geoprocessing-for-ecology

name	geoprocessing-for-ecology
description	Handles spatial data operations: reprojection, raster stacking, clipping, extraction, and environmental predictor downloads for ecological analyses. Use this skill when the user needs CRS reprojection, raster masking or cropping, spatial extraction, buffer creation, raster resampling, spatial joins, GeoTIFF processing, shapefile operations, WorldClim/CHELSA/ERA5 predictor downloads, GDAL operations, or predictor stack preparation.
skill_version	1.0.0

Skill: geoprocessing-for-ecology

Domain: Spatial analysis · CRS · Raster · Vector · Extraction
Phase: 1 — Foundation
Used by: run-sdm-study, assess-ecological-impact, build-fire-risk-map, analyze-environmental-change, assess-ecosystem-services

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Purpose

Guides the agent through spatial data operations needed in quantitative ecology: coordinate reference system management, raster and vector processing, spatial masking, buffer creation, intersection, and extraction of environmental values at occurrence or sample points.

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When to Invoke

• Reprojecting layers to a common CRS
• Clipping rasters or shapefiles to a study area
• Extracting environmental variables at point locations
• Creating buffers around sites or features
• Computing landscape metrics from a land cover layer
• Stacking multi-band rasters for modeling

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Inputs

Input	Format	Required
Occurrence / sample points	CSV with lat/lon, SHP, GPKG	Yes
Study area polygon	SHP, GPKG, GeoJSON	Yes
Environmental rasters	GeoTIFF, NetCDF	Yes
Land cover or habitat layer	GeoTIFF, SHP	Optional

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Outputs

Output	Description
layers_reprojected/	All layers in common CRS
predictors_stack.tif	Aligned, masked raster stack
points_with_env.csv	Points with extracted environmental values
study_area_buffered.gpkg	Study area with optional buffer
spatial_qa_report.md	CRS audit and alignment report

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Steps

1. CRS Audit

• Identify the CRS of each input layer
• Define the project CRS (default: EPSG:4326 for global; UTM zone for regional)
• Reproject all layers to the project CRS
• Document the CRS choice and rationale

2. Extent and Resolution Alignment

• Define the study area extent from the provided polygon
• Clip all rasters to the study area extent
• Resample rasters to a common resolution (define target resolution explicitly)
• Apply the study area mask to remove cells outside the polygon

3. Vector Operations

• Dissolve or simplify study area polygon if needed
• Create buffers (specify distance and unit)
• Perform intersection or spatial join between occurrence points and polygon layers
• Check for topology errors (self-intersections, gaps)

4. Environmental Extraction

• Extract raster values at occurrence and background/pseudo-absence points
• Handle NA values (edge cells, masked areas): document strategy
• Check for spatial autocorrelation in extracted values if relevant

5. Raster Stack QA

• Verify all rasters share the same extent, resolution, CRS, and NA mask
• Report NA cell counts per layer
• Visualise layer histograms for anomaly detection

6. Generate Outputs

• Write reprojected layers to layers_reprojected/
• Write stacked predictor raster to predictors_stack.tif
• Write points with environmental values to points_with_env.csv
• Write spatial QA report

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Decision Points

Condition	Diagnosis	Recommended Action
Layers have mismatched CRS	Spatial operations will fail or produce incorrect results	Reproject all layers to a common CRS before any operation; document chosen EPSG in decision_log.md
Rasters have different resolutions	Extraction or stacking will fail	Resample to the coarsest resolution; prefer bilinear for continuous data, nearest-neighbour for categorical
Extents do not overlap	Study area definition error or wrong file	Verify study area shapefile; clip all layers to intersection; warn if intersection is < 50% of study area
NoData fraction > 30% after masking	Data gap too large for reliable analysis	Report NoData extent; consult alternative data source; do not interpolate silently

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Key Decisions to Document

• Project CRS (EPSG code)
• Target raster resolution (and rationale)
• Resampling method (nearest, bilinear, cubic)
• NA handling strategy
• Buffer distance (if used)

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Tools and Libraries

R: terra, sf, landscapemetrics
Python: rasterio, geopandas, pyproj, rasterstats, fiona
CLI: GDAL/OGR (gdalwarp, ogr2ogr, gdal_calc)

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Resources

• resources/crs-reference.md — common CRS codes for South American biomes
• resources/resampling-methods.md — when to use each resampling method
• examples/ — example extraction workflows

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Notes

• Always keep original layers intact; write outputs to separate directories
• Document the GDAL version used for reproducibility
• For very large rasters (>4 GB), use tiled/chunked processing