geoprocessing-for-ecology

Coordinates CRS reprojection, raster/vector processing and predictor downloads for ecological analyses.

2|Updated Mar 2, 2026

Search Tags:#automation #geospatial #ecology #gis #predictors #sdm

Ready to run

npx skills add https://github.com/baratadiego/ecological-agent-skills --skill geoprocessing-for-ecology

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: geoprocessing-for-ecology
Source: https://github.com/baratadiego/ecological-agent-skills/tree/main/skills/geoprocessing-for-ecology
Command: npx skills add https://github.com/baratadiego/ecological-agent-skills --skill geoprocessing-for-ecology

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests, rasterio, shapely, geopandas, rasterstats, numpy, pandas, cdsapi, and includes scripts (resource) components.

What problem does it solve?

Spatial data processing for ecological analyses can be tedious and error-prone, requiring consistent reprojection, clipping, extraction, and predictor management across multiple projects.

Core Features & Use Cases

Reproject and align rasters and vectors to a common CRS
Clip rasters/shapefiles to study areas, create buffers, and prepare predictor stacks
Extract environmental values at occurrence points or samples
Download WorldClim/CHELSA/ERA5 predictor datasets with reproducible metadata
Generate outputs and metadata for downstream modelling workflows (SDMs, occupancy, PVA, etc.)

Quick Start

Reproduce a typical ecology workflow by reprojecting inputs to a common CRS, clipping rasters to the study area, stacking predictors, and extracting values at sample points.

Frequently Asked Questions about geoprocessing-for-ecology

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I extract environmental predictor values at occurrence points for species distribution modeling?▼

To extract environmental predictor values for species distribution modeling, this Skill processes occurrence points by clipping rasters to study areas, stacking predictors, and sampling raster values to generate downstream modeling inputs.

Can I download WorldClim, CHELSA, and ERA5 datasets reproducibly for ecological GIS workflows?▼

Yes, you can download WorldClim, CHELSA, and ERA5 datasets reproducibly. The Skill handles predictor downloads using the cdsapi while generating explicit metadata to ensure deterministic execution and reproducibility tracking.

What is the best way to reproject and align rasters and vectors to a common CRS for landscape analysis?▼

The best way to reproject and align spatial data for landscape analysis is using GDAL and terra-powered workflows. This Skill coordinates CRS reprojection and raster/vector processing to ensure consistent predictor alignment.

Does this geospatial processing workflow handle raster clipping and buffer creation around study areas?▼

Yes, this geospatial processing workflow handles raster clipping and buffer creation. It prepares predictor stacks by clipping rasters and shapefiles to specific study areas and generating buffers for spatial ecological analyses.

Do I need Python libraries like rasterio and geopandas to prepare predictor stacks for occupancy analysis?▼

Yes, preparing predictor stacks for occupancy analysis requires Python libraries like rasterio, geopandas, rasterstats, and shapely. These dependencies enable the raster/vector processing and spatial extractions needed for ecological workflows.

Why does spatial data processing for ecology require consistent reprojection and metadata generation?▼

Spatial data processing for ecology requires consistent reprojection and metadata generation to avoid alignment errors across projects. This ensures deterministic execution and provides explicit inputs, outputs, and metadata for downstream modeling workflows.