From a82fe2fc00f2d802ec442be6131ffff261103227 Mon Sep 17 00:00:00 2001
From: Laura Brenskelle <160157051+laurabrenskelle@users.noreply.github.com>
Date: Wed, 18 Sep 2024 14:57:13 -0400
Subject: [PATCH] Updating OBIS EOV file
---
.../2024-09-13-OBIS_EOVs.ipynb | 218 +++++++-----------
1 file changed, 78 insertions(+), 140 deletions(-)
diff --git a/jupyterbook/content/code_gallery/data_analysis_and_visualization_notebooks/2024-09-13-OBIS_EOVs.ipynb b/jupyterbook/content/code_gallery/data_analysis_and_visualization_notebooks/2024-09-13-OBIS_EOVs.ipynb
index 975f28d4..d42ae8e5 100644
--- a/jupyterbook/content/code_gallery/data_analysis_and_visualization_notebooks/2024-09-13-OBIS_EOVs.ipynb
+++ b/jupyterbook/content/code_gallery/data_analysis_and_visualization_notebooks/2024-09-13-OBIS_EOVs.ipynb
@@ -7,38 +7,22 @@
"source": [
"# Using aphiaIDs to download OBIS occurrences for Bio/Eco EOVs\n",
"\n",
- "Created: 2024-09-13"
+ "Created: 2024-09-13\n",
+ "\n",
+ "The [IOOS Marine Life Data Network](https://ioos.github.io/marine_life_data_network/) has developed a list of aphiaIDs for taxa mentioned in the [GOOS Biology and Ecosystems Variables](https://goosocean.org/what-we-do/framework/essential-ocean-variables/). These lists are meant to assist in querying OBIS to evaluate the abundance and distribution of these taxa. Here is an R notebook of how this might work using mangroves as an example."
]
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"id": "390bf2be-3828-4705-b64b-ed68005ff98b",
"metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "\n",
- "Attaching package: 'dplyr'\n",
- "\n",
- "\n",
- "The following objects are masked from 'package:stats':\n",
- "\n",
- " filter, lag\n",
- "\n",
- "\n",
- "The following objects are masked from 'package:base':\n",
- "\n",
- " intersect, setdiff, setequal, union\n",
- "\n",
- "\n"
- ]
+ "scrolled": true,
+ "vscode": {
+ "languageId": "r"
}
- ],
+ },
+ "outputs": [],
"source": [
"library(gh)\n",
"library(readr)\n",
@@ -47,153 +31,95 @@
"library(htmlwidgets)"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "3089302a",
+ "metadata": {},
+ "source": [
+ "First, we will pull the file with the mangrove aphiaIDs from the Marine Life Data Network GitHub repo. **Note**: the acceptedTaxonIds in these files are based on what was up-to-date in the [WoRMS](https://marinespecies.org/) database as of the date this script was written."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 5,
"id": "43964864-7fe8-4139-a9d0-2a844161ccd1",
- "metadata": {},
+ "metadata": {
+ "vscode": {
+ "languageId": "r"
+ }
+ },
"outputs": [],
"source": [
- "# first we will pull the files where the EOV taxonomy are stored from GitHub\n",
- "repo_files <- gh(\"GET /repos/:owner/:repo/contents/:path\",\n",
- " owner = \"ioos\",\n",
- " repo = \"marine_life_data_network\",\n",
- " path = \"eov_taxonomy\")\n",
- "\n",
- "for (file_info in repo_files) {\n",
- " if (file_info$type == \"file\") {\n",
- " download.file(file_info$download_url, destfile = file_info$name)\n",
- " }\n",
- "}"
+ "mangroves <- read.csv(\"https://raw.githubusercontent.com/ioos/marine_life_data_network/main/eov_taxonomy/mangroves.csv\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "45fa26db",
+ "metadata": {},
+ "source": [
+ "Now we will do a bit of cleanup to get a list of aphiaIDs for mangroves so we can run our `robis` query using these as taxon identifiers."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "0bb6ab17-d239-410c-90d0-dcb50359b095",
- "metadata": {},
+ "metadata": {
+ "vscode": {
+ "languageId": "r"
+ }
+ },
"outputs": [],
"source": [
- "# let's try mangroves as our first example\n",
- "# we will open the mangroves taxonomy CSV that we downloaded and grab the taxonIDs\n",
- "mangroves <- read.csv(\"mangroves.csv\")\n",
"mangroves$ID <- gsub(\"urn:lsid:marinespecies.org:taxname.\", \"\", mangroves$acceptedTaxonId)\n",
"mangroves$ID <- as.numeric(mangroves$ID)\n",
"mangroveIdentifiers <- paste(mangroves$ID, collapse = \", \")"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "8a6cefa9",
+ "metadata": {},
+ "source": [
+ "Using the taxonIDs from the last step, let's query OBIS for occurrence data for mangroves. This step may take a bit of time to run. When this script was written, there were over 130,000 records for mangroves in OBIS."
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"id": "d9aa2697-4ca0-4631-8d77-b0407e7a73b5",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Retrieved 5000 records of approximately 132901 (3%)\n",
- "Retrieved 10000 records of approximately 132901 (7%)\n",
- "Retrieved 15000 records of approximately 132901 (11%)\n",
- "Retrieved 20000 records of approximately 132901 (15%)\n",
- "Retrieved 25000 records of approximately 132901 (18%)\n",
- "Retrieved 30000 records of approximately 132901 (22%)\n",
- "Retrieved 35000 records of approximately 132901 (26%)\n",
- "Retrieved 40000 records of approximately 132901 (30%)\n",
- "Retrieved 45000 records of approximately 132901 (33%)\n",
- "Retrieved 50000 records of approximately 132901 (37%)\n",
- "Retrieved 55000 records of approximately 132901 (41%)\n",
- "Retrieved 60000 records of approximately 132901 (45%)\n",
- "Retrieved 65000 records of approximately 132901 (48%)\n",
- "Retrieved 70000 records of approximately 132901 (52%)\n",
- "Retrieved 75000 records of approximately 132901 (56%)\n",
- "Retrieved 80000 records of approximately 132901 (60%)\n",
- "Retrieved 85000 records of approximately 132901 (63%)\n",
- "Retrieved 90000 records of approximately 132901 (67%)\n",
- "Retrieved 95000 records of approximately 132901 (71%)\n",
- "Retrieved 1e+05 records of approximately 132901 (75%)\n",
- "Retrieved 105000 records of approximately 132901 (79%)\n",
- "Retrieved 110000 records of approximately 132901 (82%)\n",
- "Retrieved 115000 records of approximately 132901 (86%)\n",
- "Retrieved 120000 records of approximately 132901 (90%)\n",
- "Retrieved 125000 records of approximately 132901 (94%)\n",
- "Retrieved 130000 records of approximately 132901 (97%)\n",
- "Retrieved 132901 records of approximately 132901 (100%)\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "132901"
- ],
- "text/latex": [
- "132901"
- ],
- "text/markdown": [
- "132901"
- ],
- "text/plain": [
- "[1] 132901"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
+ "metadata": {
+ "vscode": {
+ "languageId": "r"
}
- ],
+ },
+ "outputs": [],
"source": [
- "# using the taxonIDs from the last step, let's search OBIS for occurrence data for this EOV\n",
- "# this will take a bit to download because there were over 130k records when this script was written in Sept 2024\n",
"mangrove_occ <- robis::occurrence(taxonid = mangroveIdentifiers)\n",
"# let's check how many occurrences we got from OBIS\n",
"nrow(mangrove_occ)"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "c837a3ac",
+ "metadata": {},
+ "source": [
+ "Now that we have all of our mangrove records from OBIS, we will map the global distribution of records using the `map_leaflet` function found in the `robis` package. With the leaflet functionality, you can zoom into records, click them, and it list the scientific name for that occurrence record."
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"id": "1ccc9837-4585-4020-852b-cdc6b33ae62d",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "\t\n",
- "\t\t\n",
- "\t\t\n",
- "\n",
- "\n",
- "\n",
- "\n",
- "\n",
- "\n",
- "\n",
- "\n",
- "\n",
- "\n",
- "\n",
- "\t\n",
- "\t\n",
- "\t\t\n",
- "\n",
- "\t\n",
- "\n"
- ],
- "text/plain": [
- "HTML widgets cannot be represented in plain text (need html)"
- ]
- },
- "metadata": {
- "text/html": {
- "isolated": true
- }
- },
- "output_type": "display_data"
+ "metadata": {
+ "vscode": {
+ "languageId": "r"
}
- ],
+ },
+ "outputs": [],
"source": [
- "# use the built in leaflet capability from robis to map the occurrences\n",
"m <- map_leaflet(mangrove_occ,\n",
" provider_tiles = \"Esri.WorldGrayCanvas\",\n",
" popup = function(x) { x[\"scientificName\"] },\n",
@@ -201,14 +127,26 @@
"m"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "202e2512",
+ "metadata": {},
+ "source": [
+ "This next step is not required, but if you'd like to save this map to view it outside of R, here's how."
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
"id": "13a8bc6c-5963-4e4e-acab-f0f6de776045",
- "metadata": {},
+ "metadata": {
+ "vscode": {
+ "languageId": "r"
+ }
+ },
"outputs": [],
"source": [
- "# if you want to save the leaflet map\n",
+ "\n",
"saveWidget(m, \"mangroveMap.html\", selfcontained = TRUE)"
]
}