# GBIF Species Occurrence Normalizer (`wakey7dev/gbif-species-normalizer`) Actor Search 3.9B+ biodiversity records with intelligent species name normalization. Enter common or scientific names — get cleaned, taxonomy-enriched occurrence data with geographic distribution. - **URL**: https://apify.com/wakey7dev/gbif-species-normalizer.md - **Developed by:** [Chris Wakefield](https://apify.com/wakey7dev) (community) - **Categories:** Business, AI - **Stats:** 2 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $1.00 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. - **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use). - **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`. - **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`. - **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`. - **Any other language** — the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ![Chris The Dev](https://raw.githubusercontent.com/chriswakefield87/appstore-screenshot-translator/main/assets/actor-banner.png) ## GBIF Species Occurrence Normalizer 🌍🐾 Search GBIF's 3.9 billion biodiversity occurrence records with intelligent species name normalization. Enter common names like "mountain lion" or scientific names like "Panthera leo" — get cleaned, taxonomy-enriched occurrence data with geographic distribution summaries. **Value-add over raw GBIF API:** Common name → scientific name resolution (200+ species), taxonomic hierarchy enrichment, country name normalization, human-readable output with geographic distribution tables. ### Input Parameters | Parameter | Type | Required | Description | |---|---|---|---| | `searchQuery` | string | ✅ | Species name — common or scientific (e.g. "blue whale", "Puma concolor") | | `country` | string | ❌ | Two-letter ISO country code filter (e.g. "GB", "US", "BR") | | `yearStart` | string | ❌ | Start year for occurrence records (e.g. "2020") | | `yearEnd` | string | ❌ | End year for occurrence records (e.g. "2024") | | `maxResults` | integer | ❌ | Max records to return (1-500, default: 50) | ### Example Input ```json { "searchQuery": "Puma concolor", "country": "US", "yearStart": "2023", "yearEnd": "2024", "maxResults": 50 } ``` ### Example Output ``` ================================================================================ GBIF SPECIES OCCURRENCE RESULTS ================================================================================ Query: Puma concolor Scientific: Puma concolor Taxonomy: Animalia > Chordata > Mammalia > Carnivora > Felidae Rank: SPECIES | Status: ACCEPTED Retrieved: 50 occurrences Countries: 12 countries # Date Country Lat Lon Basis of Record ─── ──────────── ──────────────────────── ──────── ───────── ──────────────────── 1 2024-01-07 United States of America 34.7832 -119.5579 Human Observation 2 2024-01-04 Mexico 18.4429 -89.9585 Human Observation 3 2023-12-15 Canada 51.0234 -115.1234 Preserved Specimen ... Geographic Distribution: United States of America 23 occurrences Mexico 8 occurrences Canada 5 occurrences Brazil 4 occurrences Argentina 3 occurrences ... Data source: GBIF.org ================================================================================ ``` ### Use Cases - **Environmental Impact Assessments** — Check species presence in project areas before development - **Conservation Research** — Track species distribution across countries and time periods - **Biodiversity Monitoring** — Monitor species occurrence trends for conservation planning - **Academic Research** — Access cleaned, normalized biodiversity data for papers and studies - **Biosecurity** — Track invasive species spread across geographic regions ### Data Source This Actor uses the [GBIF Occurrence API](https://www.gbif.org/developer/summary), which is free and requires no API key. GBIF — the Global Biodiversity Information Facility — is an international network funded by governments worldwide, providing open access to biodiversity data. Data license: Varies by dataset; most GBIF-mediated data is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) or [CC0](https://creativecommons.org/publicdomain/zero/1.0/). ### Pricing $1.00 per 1,000 results. Each occurrence record counts as one result. *** Built by [Chris The Dev](https://apify.com/wakey7dev) — passionate about making biodiversity data accessible and useful. # Actor input Schema ## `searchQuery` (type: `string`): Species name to search for. Can be a common name (e.g. 'mountain lion', 'blue whale') or scientific name (e.g. 'Puma concolor', 'Balaenoptera musculus'). ## `country` (type: `string`): Optional two-letter ISO country code to filter by (e.g. GB, US, DE, BR). Leave empty for worldwide results. ## `yearStart` (type: `string`): Optional start year for occurrence records (e.g. 2020). Leave empty for all years. ## `yearEnd` (type: `string`): Optional end year for occurrence records (e.g. 2024). Leave empty for all years. ## `maxResults` (type: `integer`): Maximum number of occurrence records to return (1-500). ## Actor input object example ```json { "searchQuery": "Puma concolor", "maxResults": 50 } ``` # Actor output Schema ## `results` (type: `string`): Complete occurrence records with normalized names, taxonomy, and geographic data. ## `summary` (type: `string`): Human-readable formatted summary of search results. ## `stats` (type: `string`): Machine-readable statistics about the search results. # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "searchQuery": "Puma concolor", "country": "", "yearStart": "", "yearEnd": "" }; // Run the Actor and wait for it to finish const run = await client.actor("wakey7dev/gbif-species-normalizer").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "searchQuery": "Puma concolor", "country": "", "yearStart": "", "yearEnd": "", } # Run the Actor and wait for it to finish run = client.actor("wakey7dev/gbif-species-normalizer").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "searchQuery": "Puma concolor", "country": "", "yearStart": "", "yearEnd": "" }' | apify call wakey7dev/gbif-species-normalizer --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=wakey7dev/gbif-species-normalizer", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification Download the OpenAPI definition: https://api.apify.com/v2/acts/XliUciwUjAXaJO9Lc/builds/IY304OjnyfJrUPcnu/openapi.json