Comparing_MLs.ipynb

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    "## Assessing Climate Change Impacts on *Xantusia vigilis* lizards and their Joshua tree habitats with Ensemble Species Distribution Models\n",
    "\n",
    "*What can we learn about jointly projecting range models of symbiotic species to future climates? How will climate change impact geographic overlap between Mojave Desert species?*\n",
    "\n",
    "Author : [Daniel Furman](mailto:dryanfurman@gmail.com) **|** Harvey Mudd College **|** NSF Data Science Award (#1757952; 2019)\n",
    "\n",
    "---\n",
    "\n",
    "Scripts for reproducing [SICB 2021](https://sicbannualmeeting.pathable.co/meetings/virtual/b5bEwkdpSNA3kaCLq) and [SCCUR 2019](https://drive.google.com/file/d/114wmqQgjkc5DHLQmVI19AvlTw4K_daYQ/view?usp=sharing) pubs. The notebook is contained within a [GitHub Repository](https://github.com/daniel-furman/ensemble-climate-projections) alongside its accompanying files and data. [Online Render of Notebook](https://nbviewer.jupyter.org/github/daniel-furman/ensemble-climate-projections/blob/main/Comparing_MLs.ipynb).\n",
    "\n",
    "---\n",
    "\n",
    "**Intro:** Our research can serve as a case study for projecting joint distributions of symbiotic species to explore ecological climate change affects. Habitat degradation for Mojave Desert species will likely result from changing climate and from discrete events of environmental catastrophe, such as wildfire and drought. Future research will include an assessment of lag between Xantusia and Yucca range shifts, topographic analyses, and field work at recently burned Joshua tree strands near the southern California/Arizona border (see [NYT article](https://www.nytimes.com/interactive/2020/12/09/climate/redwood-sequoia-tree-fire.html?))."
   ]
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   "source": [
    "### Part 1: Constructing Soft Voting Machine Learning Ensemble\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
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   "outputs": [
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       "        }</style><table id=\"T_65464648_52bd_11eb_ac68_dca90481a814\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Model</th>        <th class=\"col_heading level0 col1\" >Accuracy</th>        <th class=\"col_heading level0 col2\" >AUC</th>        <th class=\"col_heading level0 col3\" >Recall</th>        <th class=\"col_heading level0 col4\" >Prec.</th>        <th class=\"col_heading level0 col5\" >F1</th>        <th class=\"col_heading level0 col6\" >Kappa</th>        <th class=\"col_heading level0 col7\" >MCC</th>        <th class=\"col_heading level0 col8\" >TT (Sec)</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_65464648_52bd_11eb_ac68_dca90481a814level0_row0\" class=\"row_heading level0 row0\" >catboost</th>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row0_col0\" class=\"data row0 col0\" >CatBoost Classifier</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row0_col1\" class=\"data row0 col1\" >0.8608</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row0_col2\" class=\"data row0 col2\" >0.9220</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row0_col3\" class=\"data row0 col3\" >0.8606</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row0_col4\" class=\"data row0 col4\" >0.8629</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row0_col5\" class=\"data row0 col5\" >0.8606</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row0_col6\" class=\"data row0 col6\" >0.7215</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row0_col7\" class=\"data row0 col7\" >0.7236</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row0_col8\" class=\"data row0 col8\" >1.7820</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_65464648_52bd_11eb_ac68_dca90481a814level0_row1\" class=\"row_heading level0 row1\" >et</th>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row1_col0\" class=\"data row1 col0\" >Extra Trees Classifier</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row1_col1\" class=\"data row1 col1\" >0.8559</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row1_col2\" class=\"data row1 col2\" >0.9218</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row1_col3\" class=\"data row1 col3\" >0.8558</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row1_col4\" class=\"data row1 col4\" >0.8576</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row1_col5\" class=\"data row1 col5\" >0.8557</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row1_col6\" class=\"data row1 col6\" >0.7117</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row1_col7\" class=\"data row1 col7\" >0.7135</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row1_col8\" class=\"data row1 col8\" >0.2940</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_65464648_52bd_11eb_ac68_dca90481a814level0_row2\" class=\"row_heading level0 row2\" >gbc</th>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row2_col0\" class=\"data row2 col0\" >Gradient Boosting Classifier</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row2_col1\" class=\"data row2 col1\" >0.8529</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row2_col2\" class=\"data row2 col2\" >0.9173</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row2_col3\" class=\"data row2 col3\" >0.8526</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row2_col4\" class=\"data row2 col4\" >0.8558</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row2_col5\" class=\"data row2 col5\" >0.8525</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row2_col6\" class=\"data row2 col6\" >0.7056</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row2_col7\" class=\"data row2 col7\" >0.7086</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row2_col8\" class=\"data row2 col8\" >0.1400</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_65464648_52bd_11eb_ac68_dca90481a814level0_row3\" class=\"row_heading level0 row3\" >rf</th>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row3_col0\" class=\"data row3 col0\" >Random Forest Classifier</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row3_col1\" class=\"data row3 col1\" >0.8509</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row3_col2\" class=\"data row3 col2\" >0.9227</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row3_col3\" class=\"data row3 col3\" >0.8507</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row3_col4\" class=\"data row3 col4\" >0.8530</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row3_col5\" class=\"data row3 col5\" >0.8506</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row3_col6\" class=\"data row3 col6\" >0.7017</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row3_col7\" class=\"data row3 col7\" >0.7038</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row3_col8\" class=\"data row3 col8\" >0.3300</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_65464648_52bd_11eb_ac68_dca90481a814level0_row4\" class=\"row_heading level0 row4\" >lightgbm</th>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row4_col0\" class=\"data row4 col0\" >Light Gradient Boosting Machine</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row4_col1\" class=\"data row4 col1\" >0.8480</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row4_col2\" class=\"data row4 col2\" >0.9145</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row4_col3\" class=\"data row4 col3\" >0.8478</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row4_col4\" class=\"data row4 col4\" >0.8495</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row4_col5\" class=\"data row4 col5\" >0.8477</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row4_col6\" class=\"data row4 col6\" >0.6958</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row4_col7\" class=\"data row4 col7\" >0.6974</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row4_col8\" class=\"data row4 col8\" >0.0620</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_65464648_52bd_11eb_ac68_dca90481a814level0_row5\" class=\"row_heading level0 row5\" >xgboost</th>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row5_col0\" class=\"data row5 col0\" >Extreme Gradient Boosting</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row5_col1\" class=\"data row5 col1\" >0.8430</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row5_col2\" class=\"data row5 col2\" >0.9139</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row5_col3\" class=\"data row5 col3\" >0.8428</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row5_col4\" class=\"data row5 col4\" >0.8454</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row5_col5\" class=\"data row5 col5\" >0.8426</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row5_col6\" class=\"data row5 col6\" >0.6859</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row5_col7\" class=\"data row5 col7\" >0.6883</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row5_col8\" class=\"data row5 col8\" >0.1900</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_65464648_52bd_11eb_ac68_dca90481a814level0_row6\" class=\"row_heading level0 row6\" >ada</th>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row6_col0\" class=\"data row6 col0\" >Ada Boost Classifier</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row6_col1\" class=\"data row6 col1\" >0.8292</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row6_col2\" class=\"data row6 col2\" >0.8883</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row6_col3\" class=\"data row6 col3\" >0.8288</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row6_col4\" class=\"data row6 col4\" >0.8350</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row6_col5\" class=\"data row6 col5\" >0.8282</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row6_col6\" class=\"data row6 col6\" >0.6581</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row6_col7\" class=\"data row6 col7\" >0.6640</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row6_col8\" class=\"data row6 col8\" >0.0870</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_65464648_52bd_11eb_ac68_dca90481a814level0_row7\" class=\"row_heading level0 row7\" >dt</th>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row7_col0\" class=\"data row7 col0\" >Decision Tree Classifier</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row7_col1\" class=\"data row7 col1\" >0.8056</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row7_col2\" class=\"data row7 col2\" >0.8081</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row7_col3\" class=\"data row7 col3\" >0.8055</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row7_col4\" class=\"data row7 col4\" >0.8076</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row7_col5\" class=\"data row7 col5\" >0.8052</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row7_col6\" class=\"data row7 col6\" >0.6111</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row7_col7\" class=\"data row7 col7\" >0.6131</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row7_col8\" class=\"data row7 col8\" >0.0120</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_65464648_52bd_11eb_ac68_dca90481a814level0_row8\" class=\"row_heading level0 row8\" >qda</th>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row8_col0\" class=\"data row8 col0\" >Quadratic Discriminant Analysis</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row8_col1\" class=\"data row8 col1\" >0.7966</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row8_col2\" class=\"data row8 col2\" >0.8944</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row8_col3\" class=\"data row8 col3\" >0.7959</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row8_col4\" class=\"data row8 col4\" >0.8085</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row8_col5\" class=\"data row8 col5\" >0.7941</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row8_col6\" class=\"data row8 col6\" >0.5925</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row8_col7\" class=\"data row8 col7\" >0.6046</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row8_col8\" class=\"data row8 col8\" >0.0120</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_65464648_52bd_11eb_ac68_dca90481a814level0_row9\" class=\"row_heading level0 row9\" >knn</th>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row9_col0\" class=\"data row9 col0\" >K Neighbors Classifier</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row9_col1\" class=\"data row9 col1\" >0.7917</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row9_col2\" class=\"data row9 col2\" >0.8624</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row9_col3\" class=\"data row9 col3\" >0.7912</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row9_col4\" class=\"data row9 col4\" >0.7977</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row9_col5\" class=\"data row9 col5\" >0.7905</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row9_col6\" class=\"data row9 col6\" >0.5830</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row9_col7\" class=\"data row9 col7\" >0.5892</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row9_col8\" class=\"data row9 col8\" >0.0740</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_65464648_52bd_11eb_ac68_dca90481a814level0_row10\" class=\"row_heading level0 row10\" >nb</th>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row10_col0\" class=\"data row10 col0\" >Naive Bayes</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row10_col1\" class=\"data row10 col1\" >0.7729</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row10_col2\" class=\"data row10 col2\" >0.8587</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row10_col3\" class=\"data row10 col3\" >0.7723</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row10_col4\" class=\"data row10 col4\" >0.7828</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row10_col5\" class=\"data row10 col5\" >0.7705</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row10_col6\" class=\"data row10 col6\" >0.5452</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row10_col7\" class=\"data row10 col7\" >0.5553</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row10_col8\" class=\"data row10 col8\" >0.0110</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_65464648_52bd_11eb_ac68_dca90481a814level0_row11\" class=\"row_heading level0 row11\" >lr</th>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row11_col0\" class=\"data row11 col0\" >Logistic Regression</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row11_col1\" class=\"data row11 col1\" >0.7680</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row11_col2\" class=\"data row11 col2\" >0.8344</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row11_col3\" class=\"data row11 col3\" >0.7675</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row11_col4\" class=\"data row11 col4\" >0.7755</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row11_col5\" class=\"data row11 col5\" >0.7662</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row11_col6\" class=\"data row11 col6\" >0.5354</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row11_col7\" class=\"data row11 col7\" >0.5431</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row11_col8\" class=\"data row11 col8\" >0.1220</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_65464648_52bd_11eb_ac68_dca90481a814level0_row12\" class=\"row_heading level0 row12\" >ridge</th>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row12_col0\" class=\"data row12 col0\" >Ridge Classifier</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row12_col1\" class=\"data row12 col1\" >0.7650</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row12_col2\" class=\"data row12 col2\" >0.0000</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row12_col3\" class=\"data row12 col3\" >0.7643</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row12_col4\" class=\"data row12 col4\" >0.7749</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row12_col5\" class=\"data row12 col5\" >0.7622</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row12_col6\" class=\"data row12 col6\" >0.5293</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row12_col7\" class=\"data row12 col7\" >0.5393</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row12_col8\" class=\"data row12 col8\" >0.0110</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_65464648_52bd_11eb_ac68_dca90481a814level0_row13\" class=\"row_heading level0 row13\" >lda</th>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row13_col0\" class=\"data row13 col0\" >Linear Discriminant Analysis</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row13_col1\" class=\"data row13 col1\" >0.7650</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row13_col2\" class=\"data row13 col2\" >0.8353</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row13_col3\" class=\"data row13 col3\" >0.7643</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row13_col4\" class=\"data row13 col4\" >0.7749</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row13_col5\" class=\"data row13 col5\" >0.7622</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row13_col6\" class=\"data row13 col6\" >0.5293</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row13_col7\" class=\"data row13 col7\" >0.5393</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row13_col8\" class=\"data row13 col8\" >0.0140</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_65464648_52bd_11eb_ac68_dca90481a814level0_row14\" class=\"row_heading level0 row14\" >svm</th>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row14_col0\" class=\"data row14 col0\" >SVM - Linear Kernel</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row14_col1\" class=\"data row14 col1\" >0.6920</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row14_col2\" class=\"data row14 col2\" >0.0000</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row14_col3\" class=\"data row14 col3\" >0.6916</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row14_col4\" class=\"data row14 col4\" >0.7440</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row14_col5\" class=\"data row14 col5\" >0.6707</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row14_col6\" class=\"data row14 col6\" >0.3835</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row14_col7\" class=\"data row14 col7\" >0.4291</td>\n",
       "                        <td id=\"T_65464648_52bd_11eb_ac68_dca90481a814row14_col8\" class=\"data row14 col8\" >0.0130</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f943f2ac690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 10-fold CV model fitting\n",
    "exec(open('ml_sdms_train.py').read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### | 1.1 Validation set prediction\n",
    "---\n",
    "\n",
    "Now that we have trained and tuned our ML models, we are ready to test their validation set performance. We first print the validation F statistic and 2x2 confusion matrix and then visualize the AUC statistic with the ROC curve. A blended model was also created with the three most predictive PyCaret classifiers, based on the validation F statistic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Successfully Loaded\n",
      "Transformation Pipeline and Model Successfully Loaded\n",
      "Transformation Pipeline and Model Successfully Loaded\n",
      "Transformation Pipeline and Model Successfully Loaded\n",
      "\n",
      "\n",
      "Fraction correct validation Random Forest : 0.9806629834254144\n",
      "[[177   4]\n",
      " [  3 178]]\n",
      "\n",
      "\n",
      "Fraction correct validation LGBoost Machine : 0.9696132596685083\n",
      "[[173   8]\n",
      " [  3 178]]\n",
      "\n",
      "\n",
      "Fraction correct validation Extra Trees : 0.9834254143646409\n",
      "[[178   3]\n",
      " [  3 178]]\n",
      "\n",
      "\n",
      "Fraction correct validation Blend (rf, et, lgbm) : 0.9779005524861878\n",
      "[[176   5]\n",
      " [  3 178]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as plt\n",
    "plt.rcParams[\"figure.figsize\"] = (6, 4)\n",
    "exec(open('ml_sdms_predict.py').read())"
   ]
  },
  {
   "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>Extra Trees</th>\n",
       "      <th>Random Forest</th>\n",
       "      <th>Blend (rf, et, lgbm)</th>\n",
       "      <th>LGBoost Machine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F-statistic :</th>\n",
       "      <td>0.983425</td>\n",
       "      <td>0.980716</td>\n",
       "      <td>0.978022</td>\n",
       "      <td>0.970027</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Extra Trees  Random Forest  Blend (rf, et, lgbm)  \\\n",
       "F-statistic :     0.983425       0.980716              0.978022   \n",
       "\n",
       "               LGBoost Machine  \n",
       "F-statistic :         0.970027  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number species training presences 725\n",
      "number species training absences 723\n",
      "number species validation presences 181\n",
      "total number of data points (confirm .geoson below) 1810\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <style>\n",
       "    table.eli5-weights tr:hover {\n",
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       "\n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "        <table class=\"eli5-weights eli5-feature-importances\" style=\"border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto;\">\n",
       "    <thead>\n",
       "    <tr style=\"border: none;\">\n",
       "        <th style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">Weight</th>\n",
       "        <th style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">Feature</th>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 80.00%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0608\n",
       "                \n",
       "                    &plusmn; 0.0061\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                bclim8\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 85.13%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0398\n",
       "                \n",
       "                    &plusmn; 0.0190\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                bclim12\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 88.81%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0265\n",
       "                \n",
       "                    &plusmn; 0.0142\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                bclim6\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 89.30%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0249\n",
       "                \n",
       "                    &plusmn; 0.0078\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                bclim15\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 91.95%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0166\n",
       "                \n",
       "                    &plusmn; 0.0175\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                bclim18\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 92.71%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0144\n",
       "                \n",
       "                    &plusmn; 0.0095\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                bclim4\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 93.52%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0122\n",
       "                \n",
       "                    &plusmn; 0.0166\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                bclim14\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 93.73%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0116\n",
       "                \n",
       "                    &plusmn; 0.0142\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                bclim2\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 94.81%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0088\n",
       "                \n",
       "                    &plusmn; 0.0081\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                bclim9\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 95.28%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0077\n",
       "                \n",
       "                    &plusmn; 0.0118\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                bclim11\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 96.01%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0061\n",
       "                \n",
       "                    &plusmn; 0.0073\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                bclim3\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 98.79%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0011\n",
       "                \n",
       "                    &plusmn; 0.0056\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                bclim7\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "    \n",
       "    </tbody>\n",
       "</table>\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# blended (BRTs & RF) feature importance from the validation set\n",
    "print('number species training presences', len(data[data.pa == 1]))\n",
    "print('number species training absences', len(data[data.pa == 0]))\n",
    "print('number species validation presences', len(validation_class[validation_class == 1]))\n",
    "print('total number of data points (confirm .geoson below)', len(data)+len(validation_class))\n",
    "feature_importances[len(feature_importances)-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 2: Geospatial Predictions of Species Distributions\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyimpute import load_training_vector\n",
    "from pyimpute import load_targets\n",
    "from pyimpute import impute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "There are 12 .asc features.\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>CLASS</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>POINT (-120.65644 36.51553)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>POINT (-112.27083 37.47917)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>POINT (-111.60417 32.06250)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>POINT (-119.41123 34.82431)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>POINT (-116.10417 37.93750)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>POINT (-117.85117 34.65556)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0</td>\n",
       "      <td>POINT (-118.60656 34.78263)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>POINT (-110.03113 37.59979)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.0</td>\n",
       "      <td>POINT (-115.07847 36.69694)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>POINT (-116.47987 34.70059)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CLASS                     geometry\n",
       "0    1.0  POINT (-120.65644 36.51553)\n",
       "1    0.0  POINT (-112.27083 37.47917)\n",
       "2    0.0  POINT (-111.60417 32.06250)\n",
       "3    1.0  POINT (-119.41123 34.82431)\n",
       "4    0.0  POINT (-116.10417 37.93750)\n",
       "5    1.0  POINT (-117.85117 34.65556)\n",
       "6    1.0  POINT (-118.60656 34.78263)\n",
       "7    1.0  POINT (-110.03113 37.59979)\n",
       "8    1.0  POINT (-115.07847 36.69694)\n",
       "9    1.0  POINT (-116.47987 34.70059)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import glob \n",
    "import geopandas as gpd\n",
    "import rasterio\n",
    "import matplotlib.pyplot as plt\n",
    "# grab features, see Appendix section 1 below for feature selection methods\n",
    "explanatory_rasters = ['bclim11', 'bclim12', 'bclim14', 'bclim15', 'bclim18',\n",
    "               'bclim2', 'bclim3', 'bclim4', 'bclim6', 'bclim7', 'bclim8','bclim9']\n",
    "i = 0\n",
    "for f in explanatory_rasters:\n",
    "    explanatory_rasters[i] = ('data_2.0/train-rasters-2.5m/' + f + '.asc')\n",
    "    i = (i + 1)\n",
    "print('\\nThere are', len(explanatory_rasters), '.asc features.') # check there are ten \n",
    "# grab shapefile containing 100% of the final presence/absence locations\n",
    "response_data = gpd.GeoDataFrame.from_file('data_2.0/geofile-xv-presences/xv_two_step.shp')\n",
    "response_data.drop(287, axis = 0, inplace = True) # drop coordinate above pinnacles\n",
    "response_data.head(10) # check geopandas data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of duplicates:  0\n",
      "number of NA's:  0\n",
      "Coordinate reference system is: epsg:4326\n",
      "1810 observations with 2 columns\n"
     ]
    }
   ],
   "source": [
    "print(\"number of duplicates: \", response_data.duplicated(subset='geometry', keep='first').sum())\n",
    "print(\"number of NA's: \", response_data['geometry'].isna().sum())\n",
    "print(\"Coordinate reference system is: {}\".format(response_data.crs))\n",
    "print(\"{} observations with {} columns\".format(*response_data.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can map the species presences (`pa==1`).\n",
    "* Coordinates downloaded from open-source repositories (GBIF) (Leavitt et al., 2007)\n",
    "* Filtered erroneous, spatially biased, and duplicated data (907 X.v. & 1325 Y.b.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of species presences: 906\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('number of species presences:', len(response_data[response_data.CLASS == 1]))\n",
    "response_data[response_data.CLASS == 1].plot(marker='.', color='green', markersize=2)\n",
    "print('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also map the background points (`pa==0`).\n",
    "\n",
    "* Study space set from 109.3°W to 122.8°W and 31.9°N to 38.2°N\n",
    "* Background points randomly drawn (same # presences), removed spatially biased and clear false negative (within 1 km radius of species presences) coordinates\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of background points: 904\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('number of background points:', len(response_data[response_data.CLASS == 0]))\n",
    "response_data[response_data.CLASS == 0].plot(marker='.', color='black', markersize=2)\n",
    "print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1810, 12), (1810,))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create pyimpute framework\n",
    "train_xs, train_y = load_training_vector(response_data,\n",
    "                                         explanatory_rasters,\n",
    "                                         response_field='CLASS') \n",
    "train_xs.shape, train_y.shape # check shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Blend (rf, et, lgbm) 5-fold Cross Validation Accuracy: 87.24 (+/- 3.30)\n"
     ]
    }
   ],
   "source": [
    "# spatial prediction with the 3-model blended classifier\n",
    "from sklearn import model_selection\n",
    "import os\n",
    "session_number = 'exp_id=101, xantusia_after'\n",
    "\n",
    "target_xs, raster_info = load_targets(explanatory_rasters)\n",
    "k = 5 \n",
    "kf = model_selection.KFold(n_splits=k)\n",
    "accuracy_scores = model_selection.cross_val_score(blender_specific, train_xs, train_y, cv=kf, scoring='accuracy')\n",
    "print(name + \" %d-fold Cross Validation Accuracy: %0.2f (+/- %0.2f)\"\n",
    "          % (k, accuracy_scores.mean() * 100, accuracy_scores.std() * 200))\n",
    "blender_specific.fit(train_xs, train_y)\n",
    "# spatial prediction with impute function, saves tifs to file\n",
    "#os.mkdir('outputs' + session_number)\n",
    "#os.mkdir('outputs' + session_number + '/blender-baseline')\n",
    "impute(target_xs, blender_specific, raster_info, outdir='outputs' + session_number + '/blender-baseline',\n",
    "       class_prob=True, certainty=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the spatial output of the 3-model blended classifier\n",
    "from pylab import plt\n",
    "# define spatial plotter\n",
    "def plotit(x, title, cmap=\"Blues\"):\n",
    "    plt.imshow(x, cmap=cmap, interpolation='nearest')\n",
    "    plt.colorbar()\n",
    "    plt.title(title, fontweight = 'bold')\n",
    "blended_curr = rasterio.open('outputs'+ session_number + '/blender-baseline/responses.tif')\n",
    "plotit(blended_curr.read(1),'Night lizard distribution \\n(soft-voting ensemble, baseline)',\n",
    "        cmap='Greens')\n",
    "plt.figure()\n",
    "blended_curr = rasterio.open('outputs' + session_number + '/blender-baseline/probability_1.0.tif') \n",
    "plotit(blended_curr.read(1),'Night lizard distribution \\n(soft-voting ensemble, baseline)',\n",
    "       cmap='Greens')\n",
    "print('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 3: Predicting Suitability Under Climate Change Scenarios\n",
    "---\n",
    "Part 4 is unable to re-run without additional data/scripts (not in the GitHub repo)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extrapolate model to 128 future scenarios\n",
    "exec(open('future_projection.py').read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in ['2041-2060','2061-2080','2081-2100']:\n",
    "    future = rasterio.open('data_2.0/CMIP6_preds/ssp245_' + i + '/BCC/probability_1.0.tif').read(1)\n",
    "    plotit(future, 'Night lizard distribution \\n ('+ i + ', BCC & ssp245)')\n",
    "    plt.figure()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Appendix 0: Introduction to Study\n",
    "---\n",
    "\n",
    "Blends of tree-based machine learning classifiers performed best at predicting the geographic range for *Xantusia vigilis* lizards and Yucca brevifolia, a pair of iconic Mojave desert species. The spatial overlap between the two species’ near-current (1970-2000) distributions was ~fifty percent overall (intersection/union) and ~sixty-five percent solely within Xantusia’s geographic range. Across future bioclimatic scenarios, we predicted severe constriction for both species' distributions (Fig. 6) and a significant reduction in their geographic overlap (down by ~56% from baseline conditions, on average). Equipped with these 21st century predictions, we pinpoint areas with a high likelihood of retaining Xantusia and Yucca overlap and suggest ways to target their ecological conservation. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Appendix 1: Removing Correlated Features\n",
    "---\n",
    "\n",
    "Recursively selects de-correlated features for modeling below a Spearman's threshold, using the rank of feature importance scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "exec(open('recursive-ranker.py').read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Comparing bclim16 or bclim19  | Dropping bclim16\n",
      "Comparing bclim13 or bclim19  | Dropping bclim13\n",
      "Comparing bclim1 or bclim10  | Dropping bclim10\n",
      "Comparing bclim14 or bclim17  | Dropping bclim17\n",
      "Comparing bclim12 or bclim19  | Dropping bclim19\n",
      "Comparing bclim1 or bclim5  | Dropping bclim5\n",
      "Comparing bclim1 or bclim11  | Dropping bclim1\n",
      "\n",
      "final set of variables:  ['bclim11', 'bclim12', 'bclim14', 'bclim15', 'bclim18', 'bclim2', 'bclim3', 'bclim4', 'bclim6', 'bclim7', 'bclim8', 'bclim9']\n",
      "\n",
      "Covariance matrix (r <  0.85 ):\n",
      "\n",
      "         bclim11   bclim12   bclim14   bclim15   bclim18    bclim2    bclim3  \\\n",
      "bclim11      1.0  0.362862  0.599233  0.150592  0.321581  0.157325  0.083554   \n",
      "bclim12      0.0  1.000000  0.180278  0.520403  0.097076  0.312577  0.354639   \n",
      "bclim14      0.0  0.000000  1.000000  0.634341  0.831801  0.358903  0.495674   \n",
      "bclim15      0.0  0.000000  0.000000  1.000000  0.714498  0.018052  0.623828   \n",
      "bclim18      0.0  0.000000  0.000000  0.000000  1.000000  0.413657  0.574733   \n",
      "bclim2       0.0  0.000000  0.000000  0.000000  0.000000  1.000000  0.606145   \n",
      "bclim3       0.0  0.000000  0.000000  0.000000  0.000000  0.000000  1.000000   \n",
      "bclim4       0.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
      "bclim6       0.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
      "bclim7       0.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
      "bclim8       0.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
      "bclim9       0.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
      "\n",
      "           bclim4    bclim6    bclim7    bclim8    bclim9  \n",
      "bclim11  0.079319  0.789366  0.127814  0.835458  0.679486  \n",
      "bclim12  0.716239  0.010688  0.791650  0.370837  0.256055  \n",
      "bclim14  0.371416  0.348696  0.001547  0.350766  0.744075  \n",
      "bclim15  0.818184  0.213067  0.572597  0.071294  0.462004  \n",
      "bclim18  0.443208  0.055514  0.032066  0.093340  0.684926  \n",
      "bclim2   0.004078  0.349495  0.606813  0.045140  0.271740  \n",
      "bclim3   0.746685  0.136988  0.182854  0.125241  0.266772  \n",
      "bclim4   1.000000  0.045182  0.738623  0.262726  0.107488  \n",
      "bclim6   0.000000  1.000000  0.304482  0.677804  0.462065  \n",
      "bclim7   0.000000  0.000000  1.000000  0.196553  0.150691  \n",
      "bclim8   0.000000  0.000000  0.000000  1.000000  0.506171  \n",
      "bclim9   0.000000  0.000000  0.000000  0.000000  1.000000  \n"
     ]
    }
   ],
   "source": [
    "# load csv files for recursion function\n",
    "covariance = pd.read_csv('data_2.0/collinearity/xv-baseline.csv')\n",
    "feature_importance = pd.read_csv('maxent/maxent_19_variables_xv/maxent-jacknife.csv')\n",
    "recursive_ranker(covariance, feature_importance, 0.85,\n",
    "                 pd.read_csv('data_2.0/collinearity/raw_data.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Appendix 2 : Feature Descriptions\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Description for features is as follows :**\n",
    "\n",
    "|Variable|\tDefinition|\tUnits|   \n",
    "|:---  |:--- |:---|\n",
    "|BioClim11| Mean Temp of Coldest Quarter | $^o$C\n",
    "|BioClim12| Annual Precipitation | mm\n",
    "|BioClim14| Precip Driest Month| mm\n",
    "|BioClim15| Precip Seasonality | (coefficient of variation)\n",
    "|BioClim18| Precip Warmest Quarter | mm\n",
    "|BioClim2| Diurnal Range | (Mean of monthly (max temp - min temp))\n",
    "|BioClim3| Isothermality | (BIO2/BIO7) (×100)\n",
    "|BioClim4| Temperature Seasonality | (standard deviation(C) ×100)|\n",
    "|BioClim6| Min Temp Coldest Month| $^o$C\n",
    "|BioClim7| Temp Annual Range| $^o$C\n",
    "|BioClim8| Mean Temp Wettest Quareter | $^o$C\n",
    "|BioClim9| Mean Temp Driest Quareter | $^o$C\n",
    "\n",
    "**Description for the target variable is as follows :**\n",
    "\n",
    "|Variable|\tDefinition|\tUnits|   \n",
    "|:---  |:--- |:---|\n",
    "|Pa| Binary class | 1 (presence) & 0 (background)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Appendix 3 : Additional Model Comparisons\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rf 5-fold Cross Validation Accuracy: 87.07 (+/- 3.11)\n",
      "et 5-fold Cross Validation Accuracy: 87.24 (+/- 4.27)\n",
      "xgb 5-fold Cross Validation Accuracy: 86.02 (+/- 4.34)\n",
      "lgbm 5-fold Cross Validation Accuracy: 86.74 (+/- 2.45)\n",
      "cb 5-fold Cross Validation Accuracy: 86.63 (+/- 3.06)\n"
     ]
    }
   ],
   "source": [
    "# Compare the blended ensemble to five tree-based classifiers\n",
    "CLASS_MAP = {\n",
    "    'rf': (rf),\n",
    "    'et': (etrees),\n",
    "    'xgb': (xgboost),\n",
    "    'lgbm': (lgbm),\n",
    "    'cb': (catboost)\n",
    "    }\n",
    "for name, (model) in CLASS_MAP.items():\n",
    "    # Cross val for accuracy scores (as a %) for the entirety of the presence/background database\n",
    "    k = 5 # k-fold\n",
    "    kf = model_selection.KFold(n_splits=k)\n",
    "    accuracy_scores = model_selection.cross_val_score(model, train_xs, train_y, cv=kf, scoring='accuracy')\n",
    "    print(name + \" %d-fold Cross Validation Accuracy: %0.2f (+/- %0.2f)\"\n",
    "          % (k, accuracy_scores.mean() * 100, accuracy_scores.std() * 200))\n",
    "    # spatial prediction\n",
    "    model.fit(train_xs, train_y)\n",
    "    #os.mkdir('outputs' + session_number + '/' + name + '-baseline')\n",
    "    impute(target_xs, model, raster_info, outdir='outputs' + session_number + '/' + name + '-baseline',\n",
    "           class_prob=True, certainty=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Appendix 4 : Soft Voting Model Prams, MLFlow backend server\n",
    "---\n",
    "\n",
    "We first print the parameters for the three individual models which comprise the blended model, as well as the blended model itself, and then print the backend server log of our experiment. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesClassifier(bootstrap=False, ccp_alpha=0.0, class_weight=None,\n",
       "                     criterion='gini', max_depth=None, max_features='auto',\n",
       "                     max_leaf_nodes=None, max_samples=None,\n",
       "                     min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                     min_samples_leaf=1, min_samples_split=2,\n",
       "                     min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=-1,\n",
       "                     oob_score=False, random_state=110, verbose=0,\n",
       "                     warm_start=False)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "etrees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
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       "                          start_time                         end_time  \\\n",
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       "2   2021-01-09 20:58:15.737000+00:00 2021-01-09 20:58:15.919000+00:00   \n",
       "3   2021-01-09 20:58:15.371000+00:00 2021-01-09 20:58:15.562000+00:00   \n",
       "4   2021-01-09 20:58:15.045000+00:00 2021-01-09 20:58:15.216000+00:00   \n",
       "..                               ...                              ...   \n",
       "250 2020-12-03 00:25:01.763000+00:00 2020-12-03 00:25:02.372000+00:00   \n",
       "251 2020-12-03 00:24:56.671000+00:00 2020-12-03 00:24:57.071000+00:00   \n",
       "252 2020-12-03 00:24:30.165000+00:00 2020-12-03 00:24:30.541000+00:00   \n",
       "253 2020-12-03 00:24:25.778000+00:00 2020-12-03 00:24:26.438000+00:00   \n",
       "254 2020-12-03 00:24:16.903000+00:00 2020-12-03 00:24:17.072000+00:00   \n",
       "\n",
       "     metrics.Recall  metrics.AUC  metrics.MCC  metrics.Accuracy  ...  \\\n",
       "0          0.691600     0.000000     0.429100          0.692000  ...   \n",
       "1          0.764300     0.835300     0.539300          0.765000  ...   \n",
       "2          0.764300     0.000000     0.539300          0.765000  ...   \n",
       "3          0.767500     0.834400     0.543100          0.768000  ...   \n",
       "4          0.772300     0.858700     0.555300          0.772900  ...   \n",
       "..              ...          ...          ...               ...  ...   \n",
       "250        0.855561     0.934166     0.714359          0.855931  ...   \n",
       "251        0.868307     0.930217     0.741000          0.868783  ...   \n",
       "252        0.851637     0.922604     0.706356          0.852019  ...   \n",
       "253        0.865481     0.936462     0.734520          0.865832  ...   \n",
       "254             NaN          NaN          NaN               NaN  ...   \n",
       "\n",
       "     tags.mlflow.user     tags.Source  \\\n",
       "0        danielfurman  compare_models   \n",
       "1        danielfurman  compare_models   \n",
       "2        danielfurman  compare_models   \n",
       "3        danielfurman  compare_models   \n",
       "4        danielfurman  compare_models   \n",
       "..                ...             ...   \n",
       "250      danielfurman    create_model   \n",
       "251      danielfurman    create_model   \n",
       "252      danielfurman    create_model   \n",
       "253      danielfurman    create_model   \n",
       "254      danielfurman           setup   \n",
       "\n",
       "                               tags.mlflow.source.name  \\\n",
       "0    /opt/anaconda3/lib/python3.7/site-packages/ipy...   \n",
       "1    /opt/anaconda3/lib/python3.7/site-packages/ipy...   \n",
       "2    /opt/anaconda3/lib/python3.7/site-packages/ipy...   \n",
       "3    /opt/anaconda3/lib/python3.7/site-packages/ipy...   \n",
       "4    /opt/anaconda3/lib/python3.7/site-packages/ipy...   \n",
       "..                                                 ...   \n",
       "250  /opt/anaconda3/lib/python3.7/site-packages/ipy...   \n",
       "251  /opt/anaconda3/lib/python3.7/site-packages/ipy...   \n",
       "252  /opt/anaconda3/lib/python3.7/site-packages/ipy...   \n",
       "253  /opt/anaconda3/lib/python3.7/site-packages/ipy...   \n",
       "254  /opt/anaconda3/lib/python3.7/site-packages/ipy...   \n",
       "\n",
       "                          tags.Run ID tags.USI tags.Run Time  \\\n",
       "0    1e32e22c09d74a4a868e11f94671f72b     c85d          0.32   \n",
       "1    00d4f462cada45b59de03cd9e33c5690     c85d           0.3   \n",
       "2    88012d50be834c27a03319582dfc9ed9     c85d          0.29   \n",
       "3    d7572757f281482c87315392aa129435     c85d           1.5   \n",
       "4    9b97d1250c3f455e9488197f04abfc5a     c85d           0.3   \n",
       "..                                ...      ...           ...   \n",
       "250  630108adb70d4b60a7e5e96b8efcf2f7     6e98          4.17   \n",
       "251  1073e6d61a704cce96060cb96d0ee24f     6e98         25.74   \n",
       "252  91a7b8d5c3104467a51c9025e52004c0     6e98          3.28   \n",
       "253  5103f5471a324d72b9124124fbc69c3b     6e98          7.43   \n",
       "254  5c8c73cea9c64a66a4ed322f503b7310     6e98          1.66   \n",
       "\n",
       "              tags.mlflow.runName tags.mlflow.source.type  tags.URI  \\\n",
       "0             SVM - Linear Kernel                   LOCAL  1997d64f   \n",
       "1    Linear Discriminant Analysis                   LOCAL  1997d64f   \n",
       "2                Ridge Classifier                   LOCAL  1997d64f   \n",
       "3             Logistic Regression                   LOCAL  1997d64f   \n",
       "4                     Naive Bayes                   LOCAL  1997d64f   \n",
       "..                            ...                     ...       ...   \n",
       "250      Random Forest Classifier                   LOCAL  9aa09ea5   \n",
       "251           CatBoost Classifier                   LOCAL  3f028743   \n",
       "252     Extreme Gradient Boosting                   LOCAL  16a59a86   \n",
       "253        Extra Trees Classifier                   LOCAL  03a50304   \n",
       "254      Session Initialized 6e98                   LOCAL  66333467   \n",
       "\n",
       "                         tags.mlflow.log-model.history  \n",
       "0    [{\"run_id\": \"1e32e22c09d74a4a868e11f94671f72b\"...  \n",
       "1    [{\"run_id\": \"00d4f462cada45b59de03cd9e33c5690\"...  \n",
       "2    [{\"run_id\": \"88012d50be834c27a03319582dfc9ed9\"...  \n",
       "3    [{\"run_id\": \"d7572757f281482c87315392aa129435\"...  \n",
       "4    [{\"run_id\": \"9b97d1250c3f455e9488197f04abfc5a\"...  \n",
       "..                                                 ...  \n",
       "250  [{\"run_id\": \"630108adb70d4b60a7e5e96b8efcf2f7\"...  \n",
       "251  [{\"run_id\": \"1073e6d61a704cce96060cb96d0ee24f\"...  \n",
       "252  [{\"run_id\": \"91a7b8d5c3104467a51c9025e52004c0\"...  \n",
       "253  [{\"run_id\": \"5103f5471a324d72b9124124fbc69c3b\"...  \n",
       "254                                               None  \n",
       "\n",
       "[255 rows x 294 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pycaret.classification import get_logs\n",
    "get_logs(experiment_name = 'xv-21')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Appendix 5 : Citations\n",
    "---\n",
    "\n",
    "1. <a target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://cran.r-project.org/web/packages/dismo/vignettes/sdm.pdf\">Species distribution modeling with R</a> (Hijmans and Elith, 2017)\n",
    "2. Pyimpute's <a target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/perrygeo/pyimpute/blob/master/README.md\">README.md</a>\n",
    "3. A study <a target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://www.researchgate.net/publication/229149956_Selecting_Pseudo-Absences_for_Species_Distribution_Models_How_Where_and_How_Many\">on generating pseudo absence points</a>  (Barbet-Massin et al., 2012)\n",
    "4. A study <a target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://www.nature.com/articles/s41598-018-25437-1\n",
    "\">on SDM transferability and pixel size</a>  (Manzoor et al., 2018)\n",
    "5.  A study <a target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://onlinelibrary.wiley.com/doi/full/10.1111/ddi.13161\">on SDMs for invasive species</a>  (Lake et al., 2020)\n",
    "6. A book <a target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://www.amazon.com/Mapping-Species-Distributions-Biodiversity-Conservation/dp/0521700027\n",
    "\">on mapping SDMs </a>  (Franklin, 2009)\n",
    "7. A more modern <a target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://damariszurell.github.io/SDM-Intro/\"> SDMs tutorial</a> (Zurell, 2020) \n",
    "8. A study <a target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://onlinelibrary.wiley.com/doi/full/10.1111/j.1600-0587.2012.07348.x\">on collinearity among model variables</a> (C. F. Dormann et al., 2012) \n",
    "\n",
    "### Data Citations\n",
    "---\n",
    "\n",
    "1. GBIF.org (01 November 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.ceutzd \n",
    "2. Fick, S.E. and R.J. Hijmans, 2017. WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37 (12): 4302-4315.\n",
    "\n"
   ]
  }
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