{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Open Ocean\n", "# Open Earth Fundation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Step 2: calculate different metrics for each modulating factor

\n", "\n", "This notebook is the second part of the `Step1_Curate_IUCN_RedList.ipynb`\n", "\n", "

Modulating Factor 1: Normalize Biodiversity Score

\n", "\n", "Species diversity refers to the variety of different species present in a given area, as well as their abundance and distribution. This includes the number of species, their relative abundances, and how evenly or unevenly distributed they are.\n", "Our proposal is: apply the Simpson and Shannon Index to obtain a local value of the MPA and normalize each sqd km value" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Data needed for this project\n", "\n", "- Species names\n", "- Species abundance\n", "- Species distribution\n", "\n", "Next Steps:\n", "\n", "1. Find a database or datasets with abundance and distribution information for the entire ACMC\n", "2. If it isn't reallistic, try to simulate that data\n", "\n", "Options:\n", "1. IUCN RED List and simulate abundance information\n", "2. GBIF species information and simulate abundance and distribution information" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. Importing libraries" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# load basic libraries\n", "import os\n", "import glob\n", "import boto3\n", "\n", "import math\n", "import random\n", "import numpy as np\n", "import pandas as pd\n", "\n", "# to plot\n", "import matplotlib.pyplot as plt\n", "\n", "# to manage shapefiles\n", "import shapely\n", "import geopandas as gpd\n", "from shapely.geometry import Polygon, Point, box\n", "from shapely.ops import linemerge, unary_union, polygonize" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import fiona; #help(fiona.open)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Import OEF functions**" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "%load_ext autoreload" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "#Run this to reload the python file\n", "%autoreload 2\n", "from MBU_utils import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Load data" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "ACMC = gpd.read_file('https://ocean-program.s3.amazonaws.com/data/raw/MPAs/ACMC.geojson')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "df = gpd.read_file('https://ocean-program.s3.amazonaws.com/data/processed/ACMC_IUCN_RedList/gdf_ACMC_IUCN_range_status_filtered.shp')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "#Locally\n", "df = gpd.read_file('/Users/maureenfonseca/Desktop/Data-Oceans/ACMC_IUCN_data/gdf_ACMC_IUCN_range_status_filtered.shp')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "grid = create_grid(ACMC, grid_shape=\"hexagon\", grid_size_deg=0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3. Preliminary calculations\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Shannon Index**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$\\text{H} = -\\sum[{p_i}\\times\\ln(p_i)]$\n", "\n", "\n", "where, pi is the proportion of the entire community made up of species i\n", "\n", "$p_i = {n/N}$" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def shannon(gdf_abundance_col):\n", " \"\"\"\n", " Calculates the value of H using the given values of abundance.\n", " \n", " Parameters:\n", " - gdf_abundance_col (list): A list of species of the entire community made up of each species\n", " \n", " Returns:\n", " - H (float): The calculated value of H\n", " \"\"\"\n", " \n", " abundance = np.array(gdf_abundance_col)\n", " N = np.sum(abundance)\n", " \n", " p = (abundance/N)\n", " \n", " H = 0\n", " for pi in p:\n", " if pi > 0:\n", " H += pi * math.log(pi)\n", " H = -H\n", " return H" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fake_abundance = np.random.randint(50, size = (len(df)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df['abundance'] = fake_abundance" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def shannon(roi, gdf, grid_gdf, gdf_col_name):\n", " \"\"\"\n", " This function calculates the Shannon Index per grid cell and its corresponding MBU value\n", " pi = (n/N): where n is the abundance number per species and N is the total abundance number in the dataset \n", " \n", " input(s):\n", " roi : region of interest or the total project area\n", " gdf : contains at least the name of the species, the distribution polygons of each of them \n", " :and their abundance\n", " grid_gdf : consists of polygons of grids typically generated by the gridding function\n", " : containts at least a geometry column and a unique grid_id\n", " gdf_col_name : corresponds to the name of the abundance information column in the gdf\n", " \n", " output(s):\n", " gdf : with an additional column ('mbu_habitat_survey') containing the number\n", " : of units for that grid or geometry\n", " \"\"\"\n", " \n", " #Join in a gdf all the geometries within ROI\n", " gdf = gpd.clip(gdf.set_crs(epsg=4326, allow_override=True), roi)\n", "\n", " #This function calculates the sum of all abundances of overlapping species\n", " overlap = map_algebra(gdf, gdf_col_name, 'sum')\n", "\n", " #Merged the overlap values of overlapping geometries with the grid gdf\n", " merged = gpd.sjoin(overlap, grid_gdf, how='left')\n", " merged['n_value'] = overlap['algebra_overlaps']\n", " \n", " #Calculate the pi value per row\n", " pi = merged['n_value']/np.sum(merged['n_value'])\n", " pi = pi.fillna(0)\n", " merged['pilogpi'] = pi*np.log(pi)\n", "\n", " #Dissolve the DataFrame by 'index_right' and aggregate using the calculated Shannon entropy\n", " dissolve = merged.dissolve(by=\"index_right\", aggfunc={'pilogpi': 'sum'})\n", " \n", " #Calculate the Shannon index per grid\n", " dissolve['pilogpi'] = (-1)*dissolve['pilogpi']\n", "\n", " #Put this into cell\n", " grid_gdf.loc[dissolve.index, 'Shannon'] = dissolve.pilogpi.values\n", "\n", " #Normalization factor\n", " Norm_factor = grid_gdf['Shannon']/grid_gdf['Shannon'].max()\n", " \n", " return grid_gdf" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Shannon = shannon(ACMC, df, grid, 'abundance')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig, ax = plt.subplots(figsize = (8,8))\n", "\n", "ax.set_aspect('equal')\n", "Shannon.plot(ax = ax, column = 'Shannon', cmap = 'viridis', edgecolor = 'grey', linewidth = 0.01)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**OBIS data**" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "from pyobis import occurrences" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "#create a polygon to access the OBIS data\n", "min_x, min_y, max_x, max_y = ACMC.total_bounds\n", "geometry = f\"POLYGON(({max_x} {min_y}, {min_x} {min_y}, {min_x} {max_y}, {max_x} {max_y}, {max_x} {min_y}))\"" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fetching: [████████████████████████████████████████████████████████████████████████████████████████████████████] 4216/4216\n", "Fetched 4216 records.\n" ] }, { "data": { "text/html": [ "
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2KUGnathostomataCosta Rica1954.0urn:lsid:marinespecies.org:taxname:125636GymnothoraxNaNFalse10194.0125636...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3NaNGnathostomataNaNNaNurn:lsid:marinespecies.org:taxname:105857Mobula birostrisNaNFalseNaN1026118...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
4CASGnathostomataPanama1956.0urn:lsid:marinespecies.org:taxname:276587Polydactylus approximans2False10194.0276587...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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4211NaNGnathostomataCosta Rica1975.0urn:lsid:marinespecies.org:taxname:276587Polydactylus approximansNaNFalse10194.0276587...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
4212NaNGnathostomataNaNNaNurn:lsid:marinespecies.org:taxname:127401Diodon eydouxiiNaNFalse10194.0127401...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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NaN \n", "\n", " subsectionid minimumElevationInMeters maximumElevationInMeters hab \\\n", "0 NaN NaN NaN NaN \n", "1 NaN NaN NaN NaN \n", "2 NaN NaN NaN NaN \n", "3 NaN NaN NaN NaN \n", "4 NaN NaN NaN NaN \n", "... ... ... ... ... \n", "4211 NaN NaN NaN NaN \n", "4212 NaN NaN NaN NaN \n", "4213 NaN NaN NaN NaN \n", "4214 NaN NaN NaN NaN \n", "4215 NaN NaN NaN NaN \n", "\n", " varietyid variety parvorder parvorderid associatedMedia \n", "0 NaN NaN NaN NaN NaN \n", "1 NaN NaN NaN NaN NaN \n", "2 NaN NaN NaN NaN NaN \n", "3 NaN NaN NaN NaN NaN \n", "4 NaN NaN NaN NaN NaN \n", "... ... ... ... ... ... \n", "4211 NaN NaN NaN NaN NaN \n", "4212 NaN NaN NaN NaN NaN \n", "4213 NaN NaN NaN NaN NaN \n", "4214 NaN NaN NaN NaN NaN \n", "4215 NaN NaN NaN NaN NaN \n", "\n", "[4216 rows x 190 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "query = occurrences.search(geometry=geometry)\n", "query.execute()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# Returns the data\n", "obis = query.data " ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/maureenfonseca/opt/anaconda3/lib/python3.9/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n" ] } ], "source": [ "# convert OBIS dataframe to geodataframe\n", "obis = gpd.GeoDataFrame(obis, \n", " geometry=gpd.points_from_xy(obis.decimalLongitude, \n", " obis.decimalLatitude))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fake_abundance = [random.randint(1, 100) for _ in range(len(obis))]\n", "obis['abundance'] = fake_abundance" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot protected area with marine species\n", "# select column name to plot\n", "column_name = 'scientificName'\n", "ax = ACMC.boundary.plot()\n", "obis.plot(ax=ax, markersize=5, column=column_name, \n", " antialiased=False, legend_kwds={'bbox_to_anchor': (1, 1)})" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "#Join in a gdf all the geometries within ACMC\n", "df2 = gpd.clip(obis.set_crs(epsg=4326, allow_override=True), ACMC)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "#Spatial join of gdf and grid_gdf\n", "pointInPolys = sjoin(df2, grid, how='inner')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "'individualCount' refers to the number of individual organisms observed or sampled for a particular species at a particular location and time." ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [], "source": [ "pointInPolys = pointInPolys.dropna(subset='individualCount')\n", "pointInPolys['individualCount'] = pointInPolys['individualCount'].astype(float).astype(int)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "N = pd.DataFrame()\n", "N['N'] = pointInPolys.groupby('Grid_ID').apply(lambda x: x['individualCount'].sum())" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [], "source": [ "new = pd.merge(pointInPolys, N, on='Grid_ID')" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/maureenfonseca/opt/anaconda3/lib/python3.9/site-packages/pandas/core/arraylike.py:397: RuntimeWarning: divide by zero encountered in log\n", " result = getattr(ufunc, method)(*inputs, **kwargs)\n" ] } ], "source": [ "new['pi'] = new['individualCount']/new['N']\n", "new['shannon'] = (-1)*new['pi']*np.log(new['pi'])" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "new = new.dissolve(by='Grid_ID', aggfunc={'shannon': 'sum'})" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [], "source": [ "new = new.drop(['geometry'], axis = 1)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [], "source": [ "merge = new.merge(grid, how='right', on='Grid_ID')" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [], "source": [ "merge = gpd.GeoDataFrame(merge)" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [], "source": [ "def test(roi, gdf, grid_gdf):\n", " #Join in a gdf all the geometries within ACMC\n", " obis = gpd.clip(gdf.set_crs(epsg=4326, allow_override=True), roi)\n", " \n", " #Spatial join of gdf and grid_gdf\n", " pointInPolys = sjoin(obis, grid_gdf, how='inner')\n", " \n", " # 'individualCount' refers to the number of individual organisms observed or sampled \n", " # for a particular species at a particular location and time.\n", " pointInPolys = pointInPolys.dropna(subset='individualCount')\n", " pointInPolys['individualCount'] = pointInPolys['individualCount'].astype(float).astype(int)\n", " \n", " #To calculate the total number of species\n", " N = pd.DataFrame()\n", " N['N'] = pointInPolys.groupby('Grid_ID').apply(lambda x: x['individualCount'].sum())\n", " \n", " new = pd.merge(pointInPolys, N, on='Grid_ID')\n", " \n", " #Calculate the Shanoon index with the information available\n", " new['pi'] = new['individualCount']/new['N']\n", " new['shannon'] = (-1)*new['pi']*np.log(new['pi'])\n", " \n", " new = new.dissolve(by='Grid_ID', aggfunc={'shannon': 'sum'})\n", " \n", " new = new.drop(['geometry'], axis = 1)\n", " \n", " merge = new.merge(grid, how='right', on='Grid_ID')\n", " \n", " grid_gdf = gpd.GeoDataFrame(merge)\n", " \n", " return grid_gdf" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(-90.02200586479142,\n", " -85.62200586479142,\n", " 1.4361630298765322,\n", " 7.6282446669352675)" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = df.plot(column='shannon', figsize=(4, 4), cmap='viridis', legend = True)\n", "\n", "gpd.GeoSeries(ACMC.geometry).plot(ax=ax, edgecolor='black', facecolor='none', label='ACMC')\n", "ax.axis('off')" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/maureenfonseca/opt/anaconda3/lib/python3.9/site-packages/pandas/core/arraylike.py:397: RuntimeWarning: divide by zero encountered in log\n", " result = getattr(ufunc, method)(*inputs, **kwargs)\n" ] }, { "data": { "text/html": [ "
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4444NaNPOLYGON ((-85.07201 7.77980, -85.32201 8.21281...
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" ], "text/plain": [ " Grid_ID shannon geometry\n", "0 0 NaN POLYGON ((-88.82201 2.15063, -89.07201 2.58365...\n", "1 1 0.660915 POLYGON ((-88.82201 3.01666, -89.07201 3.44967...\n", "2 2 0.173205 POLYGON ((-88.82201 3.88268, -89.07201 4.31570...\n", "3 3 0.684616 POLYGON ((-88.82201 4.74871, -89.07201 5.18172...\n", "4 4 1.247920 POLYGON ((-88.82201 5.61474, -89.07201 6.04775...\n", "5 5 0.693147 POLYGON ((-88.82201 6.48076, -89.07201 6.91377...\n", "6 6 NaN POLYGON ((-88.82201 7.34679, -89.07201 7.77980...\n", "7 7 NaN POLYGON ((-88.07201 1.71762, -88.32201 2.15063...\n", "8 8 NaN POLYGON ((-88.07201 2.58365, -88.32201 3.01666...\n", "9 9 1.184691 POLYGON ((-88.07201 3.44967, -88.32201 3.88268...\n", "10 10 1.580088 POLYGON ((-88.07201 4.31570, -88.32201 4.74871...\n", "11 11 1.597597 POLYGON ((-88.07201 5.18172, -88.32201 5.61474...\n", "12 12 1.561628 POLYGON ((-88.07201 6.04775, -88.32201 6.48076...\n", "13 13 0.498367 POLYGON ((-88.07201 6.91377, -88.32201 7.34679...\n", "14 14 NaN POLYGON ((-88.07201 7.77980, -88.32201 8.21281...\n", "15 15 NaN POLYGON ((-87.32201 2.15063, -87.57201 2.58365...\n", "16 16 1.950728 POLYGON ((-87.32201 3.01666, -87.57201 3.44967...\n", "17 17 1.619029 POLYGON ((-87.32201 3.88268, -87.57201 4.31570...\n", "18 18 0.963099 POLYGON ((-87.32201 4.74871, -87.57201 5.18172...\n", "19 19 3.595494 POLYGON ((-87.32201 5.61474, -87.57201 6.04775...\n", "20 20 2.015293 POLYGON ((-87.32201 6.48076, -87.57201 6.91377...\n", "21 21 NaN POLYGON ((-87.32201 7.34679, -87.57201 7.77980...\n", "22 22 NaN POLYGON ((-86.57201 1.71762, -86.82201 2.15063...\n", "23 23 1.766947 POLYGON ((-86.57201 2.58365, -86.82201 3.01666...\n", "24 24 2.170191 POLYGON ((-86.57201 3.44967, -86.82201 3.88268...\n", "25 25 1.768924 POLYGON ((-86.57201 4.31570, -86.82201 4.74871...\n", "26 26 5.516342 POLYGON ((-86.57201 5.18172, -86.82201 5.61474...\n", "27 27 3.737960 POLYGON ((-86.57201 6.04775, -86.82201 6.48076...\n", "28 28 NaN POLYGON ((-86.57201 6.91377, -86.82201 7.34679...\n", "29 29 NaN POLYGON ((-86.57201 7.77980, -86.82201 8.21281...\n", "30 30 0.693949 POLYGON ((-85.82201 2.15063, -86.07201 2.58365...\n", "31 31 1.766390 POLYGON ((-85.82201 3.01666, -86.07201 3.44967...\n", "32 32 3.204350 POLYGON ((-85.82201 3.88268, -86.07201 4.31570...\n", "33 33 1.994854 POLYGON ((-85.82201 4.74871, -86.07201 5.18172...\n", "34 34 2.964282 POLYGON ((-85.82201 5.61474, -86.07201 6.04775...\n", "35 35 0.961999 POLYGON ((-85.82201 6.48076, -86.07201 6.91377...\n", "36 36 NaN POLYGON ((-85.82201 7.34679, -86.07201 7.77980...\n", "37 37 NaN POLYGON ((-85.07201 1.71762, -85.32201 2.15063...\n", "38 38 NaN POLYGON ((-85.07201 2.58365, -85.32201 3.01666...\n", "39 39 NaN POLYGON ((-85.07201 3.44967, -85.32201 3.88268...\n", "40 40 NaN POLYGON ((-85.07201 4.31570, -85.32201 4.74871...\n", "41 41 NaN POLYGON ((-85.07201 5.18172, -85.32201 5.61474...\n", "42 42 NaN POLYGON ((-85.07201 6.04775, -85.32201 6.48076...\n", "43 43 NaN POLYGON ((-85.07201 6.91377, -85.32201 7.34679...\n", "44 44 NaN POLYGON ((-85.07201 7.77980, -85.32201 8.21281..." ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test(ACMC, obis, grid)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.13" } }, "nbformat": 4, "nbformat_minor": 4 }