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"cells": [
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"cell_type": "code",
"execution_count": null,
"id": "37fbaefa",
"metadata": {},
"outputs": [],
"source": [
"# !pip install pandas plotly requests\n",
"import zipfile\n",
"from pathlib import Path\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import plotly.express as px\n",
"import requests\n",
"\n",
"\n",
"DATA_DIR = Path(\"./data\")\n",
"DATA_DIR.mkdir(parents=True, exist_ok=True)\n",
"OUTPUT_DIR = Path(\"./output\")\n",
"OUTPUT_DIR.mkdir(parents=True, exist_ok=True)"
]
},
{
"cell_type": "markdown",
"id": "86adff3d",
"metadata": {},
"source": [
"### Más información:\n",
"- [6.1 Rate of invasive alien species establishment](https://drive.google.com/drive/u/0/folders/1jOzMGO5YSKRVAB6K8JyEa1l6i7CaA6vm)\n",
"- [datos](https://drive.google.com/drive/u/0/folders/1vbfu_PNFdEdclxO-DbocAz19SrpIS-kB)"
]
},
{
"cell_type": "markdown",
"id": "cc78b2de",
"metadata": {},
"source": [
"## Descarga data desde GBIF"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "eadf1e5c",
"metadata": {},
"outputs": [],
"source": [
"def download_data_from_gbif(url: str, output_dir: Path) -> Path:\n",
" file_name = url.split(\"?r=\")[-1] + \".zip\"\n",
" response = requests.get(url, stream=True)\n",
" with open(output_dir / file_name, \"wb\") as f:\n",
" for chunk in response.iter_content(chunk_size=1024):\n",
" f.write(chunk)\n",
"\n",
" return output_dir / file_name\n",
"\n",
"\n",
"def unzip_file(zip_file_path: Path, extract_folder: Path):\n",
" with zipfile.ZipFile(zip_file_path, \"r\") as zip_ref:\n",
" zip_ref.extractall(extract_folder)\n",
"\n",
"\n",
"urls = [\n",
" \"https://cloud.gbif.org/griis/archive.do?r=juan-fernandez-islands-chile-griis\",\n",
" \"https://cloud.gbif.org/griis/archive.do?r=chile-griis-gbif\",\n",
" \"https://cloud.gbif.org/griis/archive.do?r=rapa-nui-chile-griis-gbif\",\n",
"]\n",
"\n",
"for url in urls:\n",
" file_name = download_data_from_gbif(url, DATA_DIR)\n",
" unzip_file(zip_file_path=file_name, extract_folder=DATA_DIR / str(file_name.stem))"
]
},
{
"cell_type": "markdown",
"id": "ab281c2b",
"metadata": {},
"source": [
"## Descarga data desde Zenodo"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "594cf181",
"metadata": {},
"outputs": [],
"source": [
"def download_zenodo_dataset(url: str, output_dir: Path, file_name: str):\n",
" response = requests.get(url, stream=True)\n",
" with open(output_dir / file_name, \"wb\") as f:\n",
" for chunk in response.iter_content(chunk_size=1024):\n",
" f.write(chunk)\n",
"\n",
"\n",
"url = \"https://zenodo.org/records/10039630/files/GlobalAlienSpeciesFirstRecordDatabase_v3.1_freedata.xlsx?download=1\"\n",
"download_zenodo_dataset(\n",
" url=url,\n",
" output_dir=DATA_DIR,\n",
" file_name=\"GlobalAlienSpeciesFirstRecordDatabase_v3.1_freedata.xlsx\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "1a7233de",
"metadata": {},
"outputs": [],
"source": [
"df_alien_species = pd.read_excel(\n",
" DATA_DIR / \"GlobalAlienSpeciesFirstRecordDatabase_v3.1_freedata.xlsx\",\n",
" sheet_name=\"FirstRecords\",\n",
" dtype={\n",
" \"TaxonName\": \"category\",\n",
" \"OrigName\": \"category\",\n",
" \"scientificName\": \"category\",\n",
" \"FirstRecord_orig\": str,\n",
" \"Family\": \"category\",\n",
" \"Order\": \"category\",\n",
" },\n",
")\n",
"\n",
"df_alien_species_chile = (\n",
" df_alien_species[df_alien_species[\"Region\"] == \"Chile\"]\n",
" .reset_index(drop=True)\n",
" .copy()\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "e4e0efdd",
"metadata": {},
"outputs": [
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"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
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"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
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}
},
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},
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},
"bgcolor": "#E5ECF6",
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}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
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},
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"automargin": true,
"gridcolor": "white",
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"ticks": "",
"title": {
"standoff": 15
},
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}
}
},
"title": {
"text": "Primer año de registro de especies invasoras entre 1500 y 2019"
},
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"anchor": "y",
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0,
1
],
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}
},
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0,
1
],
"title": {
"text": "count"
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}
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}
},
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],
"source": [
"# df_alien_species_chile[df_alien_species_chile[\"FirstRecord\"] >= 1970].hist()\n",
"fig = px.histogram(\n",
" df_alien_species_chile,\n",
" x=\"FirstRecord\",\n",
" title=f\"Primer año de registro de especies invasoras entre {df_alien_species_chile['FirstRecord'].min()} y {df_alien_species_chile['FirstRecord'].max()}\",\n",
")\n",
"fig.show()"
]
},
{
"cell_type": "code",
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"id": "48668aa1",
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"showlakes": true,
"showland": true,
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},
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},
"title": {
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"xaxis": {
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"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
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},
"yaxis": {
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"ticks": "",
"title": {
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}
},
"title": {
"text": "Primer año de registro de especies invasoras entre 1970 y 2019"
},
"xaxis": {
"anchor": "y",
"domain": [
0,
1
],
"title": {
"text": "FirstRecord"
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},
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0,
1
],
"title": {
"text": "count"
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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = px.histogram(\n",
" df_alien_species_chile[df_alien_species_chile[\"FirstRecord\"] >= 1970],\n",
" x=\"FirstRecord\",\n",
" title=f\"Primer año de registro de especies invasoras entre {df_alien_species_chile[df_alien_species_chile['FirstRecord'] >= 1970]['FirstRecord'].min()} y {df_alien_species_chile[df_alien_species_chile['FirstRecord'] >= 1970]['FirstRecord'].max()}\",\n",
" nbins=50,\n",
")\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"id": "1f6839c2",
"metadata": {},
"source": [
"# Alien Species"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "4129f21a",
"metadata": {},
"outputs": [],
"source": [
"df_alien_species_chile[\"FirstRecordDate\"] = (\n",
" df_alien_species_chile[\"FirstRecord\"]\n",
" .apply(lambda x: str(x) + \"-01-01\")\n",
" .apply(np.datetime64)\n",
")"
]
},
{
"cell_type": "markdown",
"id": "68be9a3d",
"metadata": {},
"source": [
"# Data Chile"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "48865231",
"metadata": {},
"outputs": [],
"source": [
"def get_data(dir: Path | str) -> pd.DataFrame:\n",
" if isinstance(dir, str):\n",
" dir = Path(dir)\n",
"\n",
" df_distribution = pd.read_csv(dir / \"distribution.txt\", sep=\"\\t\")\n",
" df_distribution.set_index(\"id\", inplace=True)\n",
"\n",
" df_taxon = pd.read_csv(dir / \"taxon.txt\", sep=\"\\t\")\n",
" df_taxon.set_index(\"id\", inplace=True)\n",
"\n",
" df_species_profile = pd.read_csv(dir / \"speciesprofile.txt\", sep=\"\\t\")\n",
" df_species_profile.set_index(\"id\", inplace=True)\n",
"\n",
" return pd.concat([df_taxon, df_distribution, df_species_profile], axis=1)\n",
"\n",
"\n",
"df_chile_griis = get_data(DATA_DIR / \"chile-griis-gbif\")\n",
"df_chile_griis[\"source\"] = \"chile-griis-gbif\"\n",
"\n",
"df_chile_juan_fernandez = get_data(DATA_DIR / \"juan-fernandez-islands-chile-griis\")\n",
"df_chile_juan_fernandez[\"source\"] = \"juan-fernandez-islands-chile-griis\"\n",
"\n",
"df_chile_rapa_nui = get_data(DATA_DIR / \"rapa-nui-chile-griis-gbif\")\n",
"df_chile_rapa_nui[\"source\"] = \"rapa-nui-chile-griis-gbif\""
]
},
{
"cell_type": "markdown",
"id": "5770e47a",
"metadata": {},
"source": [
"# Cálculo tasa especies invasoras"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "a41ab3af",
"metadata": {},
"outputs": [],
"source": [
"df_alien_species_chile[\"TaxonNameId\"] = df_alien_species_chile[\"TaxonName\"].str.lower()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "e8aa259f",
"metadata": {},
"outputs": [],
"source": [
"df_griis = pd.concat([df_chile_griis, df_chile_juan_fernandez, df_chile_rapa_nui])\n",
"df_griis[\"id_name\"]: str = None\n",
"for i, r in df_griis.iterrows():\n",
" if isinstance(r[\"acceptedNameUsage\"], str):\n",
" # print(f'{\" \".join(r[\"acceptedNameUsage\"].split(\" \")[:2])}')\n",
" df_griis.at[i, \"id_name\"] = (\n",
" f'{\" \".join(r[\"acceptedNameUsage\"].split(\" \")[:2]).lower()}'\n",
" )\n",
" else:\n",
" # print(f'{\" \".join(r[\"scientificName\"].split(\" \")[:2])}')\n",
" df_griis.at[i, \"id_name\"] = (\n",
" f'{\" \".join(r[\"scientificName\"].split(\" \")[:2]).lower()}'\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "da9f1c6c",
"metadata": {},
"outputs": [],
"source": [
"df_6_1_especies_invasoras = pd.merge(\n",
" left=df_alien_species_chile[\n",
" [\"TaxonNameId\", \"TaxonName\", \"FirstRecordDate\", \"FirstRecord\"]\n",
" ],\n",
" right=df_griis[[\"id_name\", \"isInvasive\"]],\n",
" how=\"outer\",\n",
" left_on=\"TaxonNameId\",\n",
" right_on=\"id_name\",\n",
").reset_index(drop=True)\n",
"\n",
"df_6_1_especies_invasoras.drop_duplicates(inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "fd6fac27",
"metadata": {},
"outputs": [],
"source": [
"df_6_1_especies_invasoras.reset_index(drop=True, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "2e0e9a6c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Solo GRIIS: 301 | Joined: 684 | Solo introducidas: 365\n"
]
}
],
"source": [
"griis = 0\n",
"joined = 0\n",
"zenodo = 0\n",
"for i, r in df_6_1_especies_invasoras.iterrows():\n",
" if r[\"TaxonNameId\"] == r[\"id_name\"]:\n",
" joined += 1\n",
" elif isinstance(r[\"TaxonNameId\"], float) and r[\"id_name\"]:\n",
" griis += 1\n",
" elif r[\"TaxonNameId\"] and isinstance(r[\"id_name\"], float):\n",
" zenodo += 1\n",
"\n",
"print(f\"Solo GRIIS: {griis} | Joined: {joined} | Solo introducidas: {zenodo}\")"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "237b3d41",
"metadata": {},
"outputs": [],
"source": [
"df_6_1_especies_invasoras[\"isInvasiveNum\"] = df_6_1_especies_invasoras[\n",
" \"isInvasive\"\n",
"].apply(lambda x: 1 if x == \"invasive\" else 0)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "57eb4e1a",
"metadata": {},
"outputs": [],
"source": [
"N_INVASIVE_SPECIES = df_6_1_especies_invasoras.isInvasiveNum.sum()"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "97dc4f90",
"metadata": {},
"outputs": [],
"source": [
"df_6_1_especies_invasoras_v2 = (\n",
" df_6_1_especies_invasoras[df_6_1_especies_invasoras[\"FirstRecord\"] >= 1970]\n",
" .groupby(\"FirstRecord\")\n",
" .agg({\"TaxonName\": \"count\", \"isInvasiveNum\": \"sum\"})\n",
" .reset_index()\n",
")\n",
"df_6_1_especies_invasoras_v2[\"rate\"] = (\n",
" df_6_1_especies_invasoras_v2[\"isInvasiveNum\"]\n",
" / df_6_1_especies_invasoras_v2[\"TaxonName\"]\n",
" * 100\n",
")\n",
"\n",
"df_6_1_especies_invasoras_v2[\"rate2\"] = (\n",
" df_6_1_especies_invasoras_v2[\"isInvasiveNum\"] / N_INVASIVE_SPECIES * 100\n",
")\n",
"df_6_1_especies_invasoras_v2[\"rate2\"] = df_6_1_especies_invasoras_v2[\"rate2\"].round(2)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "abad035e",
"metadata": {},
"outputs": [],
"source": [
"y_data = (\n",
" df_6_1_especies_invasoras_v2[[\"TaxonName\", \"isInvasiveNum\"]]\n",
" .groupby(df_6_1_especies_invasoras_v2.index // 5)\n",
" .sum()\n",
")\n",
"y_data[\"rate\"] = y_data[\"isInvasiveNum\"] / y_data[\"TaxonName\"] * 100\n",
"y_data[\"rate\"] = y_data[\"rate\"].round(2)\n",
"y_data[\"rate2\"] = y_data[\"isInvasiveNum\"] / N_INVASIVE_SPECIES * 100\n",
"y_data[\"rate2\"] = y_data[\"rate2\"].round(2)\n",
"\n",
"x_labels = (\n",
" df_6_1_especies_invasoras_v2[\"FirstRecord\"]\n",
" .groupby(df_6_1_especies_invasoras_v2.index // 5)\n",
" .first()\n",
")\n",
"df_6_1_especies_invasoras_v3 = y_data.join(x_labels)\n",
"# df_6_1_especies_invasoras_v2.rolling(5).sum()"
]
},
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