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computational-ecology

스타5

포크1

업데이트2026년 7월 9일 05:17

Domain toolkit and rigor for geospatial and ecological analysis — vector/raster IO, CRS handling, spatial statistics, point patterns, landscape metrics, terrain/hydrology, remote-sensing indices, movement tracks, habitat connectivity, and species distribution modelling. Use for shapefiles, GeoPackage, GeoTIFF, NetCDF/COG rasters, reprojection, clustered-vs-random spatial tests, patch metrics, NDVI/EVI, zonal statistics, GPS telemetry, least-cost corridors, or occurrence prediction. Read before hand-rolling spatial weights, Moran's I, landscape metrics, NDVI loops, or resistance-surface nulls.

설치

Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.

Manus에서 실행

출처

amilworks

amilworks/ultra

GitHub 저장소 열기 Creator 저장소 보기

다운로드

Manus에서 실행

Computational Ecology (geospatial)

When to use

Read this before any geospatial or spatial-ecology analysis — spatial statistics, landscape metrics, remote-sensing rasters, movement, connectivity, or species distribution. The point is to use the field's actual tools and inferential safeguards instead of re-deriving weaker surrogates from numpy/pandas, and to prove any spatial pattern you report is real rather than an artifact of the weights matrix, the aggregation scale, or an aspatial null. This is domain tooling; the numerical-rigor protocol in /skills/computational-experiment-rigor/SKILL.md (uncertainty, thresholds, reproducibility, honest accounting) still applies on top of it.

0. Environment — one interpreter, and two install blockers

Everything runs in the default python (numpy 1.26.4) alongside the baked scipy/pandas/scikit-image/scikit-learn/networkx/xarray/dask/zarr/pyarrow — no isolated env. State the interpreter and package versions you used. Two packages are deliberately not baked and must not be reached for: fiona (its sdist needs GDAL dev headers absent from the image) and richdem (no py3.11 wheel). So do vector IO through the pyogrio engine (geopandas.read_file(..., engine="pyogrio")), never fiona; and do terrain/hydrology through pysheds (conditioning, flow direction/accumulation) and xarray-spatial (xrspatial: slope/aspect/hillshade/curvature), never richdem. If you find yourself importing fiona or richdem, stop — you are on an unbaked path.

Protocol

1. Use the field-standard named tool, name the method, pin its parameters

Do not hand-roll a spatial primitive when the canonical library exists. Before writing analysis code, copy the vetted recipe — correct call + a runnable self-check + the named anti-pattern — from references/geospatial-recipes.md (CRS-before-area, Moran's I, spatially-blocked SDM CV, rarefied diversity, landscape metrics, Ripley's K, pysheds hydrology, spatial regression). Hand-rolling these is how wrong-but-plausible results ship; run the self-check before trusting output.

Vector/raster IO + CRS: geopandas(pyogrio)/shapely/rioxarray/rasterio; reproject with pyproj. Reproject to an appropriate equal-area or projected CRS before any distance/area/weights computation — never compute distance or area in EPSG:4326 degrees. State the CRS by EPSG code.
Spatial autocorrelation & statistics: build the weights explicitly with libpysal.weights (Queen/Rook/KNN/DistanceBand — state which, and k or the band + units) and row-standardize; then esda for global Moran's I, local LISA (esda.Moran_Local), Getis-Ord G/G* (esda.G_Local). Do not reimplement I by hand.
Spatial regression: spreg (SAR/spatial-error/GNS — name the model and the LM-lag/LM- error diagnostics that motivated it), not an OLS that ignores autocorrelated residuals.
Point patterns: pointpats (Ripley's K/L, G, F, pair-correlation), not an eyeballed scatter.
Landscape metrics: pylandstats (the Python FRAGSTATS analogue) — name the metrics (patch/edge density, mean patch area, contagion, LANDSCAPE aggregation) and the neighbor rule (4- vs 8-connectivity) and the classified raster.
Zonal statistics: rasterstats.zonal_stats (state the statistic and nodata handling).
Remote sensing / raster time series: load with rioxarray into xarray; spectral indices via xarray-spatial or explicit band math with the mapping stated (NDVI = (NIR−Red)/(NIR+Red); name EVI/NDWI/SAVI coefficients); reduce over time with named reductions.
Terrain/hydrology: pysheds (pit-filling, D8 flow direction/accumulation, stream extraction) and xarray-spatial for slope/aspect/hillshade.
Movement ecology: movingpandas TrajectoryCollection (state CRS, time index, speed/gap splitting), not differenced lat/lon columns.
Connectivity: build the habitat graph on baked networkx (nodes = patches/cells, edges weighted by a stated resistance/cost); least-cost paths / corridors / betweenness by name.
SDM: baked scikit-learn (name the estimator) with an explicit feature stack.
Basemaps via contextily (needs network at map time — figures only), choropleth binning via mapclassify (name the scheme — Quantiles/FisherJenks/NaturalBreaks — and k). Name the method at the level you are confident in; never fabricate a citation or benchmark number.

2. Validate against a principled spatial null, not an aspatial one

A spatial statistic is meaningless without the right null, and the right null is almost never IID.

Autocorrelation: report Moran's I / G* / LISA against the tool's conditional-permutation null (permutations=999, fixed seed); the analytic normal-approximation p is a fallback, not the headline. Report E[I] = −1/(n−1), observed I, and the pseudo p — a positive I is only "clustering" if it beats the permutation null.
Point patterns: compare Ripley's K/L against a CSR envelope from ≥99 simulations within the observed study window (edge correction on), not an unbounded Poisson intuition.
Landscape metrics: one landscape's metric is descriptive; if you claim fragmentation changed or differs, compare against a matched null (neutral landscape / permuted class map) or across dates/sites with uncertainty.
SDM: evaluate on spatially-blocked cross-validation (spatially disjoint train/test), never random k-fold (it leaks through autocorrelation and inflates AUC), and compare against a prevalence-only / random-background null. State the null, the permutation/simulation count, and the seed for every inferential claim.

3. Quantify uncertainty and stability

Report pseudo p-values and, where given, standardized z-scores with the permutation count. Show robustness to the choices known to move spatial results: re-run Moran's I / hot-spot maps under ≥2 weights definitions (Queen vs KNN-8 vs a distance band) and report whether the significant-cluster set is stable; flag MAUP (aggregation scale/zoning) sensitivity explicitly. For SDM report mean ± spread across spatial folds (AUC, TSS, Boyce) and variable-importance stability, not one split. Apply the rigor skill's threshold rule: if an estimate is within ~3× its spread of its null expectation, label it "marginal / not resolved."

4. Respect the geospatial substrate (CRS, resolution, alignment, nodata)

The substrate drives the answer more than the algorithm. Confirm every layer's CRS and reproject to a common equal-area/projected CRS before area/distance/weights work. When combining rasters, ensure identical grid/resolution/extent — resample explicitly with a stated method (nearest for categorical, bilinear/cubic for continuous). Honor nodata/masks (cloud/QA masks, nan-aware reductions) before every reduction. State pixel size and analysis scale (a metric at 30 m ≠ at 300 m), and the classification scheme + breaks for landscape metrics and choropleths.

5. Reproducibility record

Record the interpreter and versions (geopandas.show_versions()), the IO engine (pyogrio), every EPSG code and reprojection, the weights definition (type + k/band + row-standardization), permutation counts and seeds, raster resolution / resampling / nodata handling, the classification scheme and breaks, and the exact commands. Write intermediate spatial data to inspectable files in /workspace (GeoPackage for vector; COG or NetCDF/Zarr for raster).

6. Honest accounting

If a canonical tool was unavailable and you used a surrogate, say so and name what was lost (e.g. "used xarray-spatial slope, not a hydrologically-conditioned flow accumulation"). Every spatial conclusion (this area is a hotspot; these patches are fragmented; this species will occur here) gets a confidence level tied to §2–§3 — the correct null, the permutation p, the weights/scale robustness, the spatial-CV performance — not merely that a function returned a number. Report which validations you ran and which you did not (e.g. MAUP across scales).

Delegation note

When delegating a verification subtask, give the subagent this skill's standards explicitly — the spatial null, the weights/scale robustness, the spatial-CV protocol — and reconcile its numbers to yours against the spread, not just "consistent".

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name

computational-ecology

description

Computational Ecology (geospatial)

When to use

0. Environment — one interpreter, and two install blockers

Protocol

1. Use the field-standard named tool, name the method, pin its parameters

Vector/raster IO + CRS: geopandas(pyogrio)/shapely/rioxarray/rasterio; reproject with pyproj. Reproject to an appropriate equal-area or projected CRS before any distance/area/weights computation — never compute distance or area in EPSG:4326 degrees. State the CRS by EPSG code.
Spatial autocorrelation & statistics: build the weights explicitly with libpysal.weights (Queen/Rook/KNN/DistanceBand — state which, and k or the band + units) and row-standardize; then esda for global Moran's I, local LISA (esda.Moran_Local), Getis-Ord G/G* (esda.G_Local). Do not reimplement I by hand.
Spatial regression: spreg (SAR/spatial-error/GNS — name the model and the LM-lag/LM- error diagnostics that motivated it), not an OLS that ignores autocorrelated residuals.
Point patterns: pointpats (Ripley's K/L, G, F, pair-correlation), not an eyeballed scatter.
Landscape metrics: pylandstats (the Python FRAGSTATS analogue) — name the metrics (patch/edge density, mean patch area, contagion, LANDSCAPE aggregation) and the neighbor rule (4- vs 8-connectivity) and the classified raster.
Zonal statistics: rasterstats.zonal_stats (state the statistic and nodata handling).
Remote sensing / raster time series: load with rioxarray into xarray; spectral indices via xarray-spatial or explicit band math with the mapping stated (NDVI = (NIR−Red)/(NIR+Red); name EVI/NDWI/SAVI coefficients); reduce over time with named reductions.
Terrain/hydrology: pysheds (pit-filling, D8 flow direction/accumulation, stream extraction) and xarray-spatial for slope/aspect/hillshade.
Movement ecology: movingpandas TrajectoryCollection (state CRS, time index, speed/gap splitting), not differenced lat/lon columns.
Connectivity: build the habitat graph on baked networkx (nodes = patches/cells, edges weighted by a stated resistance/cost); least-cost paths / corridors / betweenness by name.
SDM: baked scikit-learn (name the estimator) with an explicit feature stack.
Basemaps via contextily (needs network at map time — figures only), choropleth binning via mapclassify (name the scheme — Quantiles/FisherJenks/NaturalBreaks — and k). Name the method at the level you are confident in; never fabricate a citation or benchmark number.

2. Validate against a principled spatial null, not an aspatial one

A spatial statistic is meaningless without the right null, and the right null is almost never IID.

Autocorrelation: report Moran's I / G* / LISA against the tool's conditional-permutation null (permutations=999, fixed seed); the analytic normal-approximation p is a fallback, not the headline. Report E[I] = −1/(n−1), observed I, and the pseudo p — a positive I is only "clustering" if it beats the permutation null.
Point patterns: compare Ripley's K/L against a CSR envelope from ≥99 simulations within the observed study window (edge correction on), not an unbounded Poisson intuition.
Landscape metrics: one landscape's metric is descriptive; if you claim fragmentation changed or differs, compare against a matched null (neutral landscape / permuted class map) or across dates/sites with uncertainty.
SDM: evaluate on spatially-blocked cross-validation (spatially disjoint train/test), never random k-fold (it leaks through autocorrelation and inflates AUC), and compare against a prevalence-only / random-background null. State the null, the permutation/simulation count, and the seed for every inferential claim.