{ "cells": [ { "cell_type": "code", "execution_count": 0, "metadata": { "collapsed": false }, "outputs": [ ], "source": [ "# Name: Alejandro Gutierrez\n", "# I worked on this code with: Alette Eide, Molly, Sandra Foxx\n", "\n", "# Please do all of your work for this week's lab in this worksheet. If\n", "# you wish to create other worksheets for scratch work, you can, but\n", "# this is the one that will be graded. You do not need to do anything\n", "# to turn in your lab. It will be collected by your TA at the beginning\n", "# of (or right before) next week’s lab.\n", "\n", "# Be sure to clearly label which question you are answering as you go and to\n", "# use enough comments that you and the grader can understand your code.\n" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "collapsed": false }, "outputs": [ ], "source": [ "#1\n", "#The life cycle of mangroves begins when mangroves become potential zygotes in the form of female gametophytes. Propagules, is the stage where the seed must find a favorable place and develope. The cycle is complete when the seeds reproduce gamates of their own or by forming independent clones. Stage 0 is when young trees, trees, and older trees make propgules. Stage one is when a number of propagulus become cotyledonary seedlings. Stage 2 is when a number of Cotyledonary seedlings become seelings. Stage 3 is when some seedlings become saplings. Stage 3 is when some become young trees. Stage 5 is when some young trees become old trees." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "" ], "text/latex": [ "\\begin{math}\n", "\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left(\\begin{array}{rrrrrrr}\n", "0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 500.0 & 1000.0 \\\\\n", "0.2 & 0.666 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\\\\n", "0.0 & 0.083 & 0.825 & 0.0 & 0.0 & 0.0 & 0.0 \\\\\n", "0.0 & 0.0 & 0.01 & 0.909 & 0.0 & 0.0 & 0.0 \\\\\n", "0.0 & 0.0 & 0.0 & 0.073 & 0.963 & 0.0 & 0.0 \\\\\n", "0.0 & 0.0 & 0.0 & 0.0 & 0.008 & 0.98 & 0.0 \\\\\n", "0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.012 & 0.999\n", "\\end{array}\\right)\n", "\\end{math}" ], "text/plain": [ "[ 0.0 0.0 0.0 0.0 0.0 500.0 1000.0]\n", "[ 0.2 0.666 0.0 0.0 0.0 0.0 0.0]\n", "[ 0.0 0.083 0.825 0.0 0.0 0.0 0.0]\n", "[ 0.0 0.0 0.01 0.909 0.0 0.0 0.0]\n", "[ 0.0 0.0 0.0 0.073 0.963 0.0 0.0]\n", "[ 0.0 0.0 0.0 0.0 0.008 0.98 0.0]\n", "[ 0.0 0.0 0.0 0.0 0.0 0.012 0.999]" ] }, "execution_count": 23, "metadata": { }, "output_type": "execute_result" } ], "source": [ "#2\n", "MangroveMatrix= zero_matrix(RDF,7,7)\n", "\n", "MangroveMatrix[0,5]= 500\n", "\n", "MangroveMatrix[0,6]= 1000\n", "\n", "MangroveMatrix[1,0]= 0.2\n", "\n", "MangroveMatrix[1,1]= 0.666\n", "\n", "MangroveMatrix[2,1] = 0.083\n", "\n", "MangroveMatrix[2,2] = 0.825\n", "\n", "MangroveMatrix[3,2] = 0.010\n", "\n", "MangroveMatrix[3,3] = 0.909\n", "\n", "MangroveMatrix[4,3] = 0.073\n", "\n", "MangroveMatrix[4,4] = 0.963\n", "\n", "MangroveMatrix[5,4] = 0.008\n", "\n", "MangroveMatrix[5,5] = 0.980\n", "\n", "MangroveMatrix[6,5] = 0.012\n", "\n", "MangroveMatrix[6,6] = 0.999\n", "\n", "show(MangroveMatrix)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ ], "source": [ "#3a\n", "ics=vector([1,2,3,4,5,6,7])\n", "\n", "prop_list=[1]\n", "young_list=[5]\n", "old_list=[7]\n", "\n", "for i in srange(0,10,1):\n", " ics= MangroveMatrix*ics\n", " prop_list.append(ics[0])\n", " young_list.append(ics[4])\n", " old_list.append(ics[6])" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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", 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", "text/plain": [ "Graphics object consisting of 1 graphics primitive" ] }, "execution_count": 13, "metadata": { }, "output_type": "execute_result" } ], "source": [ "#3c\n", "list_plot(young_list, color=\"dodgerblue\",legend_label=\"Young Population\",legend_color=\"black\",plotjoined=True,title=\"Young Trees\")" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "Graphics object consisting of 1 graphics primitive" ] }, "execution_count": 14, "metadata": { }, "output_type": "execute_result" } ], "source": [ "#3d\n", "list_plot(old_list, color=\"dodgerblue\",legend_label=\"Old Population\",legend_color=\"black\",plotjoined=True,title=\"Old Trees\")" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "" ], "text/latex": [ "\\begin{math}\n", "\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left(1.048140178442451, \\left[\\left(-0.8729866710666837,\\,-0.4568934230495471,\\,-0.16994767315242945,\\,-0.012214133620844852,\\,-0.010472514512338323,\\,-0.0012295259274889008,\\,-0.00030024944144526564\\right)\\right], 1\\right)\n", "\\end{math}" ], "text/plain": [ "(1.048140178442451,\n", " [(-0.8729866710666837, -0.4568934230495471, -0.16994767315242945, -0.012214133620844852, -0.010472514512338323, -0.0012295259274889008, -0.00030024944144526564)],\n", " 1)" ] }, "execution_count": 18, "metadata": { }, "output_type": "execute_result" } ], "source": [ "#4\n", "show(max(MangroveMatrix.eigenvectors_right()))\n", "#The mangrove population will grow because the dominant eigenvalue is greater than 1." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "" ], "text/latex": [ "\\begin{math}\n", "\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left(2.90753803525668 \\times 10^{6},\\,1.52171281601813 \\times 10^{6},\\,566021.613010762,\\,40679.9545139918,\\,34879.3804975235,\\,4095.01487020431,\\,1000.00000000000\\right)\n", "\\end{math}" ], "text/plain": [ "(2.90753803525668e6, 1.52171281601813e6, 566021.613010762, 40679.9545139918, 34879.3804975235, 4095.01487020431, 1000.00000000000)" ] }, "execution_count": 16, "metadata": { }, "output_type": "execute_result" } ], "source": [ "#5\n", "DomEig=vector([-0.8729866710666837,-0.4568934230495471,-0.16994767315242945,-0.012214133620844852,-0.010472514512338323,-0.0012295259274889008,-0.00030024944144526564])\n", "kek=DomEig*(1000/-0.0003002494414452655)\n", "show(kek)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " (0, 5) 1.0482096234147757 6.944497232463931e-05\n", " (0, 6) 1.0481740847518233 3.390630937216699e-05\n", " (1, 0) 1.048243431839286 0.0001032533968348126\n", " (1, 1) 1.0483213801612705 0.0001812017188194126\n", " (2, 1) 1.0482434318392884 0.0001032533968372551\n", " (2, 2) 1.0485265341258083 0.00038635568335720905\n", " (3, 2) 1.0482434318392868 0.00010325339683570078\n", " (3, 3) 1.0488269875654848 0.0006868091230336493\n", " (4, 3) 1.0482434318392875 0.00010325339683636692\n", " (4, 4) 1.0493369974111058 0.0011968189686546449\n", " (5, 4) 1.0482434318392868 0.00010325339683570078\n", " (5, 5) 1.0496645950985202 0.0015244166560690608\n", " (6, 5) 1.048174084751823 3.390630937194494e-05\n", " (6, 6) 1.048878583487892 0.0007384050454408708" ] }, "execution_count": 44, "metadata": { }, "output_type": "execute_result" } ], "source": [ "#6\n", "#In order to promote mangrove generation after a type of disturbance, the parameter we should focus changing the survival rate and the recruitment rate of yoing trees in order to display the eigenvalue change.The only way to make the parameters comparable regardless if the parameter values are not the same, is to go with a type b disturbance such as some extinction of all trees in order to increase the fercunity and bring forth reproduction.\n", "\n", "TypeA= matrix(RDF,[[0,0,0,0,0,500,1000],[0.200,0.666,0,0,0,0,0],[0,0.083,0.825,0,0,0,0],[0,0,0.010,0.909,0,0,0],[0,0,0,0.073,0.963,0,0],[0,0,0,0,0.008,0.980,0],[0,0,0,0,0,0.012,0.999]])\n", "\n", "domeig= 1.048140178442451\n", "\n", "NewA= TypeA\n", "\n", "NewA[0,5] = TypeA[0,5]*1.005\n", "\n", "changeval = max(NewA.eigenvalues())- domeig\n", "\n", "listchangeval = []\n", "listdomval = []\n", "listy=[(0,5),(0,6),(1,0),(1,1),(2,1),(2,2),(3,2),(3,3),(4,3),(4,4),(5,4),(5,5),(6,5),(6,6)]\n", "\n", "for i in listy:\n", " NewA = matrix(RDF,[[0,0,0,0,0,500,1000],[0.200,0.666,0,0,0,0,0],[0,0.083,0.825,0,0,0,0],[0,0,0.010,0.909,0,0,0],[0,0,0,0.073,0.963,0,0],[0,0,0,0,0.008,0.980,0],[0,0,0,0,0,0.012,0.999]]) \n", " NewA[i] = NewA[i] * 1.005\n", " v = max(NewA.eigenvalues())\n", " listdomval.append(v)\n", " listchangeval.append(v - domeig)\n", "\n", "show(table(list(zip(listy,listdomval,listchangeval))))" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "" ], "text/latex": [ "\\begin{math}\n", "\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\verb|Max|\\phantom{\\verb!x!}\\verb|Eigenvectors:| \\left(1.2226157176665158, \\left[\\left(-0.7685740349651337,\\,-0.5523193187480965,\\,-0.3194879821592132,\\,-0.04584259776307626,\\,-0.007946040085247514,\\,-0.00026201237616994523,\\,-1.406049872902132 \\times 10^{-05}\\right)\\right], 1\\right)\n", "\\end{math}" ], "text/plain": [ "'Max Eigenvectors:' (1.2226157176665158,\n", " [(-0.7685740349651337, -0.5523193187480965, -0.3194879821592132, -0.04584259776307626, -0.007946040085247514, -0.00026201237616994523, -1.406049872902132e-05)],\n", " 1)" ] }, "execution_count": 53, "metadata": { }, "output_type": "execute_result" }, { "data": { "text/html": [ "" ], "text/latex": [ "\\begin{math}\n", "\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\verb|Ratio|\\phantom{\\verb!x!}\\verb|of|\\phantom{\\verb!x!}\\verb|older|\\phantom{\\verb!x!}\\verb|trees:| \\left(2907.53803525668,\\,1521.71281601813,\\,566.021613010762,\\,40.6799545139918,\\,34.8793804975235,\\,4.09501487020431,\\,1.00000000000000\\right)\n", "\\end{math}" ], "text/plain": [ "'Ratio of older trees:' (2907.53803525668, 1521.71281601813, 566.021613010762, 40.6799545139918, 34.8793804975235, 4.09501487020431, 1.00000000000000)" ] }, "execution_count": 53, "metadata": { }, "output_type": "execute_result" } ], "source": [ "#7 \n", "#Question: What would be the proportion of old trees to compared to the rest of the stages for type b?\n", "Typeb= matrix(RDF,[[0,0,0,0,100,500,1000],[0.400,0.666,0,0,0,0,0],[0,0.230,0.825,0,0,0,0],[0,0,0.045,0.909,0,0,0],[0,0,0,0.045,0.963,0,0],[0,0,0,0,0.008,0.980,0],[0,0,0,0,0,0.012,0.999]])\n", "show(\"Max Eigenvectors:\",max(Typeb.eigenvectors_right()))\n", "ratio= DomEig/(-0.00030024944144526564)\n", "show(\"Ratio of older trees:\", ratio)\n", "\n" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "collapsed": false }, "outputs": [ ], "source": [ "#8\n", "#The result of the lab does not describe the system's long-term behavior due to not being able to have negative individuals because you cannot have a negative value of a organism. The original matrix does not account for any perturbations in the long-term stages. " ] } ], "metadata": { "kernelspec": { "display_name": "SageMath 9.2", "language": "sagemath", "metadata": { "cocalc": { "description": "Open-source mathematical software system", "priority": 10, "url": "https://www.sagemath.org/" } }, "name": "sage-9.2", "resource_dir": "/ext/jupyter/kernels/sage-9.2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 }