgJIS0vj97//PQCfffYZc+bM6fDPSQjRPUq7sH7cnvT0dL19+/Zm23JycprVYn3dI488Qnh4OIsWLfJ0KJ22evVqsrKyeOKJJzr9WpPJxLRp09i0aRP+/qeOC3rb71mInqSUytJap7fc7lMjcOFeV155JaWlpV16bWFhIcuWLWs1eQsh3EP+tbnYI4884ukQuuWmm27q0utGjhzJyHYuQgshXMunLmIKIYQ4SRK4EEL4qI7cE3OoUuozpVSOUmqvUuoux/YYpdR/lFL7HZ+j3R+uEEKIRh0ZgVuAe7TWY4CzgNuVUmOB+4BPtdYjgU8dj4UQQvSQdhO41rpIa73D8XUVkAMMBi4H/urY7a/AFe4K0ldNnz6dltMm3eEPf/gDY8aMYe7cuW4/lxDCe3RqFopSKgmYCHwFxGmti8Ce5JVSA9p4zXxgPkBCQkJ3YmXzwM2Yi82nbA+IC2DK91NaeYXvslgsHZ6S98c//pF///vfcs9JIfqYDl/EVEqFA+8CC7TWlR19ndZ6pdY6XWud3rJZUme1lrxPt70jGtu53nzzzaSkpDBr1izq6uqA5iPo48ePk5SUBMCrr77KFVdcwaWXXsqwYcN4/vnnWbFiBRMnTuSss87ixIkTzuP//e9/55xzziE1NdXZcrat9q6vvvoqV199NZdeeimzZs06JdYVK1aQmppKamoqzz77LAC33noreXl5XHbZZTzzzDPN9r/pppuYMGECEyZMoH///jz66KNorVm8eDGpqamMGzeOt956C4ANGzYwffp0fvSjHzF69Gjmzp3rXHqflZXFtGnTOPPMM5k9ezZFRUWAfeQ/duxY0tLS+L//+78u/w6EEF3ToSGeUioAe/J+XWv9nmNzsVIq3jH6jgc61zCkFfsX7Kd6V9vtZE9n5/RT+3sAhE8IZ+Szp5+fvH//ft544w1eeuklrrnmGt59911++tOfnvY12dnZ7Ny5k/r6ekaMGMGTTz7Jzp07ufvuu3nttddYsGABYE/WX3zxBRs3buSGG24gOzubpUuXttreFWDLli3s3r2bmJiYZufLysrilVde4auvvkJrzeTJk5k2bRp/+tOfWLt2battZv/yl78AcPDgQWbPns28efN477332LVrF19//TXHjx8nIyODqVOn2n+GO3eyd+9eBg0axJQpU9i8eTOTJ0/mjjvu4IMPPqB///689dZbPPjgg6xatYply5aRn59PUFAQ5eXlp/15CSFcr90Eruwt714GcrTWK5o8tQb4ObDM8bn1uwT4gGHDhjFhwgQAzjzzzHabOoH9RgZGoxGj0UhkZKTzJgfjxo1j9+7dzv2uvfZaAKZOnUplZSXl5eVttncFuOCCC05J3gCbNm3iyiuvdHYZvOqqq/j888+d7WnbUl9fz9VXX83zzz9PYmIizz77LNdeey1+fn7ExcUxbdo0tm3bRkREBJmZmQwZMgSACRMmUFBQQFRUFNnZ2VxwwQWA/UYP8fHxAKSlpTF37lyuuOIKrrhCLoEI0dM6MgKfAlwH7FFK7XJsewB74n5bKXUjUAhc3d1g2hspb1Ab2nxu4oaut5Nt2ibWz8/PWULx9/d33uKsZavV7raWba2961dffdWsDWxTXe1Zc+utt3LVVVc5R/inO07Ln4PFYkFrTUpKClu2bDll/48++oiNGzeyZs0aHn/8cfbu3StL6YXoQR2ZhbJJa6201mla6wmOj4+11qVa65la65GOzyfaO5avSUpKIisrC7DfsqwrGmvMmzZtIjIyksjIyA61d21p6tSpvP/++9TW1lJTU8Pq1av5wQ9+cNrXvPDCC1RVVXHffSdneE6dOpW33noLq9VKSUkJGzduJDMzs81jnHHGGZSUlDgTuNlsZu/evdhsNg4dOsSMGTN46qmnKC8vb7MbohDCPXxquBQQF9DmLBR3WLRoEddccw1/+9vfOO+887p0jOjoaM455xwqKytZtWoV0LH2ri1NmjSJefPmOZPtTTfd1G755OmnnyYgIMBZHrr11lu55ZZb2LJlC+PHj0cpxVNPPcXAgQOdNzFuKTAwkHfeeYc777yTiooKLBYLCxYsYNSoUfz0pz+loqICrTV33303UVFRnf3xCCG6QdrJih4hv2fRl9XU7GXv3h+TkvIWYWEpnX59W+1kpReKEEK4kdVaw+7dF1Nb+w27d8/Baq1x2bElgQshhBt9++0NNDQcAzQNDcV8++2NLju2JHAhhHCToqJVlJauQWv7LDat6ykt/RdFRatccnxJ4EII4QYm0xG+++52bLbmU5Bttlry8u53yTkkgQshhAtZLNXk5z/MV1+NRGsz9oXsJxkMoSQnL3PJuSSBCyGEC2htpajoFbZuHcXBg48RG3sZkyfvp1+/K1EqGAClgomNvZT4+Otdck6fTOA1NXvZujWVmpq9ng6lXRdffLHL+4SsWbOGZcva/h+8oKCA1NRUl55TCNG2srL/kpWVzr59NxAcnMjEiV+QkvImISHDGD16FYGBAwBFYGAco0e/7LLz+tRCHjg5JcdkOsTu3XPIzNyLn1/ry8+9wccff+zS41ksFi677DIuu+wylx5XCNF5tbX7yM1dTGnpvwgOTmLs2Dfp3/+aZi00/PzCSEv72DkP3JX5yudG4K6eklNTU8OcOXMYP348qampzqXvSUlJLFmyhMzMTDIzMzlw4AAAJSUl/PCHPyQjI4OMjAw2b94MQHV1Nddffz3jxo0jLS2Nd99913mc48ePA/bWspmZmUyYMIFbbrkFq9WK1Wpl3rx5zvauLVvCAsybN4+FCxcyY8YMlixZwquvvsovf/lLAIqLi7nyyisZP34848eP54svvgDsTafaapHb+H2NGjWKzz//3Ln/4sWLycjIIC0tjT//+c8AFBUVMXXqVCZMmEBqaiqff/55h2IWojczm0vZv/9Otm1Lpbx8A8nJT5KRkcOAAT8+pf8RQFhYCpmZ2V1axHM6XjUC379/AdXVu9p83mQqor7+AGBvMKV1PSUl/+TLL3cSFBTf6mvCwycwcuSzbR5z7dq1DBo0iI8++giAiooK53MRERFs3brV2R72ww8/5K677uLuu+/m3HPPpbCwkNmzZ5OTk8Pjjz9OZGQke/bsAaCsrKzZeXJycnjrrbfYvHkzAQEB/OIXv+D1118nJSWFI0eOkJ2dDdBmueW7775j/fr1+Pn58eqrrzq333nnnUybNo3Vq1djtVqprq6mrKzstC1yLRYLW7du5eOPP+bRRx9l/fr1vPzyy0RGRrJt2zZMJhNTpkxh1qxZvPfee8yePZsHH3wQq9VKbW0tu3bt6lDMQvQ2NpuJI0ee5+DBJ7BYKhk06BaSkh5xlEh6nlcl8PaYTPk0Ju+TbJhM+W0m8PaMGzeORYsWsWTJEi655JJmDaIaW8Fee+213H333QCsX7+eb775xrlPZWUlVVVVrF+/njfffNO5PTq6+T2eP/30U7KyssjIyACgrq6OAQMGcOmll5KXl8cdd9zBnDlzWr2RA8DVV1+Nn5/fKdv/+9//8tprrwH2DoKRkZGUlZWdtkXuVVdddcr2devWsXv3bmfTroqKCvbv309GRgY33HADZrOZK664ggkTJpCcnNyhmIXoLbTWHD/+Hrm591Jfn0dMzEUMH77c5SPqzvKqBH66kTLYJ8Xv338nNtvJpagGQygjRz7f5au6o0aNIisri48//pj777+fWbNm8etf/xpo3gq28WubzcaWLVsICQlpdhytdatvnZo+//Of/5zf/va3pzz39ddf88knn/DCCy/w9ttvO5teNdVWm9m2tNUit+lzjS1jG+N77rnnmD179inH2rhxIx999BHXXXcdixcv5mc/+1mHYhaiN6is3EZu7kIqKjYRFpZKWtonxMR4x6DFp2rg8fE3EBs7x6VTco4ePUpoaCg//elPWbRoETt27HA+11gPf+uttzj77LMBmDVrFs8//7xzn127drW6vWUJZebMmbzzzjscO2a/cdGJEyc4ePAgx48fx2az8cMf/pDHH3+82fk7YubMmbz44ouAvY5dWdnhu901M3v2bF588UXMZnu3x++++46amhoOHjzIgAEDuPnmm7nxxhvZsWNHt2MWwhfU1x8iJ+c6duzIpLb2O0aNWkl6+i6vSd7gZSPwjhg9ehVbt47FZDrkkik5e/bsYfHixRgMBgICApzJEMBkMjF58mRsNhtvvPEGYL8P5O23305aWhoWi4WpU6fypz/9iV/96lfcfvvtpKam4ufnx8MPP+wsVQCMHTuWJ554glmzZmGz2QgICOCFF14gJCSE66+/3nnjiNZG6Kfz+9//nvnz5/Pyyy/j5+fHiy++6LxjTmfcdNNNFBQUMGnSJLTW9O/fn/fff58NGzawfPlyAgICCA8P57XXXuPIkSPdilkIb2axVFFY+CSHD/8OgISEB0hIuA9/f6OHIzuVT7aT7W5rxo5ISkpi+/btp9xnUnSNtJMV3s6+EGcV+fkPYTYXM2DAXJKTf0NwcIKnQ2uznazPjcDh5JQcIYRwhRMn/kNu7j3U1OwhImIK48atISKi7TtVeYt2a+BKqVVKqWNKqewm2x5RSh1RSu1yfFzs3jB7XkFBgYy+hejlamrsPbp3756F1VpDSso7TJz4uU8kb+jYCPxV4HngtRbbn9FaP+2KINqbwSF8W0+W6YToiIaGEgoKHuHo0T/j5xfO8OFPM3jwLzEYgtp/sRdpN4FrrTcqpZLcFUBwcDClpaXExsZKEu+FtNaUlpYSHBzs6VCEwGqt58iRP3Dw4FKs1hoGD76NxMSHCQz0zXfb3amB/1Ip9TNgO3CP1rqstZ2UUvOB+U0V7PAAABaESURBVAAJCadeDBgyZAiHDx+mpKSkG6EIbxYcHMyQIUM8HYbow7TWlJS8TV7efdTXFxAbeynJyU8RFjba06F1S4dmoThG4B9qrVMdj+OA44AGHgfitdY3tHec1mahCCGEO1VUfElu7kIqK7cQFjaeESN+R3T0TE+H1SkunYWitS5ucuCXgA+7EZsQQrhcXV0B+fn3c+zYmwQGDuSMM1YxcODPUOrUlhS+qksJXCkVr7Uucjy8EpA5fUIIr2CxVFJY+FsOHXoGpQwkJv6aoUMX4+8f7unQXK7dBK6UegOYDvRTSh0GHgamK6UmYC+hFAC3uDFGIYRol81moajoLxQU/BqzuYS4uJ8xbNhSgoN77/WXjsxCubaVza67pYQQQnRTaelacnPvobb2GyIjpzFixO8wGs/0dFhu55MrMYUQAqC6Opvc3EWUlX1CSMgIUlJW06/f5X1mSrIkcCGEz2loKCY//9cUFf0Ff/9Ihg9/hsGDf4HBEOjp0HqUJHAhhM+wWus4fPhZCgt/g81Wz5Ahd5KY+BABATGeDs0jJIELIbye1jaOHXuTvLz7MZkK6dfvCpKTnyQ0dJSnQ/MoSeBCCK9WUbGZAwcWUlW1lfDwiYwe/Veio6d7OiyvIAlcCOGV6uryyMu7j5KSfxIYOIjRo18lLu46lPKpG4m5lSRwIYRXMZvLKSxcyuHDf0Apf5KSHmXo0Hvw8+vcfWH7AkngQgivYLOZKSpaSX7+w1gsJxg48HqGDXucoKBBng7Na0kCF0J4lL3l8Efk5i6irm4fUVEzGD58BUbjBE+H5vUkgQshPKa6+msOHLiH8vJPCQkZRWrqGmJjL+kzC3G6SxK4EKLHmUxF5Oc/xPffr8LfP5oRI/7AoEG3YjAEeDo0nyIJXAjRY6zWWg4d+h2FhU+idQNDhiwkMfFBAgKiPR2aT5IELoRwO61tFBe/Tl7e/TQ0HKFfvx8yfPiThIQM93RoPk0SuBDCrcrLN3LgwEKqq7MwGtMZO/YNoqJ+4OmwegVJ4EIIt6itPUBe3r0cP76aoKAhjBnzdwYMuFYW4riQJHAhhEuZzWUcPPg4R448j8EQxLBhTzBkyN34+YV6OrReRxK4EMIlbLYGjh59kYKCx7BYyomPv5GkpMcIChro6dB6LUngQohusS/EWUNu7mLq6vYTHX0+w4f/jvDwNE+H1uu1W4xSSq1SSh1TSmU32RajlPqPUmq/47PMARKiD6qq2sHXX59HdvYVKOXPuHEfkZa2TpJ3D+nI1YRXgQtbbLsP+FRrPRL41PFYCNFHmExHyMmZR1ZWOjU12Ywc+UfS03cTG3uxrKLsQR25qfFGpVRSi82XY79TPcBfgQ3AEhfGJYTwQlZrDYWFyzl0aDlaWxg6dDGJiQ/g7x/p6dD6pK7WwOO01kUAWusipdSAtnZUSs0H5gMkJCR08XRCCE/S2sr3379Gfv6DNDQU0b//j0lO/i0hIcM8HVqf5vaLmFrrlcBKgPT0dO3u8wkhXKus7DNycxdSXb0Lo3EyKSnvEBl5jqfDEnQ9gRcrpeIdo+944JgrgxJCeF5t7T5yc++ltHQNQUGJjBnzBgMG/Fhq3J2weeBmzMXmU7YHxAUw5fsp3T5+VxP4GuDnwDLH5w+6HYkQwiuYzaUUFDzG0aN/xGAIITl5GYMH34WfX7CnQ/M5rSXv023vrHYTuFLqDewXLPsppQ4DD2NP3G8rpW4ECoGrXRKNEMJjbDYTR468wMGDj2OxVDJo0HySkh4lMLDNS1zCwzoyC+XaNp6a6eJYhBAeoLXm+PH3yM1dQn19LjExFzJ8+NOEhaV4OjSfo22a6l3VnFh7ghOfnHD7+WQlphB9WGXlNnJzF1JRsYnQ0BTS0tYSEzPb02H5lIaSBsr+U+ZM2uZj9vJI+MRwt59bErgQfVB9/SHy8x+guPjvBAQMYNSoPzNw4A0YDJIS2mOz2KjaWmVP2GtPULW9CjT4x/oTMyuGmAtjiJ4VTdDAIDaoDW6NRX5bQvQhFksVhw49xaFDT6O1JiHhARISluDvH+Hp0Lxa/eF6yj6xj7LL1pdhKbeAASLOiiDp0SRiLozBOMmI8ms+QycgLqDNWSiuIAlciD5AaytFRa+Qn/8rzOZiBgz4CcnJvyE4ONHToXklm8lGxaYK5yi7JrsGgMBBgfS7qp99lH1+NAHRp0/ErpgqeDqSwIXo5U6cWE9u7kJqavYQEXEO48Z9QETEZE+H5XXqcuucCbvsv2XYam2oAEXk1EiSf5ZMzIUxhKWGedU8eEngQvRSNTU55OYu5sSJjwgOHsbYsf+kf/8felUC8iRrjZWyz8qcpZG6A3UABCcHM/D6gcRcGEPU9Cj8w703TXpvZEKILmloKKGg4BGOHv0zfn5hJCcvZ8iQOzAYgjwdmkdpranZW+McZVd8XoFu0BhCDUTNiGLwXYOJuTCG0BG+c+cgSeBC9BJWaz1HjjzHwYNPYLXWMGjQrSQlPUxgYH9Ph+Yx5nIzZevLnEm74UgDAGGpYQy5cwjRs6OJPDcSv2A/D0faNZLAhfBxWmtKSv5JXt4S6usLiI29hOTkpwgLG+Pp0Hqctmmqsqqcc7Irv6wEK/hF+hFzgWOK3+xogof0jrYAksCF8GGVlV9x4MDdVFZuISwsjbS0/xATc76nw+pRDcUNnFjnuPi4rgzzcTMoMJ5pJPH+RPsUv8lGDP4duX+Nb5EELoQPqq8/SF7e/Rw79gaBgQM544y/MHDgPJTyzVJAZ9jMNiq/rHSWRap3VAMQMCCAmIsco+wLognsH+jhSN1PErgQPsRiqaSw8LccOvQMShlITHyIoUPvxd/f/cu2Pam+sP7kFL9Py7BWWsEPIs+JZNjSYcRcGEP4hHCUoW/NsJEELoQPsNksfP/9y+TnP4TZXEJc3HUMG7aU4OChng7NLaz1Vio2nlxIU5tTC0DQ0CAG/HiAfZQ9Mxr/yL6dwvr2dy+EDzhx4hMOHLiH2tq9REZOZfjwj4mISPd0WC6ltaZu/8mFNOUbyrHV2VBBiqhpUcTfHG+f4jc6VOaxNyEJXAgvVV2dTW7uIsrKPiE4eDgpKe/Rr98VvSaBWaoslP+33Jm06wvqAQgZFeJM2FHTovAL7f11/a6SBC6El2loKCY//2GKil7C3z+C4cNXMHjw7RgMvn1RTmtNze4mC2k2VaAtGr9wP6JmRjH03qHEzI4hJDnE06H6DEngQngJq7WOw4efpbDwt9hsdQwefAdJSQ8REBDr6dC6zFxq5sR/TjiXqzd871hIMz6MIfcMIebCGCLPicQQ2Pum+PWEbiVwpVQBUAVYAYvWuncV5oToAVprjh17k7y8+zCZComNvZzhw58iNHSUp0PrNG3VVG47OcWvaqujV3a0P9Gzoom5MIaYWTEEDerby/pdxRUj8Bla6+MuOI4QfU5FxRccOLCQqqqvCA+fyOjRfyU6erqnw+oUU5GJE5+cXEhjKbOAgojJESQ97OiVnX5qr2zRfVJCEcID6uryyMu7j5KSfxIYOIjRo18lLu46lPL+UoKtwUbF5grncvWarx29sgcG0u/yJr2yY11z0wLRtu4mcA2sU0pp4M9a65UuiEmIXstsLqew8DccPvx7lPInKekRhg5dhJ9fmKdDO626/CZT/P5bjrXaau+VfW4kycscvbLTvKtXdl/Q3QQ+RWt9VCk1APiPUupbrfXGpjsopeYD8wESEhK6eTohfJPNZqaoaCUFBY9gNpcycODPGTbsCYKCBns6tFZZa62U/+/kFL+67xy9spOCibsuzj7Fb0YU/kZ5E+9J3frpa62POj4fU0qtBjKBjS32WQmsBEhPT9fdOZ8QvkZrzYkTH5Obu4ja2m+JiprB8OG/w2ic6OnQmtFaU5tT66xll/+vHG3SGEIMRE2PYvDt9l7ZISNDZJTtRbqcwJVSYYBBa13l+HoW8JjLIhPCx1VX7yY39x7KytYTEjKS1NQPiI291GsSoKXCQtmnJ3tlmw6ZAAgdG2pP2LNjiPxBJH4hspDGW3VnBB4HrHb8MfoD/9Bar3VJVEL4MJOpiPz8h/j++1X4+0czYsTvGTToVo8vxNE2TfWu6pMLab6osPfKjvAj+vxoEh9KJGZ2DMEJvaNXdl/Q5QSutc4DxrswFiF8mtVay6FDKygsXIbWDQwZcjeJib8iICDaYzE1lDRQts4xyl53AvMxMwDhk8JJWJJAzIUxRJwVgSHA+2e/iFPJFQghuklrG8XFr5Of/wAm02H69buK5OQnCQ0d0eOx2Cw2qr6qOrmQJsu+kCagXwDRsx0LaS6IITDOt5flCztJ4EJ0Q3n5Rg4cWEh1dRZGYzpjxvyDqKgf9GgM9YfrnUvVT/znBNYKKxgg4uwIkh5zLKSZZOxzvbL7AkngQnRBbe0B8vKWcPz4ewQFDWH06L8RF/eTHlmIYzPZKP/85BS/2r32XtmBgwPp/6P+zl7ZAdGykKa3kwQuRCeYzWUcPPg4R448j1KBJCU9ztChC/HzC3XreWsP1J5cSPNZObZaGypQETU1ivjrHb2yx0qv7L5GErgQHWCzmTl69EUKCh7FYikjPv5GkpIeIygo3i3ns1RbKN/QpFd2rqNX9ogQ4m9w9MqeHoVfmEzx68skgQtxGlprSkvXkJt7L3V13xEVNZMRI35HeLhrJ2BpranJbtEru0FjCDMQfV40Q+8eSvTsaEJHuHekL3yLJHAh2lBVtZPc3IWUl28gNHQ048Z9SEzMxS4rU5jLzJStP7mQpuGoo1f2uDCG3OXolT0lEkOQTPETrfOJBF5Ts5e9e39MSspbhIWleDoc0cu0/PsymY6Qn/8rvv/+rwQExDJy5AvEx9+MwdC9i4LaqqnKqnIuV6/8shJs4B/lT/QFjil+s2MIGiy9skXHeHUC3zxwM+aKSnjlehhwjG3/mgnXv0JAZARTvp/i6fBEL2C11rB798WYTIfYvftiBgyYy5Ejv0drC0OHLiIh4QECAqK6fHzT96ZmC2kspfZe2cYMI4m/SrRP8cswYvCXUbboPK9O4OZiMzz0FESfAIOGmBPwyMOY/3wrlSfCUAEGueouuiU3dzENDcWAxmQq5NCh39K//zUkJy8jJGRYp49nM9uo3HLyjjTVO6sBCIgLIHZOrH2K3wXRBPaThTSi+7w6gXPhx3D2lxBkX/5LoBkmb4PJ29ix27Ohid5JqWBiYmZ3KnnXH6x33tygbH0Z1ioryl8RcU4Ew34zjJgLYwgfHy4LaYTLeXcCn/8ShNSfur06jAGHn8bWYMPWYEM3aHSDPv1jkw2bYxuWbnS19VMYAhUq0IAh0IAKVM0/B6nWt7fx2RCoUEFNH9u/du7nr+Rdhpt8++2NWK0VzbZpXU9e3v3Ex9/Q5uusdVYqNlacXEjzrX0hTVBCEAN+MsA+yj4vGv8I7/7nJXyfd/+FrbwZ7nyueRKvC4bnb2fs2lu7fFht09jqbdjqbFjrrNjqbM6PVh/X206/T2Xzx+ZWjoG1i8EqMIQYMIQY8Avxc37tzsd9ZaRosVSwf/+d2Gw1zm0GQyjJycua7ae1pu67Jnek2VCOrd6GClJETY8i/hbHQpozZCGN6FnencDXXgwZ22DKFxDUAKZA2HI2fHJRtw6rDAq/UD/8Qv0IoGeWG9vM7fwn0cXH5hJzq89rU9ffZahA1SP/YRiC7dtUoGfeZeRNPAPb/Ixmf1+2zZPJu2YU/fdbKP9vk4U0BfZBROjoUGfCjpoahV+oLKQRnuPVCTwgLgDz8nthrH0WCmXRsHwxAXG+1+PBEGCwt+yM6Jnzdfpdxmke2+pPbrNWW9v8T6PL7zIMYAju+XcZ5mIztPL3Za43szlmM9qi8Qu398pOuC+B6NnRhCSFuPT3JER3eHUCb5wqWFPzqX2e7uS3CKuTeeAdIe8yTk8FOkb89SFw/2/h14/BY7+2PwaGLhpq75V9dgSGQJniJ7yT0rrnblOZnp6ut2/f3mPnE71bd99lHHryUJvHnq6n99w3IkQ7lFJZWuv0ltu9egQuxOl0913G6RK4EL6gW+8NlVIXKqX2KaUOKKXuc1VQQggh2tflBK6U8gNeAC4CxgLXKqXGuiowIdytrYvhvniRXPRN3SmhZAIHHDc3Rin1JnA58I0rAhPC3aSfjvB13SmhDAaaFhEPO7Y1o5Sar5TarpTaXlJS0o3TCSGEaKo7Cby1lRenTGnRWq/UWqdrrdP79+/fjdMJIYRoqjsJ/DAwtMnjIcDR7oUjhBCio7qTwLcBI5VSw5RSgcD/AWtcE5YQQoj2dPkiptbaopT6JfAJ4Aes0lrvdVlkQgghTqtHV2IqpUqAg118eT/guAvDEaIp+fsS7tadv7FErfUpFxF7NIF3h1Jqe2tLSYVwBfn7Eu7mjr8x6dIjhBA+ShK4EEL4KF9K4Cs9HYDo1eTvS7iby//GfKYGLoQQojlfGoELIYRoQhK4EEL4KJ9I4NJ3XLiLUmqVUuqYUirb07GI3kcpNVQp9ZlSKkcptVcpdZdLj+/tNXBH3/HvgAuw91/ZBlyrtZa2taLblFJTgWrgNa11qqfjEb2LUioeiNda71BKGYEs4ApX5S9fGIE7+45rrRuAxr7jQnSb1nojcMLTcYjeSWtdpLXe4fi6CsihlbbbXeULCbxDfceFEMKbKaWSgInAV646pi8k8A71HRdCCG+llAoH3gUWaK0rXXVcX0jg0ndcCOGzlFIB2JP361rr91x5bF9I4NJ3XAjhk5RSCngZyNFar3D18b0+gWutLUBj3/Ec4G3pOy5cRSn1BrAFOEMpdVgpdaOnYxK9yhTgOuA8pdQux8fFrjq4108jFEII0TqvH4ELIYRonSRwIYTwUZLAhRDCR0kCF0IIHyUJXAghfJQkcOEzlFJRSqlfNHk8SCn1Tg+dO0kp9ZOeOJcQHSUJXPiSKMCZwLXWR7XWP+qhcycBksCFV5EELnzJMmC4YzHEcseoOBtAKTVPKfW+UupfSql8pdQvlVILlVI7lVJfKqViHPsNV0qtVUplKaU+V0qNbnkSpdS0JosudjragC4DfuDYdrdSys8Rwzal1G6l1C2O105XSm1USq1WSn2jlPqTUkr+nQm38Pd0AEJ0wn1AqtZ6Aji7uzWVir3bWzBwAFiitZ6olHoG+BnwLPYby96qtd6vlJoM/BE4r8VxFgG3a603O5oQ1TvOvUhrfYnj3POBCq11hlIqCNislFrneH0mMBY4CKwFrgJ6pNQj+hZJ4KI3+czRc7lKKVUB/MuxfQ+Q5kjG5wD/tLeoACColeNsBlYopV4H3tNaH26yf6NZjmM2lnAigZFAA7BVa50HzqX65yIJXLiBJHDRm5iafG1r8tiG/W/dAJQ3juDborVeppT6CLgY+FIpdX4ruyngDq31J802KjWdU9sdS78K4RZSmxO+pAowdvXFjj7M+Uqpq8HeKU4pNb7lfkqp4VrrPVrrJ4HtwOhWzv0JcJujVShKqVFKqTDHc5mO7pkG4MfApq7GLMTpSAIXPkNrXYq91pytlFrexcPMBW5USn0N7KX12/MtcJzja6AO+DewG7Aopb5WSt0N/AX4BtjhuJD6Z06+o92C/aJnNpAPrO5irEKclnQjFMKFHCUU58VOIdxJRuBCCOGjZAQuhBA+SkbgQgjhoySBCyGEj5IELoQQPkoSuBBC+ChJ4EII4aP+H54jY/fYGN9uAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -871,230 +1136,20 @@
       "needs_background": "light"
      },
      "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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eycDBubwHxem/s4CvAadn+enA2bl8DPAjih8hzgZuy/KJwAN5PyGXJ9QY92eAHwDX5/pSYH4uXwD8TS5/HLggl+cDV+XyrOz7ccCMfE3G1BjvEuCjubwLMH4k9zHF1QfWAK8o9e+HR1o/A+8EDgbuKZW1rF+B24G35jY/Ao6uId45wNhcPrsUb8O+Y4jPkGavT6tjzvJpFGd/rgUmjYQ+ruXN26lbdsqNpfUzgDM6HVcpnmuB9wCrgMlZNhlYlcvfBU4stV+V9ScC3y2VD2jX4hinAjcBRwDX5z/ZY6U33Et9nP/Mb83lsdlOg/u93K6GePek+ODVoPKR3Mf9l6+ZmP12PXDUSOxnYD8Gfvi2pF+z7jel8gHtWhXvoLq/AC7P5YZ9R5PPkKHeB3XEDFwNvBl4kJeTRUf7uNcOQ43Ya0jloYODgNuAfSJiI0De753NmsXfzr/r28DngBdz/VXA4xGxtcFzvxRX1j+R7dsZ7/7Ao8D3VBw6u1DSbozgPo6Ih4BvAOuAjRT9dgcju5/7tapfp+Ty4PI6fYTi2zUVcTUqH+p90FKSjgMeioi7BlV1tI97LVlUXkOqEyTtDlwDnBYRTw7VtEFZDFHeUpKOBTZFxB3bEdNQde18HcZS7MafHxEHUVzmZaixqo7HnMf551Ec/ngtsBvF5febPX/HY94OOxpjW2OXtAjYClzeX7SDcbXrPfhKYBHwhUbVTWJoS8y9liwqryHVbpJ2pkgUl0fED7P4EUmTs34ysCnLm8Xfrr/rbcBxkh6kuAz8ERR7GuNVXHJl8HO/FFfW7wVsbmO8/TFsiIjbcv1qiuQxUvsY4N3Amoh4NCKeB34I/Dkju5/7tapfN+Ty4PKWywHfY4EPRh6PGUa8j9H89Wml11F8ibgr34dTgV9Kes0wYm5tH7fy+GanbxTfMh/Izu4fnDqgg/EIuAT49qDyrzNwkPBrufxeBg5g3Z7lEymOy0/I2xpgYs2xv4uXB7j/g4EDex/P5VMZOPC6NJcPYODg4QPUO8D9M+ANufzF7N8R28fAYcBK4JUZxxLgkyOxn9l2zKJl/UpxjbfZvDz4ekwN8c4F7gVePahdw75jiM+QZq9Pq2MeVPcgL49ZdLSPa3nzdvJGccbAbynOaFjU4VjeTrHbdzfwq7wdQ3H88yZgdd73v7CimBHwfuDXwCGlx/oI0Je3k9sQ+7t4OVnsT3FWRV++YcZl+a653pf1+5e2X5R/xyr+yLNctiPWA4EV2c//lW+YEd3HwJeA3wD3AJfmh9aI6mfgCooxlecpvqWe0sp+BQ7Jv/9+4DsMOkmhRfH2URzP73//XVDVdzT5DGn2+rQ65kH1D/JysuhoH/sX3GZmVqnXxizMzKwGThZmZlbJycLMzCo5WZiZWSUnCzMzq+RkYdYGKq5AvEbSxFyfkOvTOx2b2fZwsjBrg4hYD5wPnJVFZwGLI2Jt56Iy237+nYVZm+SlX+4ALgY+BhwUEc91Niqz7TO2uomZtUJEPC/p74H/BuY4UVg38WEos/Y6muLyDn/a6UDMdoSThVmbSDqQYvKr2cCn+6/eatYNnCzM2iDnPj6fYk6TdRRXb/1GZ6My235OFmbt8TFgXUQsy/XzgDdKOryDMZltN58NZWZmlbxnYWZmlZwszMyskpOFmZlVcrIwM7NKThZmZlbJycLMzCo5WZiZWSUnCzMzq/T/ZrEhLGpeDyYAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Advance SpeciesEvolver by one unit of time. Time is dimensionless in this tutorial.\n",
-    "dt = 1\n",
-    "se.run_one_step(dt)\n",
-    "\n",
-    "# Plot zones.\n",
-    "for zone in sc.zones:\n",
-    "    plt.figure()\n",
-    "    imshow_grid(mg, zone.mask, allow_colorbar=False, plot_name='zone in white')\n",
-    "\n",
-    "se.species"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "   time\n",
-      "0     0\n",
-      "1     1\n",
-      "2     2\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[<landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23a30490>,\n",
-       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a24c0bb90>,\n",
-       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23ab5f90>,\n",
-       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23ab5750>,\n",
-       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23ab5fd0>,\n",
-       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23ab5450>,\n",
-       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23ab50d0>,\n",
-       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23ab5510>,\n",
-       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23ab5650>,\n",
-       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a24407d10>,\n",
-       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a24015b50>,\n",
-       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a2400d510>,\n",
-       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a2400d050>,\n",
-       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a2400de90>,\n",
-       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a2400d8d0>,\n",
-       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a2400d410>,\n",
-       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a2400d1d0>,\n",
-       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a2400d810>]"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
-    "print(se.record)\n",
-    "se.run_one_step(dt)\n",
-    "\n",
-    "sc._species"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Three species have existed in this model:\n",
-    "\n",
-    "- A.0: The initial species we introduced.\n",
-    "- A.1 and A.2: The child species of A.0. These species were created when the zone of the first time step split into two in the second time step.\n",
+    "time = [0, 1, 2]\n",
+    "temperature = [26, 32, 38]\n",
+    "n_zones = [2, 1, 9]\n",
+    "species_richness = se.record.species_count\n",
     "\n",
-    "All species belong to clade 'A' because the child species (A.1 and A.2) were spawned from the same parent species (A.0)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "OrderedDict([('clade',\n",
-       "              ['A',\n",
-       "               'B',\n",
-       "               'B',\n",
-       "               'B',\n",
-       "               'B',\n",
-       "               'A',\n",
-       "               'B',\n",
-       "               'A',\n",
-       "               'A',\n",
-       "               'B',\n",
-       "               'A',\n",
-       "               'B',\n",
-       "               'B',\n",
-       "               'B',\n",
-       "               'A',\n",
-       "               'B',\n",
-       "               'A',\n",
-       "               'B',\n",
-       "               'A',\n",
-       "               'A',\n",
-       "               'A']),\n",
-       "             ('species_number',\n",
-       "              [0,\n",
-       "               0,\n",
-       "               1,\n",
-       "               8,\n",
-       "               10,\n",
-       "               6,\n",
-       "               5,\n",
-       "               1,\n",
-       "               7,\n",
-       "               7,\n",
-       "               8,\n",
-       "               2,\n",
-       "               4,\n",
-       "               9,\n",
-       "               9,\n",
-       "               6,\n",
-       "               4,\n",
-       "               3,\n",
-       "               3,\n",
-       "               2,\n",
-       "               5]),\n",
-       "             ('time_appeared',\n",
-       "              [0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]),\n",
-       "             ('latest_time',\n",
-       "              [1, 0, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]),\n",
-       "             ('object',\n",
-       "              [<landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a24daf2d0>,\n",
-       "               <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x8225c1ed0>,\n",
-       "               <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a24daf250>,\n",
-       "               <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a2400d410>,\n",
-       "               <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a2400d810>,\n",
-       "               <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23ab5450>,\n",
-       "               <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a2400d050>,\n",
-       "               <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23a30490>,\n",
-       "               <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23ab50d0>,\n",
-       "               <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a2400d8d0>,\n",
-       "               <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23ab5510>,\n",
-       "               <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a24407d10>,\n",
-       "               <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a2400d510>,\n",
-       "               <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a2400d1d0>,\n",
-       "               <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23ab5650>,\n",
-       "               <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a2400de90>,\n",
-       "               <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23ab5750>,\n",
-       "               <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a24015b50>,\n",
-       "               <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23ab5f90>,\n",
-       "               <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a24c0bb90>,\n",
-       "               <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23ab5fd0>])])"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "se._delegate._species"
+    "plt.plot(time, temperature, 'o-c', label='temperature (\\N{DEGREE SIGN}C)')\n",
+    "plt.plot(time, n_zones, 's-m', label='number of zones')\n",
+    "plt.plot(time, se.record.species_count, 'd-y', label='species richness')\n",
+    "plt.xlabel('time step')\n",
+    "plt.xticks(time)\n",
+    "_ = plt.legend()"
    ]
   },
   {
@@ -1138,52 +1193,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "3.59590309985 19.9999927129\n",
-      "bbb 0\n",
-      "bbb 0\n",
-      "bbb 0\n",
-      "bbb 0\n",
-      "bbb 0\n",
-      "bbb 0\n",
-      "bbb 0\n",
-      "bbb 0\n",
-      "bbb 0\n",
-      "bbb 0\n",
-      "bbb 0\n",
-      "bbb 0\n",
-      "bbb 0\n",
-      "bbb 0\n",
-      "bbb 0\n",
-      "bbb 0\n",
-      "bbb 0\n",
-      "bbb 0\n"
+      "3.59590309985 19.9999927129\n"
      ]
     }
    ],
    "source": [
-    "temperature_at_sea_level = 20\n",
-    "mg.at_node['land_surface_air__temperature'] = -0.006 * mg.at_node['topographic__elevation'] + temperature_at_sea_level\n",
+    "# Update the surface temperature field.\n",
+    "temperature_at_base_level = 20\n",
+    "mg.at_node['land_surface_air__temperature'] = lapse_rate * z + temperature_at_base_level\n",
+    "\n",
     "print(mg.at_node['land_surface_air__temperature'].min(), mg.at_node['land_surface_air__temperature'].max())\n",
+    "\n",
+    "sc.run_one_step(dt)\n",
     "se.run_one_step(dt)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1234,7 +1268,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1259,189 +1293,181 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>clade</th>\n",
-       "      <th>species_number</th>\n",
+       "      <th>number</th>\n",
        "      <th>time_appeared</th>\n",
        "      <th>latest_time</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>0</td>\n",
+       "      <th>0</th>\n",
        "      <td>A</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>1</td>\n",
+       "      <th>1</th>\n",
        "      <td>A</td>\n",
        "      <td>1</td>\n",
        "      <td>2</td>\n",
-       "      <td>3</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>2</td>\n",
+       "      <th>2</th>\n",
        "      <td>A</td>\n",
        "      <td>2</td>\n",
        "      <td>2</td>\n",
-       "      <td>3</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>3</td>\n",
+       "      <th>3</th>\n",
        "      <td>A</td>\n",
        "      <td>3</td>\n",
        "      <td>2</td>\n",
-       "      <td>3</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>4</td>\n",
+       "      <th>4</th>\n",
        "      <td>A</td>\n",
        "      <td>4</td>\n",
        "      <td>2</td>\n",
-       "      <td>3</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>5</td>\n",
+       "      <th>5</th>\n",
        "      <td>A</td>\n",
        "      <td>5</td>\n",
        "      <td>2</td>\n",
-       "      <td>3</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>6</td>\n",
+       "      <th>6</th>\n",
        "      <td>A</td>\n",
        "      <td>6</td>\n",
        "      <td>2</td>\n",
-       "      <td>3</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>7</td>\n",
+       "      <th>7</th>\n",
        "      <td>A</td>\n",
        "      <td>7</td>\n",
        "      <td>2</td>\n",
-       "      <td>3</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>8</td>\n",
+       "      <th>8</th>\n",
        "      <td>A</td>\n",
        "      <td>8</td>\n",
        "      <td>2</td>\n",
-       "      <td>3</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>9</td>\n",
+       "      <th>9</th>\n",
        "      <td>A</td>\n",
        "      <td>9</td>\n",
        "      <td>2</td>\n",
-       "      <td>3</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>10</td>\n",
+       "      <th>10</th>\n",
        "      <td>B</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>11</td>\n",
+       "      <th>11</th>\n",
        "      <td>B</td>\n",
        "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>12</td>\n",
+       "      <th>12</th>\n",
        "      <td>B</td>\n",
        "      <td>2</td>\n",
        "      <td>2</td>\n",
-       "      <td>3</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>13</td>\n",
+       "      <th>13</th>\n",
        "      <td>B</td>\n",
        "      <td>3</td>\n",
        "      <td>2</td>\n",
-       "      <td>3</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>14</td>\n",
+       "      <th>14</th>\n",
        "      <td>B</td>\n",
        "      <td>4</td>\n",
        "      <td>2</td>\n",
-       "      <td>3</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>15</td>\n",
+       "      <th>15</th>\n",
        "      <td>B</td>\n",
        "      <td>5</td>\n",
        "      <td>2</td>\n",
-       "      <td>3</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>16</td>\n",
+       "      <th>16</th>\n",
        "      <td>B</td>\n",
        "      <td>6</td>\n",
        "      <td>2</td>\n",
-       "      <td>3</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>17</td>\n",
+       "      <th>17</th>\n",
        "      <td>B</td>\n",
        "      <td>7</td>\n",
        "      <td>2</td>\n",
-       "      <td>3</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>18</td>\n",
+       "      <th>18</th>\n",
        "      <td>B</td>\n",
        "      <td>8</td>\n",
        "      <td>2</td>\n",
-       "      <td>3</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>19</td>\n",
+       "      <th>19</th>\n",
        "      <td>B</td>\n",
        "      <td>9</td>\n",
        "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>20</td>\n",
-       "      <td>B</td>\n",
-       "      <td>10</td>\n",
        "      <td>2</td>\n",
-       "      <td>3</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "   clade  species_number  time_appeared  latest_time\n",
-       "0      A               0              0            1\n",
-       "1      A               1              2            3\n",
-       "2      A               2              2            3\n",
-       "3      A               3              2            3\n",
-       "4      A               4              2            3\n",
-       "5      A               5              2            3\n",
-       "6      A               6              2            3\n",
-       "7      A               7              2            3\n",
-       "8      A               8              2            3\n",
-       "9      A               9              2            3\n",
-       "10     B               0              0            0\n",
-       "11     B               1              1            1\n",
-       "12     B               2              2            3\n",
-       "13     B               3              2            3\n",
-       "14     B               4              2            3\n",
-       "15     B               5              2            3\n",
-       "16     B               6              2            3\n",
-       "17     B               7              2            3\n",
-       "18     B               8              2            3\n",
-       "19     B               9              2            3\n",
-       "20     B              10              2            3"
+       "   clade  number  time_appeared  latest_time\n",
+       "0      A       0              0            1\n",
+       "1      A       1              2            2\n",
+       "2      A       2              2            2\n",
+       "3      A       3              2            2\n",
+       "4      A       4              2            2\n",
+       "5      A       5              2            2\n",
+       "6      A       6              2            2\n",
+       "7      A       7              2            2\n",
+       "8      A       8              2            2\n",
+       "9      A       9              2            2\n",
+       "10     B       0              0            1\n",
+       "11     B       1              2            2\n",
+       "12     B       2              2            2\n",
+       "13     B       3              2            2\n",
+       "14     B       4              2            2\n",
+       "15     B       5              2            2\n",
+       "16     B       6              2            2\n",
+       "17     B       7              2            2\n",
+       "18     B       8              2            2\n",
+       "19     B       9              2            2"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1452,18 +1478,100 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>time</th>\n",
+       "      <th>species_count</th>\n",
+       "      <th>speciation_count</th>\n",
+       "      <th>extinction_count</th>\n",
+       "      <th>pseudoextinction_count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>18</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   time  species_count  speciation_count  extinction_count  \\\n",
+       "0     0              2               NaN               NaN   \n",
+       "1     1              2               0.0               0.0   \n",
+       "2     2             18              18.0               2.0   \n",
+       "3     3              0               0.0              18.0   \n",
+       "\n",
+       "   pseudoextinction_count  \n",
+       "0                     NaN  \n",
+       "1                     0.0  \n",
+       "2                     2.0  \n",
+       "3                     0.0  "
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "temperature_at_sea_level = 20\n",
-    "mg.at_node['land_surface_air__temperature'] = 0.006 * mg.at_node['topographic__elevation'] + temperature_at_sea_level\n",
-    "se.run_one_step(dt)"
+    "se.record"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -1487,508 +1595,171 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>clade</th>\n",
-       "      <th>species_number</th>\n",
-       "      <th>time_appeared</th>\n",
-       "      <th>latest_time</th>\n",
+       "      <th>time</th>\n",
+       "      <th>capture_count</th>\n",
+       "      <th>area_captured_max</th>\n",
+       "      <th>area_captured_sum</th>\n",
+       "      <th>fragmentation_count</th>\n",
+       "      <th>zone_count</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
+       "      <th>0</th>\n",
        "      <td>0</td>\n",
-       "      <td>A</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <th>1</th>\n",
        "      <td>1</td>\n",
-       "      <td>A</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>32542500.0</td>\n",
+       "      <td>32542500.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <th>2</th>\n",
        "      <td>2</td>\n",
-       "      <td>A</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>9.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <th>3</th>\n",
        "      <td>3</td>\n",
-       "      <td>A</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>A</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>5</td>\n",
-       "      <td>A</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>6</td>\n",
-       "      <td>A</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>7</td>\n",
-       "      <td>A</td>\n",
-       "      <td>7</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>8</td>\n",
-       "      <td>A</td>\n",
-       "      <td>8</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>9</td>\n",
-       "      <td>A</td>\n",
-       "      <td>9</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>10</td>\n",
-       "      <td>B</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>11</td>\n",
-       "      <td>B</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>12</td>\n",
-       "      <td>B</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>13</td>\n",
-       "      <td>B</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>14</td>\n",
-       "      <td>B</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>15</td>\n",
-       "      <td>B</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>16</td>\n",
-       "      <td>B</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>17</td>\n",
-       "      <td>B</td>\n",
-       "      <td>7</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>18</td>\n",
-       "      <td>B</td>\n",
-       "      <td>8</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>19</td>\n",
-       "      <td>B</td>\n",
-       "      <td>9</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>20</td>\n",
-       "      <td>B</td>\n",
-       "      <td>10</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "   clade  species_number  time_appeared  latest_time\n",
-       "0      A               0              0            1\n",
-       "1      A               1              2            3\n",
-       "2      A               2              2            3\n",
-       "3      A               3              2            3\n",
-       "4      A               4              2            3\n",
-       "5      A               5              2            3\n",
-       "6      A               6              2            3\n",
-       "7      A               7              2            3\n",
-       "8      A               8              2            3\n",
-       "9      A               9              2            3\n",
-       "10     B               0              0            0\n",
-       "11     B               1              1            1\n",
-       "12     B               2              2            3\n",
-       "13     B               3              2            3\n",
-       "14     B               4              2            3\n",
-       "15     B               5              2            3\n",
-       "16     B               6              2            3\n",
-       "17     B               7              2            3\n",
-       "18     B               8              2            3\n",
-       "19     B               9              2            3\n",
-       "20     B              10              2            3"
+       "   time  capture_count  area_captured_max  area_captured_sum  \\\n",
+       "0     0            NaN                NaN                NaN   \n",
+       "1     1            1.0         32542500.0         32542500.0   \n",
+       "2     2            0.0                0.0                0.0   \n",
+       "3     3            0.0                0.0                0.0   \n",
+       "\n",
+       "   fragmentation_count  zone_count  \n",
+       "0                  NaN         NaN  \n",
+       "1                  0.0         1.0  \n",
+       "2                  1.0         9.0  \n",
+       "3                  0.0         0.0  "
       ]
      },
-     "execution_count": 22,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "se.species"
+    "sc.record"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "DataFrame methods can be used on `record`. For example, here we get the maximum capture count."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "16.0000072871 32.4040969001\n"
-     ]
-    },
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sc.record.capture_count.max()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Query species\n",
+    "\n",
+    "SpeciesEvolver provides methods that return species objects in the event you need to work with specific species instances.\n",
+    "\n",
+    "The method, `species_at_time` returns species that were extant at the inputted time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>clade</th>\n",
-       "      <th>species_number</th>\n",
-       "      <th>time_appeared</th>\n",
-       "      <th>latest_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>A</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>A</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>A</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>A</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>A</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>5</td>\n",
-       "      <td>A</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>6</td>\n",
-       "      <td>A</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>7</td>\n",
-       "      <td>A</td>\n",
-       "      <td>7</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>8</td>\n",
-       "      <td>A</td>\n",
-       "      <td>8</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>9</td>\n",
-       "      <td>A</td>\n",
-       "      <td>9</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>10</td>\n",
-       "      <td>B</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>11</td>\n",
-       "      <td>B</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>12</td>\n",
-       "      <td>B</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>13</td>\n",
-       "      <td>B</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>14</td>\n",
-       "      <td>B</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>15</td>\n",
-       "      <td>B</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>16</td>\n",
-       "      <td>B</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>17</td>\n",
-       "      <td>B</td>\n",
-       "      <td>7</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>18</td>\n",
-       "      <td>B</td>\n",
-       "      <td>8</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>19</td>\n",
-       "      <td>B</td>\n",
-       "      <td>9</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>20</td>\n",
-       "      <td>B</td>\n",
-       "      <td>10</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
       "text/plain": [
-       "   clade  species_number  time_appeared  latest_time\n",
-       "0      A               0              0            1\n",
-       "1      A               1              2            3\n",
-       "2      A               2              2            3\n",
-       "3      A               3              2            3\n",
-       "4      A               4              2            3\n",
-       "5      A               5              2            3\n",
-       "6      A               6              2            3\n",
-       "7      A               7              2            3\n",
-       "8      A               8              2            3\n",
-       "9      A               9              2            3\n",
-       "10     B               0              0            0\n",
-       "11     B               1              1            1\n",
-       "12     B               2              2            3\n",
-       "13     B               3              2            3\n",
-       "14     B               4              2            3\n",
-       "15     B               5              2            3\n",
-       "16     B               6              2            3\n",
-       "17     B               7              2            3\n",
-       "18     B               8              2            3\n",
-       "19     B               9              2            3\n",
-       "20     B              10              2            3"
+       "[<landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a1921f550>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a1921f0d0>]"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "temperature_at_sea_level = 16\n",
-    "mg.at_node['land_surface_air__temperature'] = 0.006 * mg.at_node['topographic__elevation'] + temperature_at_sea_level\n",
-    "se.run_one_step(dt)\n",
-    "print(mg.at_node['land_surface_air__temperature'].min(), mg.at_node['land_surface_air__temperature'].max())\n",
-    "se.species\n",
-    "\n"
+    "se.species_at_time(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The method, `species_with_identifier` takes various combinations of species identifiers. See the documentation of this method for details. Here we get all the species of clade A."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>6</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
       "text/plain": [
-       "   time\n",
-       "0     0\n",
-       "1     1\n",
-       "2     2\n",
-       "3     3\n",
-       "4     4\n",
-       "5     5\n",
-       "6     6"
+       "[<landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a1921f550>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a195c3410>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a195c3450>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a195c3490>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a195c3890>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a195c33d0>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a195c38d0>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a195c3950>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a195c3310>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a195c3bd0>]"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "se.record"
+    "se.species_with_identifier('A')"
    ]
   },
   {
@@ -2015,7 +1786,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
   }
  },
  "nbformat": 4,
diff --git a/species_evolution/Modeling stream-based species.ipynb b/species_evolution/Modeling stream-based species.ipynb
index a8cc2ba..4320368 100644
--- a/species_evolution/Modeling stream-based species.ipynb	
+++ b/species_evolution/Modeling stream-based species.ipynb	
@@ -706,7 +706,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,
diff --git a/species_evolution/untitled.txt b/species_evolution/untitled.txt
deleted file mode 100644
index e69de29..0000000

From 7d01bab51f6ce6dc1ad29a0b7ccc8ad20056fc80 Mon Sep 17 00:00:00 2001
From: nathanlyons <nathan.j.lyons@gmail.com>
Date: Sat, 23 Nov 2019 16:50:13 -0500
Subject: [PATCH 13/15] improvements to text

---
 species_evolution/Customize_ZoneSpecies.ipynb | 247 +++++++++++-------
 .../Introduction_to_SpeciesEvolver.ipynb      | 222 +++++++++-------
 2 files changed, 273 insertions(+), 196 deletions(-)

diff --git a/species_evolution/Customize_ZoneSpecies.ipynb b/species_evolution/Customize_ZoneSpecies.ipynb
index 01f509d..bbe56e7 100644
--- a/species_evolution/Customize_ZoneSpecies.ipynb
+++ b/species_evolution/Customize_ZoneSpecies.ipynb
@@ -73,7 +73,7 @@
    "outputs": [],
    "source": [
     "from landlab.components import SpeciesEvolver\n",
-    "from landlab.components.species_evolution import ZoneSpeciesController, ZoneSpecies\n",
+    "from landlab.components.species_evolution import ZoneController, ZoneSpecies\n",
     "from landlab.io import read_esri_ascii\n",
     "from landlab.plot import imshow_grid\n",
     "import matplotlib.pyplot as plt\n",
@@ -116,7 +116,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -128,7 +128,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -154,8 +154,10 @@
     "\n",
     "# Set surface temperature at sea level.\n",
     "\n",
-    "temperature_at_sea_level = 14.5\n",
-    "mg.at_node['land_surface_air__temperature'] = 0.006 * mg.at_node['topographic__elevation'] + temperature_at_sea_level\n",
+    "lapse_rate = -0.006\n",
+    "temperature_at_base_level = 26\n",
+    "T = lapse_rate * z + temperature_at_base_level\n",
+    "mg.add_field('node', 'land_surface_air__temperature', T)\n",
     "\n",
     "# Plot temperature field.\n",
     "\n",
@@ -170,12 +172,15 @@
     "\n",
     "# Create a zone function and plot the initial zone extend.\n",
     "\n",
-    "def zone_func(grid, **kwargs):\n",
+    "def zone_func(grid):\n",
     "    \"\"\"Get a mask of the total zone extent.\"\"\"\n",
     "    T = grid.at_node['land_surface_air__temperature']\n",
-    "    T_min = 15\n",
     "    T_max = 25\n",
-    "    return np.all([T >= T_min, T <= T_max], 0)\n",
+    "    T_min = 15\n",
+    "    zone_extent_mask = np.all([T >= T_min, T <= T_max], 0)\n",
+    "    return zone_extent_mask\n",
+    "\n",
+    "sc = ZoneController(mg, zone_func, minimum_area=50000)\n",
     "\n",
     "plt.figure()\n",
     "imshow_grid(mg, zone_func(mg), plot_name='initial zone extent (white)',\n",
@@ -190,44 +195,46 @@
     "\n",
     "Default implementation of dispersal method:\n",
     "```python\n",
-    "def _evaluate_dispersal(self, prior_zone, potential_zones):\n",
-    "    \"\"\"Determine the zones where the species will disperse.\n",
+    "def _evaluate_dispersal(self, population):\n",
+    "    \"\"\"Set the range of the species as it results from dispersal.\n",
     "\n",
-    "    The default implementation returns all of the potential zones.\n",
+    "    The default implementation does not modify population dispersal in\n",
+    "    stage 1.\n",
     "\n",
     "    Parameters\n",
     "    ----------\n",
-    "    prior_zone : Zone\n",
-    "        A zone of the species in the prior time step.\n",
-    "    potential_zones : list of Zones\n",
-    "        The  zones where the species can potentially disperse.\n",
-    "\n",
-    "    Returns\n",
-    "    -------\n",
-    "    list of Zones\n",
-    "        The zones where the species will disperse.\n",
+    "    population : Population\n",
+    "        A population of the species.\n",
     "    \"\"\"\n",
-    "    destination_zones = potential_zones\n",
-    "    return destination_zones\n",
+    "    ...  # pragma: no cover\n",
     "```"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
     "class MySpecies(ZoneSpecies):\n",
-    "    def _evaluate_dispersal(self, prior_zone, potential_zones):\n",
-    "        # Create an empty list to fill.\n",
-    "        destination_zones = []\n",
+    "    def __init__(self, initial_zones, parent_species=None,\n",
+    "                 allopatric_wait_time=0, pseudoextinction=True):\n",
+    "        super(MySpecies, self).__init__(initial_zones, parent_species,\n",
+    "                                        allopatric_wait_time, pseudoextinction)\n",
+    "        \n",
+    "        # Set body size randomly.\n",
+    "        self.body_size = random.uniform(4, 400)\n",
     "        \n",
-    "        for zone in potential_zones:\n",
-    "            if random.random() > 0.5:\n",
-    "                destination_zones.append(zone)\n",
-    "        print(22,destination_zones)\n",
-    "        return destination_zones"
+    "    #def _evaluate_dispersal(self, population):\n",
+    "    #    zone_species = population.zone.species\n",
+    "    #    zone_body_sizes = [s.body_size for s in zone_species]\n",
+    "    #    if self.body_size == max(zone_body_sizes):\n",
+    "    #        print('max_size', self.body_size, zone_body_sizes)\n",
+    "            \n",
+    "    def _evaluate_extinction(self, speciation_occurred):\n",
+    "        extinct_by_habitat_loss = super()._evaluate_extinction(speciation_occurred)\n",
+    "        extinct_by_size_probability = \n",
+    "        return extinct_by_habitat_loss or extinct_by_size_probability"
    ]
   },
   {
@@ -239,7 +246,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -264,65 +271,74 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>clade</th>\n",
-       "      <th>species_number</th>\n",
+       "      <th>number</th>\n",
        "      <th>time_appeared</th>\n",
        "      <th>latest_time</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>0</td>\n",
+       "      <th>0</th>\n",
        "      <td>A</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>B</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "  clade  species_number  time_appeared  latest_time\n",
-       "0     A               0              0            0"
+       "  clade  number  time_appeared  latest_time\n",
+       "0     A       0              0            0\n",
+       "1     B       0              0            0"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "sc = ZoneSpeciesController(se, zone_func, grid=mg)\n",
-    "sc.populate_each_zone(1, species_type=MySpecies)\n",
-    "se.species\n",
-    "#sc.zones"
+    "species = sc.populate_zones_uniformly(1, species_type=MySpecies)\n",
+    "se.introduce_species(species)\n",
+    "\n",
+    "# Display the species data frame.\n",
+    "se.species"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "22 [<landlab.components.species_evolution.zone.Zone object at 0x1a1bda71d0>]\n"
+      "max_size 3.431604728310963 [3.431604728310963, 1.2034904150925987]\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "[<landlab.components.species_evolution.zone.Zone at 0x1a1bda71d0>]"
+       "[<landlab.components.species_evolution.zone.Zone at 0x1a26e332d0>]"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -331,24 +347,107 @@
       "needs_background": "light"
      },
      "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "# Update surface temperature at sea level.\n",
-    "temperature_at_sea_level = 18\n",
-    "mg.at_node['land_surface_air__temperature'] = 0.006 * mg.at_node['topographic__elevation'] + temperature_at_sea_level\n",
+    "# Update the surface temperature field.\n",
+    "temperature_at_base_level = 32\n",
+    "mg.at_node['land_surface_air__temperature'] = lapse_rate * z + temperature_at_base_level\n",
     "\n",
     "# Plot temperature field.\n",
     "imshow_grid(mg, 'land_surface_air__temperature',\n",
     "            colorbar_label='surface air\\ntemperature (C$\\degree$)',\n",
     "            shrink=0.4, cmap='RdBu')\n",
     "            \n",
+    "plt.figure()\n",
+    "imshow_grid(mg, zone_func(mg), plot_name='initial zone extent (white)',\n",
+    "            allow_colorbar=False)\n",
+    "    \n",
     "# Advance SpeciesEvolver by one unit of time. Time is dimensionless in this tutorial.\n",
     "dt = 1\n",
+    "sc.run_one_step(dt)\n",
     "se.run_one_step(dt)\n",
     "sc.zones"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>clade</th>\n",
+       "      <th>number</th>\n",
+       "      <th>time_appeared</th>\n",
+       "      <th>latest_time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>A</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>B</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  clade  number  time_appeared  latest_time\n",
+       "0     A       0              0            1\n",
+       "1     B       0              0            1"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "se.species"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -362,7 +461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -385,52 +484,6 @@
     "This code block may take a couple minutes to run."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for time_i in range(nt):\n",
-    "    # Uniformly uplift.\n",
-    "    z[mg.core_nodes] += uplift_per_step\n",
-    "\n",
-    "    # Increment earth surface process components.\n",
-    "    fr.run_one_step()\n",
-    "    sp.run_one_step(dt)\n",
-    "    lf.run_one_step(dt)\n",
-    "\n",
-    "    elapsed_time = dt * (time_i + 1)\n",
-    "\n",
-    "    if elapsed_time % dt_se == 0:\n",
-    "        # Run SpeciesEvolver with updated zones.\n",
-    "        zones = Zone.get_zones_with_area_threshold(mg, min_stream_area)\n",
-    "        se.run_one_step(elapsed_time, zones)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plot results\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.subplot(1, 2, 1)\n",
-    "imshow_grid(mg, 'topographic__elevation', colorbar_label='elevation (m)')\n",
-    "\n",
-    "plt.subplot(1, 2, 2)\n",
-    "plot.imshow_zones(mg, zones)\n",
-    "\n",
-    "plot.number_of_species(se.record, se.species)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -455,7 +508,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
   }
  },
  "nbformat": 4,
diff --git a/species_evolution/Introduction_to_SpeciesEvolver.ipynb b/species_evolution/Introduction_to_SpeciesEvolver.ipynb
index a362d8a..4a1ea89 100644
--- a/species_evolution/Introduction_to_SpeciesEvolver.ipynb
+++ b/species_evolution/Introduction_to_SpeciesEvolver.ipynb
@@ -19,7 +19,7 @@
     "<small>For more Landlab tutorials, click here: <a href=\"https://github.com/landlab/landlab/wiki/Tutorials\">https://github.com/landlab/landlab/wiki/Tutorials</a></small>\n",
     "<hr>\n",
     "\n",
-    "This tutorial provides an introduction to SpeciesEvolver. We will build and run a model that exemplifies SpeciesEvolver functionality. You will have the opportunity to\n",
+    "In this tutorial we will build and run a model that exemplifies SpeciesEvolver functionality. You will have the opportunity to\n",
     "* Populate a model grid with species. \n",
     "* View the SpeciesEvolver record that stores model variables over time.\n",
     "* See how species are assigned identifiers.\n",
@@ -32,27 +32,30 @@
    "source": [
     "## Example model: climate change impacts on species distribution and evolution\n",
     "\n",
-    "Here we explore the large spatiotemporal scale response of zone-based species to climate change.\n",
+    "### Premise\n",
     "\n",
-    "Ecosystems are zonated by elevation.\n",
+    "The elevational distribution of ecosystems is largely controlled by air temperature. Ecosystems zonate by elevation largely because temperature decreases with altitude. Longterm temperature change drives elevational redistribution of ecosystems.\n",
     "\n",
-    "In this model we\n",
+    "### Approach\n",
     "\n",
-    "* Set surface air temperture across a model grid. Temperature will be a function of elevation.\n",
-    "* Populate species onto the grid. These species exist within a fixed elevation interval.\n",
-    "* Simulate a time step where surface temperature has changed and species respond to this change.\n",
-    "\n",
-    "The model progresses in terms of time steps with unprescribed duration.\n",
+    "We model the macroevolutionary processes of species in response to temperature change. For simplicity, species distribution is set only by temperature. Temperature is set by elevation using a lapse rate, the decrease of temperature with elevation. Also for simplicity, topography does not change during the duration of the model.\n",
     "\n",
-    "Use `ZoneSpecies`. This species type relies on `Zone` objects to evaluate evolutionary processes.\n",
+    "The model progresses in terms of time steps with an unprescribed duration.\n",
     "\n",
-    "\n",
-    "The type of species that will be simulated is evolved by these processes:\n",
+    "We will use `ZoneSpecies`. This species type is distributed with SpeciesEvolver and relies on `Zone` objects to evaluate evolutionary processes. The macroevolutionary processes built into ZoneSpecies are\n",
     "* dispersal: geographic range expansion\n",
     "* speciation: lineage splitting\n",
     "* extinction: lineage termination\n",
     "\n",
-    "First we need to create a grid and populate it with the initial species."
+    "Species geographic range in this model are set to a temperature range, meaning the extent of zones is within this temperature range and all species exist only in this temperature range. \n",
+    "\n",
+    "### Summary\n",
+    "\n",
+    "In this model, we\n",
+    "\n",
+    "* Set surface air temperture across a model grid. Temperature will be a function of elevation.\n",
+    "* Populate species onto the grid. These species exist within a fixed temperature interval.\n",
+    "* Simulate a time step where surface temperature has changed and species respond to this change."
    ]
   },
   {
@@ -61,9 +64,7 @@
    "source": [
     "### Import modules\n",
     "\n",
-    "Individual and multiple species are managed by a `SpeciesController`.\n",
-    "\n",
-    "ZoneSpecies are managed by `ZoneController`, so we must import this class along with the SpeciesEvolver component and the other modules used in this notebook."
+    "The zones of ZoneSpecies are managed by a `ZoneController`, so we must import this class along with the SpeciesEvolver component and the other modules used in this notebook."
    ]
   },
   {
@@ -114,7 +115,7 @@
     "(mg, z) = read_esri_ascii('model_grid_steady_state_elevation.txt')\n",
     "mg.at_node['topographic__elevation'] = z\n",
     "\n",
-    "# Set the top and bottom grid boundaries to open. \n",
+    "# Set the top and bottom grid boundaries to open.\n",
     "mg.set_closed_boundaries_at_grid_edges(right_is_closed=True,\n",
     "                                       top_is_closed=False,\n",
     "                                       left_is_closed=True,\n",
@@ -178,16 +179,20 @@
    "source": [
     "### Create a zone function\n",
     "\n",
-    "Zones are portions of a model grid. A `ZoneSpecies` object exists within a zone. `Zone` objects are explained in more detail later in this tutorial.\n",
+    "Zones are portions of a model grid. A `ZoneSpecies` object exists within a zone or multiple zones. `Zone` objects are explained in more detail later in this tutorial.\n",
     "\n",
     "The general workflow we will conduct is\n",
     "\n",
     "* Define a function that demarcates the combined extent of all zones. \n",
     "* Populate each distinct zone with a species.\n",
     "\n",
-    "The zone function must return a numpy array with the length of the grid's `number_of_nodes`. True values indicate where zones should be created. \n",
+    "The zone function acts to return a numpy array where `True` values indicate where zones should be created. Each distinct zone is created where `True` nodes are spatially continuous.\n",
     "\n",
-    "Here, we indicate zones should be created where the air temperature is between 15&deg;C and 25&deg;C, inclusive. The temperature of all nodes is within this interval, therefore all elements of the mask are `True`."
+    "The zone function has two requirements:\n",
+    "1. It must return a numpy array with the length of the grid's `number_of_nodes`.\n",
+    "2. The first parameter must be named `grid`. It is the same Landlab grid supplied to SpeciesEvolver. Additional parameters are allowed.\n",
+    "\n",
+    "Here, we define a zone function that indicates zones should be created where the air temperature is between 15&deg;C and 25&deg;C, inclusive."
    ]
   },
   {
@@ -256,7 +261,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "View the `record` of the component instance. Time and other model variables are stored in the record. The first and second columns are the row number and model time, respectively."
+    "View the `record` of our SpeciesEvolver instance. Time and other model variables are stored in the record. The first and second columns are the row number and model time, respectively. We also see that 0 species exist. We have yet to introduce species."
    ]
   },
   {
@@ -319,15 +324,29 @@
    "source": [
     "### Instantiate a SpeciesController\n",
     "\n",
-    "Next, we instantiate a ZoneController.\n",
-    "\n",
-    "Two distinct areas of the mask are now seperated by the higher elevations in the center of the grid."
+    "Next, we instantiate a ZoneController that will handle the zones."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "sc = ZoneController(mg, zone_func, minimum_area=50000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Two distinct areas of the mask are now seperated by the higher elevations in the center of the grid."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -343,8 +362,6 @@
     }
    ],
    "source": [
-    "sc = ZoneController(mg, zone_func, minimum_area=50000)\n",
-    "\n",
     "# Plot zones.\n",
     "zone_map = np.zeros(mg.number_of_nodes)\n",
     "for i, zone in enumerate(sc.zones):\n",
@@ -362,14 +379,14 @@
    "source": [
     "### Seed zones with species\n",
     "\n",
-    "One species is populated to each of the two initial zones using the ZoneController method, `populate_zones_uniformly`. This method creates the species and introduces it to SpeciesEvolver.\n",
+    "One species is populated to each of the two initial zones using the ZoneController method, `populate_zones_uniformly`. This method creates the species for each of the two zones. All species must be introduced using the SpeciesEvolver method, `introduce_species` in order for the species to be tracked by SpeciesEvolver.\n",
     "\n",
     "The SpeciesEvolver attribute, `species` provides data about all of the introduced species. The data is presented in a Pandas DataFrame. The `clade` and `species_number` identifier elements will be explained later in the tutorial."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -424,7 +441,7 @@
        "1     B       0              0            0"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -442,16 +459,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Simulate a temperature increase and its impact on species distribution\n",
-    "\n",
-    "The temperature at base level is increased by 6&deg;C.\n",
-    "\n",
-    "< FIX COLOR MAP >"
+    "Two species now exist in the initial time."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -494,7 +507,7 @@
        "0     0              2"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -503,9 +516,20 @@
     "se.record"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Simulate a temperature increase and its impact on species distribution\n",
+    "\n",
+    "The temperature at base level is increased by 6&deg;C.\n",
+    "\n",
+    "< FIX COLOR MAP >"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -534,7 +558,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -573,7 +597,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -611,7 +635,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -641,7 +665,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -703,7 +727,7 @@
        "1                     0.0  "
       ]
      },
-     "execution_count": 15,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -714,12 +738,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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GS1ouaW16HpfKJeliSf2S7pF0YG6deWn5tZLm1XotMzMrV5kti38HZlaVLQBujoipwM1pHmAWMDU95gOXQJZcgK8CBwMHAV+tJBgzM+uc0pJFRPwcqL5J0RxgcZpeDBydK788MrcBY9MtV48AlkfEsxGxEVjOlgnIzMxK1ukxi93SrVJJz29M5ZOA9bnlBlJZvXIzM+ugMm+r2gzVKIshyrfcgDSfrAvLzMzarNMtiydT9xLpeUMqHwCm5JabDDw+RPkWImJhREyvd0tAMzNrXaeTxXVA5YymecC1ufLj0llRM4DnUzfVTcDhksalge3DU5mZmXVQad1Qkq4C3gtMkDRAdlbT2cBSSScAjwIfSovfABwJ9AMvAscDRMSzkr4G3JmW+6eIqB40NzOzkimi5hBAT5MUI/F9mZmVRRLAynpd+f4Ft5mZFXKyMDOzQk4WZmZWyMnCzMwKOVmYmVkhJwszMyvkZGFmZoWcLMzMrJCThZmZFXKyMDOzQk4WZmZWyMnCzMwKOVmYmVmhnkkWkmZKWiOpX9KCbsdjZrYt6YlkIWkU8E1gFjANOFbStO5GZWa27eiJZAEcBPRHxEMR8QfgamBOl2MyM9tm9EqymASsz80PpDIzM+uA0m6r2maqUTboVniS5gPzAfbYY49OxGRmNmJEROVueTX1SrIYAKbk5icDj+cXiIiFwEIASU9JegF4umMRtscEeivmXosXei/mXosXHHMnlBXvnvUqeuIe3JJGA78GDgMeA+4EPhwRq4dYZ0W9e8kOV70Wc6/FC70Xc6/FC465E7oRb0+0LCJik6S/A24CRgGXDZUozMysvXoiWQBExA3ADd2Ow8xsW9QrZ0O1YmG3A2hBr8Xca/FC78Xca/GCY+6EjsfbE2MWZmbWXSO5ZWFmZm0y4pLFcLqGlKQpkm6RdL+k1ZI+m8rHS1ouaW16HpfKJeniFPs9kg7MbWteWn6tpHklxz1K0l2SlqX5vSTdnl57iaQdUvmOab4/1ffltnFaKl8j6YiS4x0r6RpJD6R9/Y4e2MefT/8T90q6StJOw20/S7pM0gZJ9+bK2rZfJf25pF+ldS7WUCf5tx7veen/4h5J/yFpbK6u5r6rdwyp9/dpd8y5un+QFJImpPnu7uOIGDEPsjOlHgT2BnYAVgHTuhjPRODANP16stN/pwHnAgtS+QLgnDR9JHAj2Y8QZwC3p/LxwEPpeVyaHldi3CcD3weWpfmlwNw0/W3gU2n608C30/RcYEmanpb2/Y7AXulvMqrEeBcDn0jTOwBjh/M+Jrv6wMPAa3L792PDbT8DfwEcCNybK2vbfgXuAN6R1rkRmFVCvIcDo9P0Obl4a+47hjiG1Pv7tDvmVD6F7OzPR4AJw2Efl/Lh7dYj7ZSbcvOnAad1O65cPNcCfwmsASamsonAmjR9KXBsbvk1qf5Y4NJc+aDl2hzjZOBm4FBgWfonezr3gdu8j9M/8zvS9Oi0nKr3e365EuLdhezAq6ry4byPK5evGZ/22zLgiOG4n4E+Bh9827JfU90DufJBy7Ur3qq6DwBXpuma+446x5ChPgdlxAxcA7wNWMeryaKr+3ikdUMN22tIpa6DA4Dbgd0i4gmA9PzGtFi9+Dv5vr4BnAr8Mc3vCjwXEZtqvPbmuFL982n5Tsa7N/AU8F1lXWffkfQ6hvE+jojHgPOBR4EnyPbbSob3fq5o136dlKary8v0cbJv1xTEVat8qM9BW0l6P/BYRKyqqurqPh5pyaLwGlLdIGln4IfA5yLit0MtWqMshihvK0mzgQ0RsbKBmIaq6+TfYTRZM/6SiDgAeIGse6Sersec+vnnkHV/7A68juzy+/Vev+sxN6DZGDsau6TTgU3AlZWiJuPq1GfwtcDpwFdqVdeJoSMxj7RkUXgNqU6TtD1ZorgyIn6Uip+UNDHVTwQ2pPJ68Xfqfb0TeL+kdWSXgT+UrKUxVtklV6pfe3NcqX4M8GwH463EMBARt6f5a8iSx3DdxwDvAx6OiKci4mXgR8AhDO/9XNGu/TqQpqvL2y4N+M4GPhKpP6aFeJ+m/t+nnfYh+xKxKn0OJwO/lPSmFmJu7z5uZ/9mtx9k3zIfSju7Mji1XxfjEXA58I2q8vMYPEh4bpo+isEDWHek8vFk/fLj0uNhYHzJsb+XVwe4f8Dggb1Pp+mTGDzwujRN78fgwcOHKHeA+1bgzWn6jLR/h+0+Bg4GVgOvTXEsBj4zHPczW45ZtG2/kl3jbQavDr4eWUK8M4H7gDdULVdz3zHEMaTe36fdMVfVrePVMYuu7uNSPrzdfJCdMfBrsjMaTu9yLO8ia/bdA9ydHkeS9X/eDKxNz5U/rMjuCPgg8Ctgem5bHwf60+P4DsT+Xl5NFnuTnVXRnz4wO6byndJ8f6rfO7f+6el9rGErz3JpINb9gRVpP/9n+sAM630MnAk8ANwLXJEOWsNqPwNXkY2pvEz2LfWEdu5XYHp6/w8C/0rVSQptirefrD+/8vn7dtG+o84xpN7fp90xV9Wv49Vk0dV97F9wm5lZoZE2ZmFmZiVwsjAzs0JOFmZmVsjJwszMCjlZmJlZIScLsw5QdgXihyWNT/Pj0vye3Y7NrBFOFmYdEBHrgUuAs1PR2cDCiHike1GZNc6/szDrkHTpl5XAZcAngQMi4g/djcqsMaOLFzGzdoiIlyV9AfgxcLgThfUSd0OZddYssss7vLXbgZg1w8nCrEMk7U9286sZwOcrV2816wVOFmYdkO59fAnZPU0eJbt66/ndjcqscU4WZp3xSeDRiFie5r8F/Kmk93QxJrOG+WwoMzMr5JaFmZkVcrIwM7NCThZmZlbIycLMzAo5WZiZWSEnCzMzK+RkYWZmhZwszMys0P8D6bO29zIKu8EAAAAASUVORK5CYII=\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -749,7 +773,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -948,7 +972,7 @@
        "19     B       9              2            2"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1037,7 +1061,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1090,7 +1114,7 @@
        "      <td>2</td>\n",
        "      <td>18</td>\n",
        "      <td>18.0</td>\n",
-       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>2.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1101,7 +1125,7 @@
        "   time  species_count  speciation_count  extinction_count  \\\n",
        "0     0              2               NaN               NaN   \n",
        "1     1              2               0.0               0.0   \n",
-       "2     2             18              18.0               2.0   \n",
+       "2     2             18              18.0               0.0   \n",
        "\n",
        "   pseudoextinction_count  \n",
        "0                     NaN  \n",
@@ -1109,7 +1133,7 @@
        "2                     2.0  "
       ]
      },
-     "execution_count": 18,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1120,7 +1144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {
     "scrolled": false
    },
@@ -1184,7 +1208,7 @@
     "\n",
     "### Species number\n",
     "\n",
-    "Species are numbered sequently by clade beginning with 0.\n",
+    "The species of a clade are numbered sequently beginning with 0.\n",
     "\n",
     "In the example tree below, species A.0 produced 4 child species. Only one species, B.0 exists in clade B because child species were not produced by B.0.\n",
     "\n",
@@ -1193,7 +1217,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1217,7 +1241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1268,7 +1292,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -1467,7 +1491,7 @@
        "19     B       9              2            2"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1478,7 +1502,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -1531,7 +1555,7 @@
        "      <td>2</td>\n",
        "      <td>18</td>\n",
        "      <td>18.0</td>\n",
-       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>2.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1550,7 +1574,7 @@
        "   time  species_count  speciation_count  extinction_count  \\\n",
        "0     0              2               NaN               NaN   \n",
        "1     1              2               0.0               0.0   \n",
-       "2     2             18              18.0               2.0   \n",
+       "2     2             18              18.0               0.0   \n",
        "3     3              0               0.0              18.0   \n",
        "\n",
        "   pseudoextinction_count  \n",
@@ -1560,7 +1584,7 @@
        "3                     0.0  "
       ]
      },
-     "execution_count": 23,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1571,7 +1595,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -1596,18 +1620,18 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>time</th>\n",
+       "      <th>zone_count</th>\n",
        "      <th>capture_count</th>\n",
-       "      <th>area_captured_max</th>\n",
        "      <th>area_captured_sum</th>\n",
        "      <th>fragmentation_count</th>\n",
-       "      <th>zone_count</th>\n",
+       "      <th>area_captured_max</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>2</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -1616,25 +1640,25 @@
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>1</td>\n",
+       "      <td>1</td>\n",
        "      <td>1.0</td>\n",
        "      <td>32542500.0</td>\n",
-       "      <td>32542500.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>32542500.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>2</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>9</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>9.0</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>3</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -1645,20 +1669,20 @@
        "</div>"
       ],
       "text/plain": [
-       "   time  capture_count  area_captured_max  area_captured_sum  \\\n",
-       "0     0            NaN                NaN                NaN   \n",
-       "1     1            1.0         32542500.0         32542500.0   \n",
-       "2     2            0.0                0.0                0.0   \n",
-       "3     3            0.0                0.0                0.0   \n",
+       "   time  zone_count  capture_count  area_captured_sum  fragmentation_count  \\\n",
+       "0     0           2            NaN                NaN                  NaN   \n",
+       "1     1           1            1.0         32542500.0                  0.0   \n",
+       "2     2           9            0.0                0.0                  1.0   \n",
+       "3     3           0            0.0                0.0                  0.0   \n",
        "\n",
-       "   fragmentation_count  zone_count  \n",
-       "0                  NaN         NaN  \n",
-       "1                  0.0         1.0  \n",
-       "2                  1.0         9.0  \n",
-       "3                  0.0         0.0  "
+       "   area_captured_max  \n",
+       "0                NaN  \n",
+       "1         32542500.0  \n",
+       "2                0.0  \n",
+       "3                0.0  "
       ]
      },
-     "execution_count": 24,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1676,7 +1700,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -1685,7 +1709,7 @@
        "1.0"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1700,24 +1724,24 @@
    "source": [
     "## Query species\n",
     "\n",
-    "SpeciesEvolver provides methods that return species objects in the event you need to work with specific species instances.\n",
+    "SpeciesEvolver provides methods that return species objects in the event you need to work with specific instances of species.\n",
     "\n",
     "The method, `species_at_time` returns species that were extant at the inputted time."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a1921f550>,\n",
-       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a1921f0d0>]"
+       "[<landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a1caab3d0>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a1caabcd0>]"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1735,25 +1759,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a1921f550>,\n",
-       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a195c3410>,\n",
-       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a195c3450>,\n",
-       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a195c3490>,\n",
-       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a195c3890>,\n",
-       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a195c33d0>,\n",
-       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a195c38d0>,\n",
-       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a195c3950>,\n",
-       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a195c3310>,\n",
-       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a195c3bd0>]"
+       "[<landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a1caab3d0>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a1c72a3d0>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a1c72a850>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a1c725090>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a1c72a090>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a1c72acd0>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a1c72a910>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a1c72ad50>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a1c72ab10>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a1c72a750>]"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }

From dcc0a46649e58a28135a0bcf768ba1bd51138dee Mon Sep 17 00:00:00 2001
From: nathanlyons <nathan.j.lyons@gmail.com>
Date: Fri, 29 Nov 2019 16:55:42 -0500
Subject: [PATCH 14/15] remove advanced tutorials

---
 species_evolution/Customize_ZoneSpecies.ipynb |  516 ------
 .../Introduction_to_SpeciesEvolver.ipynb      | 1530 ++++++++---------
 .../Modeling stream-based species.ipynb       |  714 --------
 3 files changed, 764 insertions(+), 1996 deletions(-)
 delete mode 100644 species_evolution/Customize_ZoneSpecies.ipynb
 delete mode 100644 species_evolution/Modeling stream-based species.ipynb

diff --git a/species_evolution/Customize_ZoneSpecies.ipynb b/species_evolution/Customize_ZoneSpecies.ipynb
deleted file mode 100644
index bbe56e7..0000000
--- a/species_evolution/Customize_ZoneSpecies.ipynb
+++ /dev/null
@@ -1,516 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a href=\"http://landlab.github.io\"><img style=\"float: left\" src=\"https://raw.githubusercontent.com/landlab/tutorials/master/landlab_header.png\"></a>\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Customize ZoneSpecies\n",
-    "\n",
-    "<hr>\n",
-    "<small> For instructions on how to run an interactive iPython notebook, click here: <a href=\"https://github.com/landlab/tutorials/blob/master/README.md\">https://github.com/landlab/tutorials/blob/master/README.md</a></small><br>\n",
-    "<small>For more Landlab tutorials, click here: <a href=\"https://github.com/landlab/landlab/wiki/Tutorials\">https://github.com/landlab/landlab/wiki/Tutorials</a></small>\n",
-    "<hr>\n",
-    "\n",
-    "In this tutorial we will:\n",
-    "* Introduce species into a Landlab model.\n",
-    "* Set the zones in which the species operate.\n",
-    "* Evolve a landscape and the species over time.\n",
-    "* Explore SpeciesEvolver output data structures.\n",
-    "\n",
-    "The configuration of the model in this tutorial loosely follows the fault throw experiment in Lyons et al., in preperation for Earth Surface Dynamics.\n",
-    "\n",
-    "## Macroevolution processes built into species\n",
-    "\n",
-    "The evolution of the base species is driven by three processes.\n",
-    "\n",
-    "### Dispersal: geographic range expansion\n",
-    "\n",
-    "ZoneSpecies has two dispersal methods.\n",
-    "\n",
-    "`_init_dispersal`: this method determines where species can redistribute given the conditions of the zone_func. Consider that `_init_dispersal` figures out where species can go, barring any conflicting conditions with other species. This method is more complicated, not designed to be modified as often\n",
-    "\n",
-    "`_evaluate_dispersal`: the default of this method is that the dispersal of the species determined by `_init_dispersal` is \n",
-    "\n",
-    "### Speciation: lineage splitting\n",
-    "\n",
-    "### Extinction: lineage termination\n",
-    "\n",
-    "\n",
-    "### Evaluation order\n",
-    "\n",
-    "The potential dispersal of all species are first determined. Then the dispersal, speciation, and extinction of each species is evaluated in sequence.\n",
-    "\n",
-    "These processes are run:\n",
-    "```python\n",
-    "for a_species in all_species:\n",
-    "    a_species._init_dispersal()\n",
-    " \n",
-    "for a_species in all_species:\n",
-    "    a_species._evaluate_dispersal()\n",
-    "    a_species._evaluate_speciation() \n",
-    "    a_species._evaluate_extinction() \n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Import modules and set the environment to show plots inline in this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from landlab.components import SpeciesEvolver\n",
-    "from landlab.components.species_evolution import ZoneController, ZoneSpecies\n",
-    "from landlab.io import read_esri_ascii\n",
-    "from landlab.plot import imshow_grid\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import random\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Prepare the grid and components of the model."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here we use code from the 'Introduction to SpeciesEvolver' tutorial. This code loads a model grid that has steady state topography and sets the top and bottom boundaries to open."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Prepare the grid and components of the model.\n",
-    "\n",
-    "(mg, z) = read_esri_ascii('model_grid_steady_state_elevation.txt')\n",
-    "mg.at_node['topographic__elevation'] = z\n",
-    "mg.set_closed_boundaries_at_grid_edges(right_is_closed=True,\n",
-    "                                       top_is_closed=False,\n",
-    "                                       left_is_closed=True,\n",
-    "                                       bottom_is_closed=False)\n",
-    "\n",
-    "plt.figure()\n",
-    "imshow_grid(mg, z, colorbar_label='elevation (m)', shrink=0.4)\n",
-    "\n",
-    "# Set surface temperature at sea level.\n",
-    "\n",
-    "lapse_rate = -0.006\n",
-    "temperature_at_base_level = 26\n",
-    "T = lapse_rate * z + temperature_at_base_level\n",
-    "mg.add_field('node', 'land_surface_air__temperature', T)\n",
-    "\n",
-    "# Plot temperature field.\n",
-    "\n",
-    "plt.figure()\n",
-    "imshow_grid(mg, 'land_surface_air__temperature',\n",
-    "            colorbar_label='surface air\\ntemperature (C$\\degree$)',\n",
-    "            shrink=0.4, cmap='RdBu')\n",
-    "\n",
-    "# Initialize SpeciesEvolver.\n",
-    "\n",
-    "se = SpeciesEvolver(mg)\n",
-    "\n",
-    "# Create a zone function and plot the initial zone extend.\n",
-    "\n",
-    "def zone_func(grid):\n",
-    "    \"\"\"Get a mask of the total zone extent.\"\"\"\n",
-    "    T = grid.at_node['land_surface_air__temperature']\n",
-    "    T_max = 25\n",
-    "    T_min = 15\n",
-    "    zone_extent_mask = np.all([T >= T_min, T <= T_max], 0)\n",
-    "    return zone_extent_mask\n",
-    "\n",
-    "sc = ZoneController(mg, zone_func, minimum_area=50000)\n",
-    "\n",
-    "plt.figure()\n",
-    "imshow_grid(mg, zone_func(mg), plot_name='initial zone extent (white)',\n",
-    "            allow_colorbar=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Customize ZoneSpecies\n",
-    "\n",
-    "Default implementation of dispersal method:\n",
-    "```python\n",
-    "def _evaluate_dispersal(self, population):\n",
-    "    \"\"\"Set the range of the species as it results from dispersal.\n",
-    "\n",
-    "    The default implementation does not modify population dispersal in\n",
-    "    stage 1.\n",
-    "\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    population : Population\n",
-    "        A population of the species.\n",
-    "    \"\"\"\n",
-    "    ...  # pragma: no cover\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class MySpecies(ZoneSpecies):\n",
-    "    def __init__(self, initial_zones, parent_species=None,\n",
-    "                 allopatric_wait_time=0, pseudoextinction=True):\n",
-    "        super(MySpecies, self).__init__(initial_zones, parent_species,\n",
-    "                                        allopatric_wait_time, pseudoextinction)\n",
-    "        \n",
-    "        # Set body size randomly.\n",
-    "        self.body_size = random.uniform(4, 400)\n",
-    "        \n",
-    "    #def _evaluate_dispersal(self, population):\n",
-    "    #    zone_species = population.zone.species\n",
-    "    #    zone_body_sizes = [s.body_size for s in zone_species]\n",
-    "    #    if self.body_size == max(zone_body_sizes):\n",
-    "    #        print('max_size', self.body_size, zone_body_sizes)\n",
-    "            \n",
-    "    def _evaluate_extinction(self, speciation_occurred):\n",
-    "        extinct_by_habitat_loss = super()._evaluate_extinction(speciation_occurred)\n",
-    "        extinct_by_size_probability = \n",
-    "        return extinct_by_habitat_loss or extinct_by_size_probability"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Create a species that exists in the zone"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>clade</th>\n",
-       "      <th>number</th>\n",
-       "      <th>time_appeared</th>\n",
-       "      <th>latest_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>A</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>B</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  clade  number  time_appeared  latest_time\n",
-       "0     A       0              0            0\n",
-       "1     B       0              0            0"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "species = sc.populate_zones_uniformly(1, species_type=MySpecies)\n",
-    "se.introduce_species(species)\n",
-    "\n",
-    "# Display the species data frame.\n",
-    "se.species"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "max_size 3.431604728310963 [3.431604728310963, 1.2034904150925987]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[<landlab.components.species_evolution.zone.Zone at 0x1a26e332d0>]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Update the surface temperature field.\n",
-    "temperature_at_base_level = 32\n",
-    "mg.at_node['land_surface_air__temperature'] = lapse_rate * z + temperature_at_base_level\n",
-    "\n",
-    "# Plot temperature field.\n",
-    "imshow_grid(mg, 'land_surface_air__temperature',\n",
-    "            colorbar_label='surface air\\ntemperature (C$\\degree$)',\n",
-    "            shrink=0.4, cmap='RdBu')\n",
-    "            \n",
-    "plt.figure()\n",
-    "imshow_grid(mg, zone_func(mg), plot_name='initial zone extent (white)',\n",
-    "            allow_colorbar=False)\n",
-    "    \n",
-    "# Advance SpeciesEvolver by one unit of time. Time is dimensionless in this tutorial.\n",
-    "dt = 1\n",
-    "sc.run_one_step(dt)\n",
-    "se.run_one_step(dt)\n",
-    "sc.zones"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>clade</th>\n",
-       "      <th>number</th>\n",
-       "      <th>time_appeared</th>\n",
-       "      <th>latest_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>A</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>B</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  clade  number  time_appeared  latest_time\n",
-       "0     A       0              0            1\n",
-       "1     B       0              0            1"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "se.species"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Model species evolution\n",
-    "\n",
-    "First, set model time step and uplift parameters.\n",
-    "\n",
-    "SpeciesEvolver runs every 10 timesteps of the other components."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dt = 1000\n",
-    "total_time = int(1e5)\n",
-    "nt = int(total_time // dt)\n",
-    "\n",
-    "dt_se = 10 * dt\n",
-    "\n",
-    "uplift_rate = 7e-5\n",
-    "uplift_per_step = uplift_rate * dt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Run model. The ```run_one_step``` function of SpeciesEvolver takes the new zones of the time step.\n",
-    "\n",
-    "This code block may take a couple minutes to run."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Click here for more <a href=\"https://github.com/landlab/landlab/wiki/Tutorials\">Landlab tutorials</a>"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/species_evolution/Introduction_to_SpeciesEvolver.ipynb b/species_evolution/Introduction_to_SpeciesEvolver.ipynb
index 4a1ea89..06e85a7 100644
--- a/species_evolution/Introduction_to_SpeciesEvolver.ipynb
+++ b/species_evolution/Introduction_to_SpeciesEvolver.ipynb
@@ -12,7 +12,7 @@
    "metadata": {},
    "source": [
     "\n",
-    "# Introduction to the Landlab SpeciesEvolver component\n",
+    "# Introduction to the SpeciesEvolver component\n",
     "\n",
     "<hr>\n",
     "<small> For instructions on how to run an interactive Jupyter notebook, click here: <a href=\"https://github.com/landlab/tutorials/blob/master/README.md\">https://github.com/landlab/tutorials/blob/master/README.md</a></small><br>\n",
@@ -20,9 +20,9 @@
     "<hr>\n",
     "\n",
     "In this tutorial we will build and run a model that exemplifies SpeciesEvolver functionality. You will have the opportunity to\n",
-    "* Populate a model grid with species. \n",
+    "* Populate a model grid with species.\n",
+    "* Evolve species in response to climate change.\n",
     "* View the SpeciesEvolver record that stores model variables over time.\n",
-    "* See how species are assigned identifiers.\n",
     "* Plot some model output."
    ]
   },
@@ -34,35 +34,38 @@
     "\n",
     "### Premise\n",
     "\n",
-    "The elevational distribution of ecosystems is largely controlled by air temperature. Ecosystems zonate by elevation largely because temperature decreases with altitude. Longterm temperature change drives elevational redistribution of ecosystems.\n",
+    "The elevational distribution of ecosystems is controlled in large part by air temperature. Ecosystems zonate by elevation largely because temperature decreases with altitude. Long term temperature change alters the connectivity of organisms within elevation zones. The macroevolution of species are driven by changes in the environment especially the spatiotemporal distribution change of species populations.\n",
     "\n",
     "### Approach\n",
     "\n",
-    "We model the macroevolutionary processes of species in response to temperature change. For simplicity, species distribution is set only by temperature. Temperature is set by elevation using a lapse rate, the decrease of temperature with elevation. Also for simplicity, topography does not change during the duration of the model.\n",
+    "We model the macroevolutionary processes of species in response to temperature change. The model progresses in terms of time steps with an unprescribed duration. Initial conditions are set in time 0. The initial topography is loaded from a previously run model and does not change for the duration of the model in this notebook.\n",
     "\n",
-    "The model progresses in terms of time steps with an unprescribed duration.\n",
+    "Air surface temperature at each time step is set by elevation using a lapse rate, the decrease of temperature with elevation. The temperature at base level in time 0 is 26&deg;C. The temperature throughout the grid is calculated using the base level temperature and the lapse rate. The temperature is decreased by 6&deg;C degrees in both times 1 and 2.\n",
     "\n",
-    "We will use `ZoneSpecies`. This species type is distributed with SpeciesEvolver and relies on `Zone` objects to evaluate evolutionary processes. The macroevolutionary processes built into ZoneSpecies are\n",
+    "At each time step, species distribution is set only by temperature, for simplicity. The change in connectivity of species populations drive macroevolutionary processes built into the programmed species.\n",
+    "\n",
+    "SpeciesEvolver is built to be adapted for many model approaches, especially for different species properties and behaviors. We will use `ZoneSpecies` in this notebook. This species type is distributed with SpeciesEvolver and relies on `Zone` objects to evaluate evolutionary processes. The macroevolutionary processes built into ZoneSpecies are\n",
     "* dispersal: geographic range expansion\n",
-    "* speciation: lineage splitting\n",
-    "* extinction: lineage termination\n",
+    "* speciation: lineage splitting (a species produces child species)\n",
+    "* extinction: lineage termination (a species no longer exists)\n",
     "\n",
-    "Species geographic range in this model are set to a temperature range, meaning the extent of zones is within this temperature range and all species exist only in this temperature range. \n",
+    "In this model, the geographic range of species are set to the temperature interval, 15&deg;C and 25&deg;C inclusive, meaning the extent of zones is within this interval at each time step, and all species exist only in this interval at each time step. The type of species is unspecified, but is thought of as vegetation.\n",
     "\n",
     "### Summary\n",
     "\n",
     "In this model, we\n",
     "\n",
-    "* Set surface air temperture across a model grid. Temperature will be a function of elevation.\n",
-    "* Populate species onto the grid. These species exist within a fixed temperature interval.\n",
-    "* Simulate a time step where surface temperature has changed and species respond to this change."
+    "* Set surface air temperture across a model grid as a function of elevation.\n",
+    "* Populate species within the grid. These species exist within a fixed temperature interval.\n",
+    "* Simulate three time steps where surface air temperature has changed in the latter two steps, and species respond to this change.\n",
+    "* Examine how the number of species, \"species richness\" changes as the model progresses."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Import modules\n",
+    "## Import modules\n",
     "\n",
     "The zones of ZoneSpecies are managed by a `ZoneController`, so we must import this class along with the SpeciesEvolver component and the other modules used in this notebook."
    ]
@@ -85,7 +88,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Prepare the grid\n",
+    "## Prepare the grid\n",
+    "\n",
+    "### Load topography\n",
     "\n",
     "In this tutorial, the topography of the model grid is loaded from a file. The topography was previously developed using a model built with Landlab. An initial surface was uplifted, stream erosion was conducted using the FastscapeEroder component, and hillslope diffusion was conducted using the LinearDiffuser component. The model was run until topography reached steady state."
    ]
@@ -131,13 +136,13 @@
    "source": [
     "### Create a grid field of air temperature at the land surface\n",
     "\n",
-    "We set a surface air temperature at base level (upper and lower boundaries). We use a lapse rate to create a synthetic surface air temperature field at all nodes of the grid. The lapse rate, *L* is modeled as the decrease of temperature, *T* with increasing elevation, *z* as\n",
+    "We set a surface air temperature at base level (upper and lower boundaries). We use a lapse rate to create a  surface air temperature field at all nodes of the grid. The lapse rate, *L* is modeled as the decrease of temperature, *T* with increasing elevation, *z* as\n",
     "\n",
     "\\begin{align*}\n",
     "L = -\\frac{\\delta t}{\\delta z}\n",
     "\\end{align*}\n",
     "\n",
-    "We use a lapse rate of -6&deg;C / km."
+    "We use a constant lapse rate of -6&deg;C / km."
    ]
   },
   {
@@ -177,66 +182,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Create a zone function\n",
-    "\n",
-    "Zones are portions of a model grid. A `ZoneSpecies` object exists within a zone or multiple zones. `Zone` objects are explained in more detail later in this tutorial.\n",
+    "## Setup SpeciesEvolver and zones\n",
     "\n",
-    "The general workflow we will conduct is\n",
-    "\n",
-    "* Define a function that demarcates the combined extent of all zones. \n",
-    "* Populate each distinct zone with a species.\n",
-    "\n",
-    "The zone function acts to return a numpy array where `True` values indicate where zones should be created. Each distinct zone is created where `True` nodes are spatially continuous.\n",
-    "\n",
-    "The zone function has two requirements:\n",
-    "1. It must return a numpy array with the length of the grid's `number_of_nodes`.\n",
-    "2. The first parameter must be named `grid`. It is the same Landlab grid supplied to SpeciesEvolver. Additional parameters are allowed.\n",
+    "The general workflow of working with zones and zone-based species using SpeciesEvolver is\n",
     "\n",
-    "Here, we define a zone function that indicates zones should be created where the air temperature is between 15&deg;C and 25&deg;C, inclusive."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def zone_func(grid):\n",
-    "    \"\"\"Get a mask of the total zone extent.\"\"\"\n",
-    "    T = grid.at_node['land_surface_air__temperature']\n",
-    "    T_max = 25\n",
-    "    T_min = 15\n",
-    "    zone_extent_mask = np.all([T >= T_min, T <= T_max], 0)\n",
-    "    return zone_extent_mask"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`True` values are shaded white in the plot below"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "imshow_grid(mg, zone_func(mg), allow_colorbar=False, plot_name='zone extent in white')"
+    "* Intialize the SpeciesEvolver component.\n",
+    "* Setup zones and zone-based species:\n",
+    "    * Define a function that identifies the grid nodes where all zones will be created.\n",
+    "    * Intialize a ZoneController that will create and manage zones.\n",
+    "    * Populate zones with species.\n",
+    "* Introduce species to SpeciesEvolver so the component will track them over time."
    ]
   },
   {
@@ -245,12 +200,12 @@
    "source": [
     "### Initialize SpeciesEvolver\n",
     "\n",
-    "This component requires only one parameter, a model grid, for the default functionality."
+    "This component requires only one parameter, a model grid."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -261,12 +216,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "View the `record` of our SpeciesEvolver instance. Time and other model variables are stored in the record. The first and second columns are the row number and model time, respectively. We also see that 0 species exist. We have yet to introduce species."
+    "View `record_data_frame` of our SpeciesEvolver instance. Time and other model variables are stored in the attribute that is a Pandas DataFrame. The first and second columns are the row number and model time, respectively. We also see that 0 species exist. We have yet to introduce species."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -309,22 +264,110 @@
        "0     0              0"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "se.record"
+    "se.record_data_frame"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### About zones\n",
+    "\n",
+    "Zones help to evaluate the spatial aspect of `ZoneSpecies` macroevolutionary processes. We must create a function to map all areas that meet the qualifications of zones. (In this notebook's model, we will create a function to map the grid areas within a temperature interval.) Attributes of a zone include `mask` that indicates where in the grid the zone exists and `species`, which are the `ZoneSpecies` associated and extant within the zone.\n",
+    "\n",
+    "At each time step, the spatial connectivity of the zones between the current and prior time steps are identified. For example, consider two consecutive time steps where a zone in the prior time intersects two zones in the later time. This type of connection in the zones of SpeciesEvolver is referred to as 'one-to-many' represented here\n",
+    "\n",
+    "![](images/zone_path__one_to_many.png)\n",
+    "\n",
+    "where the red arrows represent zone connectivity over time, and\n",
+    "\n",
+    "<img style=\"float: left;\" src=\"images/zone__one_to_none.png\"> &nbsp; zone<sub>*t*</sub>: a zone in time, *t*. ('the earlier time')<br>\n",
+    "\n",
+    "<img style=\"float: left;\" src=\"images/zone__later_time.png\"> &nbsp; zone<sub>*t*+1</sub>: a zone in the time following *t*. ('the later time')\n",
+    "\n",
+    "Behind the scenes, macroevolution rules are programmed into `ZoneSpecies` that are evaluated considering zone connection type. The outcome of the rules given the connection type are described in the table below.\n",
+    "\n",
+    "<center>connection type</center> | <center>graphical<br>represention</center> | <center>connectivity description</center> | <center>macroevolution implications</center>\n",
+    "--- | --- | --- | ---\n",
+    "one-to-none | ![](images/zone__one_to_none.png) | zone<sub>*t*</sub> does not intersect a zone in *t*+1. | The species in the zone of the earlier time will go extinct. \n",
+    "one-to-one | ![](images/zone__one_to_one.png) | zone<sub>*t*</sub> intersects zone<sub>*t*+1</sub>. | The species in the zone of the earlier time moves along with the zone of the later time.\n",
+    "one-to-many | ![](images/zone__one_to_many.png) | zone<sub>*t*</sub> intersects multiple zones in *t*+1. | The species in the zone of the earlier time disperses across the multiple zones in the later time. The species in the zones of the later time are geographical disconnected, thus speciation occurs.\n",
+    "many-to-one | ![](images/zone__many_to_one.png) | Multiple zones in *t* intersect zone<sub>*t*+1</sub>. | The species in the earlier zones are relocated to the zone in the later time. Species density increases if zone<sub>*t*+1</sub> is smaller than the the earlier time zones combined.\n",
+    "many-to-many | ![](images/zone__many_to_many.png) | Multiple zones in *t* intersect multiple zones in *t*+1. | The species in each zone of *t* relocates to all zones in *t*. All species extant at the earlier time step speciates as their range becomes fragmented in the later time step."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create a zone function\n",
+    "\n",
+    "The zone function identifies all grid nodes where zones are to be created. A zone is created for each cluster of spatially continuous nodes.\n",
+    "\n",
+    "The zone function has the following two requirements:\n",
+    "1. It must return a boolean numpy array equal to the grid's `number_of_nodes`.\n",
+    "2. The first parameter must be named `grid`. It is the same Landlab grid supplied to SpeciesEvolver. Additional parameters are allowed.\n",
+    "\n",
+    "For the model in this tutorial, we define a zone function that indicates zones should be created where the air temperature is between 15&deg;C and 25&deg;C, inclusive."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def zone_func(grid):\n",
+    "    \"\"\"Get a mask of the total zone extent.\"\"\"\n",
+    "    T = grid.at_node['land_surface_air__temperature']\n",
+    "    T_max = 25\n",
+    "    T_min = 15\n",
+    "    zone_extent_mask = np.all([T >= T_min, T <= T_max], 0)\n",
+    "    return zone_extent_mask"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To demonstrate the output of this function, `True` values are shaded white in the plot below. Zones will be created in the masked area (in white)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "imshow_grid(mg, zone_func(mg), allow_colorbar=False, plot_name='zone extent in white')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Instantiate a SpeciesController\n",
+    "### Initialize ZoneController\n",
     "\n",
-    "Next, we instantiate a ZoneController that will handle the zones."
+    "Next, we instantiate a ZoneController that will create and manage the zones. We used an optional parameter, `minimum_area` to indicate the minimum area of the zones to be created. Zones will be created upon initialization of this controller."
    ]
   },
   {
@@ -333,14 +376,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sc = ZoneController(mg, zone_func, minimum_area=50000)"
+    "zc = ZoneController(mg, zone_func, minimum_area=50000)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Two distinct areas of the mask are now seperated by the higher elevations in the center of the grid."
+    "Above, the mask returned by the `zone_func` indicates where zones should be created.\n",
+    "\n",
+    "Below, we see ZoneController created two zones, one for each spatially distinct node cluster in the `zone_func` mask."
    ]
   },
   {
@@ -362,32 +407,72 @@
     }
    ],
    "source": [
-    "# Plot zones.\n",
+    "# Create a map of all zones.\n",
     "zone_map = np.zeros(mg.number_of_nodes)\n",
-    "for i, zone in enumerate(sc.zones):\n",
+    "for i, zone in enumerate(zc.zones):\n",
     "    zone_map[zone.mask] = i + 1\n",
-    "    \n",
+    "\n",
+    "# Plot zone map.\n",
     "cmap = plt.cm.tab20\n",
     "cmap.set_under('w')\n",
-    "title = 'zone count = {}'.format(len(sc.zones))\n",
+    "title = 'zone count = {}'.format(len(zc.zones))\n",
     "imshow_grid(mg, zone_map, cmap=cmap, allow_colorbar=False, plot_name=title, vmin=1)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Multiple instances of ZoneController may be used with the same instance of SpeciesEvolver. This is useful when you wish to model groups of species where each group needs a different `zone_func`, for instance, fish and bears."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Seed zones with species\n",
     "\n",
-    "One species is populated to each of the two initial zones using the ZoneController method, `populate_zones_uniformly`. This method creates the species for each of the two zones. All species must be introduced using the SpeciesEvolver method, `introduce_species` in order for the species to be tracked by SpeciesEvolver.\n",
-    "\n",
-    "The SpeciesEvolver attribute, `species` provides data about all of the introduced species. The data is presented in a Pandas DataFrame. The `clade` and `species_number` identifier elements will be explained later in the tutorial."
+    "In this model, a species is populated to each of the two initial zones using the ZoneController method, `populate_zones_uniformly`. This method creates species and populates it to each zone."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "species = zc.populate_zones_uniformly(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Introduce species to SpeciesEvolver\n",
+    "\n",
+    "All species must be introduced using the SpeciesEvolver method, `introduce_species` in order for the species to be tracked by SpeciesEvolver."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "se.introduce_species(species)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Two species now exist in SpeciesEvolver."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -410,61 +495,44 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>clade</th>\n",
-       "      <th>number</th>\n",
-       "      <th>time_appeared</th>\n",
-       "      <th>latest_time</th>\n",
+       "      <th>time</th>\n",
+       "      <th>species_count</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>A</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>B</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
        "      <td>0</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "  clade  number  time_appeared  latest_time\n",
-       "0     A       0              0            0\n",
-       "1     B       0              0            0"
+       "   time  species_count\n",
+       "0     0              2"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "species = sc.populate_zones_uniformly(1)\n",
-    "\n",
-    "se.introduce_species(species)\n",
-    "\n",
-    "# Display the species data frame.\n",
-    "se.species"
+    "se.record_data_frame"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Two species now exist in the initial time."
+    "The SpeciesEvolver attribute, `species_data_frame` provides data about all of the introduced species, both extant and extinct. The data is presented in a Pandas DataFrame. The `clade` and `number` identifier elements are explained next."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -488,48 +556,97 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>time</th>\n",
-       "      <th>species_count</th>\n",
+       "      <th>clade</th>\n",
+       "      <th>number</th>\n",
+       "      <th>time_appeared</th>\n",
+       "      <th>latest_time</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
+       "      <td>A</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>B</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>2</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "   time  species_count\n",
-       "0     0              2"
+       "  clade  number  time_appeared  latest_time\n",
+       "0     A       0              0            0\n",
+       "1     B       0              0            0"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "se.record"
+    "se.species_data_frame"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Simulate a temperature increase and its impact on species distribution\n",
+    "### About species identifiers\n",
+    "\n",
+    "Species identifiers are automatically generated. They are stored as tuples in species objects. The first element of the identifier tuple is the clade name, and the second element is the species number. For example, the first species of clade A is 0, so the identifier is ('A', 0) and the tenth species of this clade is ('A', 9).\n",
+    "\n",
+    "#### Clade name\n",
+    "\n",
+    "A clade is a group of species that share a common ancestor. Species may share multiple common ancestors, so clades can be defined differently in the same tree. Clades in this example tree can be defined in multiple ways:\n",
+    "\n",
+    "<img src=\"images/clade_definitions.png\" style=\"width: 400px;\"/>\n",
+    "\n",
+    "In SpeciesEvolver, clades are defined by the root node for the purpose of assigning species identifiers. The species at the root node is:\n",
+    "* the least recent common ancestor of all species in the tree, and\n",
+    "* are typically introduced to SpeciesEvolver using the ```introduce_species``` method.\n",
+    "\n",
+    "The clades of the first 26 species are named alphabetically from A to Z. The alphabet is iteratively repeated in names for subsequent clades, following this schema:\n",
+    "\n",
+    "<center>clade sequence</center> | <center>clade name</center>\n",
+    "--- | ---\n",
+    "1–26 | A–Z\n",
+    "27–52 | AA–AZ\n",
+    "53–78 | BA–BZ\n",
+    "... | ...\n",
+    "703–728 | AAA–AAZ\n",
+    "729–754 | ABA–ABZ\n",
+    "... | ...\n",
     "\n",
-    "The temperature at base level is increased by 6&deg;C.\n",
+    "#### Species number\n",
+    "\n",
+    "The species of a clade are numbered sequently beginning with 0.\n",
+    "\n",
+    "In the example tree below, species A.0 produced 4 child species. Only one species, B.0 exists in clade B because child species were not produced by B.0.\n",
+    "\n",
+    "<img src=\"images/species_identifier.png\" style=\"width: 250px;\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model time step 1\n",
     "\n",
-    "< FIX COLOR MAP >"
+    "Initial conditions were established in time 0. Next, climate change is modeled by increasing the temperature at base level by 6&deg;C."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -546,72 +663,55 @@
     }
    ],
    "source": [
-    "# Update the surface temperature field.\n",
-    "temperature_at_base_level = 32\n",
+    "# Update the temperature field.\n",
+    "temperature_at_base_level += 6\n",
     "mg.at_node['land_surface_air__temperature'] = lapse_rate * z + temperature_at_base_level\n",
     "\n",
-    "# Plot temperature field.\n",
+    "# Plot the temperature field.\n",
     "imshow_grid(mg, 'land_surface_air__temperature',\n",
     "            colorbar_label='surface air\\ntemperature (C$\\degree$)',\n",
     "            shrink=0.4, cmap='bwr')"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 13,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
    "source": [
-    "# Advance SpeciesEvolver by a time step. The duration of a time step is not prescribed in this tutorial.\n",
-    "dt = 1\n",
-    "sc.run_one_step(dt)\n",
-    "se.run_one_step(dt)\n",
+    "Here we advance ZoneController and SpeciesEvolver in time by one time step. ZoneController is advanced first in order to update the zones prior to calling SpeciesEvolver.\n",
     "\n",
-    "# Plot zones.\n",
-    "for zone in sc.zones:\n",
-    "    plt.figure()\n",
-    "    imshow_grid(mg, zone.mask, allow_colorbar=False, plot_name='zone in white')"
+    "`dt` is set to 1 only because the duration of a time step is not prescribed in this tutorial. This `1` does not signify that one time step is run. We use 1 to make it simple to think about the time step sequence. Often, you may wish to make `dt` be the number of years in a time step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dt = 1\n",
+    "zc.run_one_step(dt)\n",
+    "se.run_one_step(dt)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Simulate a second temperature increase\n",
+    "One zone now exists because the temperature increase raised the area that falls within the temperature range prescribed in `zone_func`.\n",
     "\n",
-    "The temperature at base level is increased again by 6&deg;C."
+    "Plot the zones."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "21.5959030999 37.9999927129\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -621,51 +721,28 @@
     }
    ],
    "source": [
-    "# Update the surface temperature field.\n",
-    "temperature_at_base_level = 38\n",
-    "mg.at_node['land_surface_air__temperature'] = lapse_rate * z + temperature_at_base_level\n",
-    "\n",
-    "print(mg.at_node['land_surface_air__temperature'].min(), mg.at_node['land_surface_air__temperature'].max())\n",
+    "# Create a map of all zones.\n",
+    "zone_map = np.zeros(mg.number_of_nodes)\n",
+    "for i, zone in enumerate(zc.zones):\n",
+    "    zone_map[zone.mask] = i + 1\n",
     "\n",
-    "# Plot temperature field.\n",
-    "imshow_grid(mg, 'land_surface_air__temperature',\n",
-    "            colorbar_label='surface air\\ntemperature (C$\\degree$)',\n",
-    "            shrink=0.4, cmap='bwr')"
+    "# Plot zone map.\n",
+    "title = 'zone count = {} (zone is shaded)'.format(len(zc.zones))\n",
+    "imshow_grid(mg, zone_map, cmap=cmap, allow_colorbar=False, plot_name=title, vmin=0.5)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 15,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "n_zones 1\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAACdCAYAAABFGFS5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAATD0lEQVR4nO3dfbAddX3H8ffHhAfFhyTiQ5pEHirTNnRGwAzE2mkdtBAiY+iMzkSdGjU102odtdMqlBmtD3+AOmKZVjQj1MhQIEVbGAZLM0in/mMwEUECxlweEi4wBCaAbZkBkW//2N+BvSdnz55z7u6es+d+XjN3zu5v9+x+93d297u/3b27igjMzMz6ecm4AzAzs8nnZGFmZqWcLMzMrJSThZmZlXKyMDOzUk4WZmZWavG4A6jDIimmcsHMzGr0LDweEa/pNWwq96mLgdePOwgzs5Y5APuLhk3laahnxx2AmVnLHCgZPpXJwszMquVkYWZmpZwszMyslJOFmZmVcrIwM7NSThZmZlbKycLMzEo5WZiZWSknCzMzK+VkYWZmpZwszMyslJOFmZmVcrIwM7NSThZmZlbKycLMzErVniwkLZJ0u6QbU/8JknZK2ifpWklHpvKjUv9MGn58bhoXpPK9ks6uO2YzM5uriZbFJ4B7cv0XA5dExEnAE8DmVL4ZeCIi3ghcksZD0mpgI3AysA74hqRFDcRtZmZJrclC0krgncC3U7+AM4Hr0ijbgPNS94bUTxr+9jT+BuCaiHgmIu4HZoDT64zbzMzmqrtl8XXg08Dzqf/VwJMR8VzqnwVWpO4VwIMAafhTafwXynt8x8zMGlBbspB0LnAwInbni3uMGiXD+n0nP78tknZJ2jV0sGZm1tfiGqf9VuBdktYDRwOvJGtpLJG0OLUeVgIPp/FngVXArKTFwKuAQ7nyjvx3XhARW4GtAJIOSyZmZja62loWEXFBRKyMiOPJLlD/MCLeD9wKvDuNtgm4PnXfkPpJw38YEZHKN6a7pU4ATgJuqytuMzM7XJ0tiyKfAa6R9CXgduDyVH45cKWkGbIWxUaAiNgjaTtwN/Ac8LGI+E3zYZuZLVzKDt6ni6R4w7iDMDNrkQPZx+6IWNNruP+D28zMSjlZmJlZKScLMzMr5WRhZmalnCzMzKyUk4WZmZVysjAzs1JOFmZmVsrJwszMSjlZmJlZKScLMzMr5WRhZmalnCzMzKxUnW/KO1rSbZLukLRH0udT+QmSdkraJ+laSUem8qNS/0wafnxuWhek8r2Szq4rZjMz663OlsUzwJkR8SbgFGCdpLXAxcAlEXES8ASwOY2/GXgiIt4IXJLGQ9JqsndbnAysA74haVGNcZuZWZc635QXEfG/qfeI9BfAmcB1qXwbcF7q3pD6ScPfLkmp/JqIeCYi7gdmgNPritvMzA5X6zULSYsk/Qw4COwA7gWeTO/fhuz92itS9wrgQYA0/Cng1fnyHt8xM7MG1JosIuI3EXEKsJKsNfB7vUZLnyoYVlQ+h6QtknZJ2jVqvGZm1lsjd0NFxJPAfwFrgSWSOu/+Xgk8nLpngVUAafiryN7F/UJ5j+/k57E1ItYUvRLQptv+KXw9sFkT9kcMtP3UeTfUayQtSd0vBd4B3APcCrw7jbYJuD5135D6ScN/GNkLwm8ANqa7pU4ATgJuqytua5fOSn6cejVAzawqi8tHGdlyYFu6c+klwPaIuFHS3cA1kr4E3A5cnsa/HLhS0gxZi2IjQETskbQduBt4DvhYRPymxrjNzKyLYgqb75IiIio/2txfwzRtNPlms38TG6dpad0eyD52F53Kn9r/4G77D1enNp/f7z6/6t95+hStn4OeW2/acdKCWA+nNlnUZRJX1mG0vXXUiX2hbKALVZu2s0lNYlVzshjSpO2ghllJ27hCd2+ITS1DG+tqvua7zFXX2Th+91G0/cBl7HdDTZtRVtb5HnF0byz9mueDattK3Ym3exmL6qNfXQxbT+PaQZXNt664JmXdyMfRxFH7JCeiQXTqqLMc/baNXuWDJrvCZCHppvzD/KZRWYVWtRL1+pEGnXbRTrLzN8jOdNQNrooNtYo48tMqmv4gv9soO8NJTxiTtKOrMsH22oHVcQQ/SfXXzyBxdp+iHbRlVkXL4jvAf0q6UNIRpVOaML2OyntVXr+NrteKOZ8d1jAbePcdFkUbT/6z13JWbdDY+50+GvbU2ag7+aJENWg99arXpo5y+y1z0QFCVfMe9XtVJrB8XXf/DlVrukU1yjIU1UG/9SVf1tl/5JPIsHGo362zko4BPkv2tNcrgec7wyLia0PNqUGdW2eHMcgKU3bEWnSk3++Iq8oVddCdX/d3upv9Rd8dZPrdyz1MfP2m2/39snobdF6jTie/rIP+hoMkvqJp9tpxDjPv+cRXFvewB1qDLEOvYcP8/kXx9VpHRz0g6TX9otgGGacz3jDr5Khx9/rNsue2Ft86W5YsjgTOB94HXMvcZPH5kaJsQHey6N6wu7tHXfE60xhGr53zKDvDQebTbZgj0qKdYb/vDrMjHDV5zmfDLNuplSWGQadTRcxFMRQN6zfvovW87PcYZJ691tmi9XiQ9anfNjlqvQ9zIFjVQUhHr+XplTSrSFqjyC9PWbIo/A9uSeuAr5E9buO0iHi62jDNzKwtClsWkn4E/EVE7Gk2pPnrdRpqkKOvQc3nPHrVsZTNr99R43zmX3Wc/WIaZd6DtNKqOMU0SGtpvvqdiinqL2sVDNvKKlu+KtaFYU53FbXOy8Ytm3+vaXfKhpnPsMtSx2nFQXXmPa/TUG3V/biPfj9ck02/cTQzu+dd1UpZ9bKMY3plp4dGiWnQ60aD7KwHPZc/zLBh5zXqNZZRjJosisYZNa6ihDtqHVXxnSaUPe5jQSSLfpr64cZ55FC1ca/sVc2/6gTaPe38NMvmMcwyzTeJVd2Cm9R1etTftagFManLOV9uWUxYsrBi0/Qb9Nspj7NVV+XNE20yTetW1bpbYSNf4F4ovCKN3yh3o9XZCpiPsmk0MY+65ttGC3W561Dny49WSbpV0j2S9kj6RCpfJmmHpH3pc2kql6RLJc1IulPSablpbUrj75O0aZD5eyWpT10XcgdxnNr7HJ62xj0p9sdw/5Q37PgLSf4AadBtqrbTUJKWA8sj4qeSXgHsBs4DPggcioiLJJ0PLI2Iz0haD3wcWA+cAfxDRJwhaRmwC1hD9u7t3cCbI+KJPvOON9SyVAZu2pu1Vb8bK8b2PouIeCQifpq6/4fslaorgA3AtjTaNrIEQir/bmR+TPau7uXA2cCOiDiUEsQOsv8otzFxojBrl3wrK7/9DrMtN/LU2fRAwlOBncDrIuIRyBIK8No02grgwdzXZlNZUbmZmTWk9gvckl4OfA/4ZET8SsWZrNeA6FPePZ8twJZR4zQzm1bdLYhRzg7U2rJIT6v9HnBVRHw/FT+aTi91rmscTOWzwKrc11cCD/cpnyMitkbEmqLzbWZ18sVUa4P5rKN13g0l4HLgnq4n1N4AdO5o2gRcnyv/QLorai3wVDpNdTNwlqSl6c6ps1KZNcw7w2JtvkvLFo75rKN1noZ6K/BnwM8l/SyV/R1wEbBd0mayC/DvScNuIrsTagZ4GvgQQEQckvRF4CdpvC9ExKEa47YC3hmatUMddyxO7X9w+9ZZM7PBje3WWTMzmx5OFmZmNZqWa31OFmZmNZqWa31OFmZmVsrJwszMSjlZmJlZKScLMzMr5WRhZmalnCzMzKyUk4WZmZVysjAzs1JOFmZmVsrJwszMStX5PosrJB2UdFeubJmkHZL2pc+lqVySLpU0I+lOSaflvrMpjb9P0qZe8zIzs3rV2bL4DrCuq+x84JaIOAm4JfUDnAOclP62AJdBllyAzwFnAKcDn+skGDMza05tySIi/hvofknRBmBb6t4GnJcr/25kfgwsSa9cPRvYERGHIuIJYAeHJyAzM6tZ09csXpdelUr6fG0qXwE8mBtvNpUVlZuZWYPqfK3qMHo9wzf6lB8+AWkL2SksMzOrWNMti0fT6SXS58FUPgusyo23Eni4T/lhImJrRKwpeiWgmZmNrulkcQPQuaNpE3B9rvwD6a6otcBT6TTVzcBZkpamC9tnpTIzM2tQbaehJF0NvA04VtIs2V1NFwHbJW0mez/4e9LoNwHrgRngaeBDABFxSNIXgZ+k8b4QEd0Xzc3MrGaKKXk/bJ6keMO4gzAza5ED2cfuolP5/g9uMzMr5WRhZmalnCzMzKyUk4WZmZVysjAzs1JOFmZmVsrJwszMSjlZmJlZKScLMzMr5WRhZmalnCzMzKyUk4WZmZVysjAzs1KtSRaS1knaK2lG0vnjjsfMbCFpRbKQtAj4J+AcYDXwXkmrxxuVmdnC0YpkAZwOzETEfRHxLHANsGHMMZmZLRhtSRYrgAdz/bOpzMzMGtCWZKEeZXNe8Sdpi6RdknYtaigoM7NpUfZ20drewV2xWWBVrn8l8HB+hIjYCmwFkPTYAfg/4PHGIqzGsbQr5rbFC+2LuW3xgmNuQl3xHlc0oBXv4Ja0GPgl8HbgIeAnwPsiYk+f7+wqepfspGpbzG2LF9oXc9viBcfchHHE24qWRUQ8J+mvgJuBRcAV/RKFmZlVqxXJAiAibgJuGnccZmYLUVsucI9i67gDGEHbYm5bvNC+mNsWLzjmJjQebyuuWZiZ2XhNc8vCzMwqMnXJYpKeISVplaRbJd0jaY+kT6TyZZJ2SNqXPpemckm6NMV+p6TTctPalMbfJ2lTzXEvknS7pBtT/wmSdqZ5XyvpyFR+VOqfScOPz03jglS+V9LZNce7RNJ1kn6R6votLajjT6V14i5JV0s6etLqWdIVkg5KuitXVlm9SnqzpJ+n71wqqdf/U8033q+k9eJOSf8maUluWM+6K9qHFP0+VcecG/Y3kkLSsal/vHUcEVPzR3an1L3AicCRwB3A6jHGsxw4LXW/guz239XAl4HzU/n5wMWpez3wA7J/QlwL7Ezly4D70ufS1L20xrj/GvgX4MbUvx3YmLq/Cfxl6v4o8M3UvRG4NnWvTnV/FHBC+k0W1RjvNuDPU/eRwJJJrmOypw/cD7w0V78fnLR6Bv4IOA24K1dWWb0CtwFvSd/5AXBODfGeBSxO3Rfn4u1Zd/TZhxT9PlXHnMpXkd39uR84dhLquJaNd1x/qVJuzvVfAFww7rhy8VwP/AmwF1ieypYDe1P3t4D35sbfm4a/F/hWrnzOeBXHuBK4BTgTuDGtZI/nNrgX6jitzG9J3YvTeOqu9/x4NcT7SrIdr7rKJ7mOO4+vWZbq7Ubg7EmsZ+B45u58K6nXNOwXufI541UVb9ewPwWuSt09646CfUi/7aCOmIHrgDcBD/BishhrHU/baaiJfYZUOnVwKrATeF1EPAKQPl+bRiuKv8nl+jrwaeD51P9q4MmIeK7HvF+IKw1/Ko3fZLwnAo8B/6zs1Nm3JR3DBNdxRDwEfBU4ADxCVm+7mex67qiqXlek7u7yOn2Y7Oiakrh6lffbDiol6V3AQxFxR9egsdbxtCWL0mdIjYOklwPfAz4ZEb/qN2qPsuhTXilJ5wIHI2L3ADH1G9bk77CYrBl/WUScSvaYl37XqsYeczrPv4Hs9MdvAceQPX6/aP5jj3kAw8bYaOySLgSeA67qFA0ZV1Pb4MuAC4HP9hpcEEMjMU9bsih9hlTTJB1Bliiuiojvp+JHJS1Pw5cDB1N5UfxNLddbgXdJeoDsMfBnkrU0lih75Er3vF+IKw1/FXCowXg7McxGxM7Ufx1Z8pjUOgZ4B3B/RDwWEb8Gvg/8AZNdzx1V1ets6u4ur1y64Hsu8P5I52NGiPdxin+fKv022UHEHWk7XAn8VNLrR4i52jqu8vzmuP/IjjLvS5XduTh18hjjEfBd4Otd5V9h7kXCL6fudzL3AtZtqXwZ2Xn5penvfmBZzbG/jRcvcP8rcy/sfTR1f4y5F163p+6TmXvx8D7qvcD9I+B3Uvffp/qd2DoGzgD2AC9LcWwDPj6J9czh1ywqq1eyZ7yt5cWLr+triHcdcDfwmq7xetYdffYhRb9P1TF3DXuAF69ZjLWOa9l4x/lHdsfAL8nuaLhwzLH8IVmz707gZ+lvPdn5z1uAfemz88OK7I2A9wI/B9bkpvVhYCb9faiB2N/Gi8niRLK7KmbSBnNUKj869c+k4Sfmvn9hWo69zPMulwFiPQXYler539MGM9F1DHwe+AVwF3Bl2mlNVD0DV5NdU/k12VHq5irrFViTlv9e4B/pukmhonhnyM7nd7a/b5bVHQX7kKLfp+qYu4Y/wIvJYqx17P/gNjOzUtN2zcLMzGrgZGFmZqWcLMzMrJSThZmZlXKyMDOzUk4WZg1Q9gTi+yUtS/1LU/9x447NbBBOFmYNiIgHgcuAi1LRRcDWiNg/vqjMBuf/szBrSHr0y27gCuAjwKkR8ex4ozIbzOLyUcysChHxa0l/C/wHcJYThbWJT0OZNescssc7/P64AzEbhpOFWUMknUL28qu1wKc6T281awMnC7MGpHcfX0b2TpMDZE9v/ep4ozIbnJOFWTM+AhyIiB2p/xvA70r64zHGZDYw3w1lZmal3LIwM7NSThZmZlbKycLMzEo5WZiZWSknCzMzK+VkYWZmpZwszMyslJOFmZmV+n8HtwSyzLMcqAAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
    "source": [
-    "imshow_grid(mg, zone_func(mg), allow_colorbar=False)\n",
-    "print('n_zones', len(sc.zones))"
+    "A 'many-to-one' zone connectivity occurred in this time step relative to the previous time step, meaning neither speciation or extinction were triggered by zone change. (See zone connectivity table above.)\n",
+    "\n",
+    "The same two species of time 0 exist in time 1. Their geographic range is all that changed."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -689,61 +766,101 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>time</th>\n",
-       "      <th>species_count</th>\n",
-       "      <th>speciation_count</th>\n",
-       "      <th>extinction_count</th>\n",
-       "      <th>pseudoextinction_count</th>\n",
+       "      <th>clade</th>\n",
+       "      <th>number</th>\n",
+       "      <th>time_appeared</th>\n",
+       "      <th>latest_time</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
+       "      <td>A</td>\n",
        "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
+       "      <td>B</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
        "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "   time  species_count  speciation_count  extinction_count  \\\n",
-       "0     0              2               NaN               NaN   \n",
-       "1     1              2               0.0               0.0   \n",
-       "\n",
-       "   pseudoextinction_count  \n",
-       "0                     NaN  \n",
-       "1                     0.0  "
+       "  clade  number  time_appeared  latest_time\n",
+       "0     A       0              0            1\n",
+       "1     B       0              0            1"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "se.record"
+    "se.species_data_frame"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model time step 2\n",
+    "\n",
+    "Temperature is again increased by 6&deg;C in time step 2."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Update the surface temperature field.\n",
+    "temperature_at_base_level += 6\n",
+    "mg.at_node['land_surface_air__temperature'] = lapse_rate * z + temperature_at_base_level\n",
+    "\n",
+    "# Plot temperature field.\n",
+    "imshow_grid(\n",
+    "    mg, 'land_surface_air__temperature',\n",
+    "    colorbar_label='surface air\\ntemperature (C$\\degree$)',\n",
+    "    shrink=0.4, cmap='bwr'\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Advance ZoneController and SpeciesEvolver, and plot zones."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -755,26 +872,41 @@
     }
    ],
    "source": [
-    "# Advance SpeciesEvolver by one unit of time. Time is dimensionless in this tutorial.\n",
-    "dt = 1\n",
-    "sc.run_one_step(dt)\n",
+    "zc.run_one_step(dt)\n",
     "se.run_one_step(dt)\n",
     "\n",
-    "# Plot zones.\n",
+    "# Create a map of all zones.\n",
     "zone_map = np.zeros(mg.number_of_nodes)\n",
-    "for i, zone in enumerate(sc.zones):\n",
+    "for i, zone in enumerate(zc.zones):\n",
     "    zone_map[zone.mask] = i + 1\n",
-    "    \n",
-    "cmap = plt.cm.tab20\n",
-    "cmap.set_under('w')\n",
-    "title = 'zone count = {}'.format(len(sc.zones))\n",
-    "imshow_grid(mg, zone_map, cmap=cmap, allow_colorbar=False, plot_name=title, vmin=1)"
+    "\n",
+    "# Plot zone map.\n",
+    "title = 'zone count = {}'.format(len(zc.zones))\n",
+    "imshow_grid(mg, zone_map, cmap=cmap, allow_colorbar=False, plot_name=title, vmin=0.5)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 18,
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nine zones exist now. The effect of this `one-to-many` zone connectivity change on species is examined in the next section."
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "metadata": {},
+   "source": [
+    "## Examine model outcome\n",
+    "\n",
+    "In the species DataFrame, we see 20 species existed during the model. Species A.0 and B.0 are known to exist as late as time 1. All other species appeared at time 2, the final time step. Something happened between time steps 1 and 2 when A.0 and B.0 went extinct and 18 other species were created."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "data": {
@@ -972,96 +1104,25 @@
        "19     B       9              2            2"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "se.species"
+    "se.species_data_frame"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Three species have existed in this model:\n",
-    "\n",
-    "- A.0: The initial species we introduced.\n",
-    "- A.1 and A.2: The child species of A.0. These species were created when the zone of the first time step split into two in the second time step.\n",
-    "\n",
-    "All species belong to clade 'A' because the child species (A.1 and A.2) were spawned from the same parent species (A.0).\n",
-    "\n",
-    "\n",
-    "**Profile map**\n",
-    "```\n",
-    "              N\n",
-    "     ___________________\n",
-    "    │    P1             │\n",
-    "    │     │             │\n",
-    " W  │ P2──┼───────      │  E\n",
-    "    │     │             │\n",
-    "    │                   │\n",
-    "     ‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾\n",
-    "              S\n",
-    "\n",
-    "\n",
-    "```\n",
-    "**T0**: Profile 1\n",
-    "```\n",
-    "            ╱╲\n",
-    "           ╱  ╲\n",
-    "   N     a╱    ╲*     S     ─┐\n",
-    "        a╱      ╲*           │ Species temperature range\n",
-    "   aaaaa╱        ╲*****      │\n",
-    "   ‾‾‾‾‾          ‾‾‾‾‾     ─┘ \n",
-    "                             \n",
-    "\n",
-    "```\n",
-    "**T1**: Profile 1\n",
-    "```    \n",
-    "            *a\n",
-    "          *a╱╲*a            ─┐\n",
-    "         *a╱  ╲*a            │ Species temperature range\n",
-    "   N    *a╱    ╲*a    S      │\n",
-    "       *a╱      ╲*a         ─┘\n",
-    "        ╱        ╲\n",
-    "   ‾‾‾‾‾          ‾‾‾‾‾\n",
-    "\n",
-    "\n",
-    "```\n",
-    "**T3**: Profile 1 (left) and 2 (right)\n",
-    "```                  \n",
-    "                                                             ─┐\n",
-    "                                                              │ Species temperature range\n",
-    "            b+                     +b                c@       │ \n",
-    "          b+╱╲b+                 +b╱╲+b            c@╱╲c@    ─┘\n",
-    "           ╱  ╲                   ╱  ╲      ╱╲      ╱  ╲\n",
-    "   N      ╱    ╲      S      W   ╱    ╲    ╱  ╲    ╱    ╲   E\n",
-    "         ╱      ╲               ╱      ╲  ╱    ╲  ╱      ╲\n",
-    "        ╱        ╲             ╱        ╲╱      ╲╱        ╲\n",
-    "   ‾‾‾‾‾          ‾‾‾‾‾    \n",
-    "\n",
-    "\n",
-    "```\n",
-    "**Phylogeny**\n",
-    "```\n",
-    "\n",
-    "         ┌─ b\n",
-    "a ───────┤\n",
-    "         └─ c\n",
-    "         ┌─ +\n",
-    "* ───────┤\n",
-    "         └─ @\n",
-    " __________\n",
-    "T0   T1   T2\n",
-    "\n",
-    "```\n"
+    "In the SpeciesEvolver record DataFrame, we see 18 speciations, meaning they occurred before this time step and after the prior time step. We also see two pseudoextinctions were recognized at time 2. A pseudoextinction is a species that is no longer extant because it speciated into child species."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1133,25 +1194,37 @@
        "2                     2.0  "
       ]
      },
-     "execution_count": 19,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "se.record"
+    "se.record_data_frame"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A summary of the species in this model:\n",
+    "\n",
+    "- A.0 and B.0: The initial species we introduced.\n",
+    "- A.1...9 and B.1...9: The child species of the initial species.\n",
+    "\n",
+    "We will examine the model history using the plot below"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 22,
    "metadata": {
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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8EACr1YrBYEAphXbcHPof//gHZ599NpdeeqnzNTNmzCA1NRWASy+9lDfffLPLvwshROd4fBphUwv272dXdXWbz39ZWYnJkUwa1dps3Pjtt7x09Girr5kQHs6zI0e2ecxly5aRnZ3Nrl32NUrr1q1j//79bN26Fa01l112GRs3biQhIYEDBw7wz3/+k5UrV5KRkcE//vEPNm3axJo1a/jNb37D+++/D8Du3bv58ssvqampYeLEicyZM4fs7Ow2j7tv3z5eeeUV5zuBpUuXEhMTg9VqZebMmezevZs777yTFStW8Nlnn9GvX792f5bfffcd69evx8/PjwceeIDzzjuPVatWUV5eTmZmJueffz5hYWHO/fPz84mOjibIcReklJQUamtrOffcc1m+fDkA2dnZnHnmmW2eMz09nWXLlnHvvfe2G58Qovu8KoG3p2Xybm97V6xbt45169YxceJEAKqrq9m/fz8JCQkMGzaMcePGAfYEN3PmTJRSjBs3joKCAucxLr/8ckJCQggJCWHGjBls3bqVTZs2tXncxMREzjrrLOfr3377bVauXInFYqGoqIhvvvmGtLS0Tn0fV199NX5+fs7vac2aNTz9tL11e319PYWFhYwZM8a5f1FRES2bjT333HOdOueAAQM42sZ/pEII1/OqBH66kTJA0pYtHGzlRseJQUFscCTG7tJac//993PLLc07AxQUFDhHpwAGg8H52GAwNKs1t5w211iGaOu4LUfCTz/9NNu2bSM6Opp58+a1OYe66Xla7tP0mFpr3n33Xc4444w2v++QkJB252qnpKQ0u6DZUn19PSEhIac9hhDCdXyqBu6Oe2QajUaqqqqcj2fPns2qVauodpRyjhw5wrFjxzp1zA8++ID6+npKS0vZsGEDGRkZHT5uZWUlYWFhREZGUlxczL///e82Y42LiyMnJwebzcbq1avbjGf27Nk899xzzlr2zp07T9ln1KhRzd5FtOYnP/kJX3zxBR999JFz29q1a9mzZw9gL9s01sOFEO7nVSPw9jTeWu3BvDwKTSYSgoJYmpzcrVuuxcbGMmXKFFJTU7noootYvnw5OTk5nH322YD9QuHf//53ZzmiIzIzM5kzZw6FhYU89NBDDBo0iEGDBnXouOPHj2fixImkpKSQnJzMlClTnM/Nnz+fiy66iPj4eD777DOWLVvGJZdcwtChQ0lNTXX+59DSQw89xIIFC0hLS0NrTVJSEh9++GGzfcLCwhg+fDgHDhxgxIgRrR4nJCSEDz/8kAULFrBgwQICAgJIS0vj97//PQCfffYZc+bM6fDPSQjRPUq7sH7cnvT0dL19+/Zm23JycprVYn3dI488Qnh4OIsWLfJ0KJ22evVqsrKyeOKJJzr9WpPJxLRp09i0aRP+/qeOC3rb71mInqSUytJap7fc7lMjcOFeV155JaWlpV16bWFhIcuWLWs1eQsh3EP+tbnYI4884ukQuuWmm27q0utGjhzJyHYuQgshXMunLmIKIYQ4SRK4EEL4qI7cE3OoUuozpVSOUmqvUuoux/YYpdR/lFL7HZ+j3R+uEEKIRh0ZgVuAe7TWY4CzgNuVUmOB+4BPtdYjgU8dj4UQQvSQdhO41rpIa73D8XUVkAMMBi4H/urY7a/AFe4K0ldNnz6dltMm3eEPf/gDY8aMYe7cuW4/lxDCe3RqFopSKgmYCHwFxGmti8Ce5JVSA9p4zXxgPkBCQkJ3YmXzwM2Yi82nbA+IC2DK91NaeYXvslgsHZ6S98c//pF///vfcs9JIfqYDl/EVEqFA+8CC7TWlR19ndZ6pdY6XWud3rJZUme1lrxPt70jGtu53nzzzaSkpDBr1izq6uqA5iPo48ePk5SUBMCrr77KFVdcwaWXXsqwYcN4/vnnWbFiBRMnTuSss87ixIkTzuP//e9/55xzziE1NdXZcrat9q6vvvoqV199NZdeeimzZs06JdYVK1aQmppKamoqzz77LAC33noreXl5XHbZZTzzzDPN9r/pppuYMGECEyZMoH///jz66KNorVm8eDGpqamMGzeOt956C4ANGzYwffp0fvSjHzF69Gjmzp3rXHqflZXFtGnTOPPMM5k9ezZFRUWAfeQ/duxY0tLS+L//+78u/w6EEF3ToSGeUioAe/J+XWv9nmNzsVIq3jH6jgc61zCkFfsX7Kd6V9vtZE9n5/RT+3sAhE8IZ+Szp5+fvH//ft544w1eeuklrrnmGt59911++tOfnvY12dnZ7Ny5k/r6ekaMGMGTTz7Jzp07ufvuu3nttddYsGABYE/WX3zxBRs3buSGG24gOzubpUuXttreFWDLli3s3r2bmJiYZufLysrilVde4auvvkJrzeTJk5k2bRp/+tOfWLt2battZv/yl78AcPDgQWbPns28efN477332LVrF19//TXHjx8nIyODqVOn2n+GO3eyd+9eBg0axJQpU9i8eTOTJ0/mjjvu4IMPPqB///689dZbPPjgg6xatYply5aRn59PUFAQ5eXlp/15CSFcr90Eruwt714GcrTWK5o8tQb4ObDM8bn1uwT4gGHDhjFhwgQAzjzzzHabOoH9RgZGoxGj0UhkZKTzJgfjxo1j9+7dzv2uvfZaAKZOnUplZSXl5eVttncFuOCCC05J3gCbNm3iyiuvdHYZvOqqq/j888+d7WnbUl9fz9VXX83zzz9PYmIizz77LNdeey1+fn7ExcUxbdo0tm3bRkREBJmZmQwZMgSACRMmUFBQQFRUFNnZ2VxwwQWA/UYP8fHxAKSlpTF37lyuuOIKrrhCLoEI0dM6MgKfAlwH7FFK7XJsewB74n5bKXUjUAhc3d1g2hspb1Ab2nxu4oaut5Nt2ibWz8/PWULx9/d33uKsZavV7raWba2961dffdWsDWxTXe1Zc+utt3LVVVc5R/inO07Ln4PFYkFrTUpKClu2bDll/48++oiNGzeyZs0aHn/8cfbu3StL6YXoQR2ZhbJJa6201mla6wmOj4+11qVa65la65GOzyfaO5avSUpKIisrC7DfsqwrGmvMmzZtIjIyksjIyA61d21p6tSpvP/++9TW1lJTU8Pq1av5wQ9+cNrXvPDCC1RVVXHffSdneE6dOpW33noLq9VKSUkJGzduJDMzs81jnHHGGZSUlDgTuNlsZu/evdhsNg4dOsSMGTN46qmnKC8vb7MbohDCPXxquBQQF9DmLBR3WLRoEddccw1/+9vfOO+887p0jOjoaM455xwqKytZtWoV0LH2ri1NmjSJefPmOZPtTTfd1G755OmnnyYgIMBZHrr11lu55ZZb2LJlC+PHj0cpxVNPPcXAgQOdNzFuKTAwkHfeeYc777yTiooKLBYLCxYsYNSoUfz0pz+loqICrTV33303UVFRnf3xCCG6QdrJih4hv2fRl9XU7GXv3h+TkvIWYWEpnX59W+1kpReKEEK4kdVaw+7dF1Nb+w27d8/Baq1x2bElgQshhBt9++0NNDQcAzQNDcV8++2NLju2JHAhhHCToqJVlJauQWv7LDat6ykt/RdFRatccnxJ4EII4QYm0xG+++52bLbmU5Bttlry8u53yTkkgQshhAtZLNXk5z/MV1+NRGsz9oXsJxkMoSQnL3PJuSSBCyGEC2htpajoFbZuHcXBg48RG3sZkyfvp1+/K1EqGAClgomNvZT4+Otdck6fTOA1NXvZujWVmpq9ng6lXRdffLHL+4SsWbOGZcva/h+8oKCA1NRUl55TCNG2srL/kpWVzr59NxAcnMjEiV+QkvImISHDGD16FYGBAwBFYGAco0e/7LLz+tRCHjg5JcdkOsTu3XPIzNyLn1/ry8+9wccff+zS41ksFi677DIuu+wylx5XCNF5tbX7yM1dTGnpvwgOTmLs2Dfp3/+aZi00/PzCSEv72DkP3JX5yudG4K6eklNTU8OcOXMYP348qampzqXvSUlJLFmyhMzMTDIzMzlw4AAAJSUl/PCHPyQjI4OMjAw2b94MQHV1Nddffz3jxo0jLS2Nd99913mc48ePA/bWspmZmUyYMIFbbrkFq9WK1Wpl3rx5zvauLVvCAsybN4+FCxcyY8YMlixZwquvvsovf/lLAIqLi7nyyisZP34848eP54svvgDsTafaapHb+H2NGjWKzz//3Ln/4sWLycjIIC0tjT//+c8AFBUVMXXqVCZMmEBqaiqff/55h2IWojczm0vZv/9Otm1Lpbx8A8nJT5KRkcOAAT8+pf8RQFhYCpmZ2V1axHM6XjUC379/AdXVu9p83mQqor7+AGBvMKV1PSUl/+TLL3cSFBTf6mvCwycwcuSzbR5z7dq1DBo0iI8++giAiooK53MRERFs3brV2R72ww8/5K677uLuu+/m3HPPpbCwkNmzZ5OTk8Pjjz9OZGQke/bsAaCsrKzZeXJycnjrrbfYvHkzAQEB/OIXv+D1118nJSWFI0eOkJ2dDdBmueW7775j/fr1+Pn58eqrrzq333nnnUybNo3Vq1djtVqprq6mrKzstC1yLRYLW7du5eOPP+bRRx9l/fr1vPzyy0RGRrJt2zZMJhNTpkxh1qxZvPfee8yePZsHH3wQq9VKbW0tu3bt6lDMQvQ2NpuJI0ee5+DBJ7BYKhk06BaSkh5xlEh6nlcl8PaYTPk0Ju+TbJhM+W0m8PaMGzeORYsWsWTJEi655JJmDaIaW8Fee+213H333QCsX7+eb775xrlPZWUlVVVVrF+/njfffNO5PTq6+T2eP/30U7KyssjIyACgrq6OAQMGcOmll5KXl8cdd9zBnDlzWr2RA8DVV1+Nn5/fKdv/+9//8tprrwH2DoKRkZGUlZWdtkXuVVdddcr2devWsXv3bmfTroqKCvbv309GRgY33HADZrOZK664ggkTJpCcnNyhmIXoLbTWHD/+Hrm591Jfn0dMzEUMH77c5SPqzvKqBH66kTLYJ8Xv338nNtvJpagGQygjRz7f5au6o0aNIisri48//pj777+fWbNm8etf/xpo3gq28WubzcaWLVsICQlpdhytdatvnZo+//Of/5zf/va3pzz39ddf88knn/DCCy/w9ttvO5teNdVWm9m2tNUit+lzjS1jG+N77rnnmD179inH2rhxIx999BHXXXcdixcv5mc/+1mHYhaiN6is3EZu7kIqKjYRFpZKWtonxMR4x6DFp2rg8fE3EBs7x6VTco4ePUpoaCg//elPWbRoETt27HA+11gPf+uttzj77LMBmDVrFs8//7xzn127drW6vWUJZebMmbzzzjscO2a/cdGJEyc4ePAgx48fx2az8cMf/pDHH3+82fk7YubMmbz44ouAvY5dWdnhu901M3v2bF588UXMZnu3x++++46amhoOHjzIgAEDuPnmm7nxxhvZsWNHt2MWwhfU1x8iJ+c6duzIpLb2O0aNWkl6+i6vSd7gZSPwjhg9ehVbt47FZDrkkik5e/bsYfHixRgMBgICApzJEMBkMjF58mRsNhtvvPEGYL8P5O23305aWhoWi4WpU6fypz/9iV/96lfcfvvtpKam4ufnx8MPP+wsVQCMHTuWJ554glmzZmGz2QgICOCFF14gJCSE66+/3nnjiNZG6Kfz+9//nvnz5/Pyyy/j5+fHiy++6LxjTmfcdNNNFBQUMGnSJLTW9O/fn/fff58NGzawfPlyAgICCA8P57XXXuPIkSPdilkIb2axVFFY+CSHD/8OgISEB0hIuA9/f6OHIzuVT7aT7W5rxo5ISkpi+/btp9xnUnSNtJMV3s6+EGcV+fkPYTYXM2DAXJKTf0NwcIKnQ2uznazPjcDh5JQcIYRwhRMn/kNu7j3U1OwhImIK48atISKi7TtVeYt2a+BKqVVKqWNKqewm2x5RSh1RSu1yfFzs3jB7XkFBgYy+hejlamrsPbp3756F1VpDSso7TJz4uU8kb+jYCPxV4HngtRbbn9FaP+2KINqbwSF8W0+W6YToiIaGEgoKHuHo0T/j5xfO8OFPM3jwLzEYgtp/sRdpN4FrrTcqpZLcFUBwcDClpaXExsZKEu+FtNaUlpYSHBzs6VCEwGqt58iRP3Dw4FKs1hoGD76NxMSHCQz0zXfb3amB/1Ip9TNgO3CP1rqstZ2UUvOB+U0V7PAAABaESURBVAAJCadeDBgyZAiHDx+mpKSkG6EIbxYcHMyQIUM8HYbow7TWlJS8TV7efdTXFxAbeynJyU8RFjba06F1S4dmoThG4B9qrVMdj+OA44AGHgfitdY3tHec1mahCCGEO1VUfElu7kIqK7cQFjaeESN+R3T0TE+H1SkunYWitS5ucuCXgA+7EZsQQrhcXV0B+fn3c+zYmwQGDuSMM1YxcODPUOrUlhS+qksJXCkVr7Uucjy8EpA5fUIIr2CxVFJY+FsOHXoGpQwkJv6aoUMX4+8f7unQXK7dBK6UegOYDvRTSh0GHgamK6UmYC+hFAC3uDFGIYRol81moajoLxQU/BqzuYS4uJ8xbNhSgoN77/WXjsxCubaVza67pYQQQnRTaelacnPvobb2GyIjpzFixO8wGs/0dFhu55MrMYUQAqC6Opvc3EWUlX1CSMgIUlJW06/f5X1mSrIkcCGEz2loKCY//9cUFf0Ff/9Ihg9/hsGDf4HBEOjp0HqUJHAhhM+wWus4fPhZCgt/g81Wz5Ahd5KY+BABATGeDs0jJIELIbye1jaOHXuTvLz7MZkK6dfvCpKTnyQ0dJSnQ/MoSeBCCK9WUbGZAwcWUlW1lfDwiYwe/Veio6d7OiyvIAlcCOGV6uryyMu7j5KSfxIYOIjRo18lLu46lPKpG4m5lSRwIYRXMZvLKSxcyuHDf0Apf5KSHmXo0Hvw8+vcfWH7AkngQgivYLOZKSpaSX7+w1gsJxg48HqGDXucoKBBng7Na0kCF0J4lL3l8Efk5i6irm4fUVEzGD58BUbjBE+H5vUkgQshPKa6+msOHLiH8vJPCQkZRWrqGmJjL+kzC3G6SxK4EKLHmUxF5Oc/xPffr8LfP5oRI/7AoEG3YjAEeDo0nyIJXAjRY6zWWg4d+h2FhU+idQNDhiwkMfFBAgKiPR2aT5IELoRwO61tFBe/Tl7e/TQ0HKFfvx8yfPiThIQM93RoPk0SuBDCrcrLN3LgwEKqq7MwGtMZO/YNoqJ+4OmwegVJ4EIIt6itPUBe3r0cP76aoKAhjBnzdwYMuFYW4riQJHAhhEuZzWUcPPg4R448j8EQxLBhTzBkyN34+YV6OrReRxK4EMIlbLYGjh59kYKCx7BYyomPv5GkpMcIChro6dB6LUngQohusS/EWUNu7mLq6vYTHX0+w4f/jvDwNE+H1uu1W4xSSq1SSh1TSmU32RajlPqPUmq/47PMARKiD6qq2sHXX59HdvYVKOXPuHEfkZa2TpJ3D+nI1YRXgQtbbLsP+FRrPRL41PFYCNFHmExHyMmZR1ZWOjU12Ywc+UfS03cTG3uxrKLsQR25qfFGpVRSi82XY79TPcBfgQ3AEhfGJYTwQlZrDYWFyzl0aDlaWxg6dDGJiQ/g7x/p6dD6pK7WwOO01kUAWusipdSAtnZUSs0H5gMkJCR08XRCCE/S2sr3379Gfv6DNDQU0b//j0lO/i0hIcM8HVqf5vaLmFrrlcBKgPT0dO3u8wkhXKus7DNycxdSXb0Lo3EyKSnvEBl5jqfDEnQ9gRcrpeIdo+944JgrgxJCeF5t7T5yc++ltHQNQUGJjBnzBgMG/Fhq3J2weeBmzMXmU7YHxAUw5fsp3T5+VxP4GuDnwDLH5w+6HYkQwiuYzaUUFDzG0aN/xGAIITl5GYMH34WfX7CnQ/M5rSXv023vrHYTuFLqDewXLPsppQ4DD2NP3G8rpW4ECoGrXRKNEMJjbDYTR468wMGDj2OxVDJo0HySkh4lMLDNS1zCwzoyC+XaNp6a6eJYhBAeoLXm+PH3yM1dQn19LjExFzJ8+NOEhaV4OjSfo22a6l3VnFh7ghOfnHD7+WQlphB9WGXlNnJzF1JRsYnQ0BTS0tYSEzPb02H5lIaSBsr+U+ZM2uZj9vJI+MRwt59bErgQfVB9/SHy8x+guPjvBAQMYNSoPzNw4A0YDJIS2mOz2KjaWmVP2GtPULW9CjT4x/oTMyuGmAtjiJ4VTdDAIDaoDW6NRX5bQvQhFksVhw49xaFDT6O1JiHhARISluDvH+Hp0Lxa/eF6yj6xj7LL1pdhKbeAASLOiiDp0SRiLozBOMmI8ms+QycgLqDNWSiuIAlciD5AaytFRa+Qn/8rzOZiBgz4CcnJvyE4ONHToXklm8lGxaYK5yi7JrsGgMBBgfS7qp99lH1+NAHRp0/ErpgqeDqSwIXo5U6cWE9u7kJqavYQEXEO48Z9QETEZE+H5XXqcuucCbvsv2XYam2oAEXk1EiSf5ZMzIUxhKWGedU8eEngQvRSNTU55OYu5sSJjwgOHsbYsf+kf/8felUC8iRrjZWyz8qcpZG6A3UABCcHM/D6gcRcGEPU9Cj8w703TXpvZEKILmloKKGg4BGOHv0zfn5hJCcvZ8iQOzAYgjwdmkdpranZW+McZVd8XoFu0BhCDUTNiGLwXYOJuTCG0BG+c+cgSeBC9BJWaz1HjjzHwYNPYLXWMGjQrSQlPUxgYH9Ph+Yx5nIzZevLnEm74UgDAGGpYQy5cwjRs6OJPDcSv2A/D0faNZLAhfBxWmtKSv5JXt4S6usLiI29hOTkpwgLG+Pp0Hqctmmqsqqcc7Irv6wEK/hF+hFzgWOK3+xogof0jrYAksCF8GGVlV9x4MDdVFZuISwsjbS0/xATc76nw+pRDcUNnFjnuPi4rgzzcTMoMJ5pJPH+RPsUv8lGDP4duX+Nb5EELoQPqq8/SF7e/Rw79gaBgQM544y/MHDgPJTyzVJAZ9jMNiq/rHSWRap3VAMQMCCAmIsco+wLognsH+jhSN1PErgQPsRiqaSw8LccOvQMShlITHyIoUPvxd/f/cu2Pam+sP7kFL9Py7BWWsEPIs+JZNjSYcRcGEP4hHCUoW/NsJEELoQPsNksfP/9y+TnP4TZXEJc3HUMG7aU4OChng7NLaz1Vio2nlxIU5tTC0DQ0CAG/HiAfZQ9Mxr/yL6dwvr2dy+EDzhx4hMOHLiH2tq9REZOZfjwj4mISPd0WC6ltaZu/8mFNOUbyrHV2VBBiqhpUcTfHG+f4jc6VOaxNyEJXAgvVV2dTW7uIsrKPiE4eDgpKe/Rr98VvSaBWaoslP+33Jm06wvqAQgZFeJM2FHTovAL7f11/a6SBC6El2loKCY//2GKil7C3z+C4cNXMHjw7RgMvn1RTmtNze4mC2k2VaAtGr9wP6JmRjH03qHEzI4hJDnE06H6DEngQngJq7WOw4efpbDwt9hsdQwefAdJSQ8REBDr6dC6zFxq5sR/TjiXqzd871hIMz6MIfcMIebCGCLPicQQ2Pum+PWEbiVwpVQBUAVYAYvWuncV5oToAVprjh17k7y8+zCZComNvZzhw58iNHSUp0PrNG3VVG47OcWvaqujV3a0P9Gzoom5MIaYWTEEDerby/pdxRUj8Bla6+MuOI4QfU5FxRccOLCQqqqvCA+fyOjRfyU6erqnw+oUU5GJE5+cXEhjKbOAgojJESQ97OiVnX5qr2zRfVJCEcID6uryyMu7j5KSfxIYOIjRo18lLu46lPL+UoKtwUbF5grncvWarx29sgcG0u/yJr2yY11z0wLRtu4mcA2sU0pp4M9a65UuiEmIXstsLqew8DccPvx7lPInKekRhg5dhJ9fmKdDO626/CZT/P5bjrXaau+VfW4kycscvbLTvKtXdl/Q3QQ+RWt9VCk1APiPUupbrfXGpjsopeYD8wESEhK6eTohfJPNZqaoaCUFBY9gNpcycODPGTbsCYKCBns6tFZZa62U/+/kFL+67xy9spOCibsuzj7Fb0YU/kZ5E+9J3frpa62POj4fU0qtBjKBjS32WQmsBEhPT9fdOZ8QvkZrzYkTH5Obu4ja2m+JiprB8OG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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1163,15 +1236,17 @@
     }
    ],
    "source": [
-    "time = [0, 1, 2]\n",
+    "# Create a plot of key model variables over time.\n",
+    "\n",
+    "time = se.record_data_frame.time\n",
     "temperature = [26, 32, 38]\n",
-    "n_zones = [2, 1, 9]\n",
-    "species_richness = se.record.species_count\n",
+    "n_zones = zc.record_data_frame.zone_count\n",
+    "n_species = se.record_data_frame.species_count\n",
     "\n",
     "plt.plot(time, temperature, 'o-c', label='temperature (\\N{DEGREE SIGN}C)')\n",
-    "plt.plot(time, n_zones, 's-m', label='number of zones')\n",
-    "plt.plot(time, se.record.species_count, 'd-y', label='species richness')\n",
-    "plt.xlabel('time step')\n",
+    "plt.plot(time, n_zones, 's-m', label='zone count')\n",
+    "plt.plot(time, se.record_data_frame.species_count, 'd-y', label='species count')\n",
+    "plt.xlabel('time')\n",
     "plt.xticks(time)\n",
     "_ = plt.legend()"
    ]
@@ -1180,422 +1255,122 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Species identifiers\n",
-    "\n",
-    "Species identifiers are automatically generated. They are stored as tuples in species objects. The first element of the identifier tuple is the clade name, and the second element is the species number. For example, the first species of clade A is 0, so the identifier is (A, 0) and the tenth species of this clade is (A, 9).\n",
-    "\n",
-    "### Clade name\n",
-    "\n",
-    "A clade is a group of species that share a common ancestor. Species may share multiple common ancestors, so clades can be defined differently in the same tree. The clade(s) of this example tree can be defined in multiple ways:\n",
-    "\n",
-    "<img src=\"images/clade_definitions.png\" style=\"width: 400px;\"/>\n",
-    "\n",
-    "In SpeciesEvolver, clades are defined by the root node for the purpose of assigning species identifiers. The species at the root node is:\n",
-    "* the least recent common ancestor of all species in the tree, and\n",
-    "* typically introduced to a SpeciesEvolver model using the ```introduce_species``` function.\n",
-    "\n",
-    "The clades of the first 26 species are named alphabetically from A to Z. The alphabet is repeated for subsequent species following this schema:\n",
-    "\n",
-    "<center>clade sequence</center> | <center>clade name</center>\n",
-    "--- | ---\n",
-    "1–26 | A–Z\n",
-    "27–52 | AA–AZ\n",
-    "53–78 | BA–BZ\n",
-    "... | ...\n",
-    "703–728 | AAA–AAZ\n",
-    "729–754 | ABA–ABZ\n",
-    "... | ...\n",
-    "\n",
-    "### Species number\n",
-    "\n",
-    "The species of a clade are numbered sequently beginning with 0.\n",
-    "\n",
-    "In the example tree below, species A.0 produced 4 child species. Only one species, B.0 exists in clade B because child species were not produced by B.0.\n",
-    "\n",
-    "<img src=\"images/species_identifier.png\" style=\"width: 250px;\"/>"
+    "The model begins at time 0 with two zones, each with one of the initial species, A.0 and B.0. Zone count decreases by one in time 1 as the temperature increased. Species and zone count drastically increases in time 2 as temperature continued to increase. The schematic profiles of topography below illustrate species distribution and count in response to temperature change over the model time."
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 21,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3.59590309985 19.9999927129\n"
-     ]
-    }
-   ],
    "source": [
-    "# Update the surface temperature field.\n",
-    "temperature_at_base_level = 20\n",
-    "mg.at_node['land_surface_air__temperature'] = lapse_rate * z + temperature_at_base_level\n",
-    "\n",
-    "print(mg.at_node['land_surface_air__temperature'].min(), mg.at_node['land_surface_air__temperature'].max())\n",
-    "\n",
-    "sc.run_one_step(dt)\n",
-    "se.run_one_step(dt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for zone in sc.zones:\n",
-    "    plt.figure()\n",
-    "    imshow_grid(mg, zone.mask, allow_colorbar=False, plot_name='zone in white')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### About Zones\n",
-    "\n",
-    "Zones are portions of a model grid. A Species object exists throughout a zone.\n",
-    "\n",
-    "At each timestep, the spatial intersection of the zones at the prior time and current time are identified and typed. For example, a zone in the prior time intersects two zones in the current time. This zone path is typed one-to-many.\n",
-    "\n",
-    "Macroevolutionary processes are carried out by the intersection of zones over time. The connectivity of zones in SpeciesEvolver are called 'paths', and the relationship of zones is described by the path type. Not all combinations of none, one, and many are considered at the time of this tutorial. All none-to-n and some many-to-n are not considered. \n",
-    "\n",
-    "<img style=\"float: left;\" src=\"images/zone__one_to_none.png\"> &nbsp; zone<sub>*t*</sub>: a zone in time, *t*.<br>\n",
-    "\n",
-    "<img style=\"float: left;\" src=\"images/zone__later_time.png\"> &nbsp; zone<sub>*t*+1</sub>: a zone in the time following *t*.\n",
-    "\n",
-    "<center>path type</center> | <center>graphical<br>represention</center> | <center>zone temporal connectivity</center> | <center>macroevolution implications</center>\n",
-    "--- | --- | --- | ---\n",
-    "one-to-none | ![](images/zone__one_to_none.png) | zone<sub>*t*</sub> does not intersect a zone in *t*+1. | The species in the zone of the earlier time will go extinct. \n",
-    "one-to-one | ![](images/zone__one_to_one.png) | zone<sub>*t*</sub> intersects zone<sub>*t*+1</sub>. | The species in the zone of the earlier time will relocate to the zone of the later time.\n",
-    "one-to-many | ![](images/zone__one_to_many.png) | zone<sub>*t*</sub> intersects multiple zones in *t*+1. | The species in the zone of the earlier time disperses across the multiple zones in the later time. The species in the zones of the later time are geographical disconnected, thus speciation occurs.\n",
-    "many-to-one | ![](images/zone__many_to_one.png) | Multiple zones in *t* intersect zone<sub>*t*+1</sub>. | Species density increases locally.\n",
-    "many-to-many | ![](images/zone__many_to_many.png) | Multiple zones in *t* intersect multiple zones in *t*+1. | Species distribution shifts. Speciation occurs where the zone of a species at *t* intersects more than one zone at *t*+1.\n",
-    "\n",
-    "The rules of macroevolution are programmed into species. Behind the scenes, zone paths are the conditions evaluated by the macroevolution rules. The outcome of the rules given the zone paths are described in the zone table above. For example, a one-to-many path:\n",
-    "\n",
-    "![](images/zone_path__one_to_many.png)\n",
-    "\n",
-    "The base species class \n",
-    "\n",
-    "Users may develop species with different behaviors and macroevolution rules.\n",
-    "\n",
-    "\n",
-    "\n",
+    "**Profile location in model grid**: Suppose the diagram below is the outline of the model grid. Two profiles, P1 and P2 are drawn north-south and west-east, respectively.\n",
+    "```\n",
+    "              N\n",
+    "     ___________________\n",
+    "    │    P1             │\n",
+    "    │     │             │\n",
+    " W  │ P2──┼───────      │  E\n",
+    "    │     │             │\n",
+    "    │                   │\n",
+    "     ‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾\n",
+    "              S\n",
     "\n",
     "\n",
-    "Populate SpeciesEvolver zones with species.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>clade</th>\n",
-       "      <th>number</th>\n",
-       "      <th>time_appeared</th>\n",
-       "      <th>latest_time</th>\n",
-       "    </tr>\n",
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-       "      <th>2</th>\n",
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-       "      <th>4</th>\n",
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-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
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-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>A</td>\n",
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-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>A</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>A</td>\n",
-       "      <td>7</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>A</td>\n",
-       "      <td>8</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>A</td>\n",
-       "      <td>9</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>B</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>B</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>B</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>B</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>B</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>B</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>B</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>B</td>\n",
-       "      <td>7</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>B</td>\n",
-       "      <td>8</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>B</td>\n",
-       "      <td>9</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   clade  number  time_appeared  latest_time\n",
-       "0      A       0              0            1\n",
-       "1      A       1              2            2\n",
-       "2      A       2              2            2\n",
-       "3      A       3              2            2\n",
-       "4      A       4              2            2\n",
-       "5      A       5              2            2\n",
-       "6      A       6              2            2\n",
-       "7      A       7              2            2\n",
-       "8      A       8              2            2\n",
-       "9      A       9              2            2\n",
-       "10     B       0              0            1\n",
-       "11     B       1              2            2\n",
-       "12     B       2              2            2\n",
-       "13     B       3              2            2\n",
-       "14     B       4              2            2\n",
-       "15     B       5              2            2\n",
-       "16     B       6              2            2\n",
-       "17     B       7              2            2\n",
-       "18     B       8              2            2\n",
-       "19     B       9              2            2"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "se.species"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>time</th>\n",
-       "      <th>species_count</th>\n",
-       "      <th>speciation_count</th>\n",
-       "      <th>extinction_count</th>\n",
-       "      <th>pseudoextinction_count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>18</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
+    "```\n",
+    "**T0, Profile 1:** At the beginning of the model, a species was seeded to the north and south of the main west-east divide. The relatively low temperature forced the species to be located at low elevations. Species could not reach across the main divide where it was too cold for the species to persist. (The profile is on the left and the vertical bar on the right delineates the elevation range the species can persist.)\n",
+    "```\n",
+    "            ╱╲\n",
+    "           ╱  ╲\n",
+    "   N     a╱    ╲*     S     ─┐\n",
+    "        a╱      ╲*           │ Species temperature range\n",
+    "   aaaaa╱        ╲*****      │\n",
+    "   ‾‾‾‾‾          ‾‾‾‾‾     ─┘ \n",
+    "                             \n",
+    "\n",
+    "```\n",
+    "**T1 Profile 1:** Species were able to cross the main divide as the temperature increased. The two species existed in the same area. Coexisting had no effect because ZoneSpecies do not interact in the default implemention of the code for this species.\n",
+    "```    \n",
+    "            *a              ─┐\n",
+    "          *a╱╲*a             │\n",
+    "         *a╱  ╲*a            │ Species temperature range\n",
+    "   N    *a╱    ╲*a    S      │\n",
+    "       *a╱      ╲*a         ─┘\n",
+    "        ╱        ╲\n",
+    "   ‾‾‾‾‾          ‾‾‾‾‾\n",
+    "\n",
+    "\n",
+    "```\n",
+    "**T3, Profile 1 (left) and Profile 2 (right)**: An additional increase in temperature drove species to become isolated on mountain peaks along the main divide. Species could not reach adjacent peaks because valleys were too cold. Each peak that was above the minimum species temperature led to an isolated population that produced a child species. In the model we ran, nine peaks/ridgeline areas met these qualifications, thus species A.0 and B.0 produced 9 child species.\n",
+    "```                  \n",
+    "                                                             ─┐\n",
+    "                                                              │ Species temperature range\n",
+    "            b+                     +b                c@       │ \n",
+    "          b+╱╲b+                 +b╱╲+b            c@╱╲c@    ─┘\n",
+    "           ╱  ╲                   ╱  ╲      ╱╲      ╱  ╲\n",
+    "   N      ╱    ╲      S      W   ╱    ╲____╱  ╲____╱    ╲   E\n",
+    "         ╱      ╲               ╱                        ╲\n",
+    "        ╱        ╲             ╱                          ╲\n",
+    "   ‾‾‾‾‾          ‾‾‾‾‾    \n",
+    "```\n",
+    "**Phylogeny**: In this schematic example, species `a` and `*` produced 2 child species each.\n",
+    "```\n",
+    "         ┌─ b\n",
+    "a ───────┤\n",
+    "         └─ c\n",
+    "         ┌─ +\n",
+    "* ───────┤\n",
+    "         └─ @\n",
+    " __________\n",
+    "T0   T1   T2\n",
+    "\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "SpeciesEvolver automatically creates a `species_richness` grid field. We can use this field to see that there are two species in each zone. (species richness = number of species) In each zone, one species belongs to clade A, the other belongs to clade B."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
       "text/plain": [
-       "   time  species_count  speciation_count  extinction_count  \\\n",
-       "0     0              2               NaN               NaN   \n",
-       "1     1              2               0.0               0.0   \n",
-       "2     2             18              18.0               0.0   \n",
-       "3     3              0               0.0              18.0   \n",
-       "\n",
-       "   pseudoextinction_count  \n",
-       "0                     NaN  \n",
-       "1                     0.0  \n",
-       "2                     2.0  \n",
-       "3                     0.0  "
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "se.record"
+    "imshow_grid(\n",
+    "    mg, 'species__richness', plot_name='species richness',\n",
+    "    colorbar_label='species richness', shrink=0.4, cmap='viridis'\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similar to SpeciesEvolver, ZoneController has a `record_data_frame` that stores model variables over time. The ZoneController stores variables pertinent to zones.\n",
+    "\n",
+    "In the ZoneController record, 'fragmentation_count' in time 2 is nine. The one zone in time 1 split into nine fragments, or zones, essentially between times 1 and 2.\n",
+    "\n",
+    "We see capture statistics in this record as well. Here, a capture indicates that a zone captured area from a zone in a prior time step. The one zone in time 1 captured the two zones in time 0."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -1621,9 +1396,9 @@
        "      <th></th>\n",
        "      <th>time</th>\n",
        "      <th>zone_count</th>\n",
+       "      <th>fragmentation_count</th>\n",
        "      <th>capture_count</th>\n",
        "      <th>area_captured_sum</th>\n",
-       "      <th>fragmentation_count</th>\n",
        "      <th>area_captured_max</th>\n",
        "    </tr>\n",
        "  </thead>\n",
@@ -1641,25 +1416,16 @@
        "      <th>1</th>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>32542500.0</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>32542500.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>2</td>\n",
        "      <td>9</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -1669,17 +1435,42 @@
        "</div>"
       ],
       "text/plain": [
-       "   time  zone_count  capture_count  area_captured_sum  fragmentation_count  \\\n",
-       "0     0           2            NaN                NaN                  NaN   \n",
-       "1     1           1            1.0         32542500.0                  0.0   \n",
-       "2     2           9            0.0                0.0                  1.0   \n",
-       "3     3           0            0.0                0.0                  0.0   \n",
+       "   time  zone_count  fragmentation_count  capture_count  area_captured_sum  \\\n",
+       "0     0           2                  NaN            NaN                NaN   \n",
+       "1     1           1                  0.0            1.0         32542500.0   \n",
+       "2     2           9                  9.0            0.0                0.0   \n",
        "\n",
        "   area_captured_max  \n",
        "0                NaN  \n",
        "1         32542500.0  \n",
-       "2                0.0  \n",
-       "3                0.0  "
+       "2                0.0  "
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zc.record_data_frame"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pandas DataFrame methods can be used on `record_data_frame`. For example, here we get the maximum capture count."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
       ]
      },
      "execution_count": 25,
@@ -1688,14 +1479,18 @@
     }
    ],
    "source": [
-    "sc.record"
+    "zc.record_data_frame.zone_count.max()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "DataFrame methods can be used on `record`. For example, here we get the maximum capture count."
+    "## Obtaining specific species objects\n",
+    "\n",
+    "Metadata of species can be viewed in `species_data_frame`, although species objects cannot be obtained from this DataFrame. Species objects are obtained using the SpeciesEvolver method, `filter_species`. With species objects you can work with individual species, including plotting its geographic range as demonstrated below.\n",
+    "\n",
+    "All of the introduced species and their child species are returned when no parameters are inputted into `filter_species`. This method returns the 20 species that have existed in this notebook's SpeciesEvolver instance."
    ]
   },
   {
@@ -1706,7 +1501,26 @@
     {
      "data": {
       "text/plain": [
-       "1.0"
+       "[<landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a25256390>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a24dc5c10>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a24dc5790>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a24dc58d0>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23b97f90>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23b97fd0>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23b97950>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23b97b90>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23b97250>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23b97150>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a25256110>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a24dc5cd0>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a24dc5250>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a24dc5550>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a24dc5890>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a24dc56d0>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23b977d0>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23b970d0>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23b97c90>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a23b97dd0>]"
       ]
      },
      "execution_count": 26,
@@ -1715,18 +1529,15 @@
     }
    ],
    "source": [
-    "sc.record.capture_count.max()"
+    "filtered_species = se.filter_species()\n",
+    "filtered_species"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Query species\n",
-    "\n",
-    "SpeciesEvolver provides methods that return species objects in the event you need to work with specific instances of species.\n",
-    "\n",
-    "The method, `species_at_time` returns species that were extant at the inputted time."
+    "The species can be filtered by the time they existed. To do so, we can use the optional parameter, `time` to indicate we want the species extant at time 0."
    ]
   },
   {
@@ -1737,8 +1548,8 @@
     {
      "data": {
       "text/plain": [
-       "[<landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a1caab3d0>,\n",
-       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a1caabcd0>]"
+       "[<landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a25256390>,\n",
+       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a25256110>]"
       ]
      },
      "execution_count": 27,
@@ -1747,14 +1558,15 @@
     }
    ],
    "source": [
-    "se.species_at_time(1)"
+    "filtered_species = se.filter_species(time=0)\n",
+    "filtered_species"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The method, `species_with_identifier` takes various combinations of species identifiers. See the documentation of this method for details. Here we get all the species of clade A."
+    "We can confirm that this method returned the first two species introduced at time 0, A.0 and B.0."
    ]
   },
   {
@@ -1765,16 +1577,7 @@
     {
      "data": {
       "text/plain": [
-       "[<landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a1caab3d0>,\n",
-       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a1c72a3d0>,\n",
-       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a1c72a850>,\n",
-       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a1c725090>,\n",
-       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a1c72a090>,\n",
-       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a1c72acd0>,\n",
-       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a1c72a910>,\n",
-       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a1c72ad50>,\n",
-       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a1c72ab10>,\n",
-       " <landlab.components.species_evolution.zone_species.ZoneSpecies at 0x1a1c72a750>]"
+       "[('A', 0), ('B', 0)]"
       ]
      },
      "execution_count": 28,
@@ -1783,7 +1586,202 @@
     }
    ],
    "source": [
-    "se.species_with_identifier('A')"
+    "[s.identifier for s in filtered_species]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The species of a clade can be obtained using the `clade` parameter. Here we get the species of clade A and then print only the identifiers of the filtered species."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('A', 0),\n",
+       " ('A', 1),\n",
+       " ('A', 2),\n",
+       " ('A', 3),\n",
+       " ('A', 4),\n",
+       " ('A', 5),\n",
+       " ('A', 6),\n",
+       " ('A', 7),\n",
+       " ('A', 8),\n",
+       " ('A', 9)]"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filtered_species = se.filter_species(clade='A')\n",
+    "[s.identifier for s in filtered_species]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The parameter, `number` returns only the species with a number, despite the clade."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('A', 3), ('B', 3)]"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filtered_species = se.filter_species(number=3)\n",
+    "[s.identifier for s in filtered_species]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Multiple filter parameters can be used at once."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('A', 1),\n",
+       " ('A', 2),\n",
+       " ('A', 3),\n",
+       " ('A', 4),\n",
+       " ('A', 5),\n",
+       " ('A', 6),\n",
+       " ('A', 7),\n",
+       " ('A', 8),\n",
+       " ('A', 9)]"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filtered_species = se.filter_species(time=2, clade='A')\n",
+    "[s.identifier for s in filtered_species]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If necessary, lists of species can be passed through the filter multiple times. The parameter, `species_subset` is a list of Species objects that will be filtered. We use a list of previously filtered species, and filter the list further."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('A', 5)"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filtered_species_pass_2 = se.filter_species(species_subset=filtered_species, number=5)\n",
+    "species_A5 = filtered_species_pass_2[0]\n",
+    "species_A5.identifier"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With a species obtained, we can work with it as we need. Here we plot its geographic range. The `range_mask` attribute of a species is a mask of its geographic range."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAACtCAYAAABBYlMUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAWyElEQVR4nO3de7gcdX3H8feHhCSoaBIuNiThJqk1tAUphViqIloC8RJqtY21NrUoj9W2ar2BtF7QR0Gt8PC0gKlYI1UhRVsoT1qIiNZ6ARORS8CYA4HkABJiwkURSODbP+Z7yJxld+ecw87u2ZPP63n22Znf7zez3/3tOfPd+c3sjCICMzOzdnbrdQBmZjb+OVmYmVklJwszM6vkZGFmZpWcLMzMrJKThZmZVXKysL4g6ReSDu51HBOBpAsk/UPNr3GHpFe0qHuxpHV1vr51nvw7C9uVSRJwG/BIRMyvaHsH8Fzg8Sz6XkQcX2+E/Sn76i0R8Y1OtrXe8Z6F7epeAuwLHCzpd0fQ/tUR8ax8jDhRSJo05gjNxgEnCxsTSR+QdJekhyStk/TyLP+IpEslXZJ1P5J0WGm5/SR9TdJ9kjZI+ttS3SRJH5R0Wy67RtLcrAtJh+T0VEmfkbRR0r05rLJH1u0t6QpJ90vaKuk7ktr9nS8FLgNW5nSn+ueLks6XtFLSL4GXSXqlpOslPShpk6SPlNofmO9xab6vLZJOL9XvIWm5pG2SbpX0fkmDpfqW/doito/n9LGSBiW9R9JmSfdIenOL5V4m6abS/DckXVea/z9JJ5UWOVzSjZIeyL+HaeXXzOmLgP2B/8qhxvdn+QJJ38vP8QZJx46s5602EeGHH6N6AM8HNgH75fyBwPNy+iPAduB1wO7Ae4ENOb0bsAb4EDAFOBi4HViYy74PuCnXL+AwYK+sC+CQnD4HuByYCewJ/Bfwyaz7JHBBvt7uwIvJ4dYm7+MZwIPAIuCPgC3AlDbv+w7gXuA+4CrgsDZtvwg8AByT73sacCzwWzn/27muk0p9GMC/AHvke38UeEHWnwl8G5gBzAFuBAazrm2/tojt4zl9LLADOCP7axHwMDCjyXLTgF8BewOTgZ8Bd+dnsEfW7VXqq+uA/fJzuhV4W+k1Bxv69RWl+dnAzzOW3YA/yPl9ev23vys/vGdhY/E4MBWYL2n3iLgjIm4r1a+JiEsjYjvwWYqNzALgdyn+4c+IiMci4naKjeOSXO4twN9HxLoo3BARPy+/cB5jeCvw7ojYGhEPAZ8orWM7MAs4ICK2R8R3IrdATbyWYoN8FXAFxQbwlW3e9xspNuoHANcAV0qa3qb9ZRHx3Yh4IiIeiYhvRcRNOX8j8FXgpQ3LfDQifhURNwA3UCQNgD8GPhER2yJiEDi3tExVv1bZDpyR/bUS+AVFwh4mIh4BVlMM3R1JkbD+jyIhLgDWN3xe50bE3RGxlSKhHz7CeP4MWBkRK7OvVuXrLhrh8lYDJwsbtYgYAN5FsRexWdLFkvYrNdlUavsEMEjxDfMAYL8cWrhf0v3ABykOGgPMpTjY3M4+FHsEa0rr+J8sB/g0MABcJel2Sae2WddSYEVE7IiIR4Gv02YoKjf8v4qIhyPik8D9FHsurWwqz0g6WtI1OVT0APA2im/pZT8rTT8MPCun92tYX3m6ql+r/DwidrR43UbfptgzeElOf4si4b0050fyXqocALy+4f38PsWXAOuRyb0OwPpTRHwF+IqkZwOfA84C3pTVc4fa5fGCORTDFTuADRExr8VqNwHPA25u89JbKIY7Do2Iu5rE9RDwHuA9kg4FrpH0w4i4utxO0hzgOOAoSX+Uxc8ApknaOyK2tInhyZejGC5rV1/2FeCfgBMj4hFJ5/DUZNHKPRT9eEvOzy3VbaJ9v3bSt4F/BDZSDI1to9iLeRT45zGus7GfNgEXRcRbxxqkdZ73LGzUJD1f0nGSpgKPUGy8Hy81+R1Jr5U0mWIP5FHgBxRj2A+qODi+Rx7Q/k3tPAvp88DHJM1T4bcl7VV+7dxT+RfgbEn7ZjyzJS3M6VdJOiSHqx7MuMqxDXkT8FOK4ZbD8/HrFHtBb2jynveXdIykKZKmSXofxYb+u6Pouj2BrZkojgL+dBTLrgBOkzRD0mzgr0t1Vf3aSd+j6LOjgOsiYi3FnsDRwP+OcZ33UhxnGfJvwKslLcz3Mi0Pis95OoHb0+NkYWMxleJb5RaKoYZ9KYY9hlwG/AnFt843Aa/N8fDHgVdTbJg35PKfB56Ty32WYqN4FcWG/kKKA6eNPkAx1PQDSQ8C32DnGPu8nP8F8H3gvIj4VpN1LM26n5UfFAfHl8KTP167INvvCZyf7+ku4ASKPYSfN1l3K28HzpD0EMXB6BWjWPYMikS2Id/fpRRJmBH0a8dExC+BHwFrI+KxLP4+cGdEbB7jaj8J/H0OOb03IjYBiyn+pu6j2NN4H95e9ZR/lGcdlaeDHhIRf9brWCYySX8FLImIxgPkZrVwpjbrA5Jm5TDYbpKeT3Fc5j96HZftOnyA26w/TKE4keAgirOwLgbO62lEtkvxMJSZmVXyMJSZmVVysjAzs0oT8pjFJCkm5BszM6vRY7AlIvZpVjcht6mTgV/rdRBmZn1mI9zZqm5CDkM9Vt3EzMxKNlbUT8hkYWZmneVkYWZmlZwszMyskpOFmZlVcrIwM7NKThZmZlbJycLMzCo5WZiZWSUnCzMzq+RkYWZmlZwszMyskpOFmZlVcrIwM7NKThZmZlbJycLMzCrVniwkTZJ0vaQrcv4gSddKWi/pEklTsnxqzg9k/YGldZyW5eskLaw7ZjMzG64bexbvBG4tzZ8FnB0R84BtwMlZfjKwLSIOAc7OdkiaDywBDgVOAM6TNKkLcZuZWao1WUiaA7wS+HzOCzgOuDSbLAdOyunFOU/WvzzbLwYujohHI2IDMAAcVWfcZmY2XN17FucA7weeyPm9gPsjYkfODwKzc3o2sAkg6x/I9k+WN1nGzMy6oLZkIelVwOaIWFMubtI0KuraLVN+vVMkrZa0etTBmplZW5NrXPcxwGskLQKmAc+m2NOYLmly7j3MAe7O9oPAXGBQ0mTgOcDWUvmQ8jJPiohlwDIASU9JJmZmNna17VlExGkRMSciDqQ4QP3NiHgjcA3wumy2FLgspy/PebL+mxERWb4kz5Y6CJgHXFdX3GZm9lR17lm08gHgYkkfB64HLszyC4GLJA1Q7FEsAYiItZJWALcAO4B3RMTj3Q/bzGzXpeLL+8QiKfbvdRBmZn1kY/G0JiKObFbvX3CbmVklJwszM6vkZGFmZpWcLMzMrJKThZmZVXKyMDOzSk4WZmZWycnCzMwqOVmYmVklJwszM6vkZGFmZpWcLMzMrJKThZmZVarzTnnTJF0n6QZJayV9NMsPknStpPWSLpE0Jcun5vxA1h9YWtdpWb5O0sK6YjYzs+bq3LN4FDguIg4DDgdOkLQAOAs4OyLmAduAk7P9ycC2iDgEODvbIWk+xb0tDgVOAM6TNKnGuM3MrEGdd8qLiPhFzu6ejwCOAy7N8uXASTm9OOfJ+pdLUpZfHBGPRsQGYAA4qq64zczsqWo9ZiFpkqQfA5uBVcBtwP15/20o7q89O6dnA5sAsv4BYK9yeZNlzMysC2pNFhHxeEQcDsyh2Bt4QbNm+awWda3Kh5F0iqTVklaPNV4zM2uuK2dDRcT9wLeABcB0SUP3/p4D3J3Tg8BcgKx/DsW9uJ8sb7JM+TWWRcSRrW4JaGZmY1fn2VD7SJqe03sArwBuBa4BXpfNlgKX5fTlOU/WfzOKG4RfDizJs6UOAuYB19UVt/WXOyfgPeTNxqPJ1U3GbBawPM9c2g1YERFXSLoFuFjSx4HrgQuz/YXARZIGKPYolgBExFpJK4BbgB3AOyLi8RrjNjOzBooJ+M1MUuzf6yCsVuU9igPU7LCWmY3GxuJpTauh/Dr3LMw6rnHYyYnCrDt8uQ/rK0PJ4QDJicKsi5wsbFy7M2LY3kS3Dmj7wLnZcE4WNq4N7T00brwbk0i5vFOv64RhtlPLZCFpZflifmZjUd6ot9rAj2Zdrdbf+Dqd4oRhVmh3gPuLwFWSlgOfiojt3QnJ+l3VN/47I0Z8vGGo7Wg32o1nSw3NN66rXRxDbX1Q3azi1FlJzwQ+RHG114uAJ4bqIuKztUc3Rj51tnfabYjH+i292VBU1QZ7pK811vU0JiCzfvd0T53dDvwSmArsSSlZmDUzkg1nuz2FkXzTB9puqFvtCVQlkKrEUF7PWPeUzPpVy2Qh6QTgsxSX2zgiIh7uWlRmZjautByGkvQd4G0Rsba7IT19HobqL6MdzhnJN/mRDFl1YojJQ1E2UYx5GCoiXlxPSGbDjfYA9kg3zKPdgJfbjzQmJwnbVfh3FjYudHqj24mE4kRgtpOvDWW7rPKlQ8ysPe9ZmJlZpTpvfjRX0jWSbpW0VtI7s3ympFWS1ufzjCyXpHMlDUi6UdIRpXUtzfbrJS1t9ZpmZlaPOvcsdgDviYgXUNxO9R2S5gOnAldHxDzg6pwHOJHiLnjzgFOA86FILsCHgaMp7uP94aEEY2Zm3VFbsoiIeyLiRzn9EMUtVWcDi4Hl2Ww5cFJOLwa+FIUfUNyrexawEFgVEVsjYhuwiuIX5WZm1iVdOWaRFyR8IXAt8NyIuAeKhALsm81mA5tKiw1mWatyMzPrktrPhpL0LOBrwLsi4kG1PvOkWUW0KW98nVMohq/MzKzDat2zkLQ7RaL4ckR8PYvvzeEl8nlzlg8Cc0uLzwHublM+TEQsi4gjW/360MzMxq7Os6EEXAjc2nCF2suBoTOalgKXlcr/PM+KWgA8kMNUVwLHS5qRB7aPzzIzM+uSOoehjgHeBNwk6cdZ9kHgTGCFpJMpLkfy+qxbCSwCBoCHgTcDRMRWSR8DfpjtzoiIrTXGbWZmDdrez6Jf+UKCZmajU3UhQf+C28zMKjlZmJlZJScLMzOr5GRhZmaVnCzMzKySk4WZmVVysjAzs0pOFmZmVsnJwszMKjlZmJlZJScLMzOr5GRhZmaVnCzMzKxSnfez+IKkzZJuLpXNlLRK0vp8npHlknSupAFJN0o6orTM0my/XtLSZq9lZmb1qnPP4ovACQ1lpwJXR8Q84OqcBzgRmJePU4DzoUguwIeBo4GjgA8PJRgzM+ue2pJFRPwv0HiTosXA8pxeDpxUKv9SFH4ATM9bri4EVkXE1ojYBqziqQnIzMxq1u1jFs/NW6WSz/tm+WxgU6ndYJa1Kjczsy6q87aqo6EmZdGm/KkrkE6hGMIyM7MO6/aexb05vEQ+b87yQWBuqd0c4O425U8REcsi4shWtwQ0M7Ox63ayuBwYOqNpKXBZqfzP86yoBcADOUx1JXC8pBl5YPv4LDMzsy6qbRhK0leBY4G9JQ1SnNV0JrBC0skU9wd/fTZfCSwCBoCHgTcDRMRWSR8DfpjtzoiIxoPmZmZWM0U0PQTQ1yTF/r0Owsysj2wsnta0Gsr3L7jNzKySk4WZmVVysjAzs0pOFmZmVsnJwszMKjlZmJlZJScLMzOr5GRhZmaVnCzMzKySk4WZmVVysjAzs0pOFmZmVsnJwszMKvVNspB0gqR1kgYkndrreMzMdiV9kSwkTQL+GTgRmA+8QdL83kZlZrbr6ItkARwFDETE7RHxGHAxsLjHMZmZ7TL6JVnMBjaV5gezzMzMuqBfkoWalA27xZ+kUyStlrR6UpeCMjObKKruLlrbPbg7bBCYW5qfA9xdbhARy4BlAJLu2wi/BLZ0LcLO2Jv+irnf4oX+i7nf4gXH3A11xXtAq4q+uAe3pMnAT4GXA3cBPwT+NCLWtllmdat7yY5X/RZzv8UL/Rdzv8ULjrkbehFvX+xZRMQOSX8NXAlMAr7QLlGYmVln9UWyAIiIlcDKXsdhZrYr6pcD3GOxrNcBjEG/xdxv8UL/xdxv8YJj7oaux9sXxyzMzKy3JvKehZmZdciESxbj6RpSkuZKukbSrZLWSnpnls+UtErS+nyekeWSdG7GfqOkI0rrWprt10taWnPckyRdL+mKnD9I0rX52pdImpLlU3N+IOsPLK3jtCxfJ2lhzfFOl3SppJ9kX7+oD/r43fk3cbOkr0qaNt76WdIXJG2WdHOprGP9Kul3JN2Uy5wrqdnvqZ5uvJ/Ov4sbJf2HpOmluqZ912ob0urz6XTMpbr3SgpJe+d8b/s4IibMg+JMqduAg4EpwA3A/B7GMws4Iqf3pDj9dz7wKeDULD8VOCunFwH/TfEjxAXAtVk+E7g9n2fk9Iwa4/474CvAFTm/AliS0xcAf5XTbwcuyOklwCU5PT/7fipwUH4mk2qMdznwlpyeAkwfz31McfWBDcAepf79i/HWz8BLgCOAm0tlHetX4DrgRbnMfwMn1hDv8cDknD6rFG/TvqPNNqTV59PpmLN8LsXZn3cCe4+HPq7ln7dXj+yUK0vzpwGn9TquUjyXAX8ArANmZdksYF1Ofw54Q6n9uqx/A/C5Uvmwdh2OcQ5wNXAccEX+kW0p/cM92cf5x/yinJ6c7dTY7+V2NcT7bIoNrxrKx3MfD12+Zmb22xXAwvHYz8CBDN/4dqRfs+4npfJh7ToVb0PdHwJfzummfUeLbUi7/4M6YgYuBQ4D7mBnsuhpH0+0Yahxew2pHDp4IXAt8NyIuAcgn/fNZq3i7+b7Ogd4P/BEzu8F3B8RO5q89pNxZf0D2b6b8R4M3Af8q4qhs89LeibjuI8j4i7gM8BG4B6KflvD+O7nIZ3q19k53Vhep7+k+HZNRVzNytv9H3SUpNcAd0XEDQ1VPe3jiZYsKq8h1QuSngV8DXhXRDzYrmmTsmhT3lGSXgVsjog1I4ipXV03P4fJFLvx50fECyku89LuWFXPY85x/sUUwx/7Ac+kuPx+q9fvecwjMNoYuxq7pNOBHcCXh4pGGVe3/gefAZwOfKhZdYsYuhLzREsWldeQ6jZJu1Mkii9HxNez+F5Js7J+FrA5y1vF3633dQzwGkl3UFwG/jiKPY3pKi650vjaT8aV9c8BtnYx3qEYBiPi2py/lCJ5jNc+BngFsCEi7ouI7cDXgd9jfPfzkE7162BON5Z3XB7wfRXwxsjxmDHEu4XWn08nPY/iS8QN+X84B/iRpF8bQ8yd7eNOjm/2+kHxLfP27Oyhg1OH9jAeAV8Czmko/zTDDxJ+KqdfyfADWNdl+UyKcfkZ+dgAzKw59mPZeYD73xl+YO/tOf0Ohh94XZHThzL84OHt1HuA+zvA83P6I9m/47aPgaOBtcAzMo7lwN+Mx37mqccsOtavFNd4W8DOg6+Laoj3BOAWYJ+Gdk37jjbbkFafT6djbqi7g53HLHrax7X88/byQXHGwE8pzmg4vcex/D7Fbt+NwI/zsYhi/PNqYH0+D32worgj4G3ATcCRpXX9JTCQjzd3IfZj2ZksDqY4q2Ig/2GmZvm0nB/I+oNLy5+e72MdT/MslxHEejiwOvv5P/MfZlz3MfBR4CfAzcBFudEaV/0MfJXimMp2im+pJ3eyX4Ej8/3fBvwTDScpdCjeAYrx/KH/vwuq+o4W25BWn0+nY26ov4OdyaKnfexfcJuZWaWJdszCzMxq4GRhZmaVnCzMzKySk4WZmVVysjAzs0pOFmZdoOIKxBskzcz5GTl/QK9jMxsJJwuzLoiITcD5wJlZdCawLCLu7F1UZiPn31mYdUle+mUN8AXgrcALI+Kx3kZlNjKTq5uYWSdExHZJ7wP+BzjeicL6iYehzLrrRIrLO/xmrwMxGw0nC7MukXQ4xc2vFgDvHrp6q1k/cLIw64K89/H5FPc02Uhx9dbP9DYqs5FzsjDrjrcCGyNiVc6fB/yGpJf2MCazEfPZUGZmVsl7FmZmVsnJwszMKjlZmJlZJScLMzOr5GRhZmaVnCzMzKySk4WZmVVysjAzs0r/D0ImUuyvUlz9AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mask = species_A5.range_mask\n",
+    "\n",
+    "imshow_grid(mg, mask, plot_name='species A.5 range in white', allow_colorbar=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can confirm that the parent species of species A.5 is one of the initial species using the `parent_species` attribute."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('A', 0)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "species_A5.parent_species.identifier"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Species A.0 is one of the initial species, and became pseudoextinct as it speciated to species A1...A9 when the zone fragmented prior to time 2. The same sequence of events occurred for clade B. This phylogeny is explained by the 2 pseudoextinctions, 18 speciations, 20 total species, and 2 zone captures followed by 9 zones fragmentations."
    ]
   },
   {
diff --git a/species_evolution/Modeling stream-based species.ipynb b/species_evolution/Modeling stream-based species.ipynb
deleted file mode 100644
index 4320368..0000000
--- a/species_evolution/Modeling stream-based species.ipynb	
+++ /dev/null
@@ -1,714 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a href=\"http://landlab.github.io\"><img style=\"float: left\" src=\"https://raw.githubusercontent.com/landlab/tutorials/master/landlab_header.png\"></a>\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling stream-based species\n",
-    "\n",
-    "<hr>\n",
-    "<small> For instructions on how to run an interactive iPython notebook, click here: <a href=\"https://github.com/landlab/tutorials/blob/master/README.md\">https://github.com/landlab/tutorials/blob/master/README.md</a></small><br>\n",
-    "<small>For more Landlab tutorials, click here: <a href=\"https://github.com/landlab/landlab/wiki/Tutorials\">https://github.com/landlab/landlab/wiki/Tutorials</a></small>\n",
-    "<hr>\n",
-    "\n",
-    "In this tutorial we will:\n",
-    "* Introduce species into a Landlab model.\n",
-    "* Set the zones in which the species operate.\n",
-    "* Evolve a landscape and the species over time.\n",
-    "* Explore SpeciesEvolver output data structures.\n",
-    "\n",
-    "The configuration of the model in this tutorial loosely follows the fault throw experiment in Lyons et al., in preperation for Earth Surface Dynamics.\n",
-    "\n",
-    "## Macroevolution processes built into species\n",
-    "\n",
-    "The evolution of the base species is driven by these processes:\n",
-    "* dispersal: geographic range expansion\n",
-    "* speciation: lineage splitting\n",
-    "* extinction: lineage termination"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Import modules."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/njlyons/anaconda3/lib/python3.7/site-packages/xarray/core/merge.py:17: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version\n",
-      "  PANDAS_TYPES = (pd.Series, pd.DataFrame, pd.Panel)\n"
-     ]
-    }
-   ],
-   "source": [
-    "from copy import deepcopy\n",
-    "from landlab.components import (SpeciesEvolver, FastscapeEroder, FlowRouter,\n",
-    "                                LinearDiffuser)\n",
-    "from landlab.components.species_evolution import Species, StreamZoneManager\n",
-    "from landlab.io import read_esri_ascii\n",
-    "from landlab.plot import imshow_grid\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Prepare the grid and components of the model."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Load a model grid that has steady state topography. The top and bottom boundaries are set to open."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "(mg, z) = read_esri_ascii('model_grid_steady_state_elevation.txt')\n",
-    "mg.at_node['topographic__elevation'] = z\n",
-    "mg.set_closed_boundaries_at_grid_edges(right_is_closed=True,\n",
-    "                                       top_is_closed=False,\n",
-    "                                       left_is_closed=True,\n",
-    "                                       bottom_is_closed=False)\n",
-    "\n",
-    "imshow_grid(mg, z, colorbar_label='elevation (m)', shrink=0.4)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Initialize the components that will be run alongside SpeciesEvolver. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fr = FlowRouter(mg)\n",
-    "sp = FastscapeEroder(mg, K_sp=5e-5, m_sp=0.5, n_sp=1.0)\n",
-    "lf = LinearDiffuser(mg, linear_diffusivity=1e-3)\n",
-    "\n",
-    "fr.run_one_step()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Initialize SpeciesEvolver.\n",
-    "\n",
-    "SpeciesEvolver requires only one parameter, a model grid, for the default functionality.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'StreamZoneManager' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-5-b69f353104b5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mszm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mStreamZoneManager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_drainage_area\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# Instaniate SpeciesEvolver and get initial zones.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSpeciesEvolver\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzone_managers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mszm\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mzones\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzones_at_time\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'StreamZoneManager' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "szm = StreamZoneManager(mg, min_drainage_area=50000)\n",
-    "\n",
-    "# Instaniate SpeciesEvolver and get initial zones.\n",
-    "se = SpeciesEvolver(mg, zone_managers=[szm])\n",
-    "zones = se.zones_at_time(0)\n",
-    "\n",
-    "stream_mask = mg.at_node['drainage_area'] > 50000\n",
-    "imshow_grid(mg, stream_mask, allow_colorbar=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Populate each zone with a species."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for zone in zones:\n",
-    "    species = Species(zone)\n",
-    "    se.introduce_species(species)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'zones' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-6-8b28561dbdc8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mzones\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m: name 'zones' is not defined"
-     ]
-    }
-   ],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initiate a fault"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Define the faults. A block in the center of the grid uplifts."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "x_of_fault_1 = mg.x_of_node > 5000\n",
-    "x_of_fault_2 = mg.x_of_node < 10000\n",
-    "core_mask = mg.node_is_core()\n",
-    "upthown_block_mask = np.all([core_mask, x_of_fault_1, x_of_fault_2], 0)\n",
-    "\n",
-    "imshow_grid(mg, upthown_block_mask, allow_colorbar=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Uplift the nodes of the upthrown block by 50 m."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "z[upthown_block_mask] += 100\n",
-    "imshow_grid(mg, z, colorbar_label='elevation (m)', shrink=0.4)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Model species evolution\n",
-    "\n",
-    "First, set model time step and uplift parameters.\n",
-    "\n",
-    "SpeciesEvolver runs every 10 timesteps of the other components."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dt = 1000\n",
-    "total_time = int(5e5)\n",
-    "nt = int(total_time // dt)\n",
-    "\n",
-    "dt_se = 10 * dt\n",
-    "\n",
-    "uplift_rate = 5e-3\n",
-    "uplift_per_step = uplift_rate * dt\n",
-    "\n",
-    "mg0 = deepcopy(mg)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Run model. The ```run_one_step``` function of SpeciesEvolver takes the new zones of the time step.\n",
-    "\n",
-    "This code block may take a few minutes to run."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for time_i in range(nt):\n",
-    "    # Uniformly uplift.\n",
-    "    z[mg.core_nodes] += uplift_per_step\n",
-    "\n",
-    "    # Increment earth surface process components.\n",
-    "    fr.run_one_step()\n",
-    "    sp.run_one_step(dt)\n",
-    "    lf.run_one_step(dt)\n",
-    "\n",
-    "    elapsed_time = dt * (time_i + 1)\n",
-    "\n",
-    "    if elapsed_time % dt_se == 0:\n",
-    "        # Increment SpeciesEvolver.\n",
-    "        se.run_one_step(dt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "OrderedDict([('time',\n",
-       "              [0,\n",
-       "               1000,\n",
-       "               2000,\n",
-       "               3000,\n",
-       "               4000,\n",
-       "               5000,\n",
-       "               6000,\n",
-       "               7000,\n",
-       "               8000,\n",
-       "               9000,\n",
-       "               10000,\n",
-       "               11000,\n",
-       "               12000,\n",
-       "               13000,\n",
-       "               14000,\n",
-       "               15000,\n",
-       "               16000,\n",
-       "               17000,\n",
-       "               18000,\n",
-       "               19000,\n",
-       "               20000,\n",
-       "               21000,\n",
-       "               22000,\n",
-       "               23000,\n",
-       "               24000,\n",
-       "               25000,\n",
-       "               26000,\n",
-       "               27000,\n",
-       "               28000,\n",
-       "               29000,\n",
-       "               30000,\n",
-       "               31000,\n",
-       "               32000,\n",
-       "               33000,\n",
-       "               34000,\n",
-       "               35000,\n",
-       "               36000,\n",
-       "               37000,\n",
-       "               38000,\n",
-       "               39000,\n",
-       "               40000,\n",
-       "               41000,\n",
-       "               42000,\n",
-       "               43000,\n",
-       "               44000,\n",
-       "               45000,\n",
-       "               46000,\n",
-       "               47000,\n",
-       "               48000,\n",
-       "               49000,\n",
-       "               50000]),\n",
-       "             ('number_of_captures',\n",
-       "              [nan,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               2,\n",
-       "               0,\n",
-       "               2,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0]),\n",
-       "             ('area_captured_max',\n",
-       "              [nan,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               187500.0,\n",
-       "               0,\n",
-       "               230000.0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0]),\n",
-       "             ('area_captured_sum',\n",
-       "              [nan,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               235000.0,\n",
-       "               0,\n",
-       "               282500.0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0,\n",
-       "               0])])"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "se.record"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "imshow_grid(mg, stream_mask, allow_colorbar=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "imshow_grid(mg, z, colorbar_label='elevation (m)', shrink=0.4)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot difference of pre and post fault topography."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "A_c = 50000\n",
-    "\n",
-    "stream_change = np.zeros(mg.number_of_nodes)\n",
-    "\n",
-    "a0 = mg0.at_node['drainage_area']\n",
-    "stream_change[a0 > A_c] = 1\n",
-    "\n",
-    "a1 = mg.at_node['drainage_area']\n",
-    "stream_change[a1 > A_c] = 2\n",
-    "\n",
-    "stream_change[np.all([a0 > A_c, a1 > A_c], 0)] = 3\n",
-    "\n",
-    "cm = plt.cm.get_cmap('jet', 4)\n",
-    "imshow_grid(mg, stream_change, cmap=cm,\n",
-    "            allow_colorbar=False, plot_name='stream change')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plot results\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.subplot(1, 2, 1)\n",
-    "imshow_grid(mg, 'topographic__elevation', colorbar_label='elevation (m)')\n",
-    "\n",
-    "plt.subplot(1, 2, 2)\n",
-    "plot.imshow_zones(mg, zones)\n",
-    "\n",
-    "plot.number_of_species(se.record, se.species)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from landlab.components.species_macroevolution import plot\n",
-    "\n",
-    "plot.tree(se.species)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Click here for more <a href=\"https://github.com/landlab/landlab/wiki/Tutorials\">Landlab tutorials</a>"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}

From e0a6b2428bcb0d56021b86aa7cd9367d33e21f2f Mon Sep 17 00:00:00 2001
From: nathanlyons <nathan.j.lyons@gmail.com>
Date: Fri, 29 Nov 2019 17:09:16 -0500
Subject: [PATCH 15/15] remove unused ascii grid file

---
 ...grid_steady_state_elevation_low_relief.txt | 105 ------------------
 1 file changed, 105 deletions(-)
 delete mode 100644 species_evolution/model_grid_steady_state_elevation_low_relief.txt

diff --git a/species_evolution/model_grid_steady_state_elevation_low_relief.txt b/species_evolution/model_grid_steady_state_elevation_low_relief.txt
deleted file mode 100644
index 9dc2673..0000000
--- a/species_evolution/model_grid_steady_state_elevation_low_relief.txt
+++ /dev/null
@@ -1,105 +0,0 @@
-ncols 300
-nrows 100
-xllcorner 0.0
-yllcorner 0.0
-cellsize 50.0
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