index.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Phenology of Fish and Inverebrates in Mobile Bay, AL\n",
    "Jeffrey D. Plumlee, Ph.D., Postdoctoral Fellow at the University of South Alabama\n",
    "\n",
    "March 9, 2023."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Suppress the library warning messages\n",
    "suppressPackageStartupMessages({\n",
    "    library(ggplot2)\n",
    "    library(dplyr)\n",
    "    library(tidyr) \n",
    "    library(mgcv)\n",
    "    library(lubridate)\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## About the data\n",
    "Say where the data came from.  What is a row?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File all_species_enviro.csv has 626 rows, 53 columns and contains the following:\n",
      " [1] \"year\"             \"Median\"           \"Range\"            \"n\"               \n",
      " [5] \"species\"          \"group\"            \"season\"           \"decade\"          \n",
      " [9] \"baseline_median\"  \"baseline_range\"   \"diff_median\"      \"diff_range\"      \n",
      "[13] \"spring_dp10\"      \"spring_d32\"       \"spring_emxp\"      \"spring_prcp\"     \n",
      "[17] \"spring_tavg\"      \"spring_tmax\"      \"spring_tmin\"      \"fall_dp10\"       \n",
      "[21] \"fall_d32\"         \"fall_emxp\"        \"fall_prcp\"        \"fall_tavg\"       \n",
      "[25] \"fall_tmax\"        \"fall_tmin\"        \"winter_dp10\"      \"winter_dx32\"     \n",
      "[29] \"winter_emxp\"      \"winter_prcp\"      \"winter_tavg\"      \"winter_tmax\"     \n",
      "[33] \"winter_tmin\"      \"yearly_dp10\"      \"yearly_dx32\"      \"yearly_emxp\"     \n",
      "[37] \"yearly_prcp\"      \"yearly_tavg\"      \"yearly_tmin\"      \"yearly_tmax\"     \n",
      "[41] \"spring_temp\"      \"fall_temp\"        \"winter_temp\"      \"yearly_temp\"     \n",
      "[45] \"hurricane\"        \"spring_discharge\" \"fall_discharge\"   \"winter_discharge\"\n",
      "[49] \"yearly_discharge\" \"spring_drought\"   \"fall_drought\"     \"winter_drought\"  \n",
      "[53] \"yearly_drought\"  \n",
      "Includes years 1981 to 2018 , categorized into decades 1980 to 2010 \n"
     ]
    }
   ],
   "source": [
    "#Read the file\n",
    "dirname <- getwd()\n",
    "filename <- 'all_species_enviro.csv'\n",
    "df <- read.csv(file.path(dirname,filename))\n",
    "cat(\"File\",filename,\"has\",nrow(df),\"rows,\",ncol(df),\"columns and contains the following:\\n\")\n",
    "print(names(df))\n",
    "cat(\"Includes years\", min(df$year), \"to\", max(df$year), \", categorized into decades\",min(df$decade),\"to\",max(df$decade),\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Pelagic Forage\n",
    "\n",
    "`p_prey` = pelagic prey\n",
    "\n",
    "We exclude Butterfish because ... (?)\n",
    "\n",
    "[NOAA: What are pelagic fish?](https://oceanservice.noaa.gov/facts/pelagic.html) *Pelagic fish inhabit the water column (not near the bottom or the shore) of coasts, open oceans, and lakes.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Pelagic Prey, exclude Butterfish and omit NaNs\n",
    "p_prey <- df[df$group == \"Pelagic Prey\",]\n",
    "p_prey <- na.omit(p_prey[p_prey$species != \"Butterfish\",])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Benthic Forage\n",
    "\n",
    "`b_prey` = benthic prey\n",
    "\n",
    "We exclude Mojarra because ... (?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Benthic Prey except Mojarra, omit NaN\n",
    "b_prey <- df[df$group == \"Benthic Prey\",]\n",
    "b_prey <- na.omit(b_prey[b_prey$species != \"Mojarra\",])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fishery Species\n",
    "\n",
    "`f_spec` = fishery species\n",
    "\n",
    "Whiting, Sand Seatrout, and Pigfish are excluded because..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "f_spec <- df[df$group == \"Fishery Species\",]\n",
    "f_spec <- na.omit(f_spec[f_spec$species != \"Whiting\",])\n",
    "f_spec <- na.omit(f_spec[f_spec$species != \"Sand Seatrout\",])\n",
    "f_spec <- na.omit(f_spec[f_spec$species != \"Pigfish\",])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Changes in the phenology of recruitment\n",
    "\n",
    "The following plots show changes in the phenology of recruitment from the 1980s to 2000 - 2020 for forage and economically-important fisheries species. \n",
    "\n",
    "Color corresponds to the season where peak recruitment for each species occurred."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#General plot options\n",
    "options(repr.plot.width=15, repr.plot.height=6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Difference in Median Peak Day of the Year\n",
    "### Pelagic Forage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in p_prey$decade == c(\"2000\", \"2010\"):\n",
      "“longer object length is not a multiple of shorter object length”\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 900
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#For R to respect the order in data.frame, we need to change the order of **factor** levels by specifying the order explicitly.\n",
    "p_prey$species <- factor(p_prey$species, levels = c(\"Atlantic Bumper\", \"Striped Anchovy\", \"Finger Anchovy\", \"Gulf Menhaden\", \"Bay Anchovy\", \"Striped Mullet\"))\n",
    "p_prey$season <- factor(p_prey$season, levels = c(\"Spring\", \"Summer\", \"Fall\"))\n",
    "ggplot( p_prey[p_prey$decade == c(\"2000\", \"2010\"),], \n",
    "    aes(y = species, x = diff_median, fill = season)) +\n",
    "    geom_boxplot(outlier.shape = NA, size = 1.1, color = \"black\") +\n",
    "    scale_fill_viridis_d() +\n",
    "    geom_vline(xintercept = 0, linetype = \"dashed\", linewidth = 1.1) +\n",
    "    xlim(-100,100) +\n",
    "    labs(x = \"Difference in Median Peak Day of the Year for Recruitment between 1980 and 2000-2020\", fill = \"Season\") +\n",
    "    theme(panel.background = element_rect(fill = \"white\", color = \"black\", linewidth = 1.1), \n",
    "        axis.title.y = element_blank(), panel.grid.major.y = element_line(color = \"grey\", linewidth = 1.10, linetype = \"dashed\"), \n",
    "        axis.title = element_text(color = \"black\", family = \"sans\", size = 20), axis.text = element_text(color = \"black\", family = \"sans\", size = 16), \n",
    "        legend.title = element_text(color = \"black\", family = \"sans\", size = 20), legend.text = element_text(color = \"black\", family = \"sans\", size = 18))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Difference in Median Peak Day of the Year\n",
    "### Benthic Forage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/bsS5cu7dixQw7iLMhmpLJhqXVqciw9Pb1Tp05nz54V/zZs2DAuLs7H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\ncy9cuCAGoy8oKHjqqaf+/PPPV1991SiK+muKA5d+I67eWU5kqZ2+7HvXSkN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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 900
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "b_prey$species <- factor(b_prey$species, levels = c(\"Star Drum\", \"Silver Perch\", \"Atlantic Croaker\", \"Spot\"))\n",
    "b_prey$season <- factor(b_prey$season, levels = c(\"Spring\", \"Summer\", \"Fall\"))\n",
    "ggplot(b_prey[b_prey$decade == c(\"2000\", \"2010\"),], aes(y = species, x = diff_median, fill = season)) +\n",
    "  geom_boxplot(outlier.shape = NA, size = 1.1, color = \"black\", na.rm=TRUE) +\n",
    "  scale_fill_viridis_d() +\n",
    "  geom_vline(xintercept = 0, linetype = \"dashed\", linewidth = 1.1) +\n",
    "  xlim(-100,100) +\n",
    "  labs(x = \"Difference in Median Peak Day of the Year for Recruitment between 1980 and 2000-2020\", fill = \"Season\") +\n",
    "    theme(panel.background = element_rect(fill = \"white\", color = \"black\", linewidth = 1.1), \n",
    "        axis.title.y = element_blank(), panel.grid.major.y = element_line(color = \"grey\", linewidth = 1.10, linetype = \"dashed\"), \n",
    "        axis.title = element_text(color = \"black\", family = \"sans\", size = 20), axis.text = element_text(color = \"black\", family = \"sans\", size = 16), \n",
    "        legend.title = element_text(color = \"black\", family = \"sans\", size = 20), legend.text = element_text(color = \"black\", family = \"sans\", size = 18))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Difference in Median Peak Day of the Year\n",
    "### Fisheries Species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8/i\nxYuFEG3atBk1apRpylu3bsmqTKGhoZ988oky3ugl5+jRoxcsWCCHg4ODW7VqFR4enpOTc/Dg\nQXlFX7Ro0bVr1zZt2mThWrty5coXX3xRCOHv79+4ceOAgIDk5GS9Xm/ftWzYsGHw4MF6vT40\nNPThhx8ODAw8d+7cH3/8IYRIT0/v3bv3qVOnzL7C/cc//rFkyRI57OHhERcXV7duXV9f35s3\nbx49elQ+scgG43YvGVtY/XvV8/T0HDRo0Jw5c65du7Z9+3blI86SUo1UqVhqmS0llpKS0rlz\n54sXL8p/mzRpEhcX5+HhcfTo0SNHjixZskSJQ2klPy0qhKhWrVqTJk3CwsJ8fHxSUlIOHjwo\na/GsWbMmPT19y5Ytjtj3bJeWliYHKlSoYDjeltK2S5kYuX//fr9+/bZs2SIXu3XrVsMvtzqC\n5ZOndb9x0KBBr776alZW1s6dOy9cuKDcxBtZtWrV/fv3hRDx8fH169fXlO3MzMxHHnnk999/\nl/+Gh4e3a9fO39//zJkz8uE8OTm5a9euGzZs6Nq1q0xT4mlf6W/BgpCQELmQl19++e7du0KI\n2bNnV6xY0TBNo0aNTGe0eud39Cn0oYceevPNN2VFqgkTJnTr1q1atWovv/yybOoYExNjGFaw\nMT92OWRKvFAWR9MOYMUOZsesaqJUPiquCZ6Nu5B1l37JOSVgyOojdM2aNS+88IIQIjIyMiYm\nxsPD49ChQ3K33LRp08SJEz/99NPhw4d/8803QogGDRpER0fn5OTs3btX1pT8/PPPY2NjzYZN\nnX/nZnUhlGjBggVvvPGGEKJevXpt2rTx9fVNSkrat2+fPCIef/zxPXv2mF2yFYWg/lfExcUF\nBQXdu3fvwIEDWVlZRqGoHTt2KMOWE3Ts2NH2bNtxdsmFN07FUQ5hC9k27KBM5lYRFBTUuXNn\nWf/xvffe69y5s+HXIKXXX39drqVLly5GtxAnT56UA02aNDH7JkOKjY3dv3+/EOLUqVMqftP/\n4cI7XsfdxqukFG/NmjWLm2Rdybt2dntxXPmU/rXD4RwceEVZpbXSU0ZGhnINnjFjhtFUC/Xg\nlO69ExISzC5Zqe5Xo0aN4taudOrh7+//6aefGtW6+ve//618v8Lsh8uVt4sBAQFeXl4fffRR\nVlaWMlXpbN6OawkICPjyyy8LCgqUqT/99FNQUJBMMHnyZNPZDYPCAwcOvHr1qlGCgwcPjhgx\nwugVqI15LpGaGqPW/d4SGdYY1ev1SpOoIUOGGCYrKiqSr9YrVaok3+BZrjFqY4kNGDBATq1Q\nocKmTZsMJ/3yyy/yDky5XmqqMfrSSy+9/fbbJ0+eNBpfWFj47bffKs8qS5cuNc2V07aFhdNF\ngwYNZJpOnTopI20sbbuUieFK09LSlFaxdevWvXjxoroC+D/se/K0+jeOGDFCTnrjjTeKW3WL\nFi1kmmXLlqnJqiGloquXl9c///lPw5raR48eVcKslStXNq1lVuJpXw2tH1+ybud39ClUys/P\nb9asmVxO7969lY+2e3p6Hjp0yI75sddpxMKFUg01O4AtO5gds1ri4VxQUKA8IA0bNsw0gY2b\nzLpLv8oScESNUYUVR2hwcPB3332nTMrLy5swYYKc6uvrO3PmTCFE/fr1ZRxQunPnTrdu3WSa\nqKgo0y8Fu/bOze4fX/L29vb19TWqAHjgwAGlt8rY2FjDHEo2FoKaX6G8n9i6davh+KysLHnP\no3x1pLgEAQEBRtWsbMy2aze9FdTXGFWu3ePHjy8ujWHHoL179zaaeuTIEeXTfw0aNFi+fPn5\n8+ezs7OTk5N/+OGH9u3by0nR0dEyRmlI6ay/f//+FjI5Y8YMmSwmJsbyzzHlwjteG2/jbXTu\n3Dkl1Lt582ajqTaWvGtntwuHlk8pXzucgMAozNP6bK83uHWeMGGC0SSHBkZv374tL3IeHh5G\n91uKXbt2ydBD5cqV79+/bzTVsJWZ0e2mI9bi4eFhtgnV/PnzZYKoqCijSTdv3lRe544ZM8bs\n2h2R5xKpCYxa8XvVMAqM6vV62UtaYGCgYfuaXbt2yWRKOzsLgVEbS+zYsWPKjvTzzz+bzrtv\n3z7Dd/iaAqOW7dq1S16tH374YdOpTtsWar5K//7778uRjt4/VZaJ8rB05cqVhx56SI5s1qzZ\njRs3NK1OYd+Tp2UWfqPStjEiIsL0OVmv1x8/flwmKF++vGHQRA3D6idffPGFaYKrV6+GhobK\nBKZPbi4JjFqx8zvhFKo4evSol9d/G/EoDT9fe+01Z+ZH5SEjir9QqlTiDmDjDmbHrJZ4OH/0\n0UfKuky73bBxk1l36derLoFSFRj18vLau3ev0dSioqKHH35Y+S1VqlQxPTOnpaUp/QwYbQLX\n3rmpL4QSlbhBz5w5o8S5jL5CbnshqPkVyoEgaxoqfv75Zzn+448/tpzgscces2O2Xb7praA+\nMDp69GiZsmHDhsWlmT17trLPxMfHmybYv39/1apVRTHKlSs3atSo9PR00xl79Ogh07z00ksW\nMqnc+EVGRlr+OVo57o7X9tt4Gyll27hxY9OewWwsedfObhcOLZ9SvnY4AYFRmGfFs73SeGf4\n8OFGkxwaGFXuxoyqChpRuirbsGGD0STlImpYl81xa5EtO0xlZWUpj0BGjx/K3VKdOnVyc3Mt\nZMC+eS6RmsCoFb9XDdPAqPIWzvCxQekAW+l7y0Jg1MYSGzt2rBxv+nJe8cwzz4j/sWNgVK/X\nt2nTRs6blpZmNMlp28L0dJGVlfXxxx8rXSgGBAQoj7VO2D/VlIkMjJ4+fbp69epyTIcOHcw+\nDKhk35NniSz8RqUTALOdiL388sty6vPPP691pcq8LVq0KC7N559/LtNUqFDB6KzlksCoFTu/\nE3ZRQ7KFrKJBgwY5OTlOzo+aQ8bChVKlEncAG3cwO2a1uMP57t27u3fvHjx4sLK9pk6dajq7\njZvMuku/XnUJlKrA6IgRI8wmmDVrllLI8+fPN5tm4MCBMoFRl4uuvXPTOyAw2qpVq+LSTJo0\nSaZ59NFHDcfbXghqfoXyOaDWrVsbjn/99dfl+OTkZNlXRnEJPvjgAztm2+Wb3grqA6OGX8T+\n+uuvTROkpqaGh4craZo1a1bcT/jkk09M29HrdLqhQ4cePnzY7FwdOnSQySxXlV2zZo1MFhwc\nbPnnWMFBd7y238bbQrmo6XQ6syFdG0vetbPbztHlU5rXDufgq/SwG6VtgpovY9qR8sFow0cU\nU927d5cDe/bsKS6NhT4o7bgW5bsuRgICApQe1i5fvmw4SfZ4KIR4/vnnlRhTieyYZ1tY8Xut\nk5CQIF/kKp2K5uTkrF27VgjRoEEDpeWRBTaW2H/+8x85oLSnMKWyn1MrKG3VDd94G3H0tvju\nu+86GmjevHlISMjEiRPz8vJkgs8//1x5xHLC/qmmTIQQBw8ebN++fXJyshDiySef3Lp1q1FH\nqI5my8nTwm9UOolXOihU5OXlKR+s/8c//qF1pdu3b5cDSoN9U0OHDpUnq4yMDBs/3W4XVuz8\nTj6FvvXWW0qdZQ8PjyVLlhh9M6eUHDKOO4kp7LWD2TGrc+fONfwYd/ny5ePj4+VBFBYWNnfu\n3Pfff990Lhs3mXWXfkNO2Fj2orRgNWL4FZT+/ftbTnPp0iXD8a69c3MEC3cXyqRff/01Pz9f\nGe+c81hsbKys6n7o0CHDj57L/kPr1KkTFRUluxA1m0CYdDBqY7YfvE1vqFu3bnFxcXL42Wef\nXbx4seGH40+dOtWlS5dr164pY7Kzs00XcuPGjVGjRk2ePDknJycoKKhTp079+/fv1q1b5cqV\n9Xr9119/HRsb++KLLxYWFhrNKL/2LoSwfFIyfP2s/SeWwEF3vC68jd++ffv48ePl8Lhx44w+\nliDZWPKund1GTiifUrt2OA0fX4LdKI/0Fr6dandFRUUHDhyQw6dPn5ZfqzDrzJkzcsDCdzZb\ntmzphLUU961DYdAHU0ZGhjKyoKDg8OHDcrhz587FzevQPNtC6++1WkREROfOnbdt2/bLL79c\nu3YtPDx8w4YNcskWbnEUNpZYXl6e0pG20k+lqZYtW3p4eBjewmpy4MCB1atXHzx48OzZs3fv\n3jV7WU1NTS1udkdvi7/++uuvv/4yO6l8+fLz5s1THlHsuH/aWCbbtm0bOHCgfE575plnvvzy\nS7Mf/HWoEk+e1v3GIUOGTJ48OTs7e+PGjSkpKcomFkKsX79efg6radOmyvOVSnl5eUpL53bt\n2hWXrHz58jExMYcOHRJCHDlyxMJB4Rxad37nn0J9fHyGDBki6081bNhQqRFj9/zYeMgUd6G0\nFzvuYI7OqhDC29t79uzZCQkJppNs3GTWXfqNOKEE7KW4z9wpfSaEh4crw8Wlkd8Iklx75+Yg\nFk6kjRo1KleuXGZmZk5OzunTp2V5Ou085uXl1a5duy1bthQUFOzevVsGHLOysuQR2qlTJ/n3\nX//6l9kEgYGBhm+vbcz2A7npjaxataply5Z37tzJzc0dOXLk22+/3aJFC39//wsXLhw6dKio\nqMjHx6dr166bNm0SBt2zKM6cOdOxY8cbN254eHi88847EydOVL4pVFhYuGLFirFjx2ZmZs6f\nPz8vL2/RokWG8yrVLZV33mYpX4RTOnnQysl3vM65jTfr6NGjffr0KSgoEEI8/vjjhj20GLKx\n5F07uy2cUz6lc+1wJgKjsJv09HQ5EBIS4rSV3r59W3lFozS+s+zOnTvFTSqutx37rkXp7t2U\n0p+34dv+27dvy9OxEKJ27dpq1i7snWdbaP29thg6dOi2bduKiopWrlz56quvLl++XAih0+ks\nVxmQbCyx27dvy/fqnp6eSsNDU4GBgSEhIRbu5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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 900
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f_spec$species <- factor(f_spec$species, levels = c(\"Spotted Seatrout\", \"White Shrimp\", \"Pink Shrimp\", \"Blue Crab\", \"Brown Shrimp\", \"Southern Flounder\"))\n",
    "f_spec$season <- factor(f_spec$season, levels = c(\"Spring\", \"Summer\", \"Fall\"))\n",
    "ggplot(f_spec[f_spec$decade == c(\"2000\", \"2010\"),], aes(y = species, x = diff_median, fill = season)) +\n",
    "  geom_boxplot(outlier.shape = NA, linewidth = 1.1, color = \"black\", na.rm=TRUE) +\n",
    "  scale_fill_viridis_d() +\n",
    "  geom_vline(xintercept = 0, linetype = \"dashed\", linewidth = 1.1) +\n",
    "  xlim(-100,100) +\n",
    "  labs(x = \"Difference in Median Peak Day of the Year for Recruitment between 1980 and 2000-2020\", fill = \"Season\") +\n",
    "  theme(panel.background = element_rect(fill = \"white\", color = \"black\", linewidth = 1.1), \n",
    "        axis.title.y = element_blank(), panel.grid.major.y = element_line(color = \"grey\", linewidth = 1.10, linetype = \"dashed\"), \n",
    "        axis.title = element_text(color = \"black\", family = \"sans\", size = 20), axis.text = element_text(color = \"black\", family = \"sans\", size = 16), \n",
    "        legend.title = element_text(color = \"black\", family = \"sans\", size = 20), legend.text = element_text(color = \"black\", family = \"sans\", size = 18))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Difference in Range of Recruitment\n",
    "### Pelagic Forage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in p_prey$decade == c(\"2000\", \"2010\"):\n",
      "“longer object length is not a multiple of shorter object length”\n"
     ]
    },
    {
     "data": {
      "image/png": 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6kBAQHr1683u/P4+PjExMTff/+dEPLNN9/w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\nR2j9t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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 900
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p_prey$species <- factor(p_prey$species, levels = c(\"Atlantic Bumper\", \"Striped Anchovy\", \"Finger Anchovy\", \"Bay Anchovy\", \"Gulf Menhaden\", \"Striped Mullet\"))\n",
    "p_prey$season <- factor(p_prey$season, levels = c(\"Spring\", \"Summer\", \"Fall\"))\n",
    "ggplot(p_prey[p_prey$decade == c(\"2000\", \"2010\"),], aes(y = species, x = diff_range, fill = season)) +\n",
    "  geom_boxplot(outlier.shape = NA, linewidth = 1.1, color = \"black\", na.rm=TRUE) +\n",
    "  scale_fill_viridis_d() +\n",
    "  geom_vline(xintercept = 0, linetype = \"dashed\", linewidth = 1.1) +\n",
    "  xlim(-100,100) +\n",
    "  labs(x = \"Difference in Range of Recruitment between 1980 and 2000-2020\", fill = \"Season\") +\n",
    "  theme(panel.background = element_rect(fill = \"white\", color = \"black\", linewidth = 1.1), \n",
    "        axis.title.y = element_blank(), panel.grid.major.y = element_line(color = \"grey\", linewidth = 1.10, linetype = \"dashed\"), \n",
    "        axis.title = element_text(color = \"black\", family = \"sans\", size = 20), axis.text = element_text(color = \"black\", family = \"sans\", size = 16), \n",
    "        legend.title = element_text(color = \"black\", family = \"sans\", size = 20), legend.text = element_text(color = \"black\", family = \"sans\", size = 18))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Difference in Range of Recruitment\n",
    "### Benthic Forage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 900
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "b_prey$species <- factor(b_prey$species, levels = c(\"Star Drum\", \"Silver Perch\", \"Atlantic Croaker\", \"Spot\"))\n",
    "b_prey$season <- factor(b_prey$season, levels = c(\"Spring\", \"Summer\", \"Fall\"))\n",
    "ggplot(b_prey[b_prey$decade == c(\"2000\", \"2010\"),], aes(y = species, x = diff_range, fill = season)) +\n",
    "  geom_boxplot(outlier.shape = NA, linewidth = 1.1, color = \"black\", na.rm=TRUE) +\n",
    "  scale_fill_viridis_d() +\n",
    "  geom_vline(xintercept = 0, linetype = \"dashed\", linewidth = 1.1) +\n",
    "  xlim(-100,100) +\n",
    "  labs(x = \"Difference in Range of Recruitment between 1980 and 2000-2020\", fill = \"Season\") +\n",
    "  theme(panel.background = element_rect(fill = \"white\", color = \"black\", linewidth = 1.1), \n",
    "        axis.title.y = element_blank(), panel.grid.major.y = element_line(color = \"grey\", linewidth = 1.10, linetype = \"dashed\"), \n",
    "        axis.title = element_text(color = \"black\", family = \"sans\", size = 20), axis.text = element_text(color = \"black\", family = \"sans\", size = 16), \n",
    "        legend.title = element_text(color = \"black\", family = \"sans\", size = 20), legend.text = element_text(color = \"black\", family = \"sans\", size = 18))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Difference in Range of Recruitment\n",
    "### Fisheries Species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kQ/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\nOvPaa/9fe3cfEsXzxwF87868LUqlfEpPetAKU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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 900
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f_spec$species <- factor(f_spec$species, levels = c(\"Spotted Seatrout\", \"White Shrimp\", \"Pink Shrimp\", \"Blue Crab\", \"Brown Shrimp\", \"Southern Flounder\"))\n",
    "f_spec$season <- factor(f_spec$season, levels = c(\"Spring\", \"Summer\", \"Fall\"))\n",
    "ggplot(f_spec[f_spec$decade == c(\"2000\", \"2010\"),], aes(y = species, x = diff_range, fill = season)) +\n",
    "  geom_boxplot(outlier.shape = NA, linewidth = 1.1, color = \"black\") +\n",
    "  scale_fill_viridis_d() +\n",
    "  geom_vline(xintercept = 0, linetype = \"dashed\", linewidth = 1.1) +\n",
    "  xlim(-100,100) +\n",
    "  labs(x = \"Difference in Range of Recruitment between 1980 and 2000-2020\", fill = \"Season\") +\n",
    "  theme(panel.background = element_rect(fill = \"white\", color = \"black\", linewidth = 1.1), \n",
    "        axis.title.y = element_blank(), panel.grid.major.y = element_line(color = \"grey\", linewidth = 1.10, linetype = \"dashed\"), \n",
    "        axis.title = element_text(color = \"black\", family = \"sans\", size = 20), axis.text = element_text(color = \"black\", family = \"sans\", size = 16), \n",
    "        legend.title = element_text(color = \"black\", family = \"sans\", size = 20), legend.text = element_text(color = \"black\", family = \"sans\", size = 18))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Generalized additive models\n",
    "Generalized additive models depicting the relationships between average annual temperature (ºC) on the left side and annual drought index on the right side."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OEln58NaRcA8BBBmOpQgCUgVuc2rZLC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\nAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 900
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "menhaden_median <- gam(Median ~ s(yearly_tavg, k=3), data=df[df$species == \"Menhaden\",], family=poisson)\n",
    "menhaden_median_p <- plot(menhaden_median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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w+EciMbPQdKE4QcFzuveKGpClj0TPZB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\noo5UQf4AAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 900
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "silverperch_median <- gam(Median ~ s(yearly_drought, k=3), data=df[df$species == \"Silver Perch\",], family=poisson)\n",
    "\n",
    "silverperch_median_p <- plot(silverperch_median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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a9bDbfCMfAhegXhX1PPay/mNYIQNifs0ubf+zQsmlKIJEXq9fALCLTLBYLverm4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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 900
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "brownshrimp_median <- gam(Median ~ s(yearly_tavg, k=3), data=df[df$species == \"Brown Shrimp\",], family=poisson)\n",
    "\n",
    "brownshrimp_median_p <- plot(brownshrimp_median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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z1BaLjza6a8+mhvqbF53C9HjCjgVFJkE2+v4IiE42p0lmW3Z/cZb9iHvgDcmWpB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\nkl8AAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 900
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stripedanchovy_median <- gam(Median ~ s(yearly_drought, k=3), data=df[df$species == \"Striped Anchovy\",], family=poisson)\n",
    "\n",
    "stripedanchovy_median_p <- plot(stripedanchovy_median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in dpois(y, mu, log = TRUE):\n",
      "“non-integer x = 97.500000”\n",
      "Warning message in dpois(y, mu, log = TRUE):\n",
      "“non-integer x = 97.500000”\n",
      "Warning message in dpois(y, mu, log = TRUE):\n",
      "“non-integer x = 97.500000”\n",
      "Warning message in dpois(y, mu, log = TRUE):\n",
      "“non-integer x = 97.500000”\n"
     ]
    },
    {
     "data": {
      "image/png": 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2JtfxK5LARgOGUr1NfbobAqglCxTbahLQdGLL2oT/PbR9fzKd13uYIHfRXrjj8g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\nAADV/gFTK3GiH1Rb6AAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 900
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pinkshrimp_median <- gam(Median ~ s(yearly_drought, k=3), data=df[df$species == \"Pink Shrimp\",], family=poisson)\n",
    "\n",
    "pinkshrimp_median_p <- plot(pinkshrimp_median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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C95e+V714IXfqY/3uvrHt\nEbv9fI1qDdsE9VH+43OXJCSmHDgnu0CXkvnrvKfv9BU1O4+o0BwxZYhvLtpGruXMIHAJXS+o\nPyhWOFRMIYLQLf3v52/mT38jXYm6ID9vIWx77fbssNCRL7w+X323Lkt2eS7q2Mo3H77eS/j3\njFy4rerDgI5tV27WvpNabkfAgghCOEvu8bwr2j58dkjf2zqpzQaihn+7u9TR+p9/smzDTn3f\nTndHaQlRoQEe1QJCoxIOOrKf7dFdPG/TqSbADRGE0NGxX7//4qO31ITr07yuEGKz0urfc9CT\nL76rvtN59rzk8szj77UzRgbVFPUqNUdMGU5eJ1qPXWOZa0qAyiMI4YDzeZNZfv7ikwN73vqn\n0rpaCN+WQTvVz83+8offGJlYBXuXvPpgK5tHpeeIKc2pb87a7Vmx2/WoC3BfBCEqJWPnxmWf\nfqgu1jOiXZMaQqxWWoNu7zt07FR1wuoTJySXZ2pZKYuiQhoJ74DwmBU6zH+9O6arV93Nju8H\ncHsEIbTl/U+sev2FkWF3q9O5dBLCy+/aTUprYcz8JT9vZwiiLo6tzJsjxsERMSWMEc0jVjj6\ntipgCQQhLnEhLSXx6/lHlNbEoNYNq4l4pTX0hrv7jRq/X2kd28t/ja5yilZNWlGZVZM0pC/+\nQbndysQxQAURhMiz4b1JTz/S9yel1UMIUavpcqW1cMqMhct/4bIGZ/k7KTYiuKaoEzwiJjFD\nr53um9HDp+Zreu0NsASC0IpO7/ll1RfqZQzT7+rQrI6Yp7RGBwSHPDxmm9I6tPXwBcn1ub+q\nrppUrmRbk1FLmYwAqBSC0DK2zn3r36MH/KC0Biuv+Tzqf6q0vpgw7eP41bq9GkH58lZNcniO\nmFKcW/rvDPWdVs7aApXlaBCWGCXIFGsuI/vI9nXfqRdhx91363VXXvau0nrmX+3vuH/YRqV1\nIGn3Scn1WdLJxJgRwT6iXnBEbIrOl/UdndOnps99/NQAVeJoEF42eK1+xRQiCKtk38KZrz0/\n4r9Ka6y6IIiPGn+Ln3r5nQVLyT25io2IWanTiJiSbmo0ZHG6M3YMWIGjQdhKiP97X/e5sgjC\nCji7b/Ma9TXf0oe6d7q6/hSl9WKDVp3v6a++PZ22Zsv+s5LrQx4954gpReaKMerqHGcquS4v\ngGIcDcLcVWHeoubQjfpVpCIIy3B86YJ3Xh6zRGlFV1df9P1baS0ZMW7qh58fkF0a/sHxVZPK\ncT4urLZX15+csWvASnQYLHN4akshOs7SM7oIwnwnd29Sx3auHfbAHe2bTlBab/hced2t9y1U\nWmnfrd9+lPkjXVTBqkk1Oo344GedzpuXZlH9QQutvEYxoBNdRo3mLn/AS1w+Sr8LeC0chOmr\n4+dMG6++2zXP10N5zTdSaS3rP3riW3N3yC4NFVGwatKVPV74bKvT/lDJXj22t7P2DViPPpdP\n7JnYSB2R/5Bef51aJwgzD209rNyljAkPufnap+3ql16nece7ZiitA1+s+mUPSxWZR872RePv\nbSK82g2KXnHEmU/0zSBfzy4znPkMgLXoEITZX/e0iate2f/t7WKgTlW5dRBmbvrvZx9M/lJp\nLbmqlvL3Q1+l9UPfR56Z9H6y7NJQRYUjYsZ8nOz86T1vCIvTYU5uABc5HIR7o/yFR7d49U2g\n3F71dKrKrYLw773qxZZ7xo/q1+2GUUrrE+HVqM1Nk5TWwfkJiX8cYAozU9u75NXQVjaPlg9O\nStjj5KfKWRvZYYmTnwOwIkeDMMRT+D6zs+CDyWXOOVNJJg/C3K0/L5k/fbHS+vlaP28hblNa\n67qFjYx8Y436MNd7uYeLI2I6O3dEzEUpQxrZgqawzhWgP0eDUNw4t2hiw+R3dahIZbIgzD2u\nXrB+9LVnh/S5bajS+k6IGk3aPaG0jsz6dNmGnVzR524KRsT493xBt1WTyjWvz5zDBj0VYDGO\nBmGSfqUU4/pBuPfX77/4SD3Pl9KpeV0h2imt327tOejJKHXRotyj5yWXB6dJS4gKDfCoFhAa\nlXDQkCfMTYrq2MWQZwKsStdJt3XjekF4Tp2YOn36i08M7PmI0toghPBt+ZDSOv727MU//HZc\ncnkwgJPniCnd+Sf8PQJf2mbU0wGWRBCW5eSODd99or6+23dHW/8aoonS2nnj7X2Hjp2rPrqH\nczVWUjAiRv9VkzSpPwRnHp2537hnBKzJkSDsXZKOVUkJQnX05oXY118YGTogVwk9DyG8r7hV\n+dyZ16fHffPzXuMLggvISlkUFeInvAPCY1YYes3C5smdbTrPXAigdI4EoSipopv/2KN+reun\naV40YEwQnktLSfx6qdI4cV9Q64bVamfb7WnX3Xj3Q49NUx/dvo/RndZ2fNVbg9s7e46YMnzU\nTLSLXG/wkwJW5UgQ7iup/A39IpSbTzzzYrO31izETgzC3MXvvfL0I49k2u1HvJUyLr/ugt2e\n8eKUGQuXb3fWU8JscnYsmhBylQFzxJTmmPqMC99JNfp5Aety9Bzhul8rs+Fg5ce8lm1i6onF\njcUCjY66BeGp1OQVS5XIPffwnR2a1q55xm4/eU1AcK+HI9W/8LdsPXxBn6eB28hMiY0IriVq\nB4+IScww/um3RXfxfNT4pwWszdEg9HigMhsOVjPuKbW5XnTT6OhYEK78+M2Jjw9T9nCutvKa\nz7PJCbv9wviJb34cv8GBncLt7V0yOW+OmAde+drZc8SUYWcb0XrsatYUAQzmaBA2CK/MhoPt\n9sfFH3nt9g01OjoUhBeaN+1w54MR6lXsm375i1VqUK4Lv857+q68VZPe/8mAOWJKc2pVrt1+\n4n3enwckcDQIH2xTib9f1SB8VOS/4XS/l0ZHV7h8ApZwKjFmRLCPqBccEZsi7aVYasxdXg2Y\nRxuQxdEg3FH/iYpPIKYG4WRxKK99RwONjgQhnM6wVZPKdb9oHrGC09WANI4G4eA7RIOuDw9W\nVWBDW/XqXmJlXrtZB42OBCGc6eIcMbcbsmqShvSvtyq3ifotaQ2gChyedLsy1xG2yTNFbSaL\nURodCUI4SdqKmPAAm2gcErUoResKHgPsm9HTp+ZcuTUAsDsehL8WqdQ+Nkb/ovEoQQjdXShY\nNUniiJgSvvTwH7n0XPn9ADgbc43C/cmcI6YU55ZOV27Tf5P8ihRAAYIQ7kzqHDGlOjynb02f\nMNlVACjG8SA8/vUHb+ep0La52xLi5idsK+dvYYIQDktfN3PUTbVE3dsiPk52ofUhGzQasphZ\nbAGX4nAQTvGpxKTbGZP88/s2maQ5fRVBCEfs+2ZyaGubR8v7X/7qL9m1FMpc8Yx6XvxwjuxC\nAFzC0SD8VNw4WTz76p0iNK78DdM7C1uHsOEjwtrbRJDW5YcEIarmwm+xz9xVX1x24/D3Ek/L\nLqaYv+PCant3TZFdBoDSOBqEt/hlHBTf2e0LPFeVv+F4MTAtv7W/v5ig0ZEgRKXljYjxFo3v\neeGzP11gRExJL/kOXMhsf4CLcjQILx9qPyTUNf1C7ix/wxaBhW8L5XS8WqMjQYjKSEuICg3w\nqBYQGpVwUHYtl8hePfZZ5S7X5aIZQCFHg7B6pP2E+ERpTKxT/obeTxW1x1TX6EgQomIujoi5\n3bVGxFz05UBfz9u0FhwD4AIcDcKmw+25tSKVxqAKBGHD3kXtXn4aHQlClOviiJgHXvn6L9m1\nlOHIv8LijssuAkB5HA3C+4Lt9hDflX9/6X1r+Rv2t8VebM71GKDRkSCEhqyURVEhfsIrIDxm\nhWuu2ZCzLrLTH7KLAFBBjgbhLI999k3qFRSeP5S/4a46okPkvPj4eZHtRd1dGh0JQpSuYESM\nq8wRU7oNQxrZgl6r+KosAOTSZWaZpIHB4RsrsuWWThcvOuykOeE+QYhLFZsjZrlrzBFTpvF9\n5xyWXQOAijN6irXk6GGhocOik7V7EYQoJjMlNiK4lqgdPCImUXMiBslyk6I6Pi67CACVpkMQ\n7lmrywVSRweEFgokCJGn2IiYPbJrKcfxUf4egS+lyi4DQKU5HITr/k+IFXb7p9etdrCSUxEj\nCt1KEFpewRwxLrNqkiZ1dd8dD87cL7sOAFXhaBD+WbNWbzUI/645ulL7OLxpk8ajvDVqaacS\nY0YEXybqBUfEprjsiJgiv0/uXN01B68CqBBHg3CA9+ajahDaQ66v1D7e1pykmyC0qJydn6sj\nYqq1HfjGcpOMN5naTLQbnyS7CgAOcDQI/frZ84NwbP1K7WNuy5YajxKE1lO0atKcJBecI6Y0\nJ9SRO9Pe+Ut2HQAc42gQVhtXEITjvHWriSC0mLQVMeEBnqJxyLjYFNOs2r7tjS6er8ouAoAe\nHA3ChkMKgvDupnqVZCcILcNVV00q18+tRJuxa0xwAhNA+RwNwj5+5/OCcJXHYN1qIgitoHDV\npHGfut6qSVpO/abc7Hh7u+w6AOjF0SD8ydZjjUjY+IyX1+8V2jZ3W0Lc/IRt5bwBRhC6s5wd\nphsRUyh1elevZrKLAKAvh68jnFEtb8o0r9iyeheXMck/f4a1JpM0ZwghCN1UsRExrrhqUnly\nbxLNI1Zkyi4DgL4cn1km5YnAZtePTKnIhumdha1D2PARYe1tIkhrTmKC0P1cnCPm/pe/+kt2\nLVWQvkSd4PSLzbLrAKA/Q+caHS8GpuW39vcXEzQ6EoTuxLQjYgrtm9HDp+Zy2VUAcBJHg/BE\nZTZsEZhzsZnT8WqNjgShmygcEfOCyUbElPCmR5NRS8/JrgKAszgahJcNXlvxDb2fKmqPqa7R\nkSA0v7SEqNAAj2otQqISDsiupcrOLf1SuU37xTRXNwKoAkeDsJUQ//d+Rd/vati7qN3LT6Mj\nQWhm6etnPXazueaIKdWh2X1q+kTIrgKA0zkahLmrwrxFzaEVWpbX3t9WOLZ0rscAjY4EoUnt\nXzolrI2nRwuTjogp4ZB3oyHx6bKrAOB8OgyWOTy1pRAdZ1UgunbVER0i58XHz4tsL+ru0uhI\nEJpOVsqiqJArhFdAeMyKo7KLcVDm8nEHlbudOeX2BOAOdBk1mrv8AS9x+agt5W65pZMo0Emz\nM0FoJqcSZ0UUrJqUZP5r7E7Fhdb2vpuVBQEL0efyiT0TGynh5vFQ+UvVJ0cPCw0dFp2s3Ysg\nNAm3GBFT0gDfQQvNeZEHgCrSIQizv+5pE1e9sv/b28VAnaoiCF1eZkpsRNf6onbwiJhErbkR\nzCPrh2dnKnfnzXuZB4CqcTgI90b5C49u8epvj9xe9Sq0/e7E8noQhK6s2IiYVNm16CV74QDf\narctkV0GABkcDcIQT+H7zM6CDyZXbM6Z0eV2Iwhd04Xf5z/btYGZ54gpy4b6/RYcl10EADkc\nDUJx49yiKTeS363Q9gShCbnXiJgiOWsj7yz/1DYAd+ZoECZV4TkJQlPJ2fn5xF7/MueqSeVZ\n/WgjW1A0ZwUBazN00u0CBKFZnFXniLnc/HPElOmBvnPcLdsBVJojQfiPwYIVHD1IEJrAicSY\n8ABP0ThkXGyK+820mZsUFfie7CIAuAhHgvCKd0ucLNrc56WKbZ+TVV4PglCiC5vjxnZtKHxu\nGPbuj+41Iuai/4309wh8iWvmAeRzJAgHiPpP/FTwjtlfM28W/j/oVRVBKMeJ72Me7egtrug+\n7pM/yv1rxbxW3DeTFARQyKFzhGtvF8K7Q48B99/mJ4RvlH7XVROERsvd9fnEXk1FtesGvP7f\nQ7KLcZrNk4Oukl0DAJfj4GCZLWOu81CnDq1z78d6LlxKEBqoYERMnS5Pzk5y69VnX2gm2kX+\nJrsKAC7H8VGjJ37/ft1unQegE4TG2L/0tbw5Yvq+FO82c8SU6nSO8qJ39Dvu/UUCqCIZl0+U\njyB0toI5YvJGxLj79eTbort4LpRdBADXpU8Q7p23KEOPai4iCJ3oxPdvP9LB/UfEXPRVK9F6\n7Gr3uwIEgG4cDcLXW5+w23+sJUQ7PUfaE4TO4YarJmk5tVe5Wf3mdtl1AHBtjgZh59vVG+/I\nYeI13WoiCPVnmRExhVKnd/W+Q3YRAMzA0SBs+ITdfkCMttvvuF6/oghCPV1cNcntR8QUd7yD\naP7kCneaHRyA0zgahF4v2u2fi//a7c9XbC3CiiEIdZGVsigq5ArhFRAes+Ko7GIMk77svPJ9\nPWuz7DoAmIWjQeg30m4fbTtttz9bQ7+iCEKHFR8RY6XFFfbN6OFz+R+yqwBgKo4G4V1XpB1u\nEKw07m+hW00EoSPy5oj5l7vPEVOGMR5NRn1rjXOgAHTjaBAmCE9v8any6/fKvvoVRRBWTWZK\nbETX+qJ28IiYRP3muzOHjKXqN/LmZK6TAFBZDl9HOPfmm9X1bFbXn6FXSXaCsPLSvn2t3zWe\normlRsQUOjS7dw2fKbKrAGBSzCxjdsVXTXL3OWLKsMHTb2h8uuwqAJgVQWhipxNnRQRfJuoF\nR8S65wLy5cpc/mqm3Z71e47sQgCYGEFoSrm7vvj3fW6/alI5TswPre3dk1eCABxEEJrN2Q2z\nHg/OnyNmk7XHR95Yf9BCPSf2A2BRBKGJnEiMCQ/wFI1DxsWmWPnNwOzVY5cpdyesdIEkAOch\nCE2BETGFMhcO9PW87UfZZQBwHwShqzvxQ8yjHa2zalK55tcOizsuuwgA7oQgdF2MiCkhZ13k\ngLx72YUAcDMEoUsqNiLGIqsmlWfZkEa2oPdkVwHAHRGErsbac8SUJffavnMOyy4CgHsiCF1H\n1ua4sd3UETFD37H6iJgiuUlRnZbLLgKAWyMIXcJJdURMdeF39/P/SWFETJE/R/p7dIxibAwA\nZyIIZUtLiAoN8PBsERKVsFt2LS5nZsjM/bJrAODuCEJ5zm748PHg2qLOrU98tClDdjEuZvPk\nzvfIrgGARRCEUlwcEdMnajEvA//h3NPNRLvxrDMPwBgEocGyUhZFhVwhvALCpyYckV2MC1JX\n0TjZbzoDZgEYxuggzN2WEDc/YVs564i7ZxCeYERMOba9cavnb7KLAGA1xgZhxiR/kafJJM2T\nYu4WhBfniAno//qyg7KLcVlzWok2zyXKrgKA5RgahOmdha1D2PARYe1tIuisRkc3CsLMlNiI\nrvVF7cDwmEStL9naTqlXSMyftkN2HQCsyNAgHC8GpuW39vcXEzQ6ukcQFhsRwykvDbtj7vIa\nJrsIANZlaBC2CCycMDmn49UaHc0ehFlb4p4rmCNmDXPEaNveVjSPWHFBdhkArMvQIPR+qqg9\nprpGRxMH4enEWRHBNUS94IjYpPOyi3Fxf69Vbo7EbJFdBwBrMzQIG/Yuavfy0+hoyiDM3f3l\ni72bCc+A/lMZEVO+vR/c4+N7UnYVAGBsEPa3xV5szvUYoNHRbEGYsfHD0bfUFrVveeJD5oip\nmAc8moz6lhWmALgAQ4NwVx3RIXJefPy8yPai7i6NjiYKwhOJMeGB3qJxyLjYFJaMrYiMb9T/\n+hXJ5VxLCgAGMfY6wi2dRIFOmieGTBGEWVsWPNetkfAJHPrOGt7iq6BDs3vXuGye7CoAoBij\nZ5ZJjh4WGjosOlm7l6sH4cnV04cEVhd+3ZgjpnIW2fyGxnM5JQCXwlyjlZO7ixExVZK5/EPl\n9lQSbx8DcDUEYYVdHBHDqkmVdmx+aG3vB2VXAQClIggr4sB3rz10bcGqSYzxqLScevUHLTot\nuwoAKJ20IHy2qcaDrhOExUfEMEdMpWWtHqcuK/i/bNmFAECZpAXhYK29uEQQqiNiWDXJAWcX\nDvT1vC1FdhkAoI0gLE1awtTwAA/PFiFRCSwgX2UT6oTFHZddBACUx9Ag7FdMM9cMwrMbP3w8\nuLaow4gYB+SsjZyo3GUzkzYAMzA0CEUJGh2lBCEjYvSR8GgjW1Bs+f0AwDUYGoQ1Wy8pdJcL\nBWHW7oSokMbCKyB8asIRI5/YHaU16jvnsOwiAKDiDA3Cm2oXvdJykXOEBXPENGKOGIflJkV1\n2Sm7CACoNEODcLQommlbehDmrZrUXHhe+9DU7w44/dncXtJIf4/AV5g9DYD5GBqEXwauLmpP\n0Ojo5CC8kBIb0bWBuDwwPCYx3ZlPZCFP3ztzv+waAKAqrDazzIHvpqojYpr1fvFLRsToYvPk\nzs/KrgEAHOA6QXjwnq6FrnVCEBbMEVM9cMj01ayapJPjTzYT7SI5MwjAzFwnCNNfHleou75B\neDpxVkRwDVEvOCI26byeO7Y09QX1H73f+Ut2HQDgGFlBeHjTJo1HdXtrNG9ETDNGxOhu2xu3\n1mDuVQBuQVYQvu30C+ovrpp0y+gPNzJHjL6mtRJtnlsnuwoA0IWsIJzbsqXGow4G4YnEmPBA\nb9E4ZFxsCgvB6uu0+t7yy9HbZdcBAHpxnXOExTkUhBeaqasmMSLGCVJj7vKaKrsIANCXGwah\nfRVzxDjFz21F84gVoU48fwAAFNtJREFUHFsAbsYdgxC6S9+q3KS8vkV2HQCgP6ODMHdbQtz8\nhG3lXMtOELqSfTN6+DSXXQQAOIuxQZgxyT9/CaYmkzQHchKEriPnFg//UUvPyS4DAJzF0CBM\n7yxsHcKGjwhrbxNBWvMzE4QuIeO/J5TbuGTmogPgzgwNwvFiYFp+a39/IXHSbVTAodl9avis\nkF0FADidoUHYIrDwqr6cjldrdCQIpXvT1mhIPEtzALAAQ4PQ+6mi9pjqGh0JQokyly9Rbv9a\nx1QEAKzB0CBs2Luo3ctPoyNBKMux+aG1vUfLrgIADGRoEPa3xV5szvUYoNGRIJRkf/X6gxae\nll0FABjJ0CDcVUd0iJwXHz8vsr2ou0ujI0FouKzVLx1T7v7Ill0IABjM2OsIt3QSBTppTlJC\nEBrrzGcDfT3vSJNdBgDIYPTMMsnRw0JDh0Una/ciCI0VWidswXHZRQCAHMw1amk5P78wV7n7\n+4LsQgBAGhlBuDuxvB4EoRFy4h9tZLspQXYZACCXjCAcXe4OCEIjJNbq+/Fh2UUAgGwEoQXl\nJr3YkwMMAPkIQsv5aaS/R+AULpMAgHwEoeXc03Pmftk1AIDrIAitY/OUm2fLrgEAXI6MIMzJ\nKq8HQai7vU82F20j98kuAwBcDtcRWsTS7tNTZdcAAK6IIHR3297o0lp2DQDgwghC9/Zia9F6\n7CbZVQCACyMI3VZGrt2eMyR6u+w6AMC1EYTuKXV6V6/FsosAADMgCN1RQjvRPGJFruwyAMAM\nCEI3k66uKrhs8mbZdQCAWRCE7mTfjB4+XWUXAQDmQhC6j6OdPJqMWnpOdhkAYC4EoVs4tyZL\nuXk3mdOCAFBZBKH5HZrdp2aNrbKrAACTIghNb4zNb2h8uuwqAMCsCEITy1yerNxuWJ8juxAA\nMDGC0KyOzQ+t7T1FdhUAYHoEoUmtq1Z/0KLTsqsAAPMjCE0na/W0LOU/bkO27EIAwC0QhOZy\neuFAX89uZ2WXAQDugyA0lw51whYcl10EALgTgtAkctZNXK3cHcqUXQgAuBmC0AwuLB7SyBa0\nRnYZAOCOCEIzmF2zz+xDsosAAPdEELq03KSooeo9b4gCgLMQhC7s+5H+HoExsqsAAPdGELqu\n7OY9Z+yTXQQAuDuC0BVtntxltewaAMAiCEKXs/XJZqLd+GOyywAAiyAIXU5Mt3dSZdcAANZB\nELqObW90uV92DQBgOQShizg3rpVoM/Z32WUAgOUQhC4gS/l3tG/0dtl1AIAVEYSy7Y65yztF\ndhEAYF0SgvDHHvVrXa8uqVc26wTh3LaiecT3sqsAAAszNAj9IpSbTzyFqneuR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vZu4g+7/Qvx9P+3d/egTYVRAIZPbFLRYGkWBX+IxToF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\nANUIIQBANUIIAFDtJ/d6gZa3dx2EAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 900
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stardrum_median <- gam(Median ~ s(yearly_tavg, k=3), data=df[df$species == \"Star Drum\",], family=poisson)\n",
    "\n",
    "stardrum_median_p <- plot(stardrum_median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "variables <- rbind(matrix(menhaden_median_p[[1]][[\"x\"]]), matrix(silverperch_median_p[[1]][[\"x\"]]), matrix(brownshrimp_median_p[[1]][[\"x\"]]), matrix(stripedanchovy_median_p[[1]][[\"x\"]]), matrix(pinkshrimp_median_p[[1]][[\"x\"]]), matrix(stardrum_median_p[[1]][[\"x\"]]))\n",
    "response <- rbind(matrix(menhaden_median_p[[1]][[\"fit\"]]), matrix(silverperch_median_p[[1]][[\"fit\"]]), matrix(brownshrimp_median_p[[1]][[\"fit\"]]), matrix(stripedanchovy_median_p[[1]][[\"fit\"]]), matrix(pinkshrimp_median_p[[1]][[\"fit\"]]), matrix(stardrum_median_p[[1]][[\"fit\"]]))\n",
    "error <- rbind(matrix(menhaden_median_p[[1]][[\"se\"]]), matrix(silverperch_median_p[[1]][[\"se\"]]), matrix(brownshrimp_median_p[[1]][[\"se\"]]), matrix(stripedanchovy_median_p[[1]][[\"se\"]]), matrix(pinkshrimp_median_p[[1]][[\"se\"]]), matrix(stardrum_median_p[[1]][[\"se\"]]))\n",
    "species <- rbind(matrix(rep(\"Menhaden\", 100)), matrix(rep(\"Silver Perch\", 100)), matrix(rep(\"Brown Shrimp\", 100)), matrix(rep(\"Striped Anchovy\", 100)), matrix(rep(\"Pink Shrimp\", 100)), matrix(rep(\"Star Drum\", 100)))\n",
    "median_tavg <- cbind(variables, response, error, species) \n",
    "colnames(median_tavg) <- c(\"Variable\", \"Response\", \"Error\", \"Species\")\n",
    "median_tavg <- as.data.frame(median_tavg)\n",
    "\n",
    "median_tavg$Variable <- as.numeric(median_tavg$Variable)\n",
    "median_tavg$Response <- as.numeric(median_tavg$Response)\n",
    "median_tavg$Error <- as.numeric(median_tavg$Error)\n",
    "median_tavg$Species <- as.factor(median_tavg$Species)\n",
    "\n",
    "menhaden <- median_tavg[median_tavg$Species == \"Menhaden\",]\n",
    "menhaden$Variable <- (menhaden$Variable-32)*5/9\n",
    "\n",
    "brownshrimp <- median_tavg[median_tavg$Species == \"Brown Shrimp\",]\n",
    "brownshrimp$Variable <- (brownshrimp$Variable-32)*5/9\n",
    "\n",
    "stardrum <- median_tavg[median_tavg$Species == \"Star Drum\",]\n",
    "stardrum$Variable <- (stardrum$Variable-32)*5/9\n",
    "\n",
    "silverperch <- median_tavg[median_tavg$Species == \"Silver Perch\",]\n",
    "silverperch <- silverperch[silverperch$Variable >= -10000,]\n",
    "\n",
    "stripedanchovy <- median_tavg[median_tavg$Species == \"Striped Anchovy\",]\n",
    "\n",
    "pinkshrimp <- median_tavg[median_tavg$Species == \"Pink Shrimp\",]\n",
    "pinkshrimp <- pinkshrimp[pinkshrimp$Variable >= -10000,]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“\u001b[1m\u001b[22mUsing `size` aesthetic for lines was deprecated in ggplot2 3.4.0.\n",
      "\u001b[36mℹ\u001b[39m Please use `linewidth` instead.”\n",
      "Warning message:\n",
      "“\u001b[1m\u001b[22mThe `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.\n",
      "\u001b[36mℹ\u001b[39m Please use the `linewidth` argument instead.”\n",
      "Warning message:\n",
      "“\u001b[1m\u001b[22mThe `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.\n",
      "\u001b[36mℹ\u001b[39m Please use the `linewidth` argument instead.”\n"
     ]
    },
    {
     "data": {
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KA5HSQY/drXvhYEwXve857bPzg5Ofn2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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 900
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
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ODAqV95DwAAAACwmjDP87Jr6KUDBw7ceeedd9999zXXXFN2LQAAAABACZ588snJ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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 900
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
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AhGAQAAAIDoCEYBAAAAgOjUZF0AqeXz+cOHDxfHdXV11dXV2dYDlUFnQTnoLCgH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\n7erVq5Mvvnv37l/96lfJ5/f09CQ/LnZkZOQXv/hF8sUXLly4YMG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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 900
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
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VEhSpeTdDm58ZYbep1ea7Pd3Gx3m91d33jpRvt0Kak10vpKzUb7K5OQAgAAAFx/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\nHcGSJBlZjZ7ESTpVM06SZry9OZDEo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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 900
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
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33oQ29961sjYrlc/uiP/uhyuXznO995cCLhL/7iL168ePHNb37z3XffvXrZO9/5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\nPK3Gks7qelKV46oal1W9PMxY0kU9n9ZPT6exF5KuOu6LU7G7SRk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2dnY2exK4DTZSUlp0u0W3e77fj4hqsRivOu6ral7X67/JwbGkB3Y3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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 900
      }
     },
     "output_type": "display_data"
    },
    {
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AKAqCUWAObYzAChiSupbt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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 900
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(menhaden, aes(x = Variable, y = Response))+\n",
    "  geom_line(size = 1.5, color = \"#440154FF\") +\n",
    "  geom_ribbon(aes(ymin = Response - Error, ymax = Response + Error), alpha = 0.8, fill = \"#440154FF\") +\n",
    "  geom_hline(yintercept = 0, color = \"black\", linetype = \"dashed\", size = 1.10) +\n",
    "  ylim(-0.25, 0.70) +\n",
    "  scale_x_continuous(breaks=seq(18,22, by= 0.5)) +\n",
    "  labs(y = \"Additive Effect (Median DOY)\", x = \"Average Yearly Temperature\")  +\n",
    "  theme(panel.background = element_rect(fill = \"white\", color = \"black\", size = 1.1), axis.title.y = element_blank(), panel.grid.major.x = element_line(color = \"grey\", size = 1, linetype = \"dashed\"), axis.title = element_text(color = \"black\", family = \"sans\", size = 12), axis.text = element_text(color = \"black\", family = \"sans\", size = 12), legend.title = element_text(color = \"black\", family = \"sans\", size = 14), legend.text = element_text(color = \"black\", family = \"sans\", size = 12))\n",
    "\n",
    "ggplot(brownshrimp, aes(x = Variable, y = Response))+\n",
    "  geom_line(size = 1.5, color = \"#440154FF\") +\n",
    "  geom_ribbon(aes(ymin = Response - Error, ymax = Response + Error), alpha = 0.8, fill = \"#440154FF\") +\n",
    "  geom_hline(yintercept = 0, color = \"black\", linetype = \"dashed\", size = 1.10) +\n",
    "  ylim(-0.25, 0.70) +\n",
    "  scale_x_continuous(breaks=seq(18,22, by= 0.5)) +\n",
    "  labs(y = \"Additive Effect (Median DOY)\", x = \"Average Yearly Temperature\")  +\n",
    "  theme(panel.background = element_rect(fill = \"white\", color = \"black\", size = 1.1), axis.title.y = element_blank(), panel.grid.major.x = element_line(color = \"grey\", size = 1, linetype = \"dashed\"), axis.title = element_text(color = \"black\", family = \"sans\", size = 12), axis.text = element_text(color = \"black\", family = \"sans\", size = 12), legend.title = element_text(color = \"black\", family = \"sans\", size = 14), legend.text = element_text(color = \"black\", family = \"sans\", size = 12))\n",
    "\n",
    "ggplot(stardrum, aes(x = Variable, y = Response))+\n",
    "  geom_line(size = 1.5, color = \"#FDE725FF\") +\n",
    "  geom_ribbon(aes(ymin = Response - Error, ymax = Response + Error), alpha = 0.8, fill = \"#FDE725FF\") +\n",
    "  geom_hline(yintercept = 0, color = \"black\", linetype = \"dashed\", size = 1.10) +\n",
    "  ylim(-0.25, 0.70) +\n",
    "  scale_x_continuous(breaks=seq(18,22, by= 0.5)) +\n",
    "  labs(y = \"Additive Effect (Median DOY)\", x = \"Average Yearly Temperature\")  +\n",
    "  theme(panel.background = element_rect(fill = \"white\", color = \"black\", size = 1.1), axis.title.y = element_blank(), panel.grid.major.x = element_line(color = \"grey\", size = 1, linetype = \"dashed\"), axis.title = element_text(color = \"black\", family = \"sans\", size = 12), axis.text = element_text(color = \"black\", family = \"sans\", size = 12), legend.title = element_text(color = \"black\", family = \"sans\", size = 14), legend.text = element_text(color = \"black\", family = \"sans\", size = 12))\n",
    "\n",
    "ggplot(silverperch, aes(x = Variable, y = Response))+\n",
    "  geom_line(size = 1.5, color = \"#440154FF\") +\n",
    "  geom_ribbon(aes(ymin = Response - Error, ymax = Response + Error), alpha = 0.8, fill = \"#440154FF\") +\n",
    "  geom_hline(yintercept = 0, color = \"black\", linetype = \"dashed\", size = 1.10) +\n",
    "  ylim(-0.35, 0.40) +\n",
    "  scale_x_continuous(breaks=seq(-10000,10000, by= 5000)) +\n",
    "  labs(y = \"Additive Effect (Median DOY)\", x = \"Annual Drought Index\")  +\n",
    "  theme(panel.background = element_rect(fill = \"white\", color = \"black\", size = 1.1), axis.title.y = element_blank(), panel.grid.major.x = element_line(color = \"grey\", size = 1, linetype = \"dashed\"), axis.title = element_text(color = \"black\", family = \"sans\", size = 12), axis.text = element_text(color = \"black\", family = \"sans\", size = 12), legend.title = element_text(color = \"black\", family = \"sans\", size = 14), legend.text = element_text(color = \"black\", family = \"sans\", size = 12))\n",
    "\n",
    "ggplot(stripedanchovy, aes(x = Variable, y = Response))+\n",
    "  geom_line(size = 1.5, color = \"#21908CFF\") +\n",
    "  geom_ribbon(aes(ymin = Response - Error, ymax = Response + Error), alpha = 0.8, fill = \"#21908CFF\") +\n",
    "  geom_hline(yintercept = 0, color = \"black\", linetype = \"dashed\", size = 1.10) +\n",
    "  ylim(-0.35, 0.40) +\n",
    "  scale_x_continuous(breaks=seq(-10000,10000, by= 5000)) +\n",
    "  labs(y = \"Additive Effect (Median DOY)\", x = \"Annual Drought Index\")  +\n",
    "  theme(panel.background = element_rect(fill = \"white\", color = \"black\", size = 1.1), axis.title.y = element_blank(), panel.grid.major.x = element_line(color = \"grey\", size = 1, linetype = \"dashed\"), axis.title = element_text(color = \"black\", family = \"sans\", size = 12), axis.text = element_text(color = \"black\", family = \"sans\", size = 12), legend.title = element_text(color = \"black\", family = \"sans\", size = 14), legend.text = element_text(color = \"black\", family = \"sans\", size = 12))\n",
    "\n",
    "ggplot(pinkshrimp, aes(x = Variable, y = Response))+\n",
    "  geom_line(size = 1.5, color = \"#21908CFF\") +\n",
    "  geom_ribbon(aes(ymin = Response - Error, ymax = Response + Error), alpha = 0.8, fill = \"#21908CFF\") +\n",
    "  geom_hline(yintercept = 0, color = \"black\", linetype = \"dashed\", size = 1.10) +\n",
    "  ylim(-0.35, 0.40) +\n",
    "  scale_x_continuous(breaks=seq(-10000,10000, by= 5000)) +\n",
    "  labs(y = \"Additive Effect (Median DOY)\", x = \"Annual Drought Index\")  +\n",
    "  theme(panel.background = element_rect(fill = \"white\", color = \"black\", size = 1.1), axis.title.y = element_blank(), panel.grid.major.x = element_line(color = \"grey\", size = 1, linetype = \"dashed\"), axis.title = element_text(color = \"black\", family = \"sans\", size = 12), axis.text = element_text(color = \"black\", family = \"sans\", size = 12), legend.title = element_text(color = \"black\", family = \"sans\", size = 14), legend.text = element_text(color = \"black\", family = \"sans\", size = 12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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