How sdarionicolas-boop/enm-bgen works
The README describes ENM-BGEN, an ecological niche-modeling pipeline that turns species-presence records and environmental layers into habitat-suitability rasters, conservation overlays, metrics, and interactive maps. Its documented workflow covers record cleaning and spatial thinning, environmental preparation and VIF selection, per-species modeling and ensemble projection, classification, and a MapBiomas land-cover cross. The supplied tree does not include the documented R pipeline or its data and results; those nodes therefore have no source path. The visible JavaScript files are bundled map libraries, not evidence of the pipeline’s internal wiring.
Main entry points: leaflet-binding-2.2.3/leaflet.js Leaflet widget binding · leaflet-1.3.1/leaflet.js Leaflet map engine
Components 20
Input data
- GBIF and iNaturalist
- Presence CSV
- WorldClim layers
- SRTM layers
- MapBiomas Argentina
Data preparation
- Clean and thin
- Environmental stack
- VIF selection
Modeling
- Pseudo-absences
- Cross-validated models
- Ensemble projection
Conservation outputs
- Suitability rasters
- Suitability classes
- Model metrics
- MapBiomas cross
- Habitat-use totals
- Interactive map
- Leaflet widget binding
outputs/ranking_partidos/mapa_prioridades_partidos_files/leaflet-binding-2.2.3/leaflet.js - Leaflet map engine
outputs/ranking_partidos/mapa_prioridades_partidos_files/leaflet-1.3.1/leaflet.js
Other components
- Analyst
Connections 20
- Analyst → Presence CSV: provides
- GBIF and iNaturalist → Presence CSV: records inform
- Presence CSV → Clean and thin: clean and thin
- WorldClim layers → Environmental stack: prepare layers
- SRTM layers → Environmental stack: prepare layers
- Environmental stack → VIF selection: select variables
- Clean and thin → Pseudo-absences: supply occurrences
- VIF selection → Cross-validated models: supply predictors
- Pseudo-absences → Cross-validated models: supply absences
- Cross-validated models → Ensemble projection: ensemble models
- Ensemble projection → Suitability rasters: write predictions
- Suitability rasters → Suitability classes: classify suitability
- Ensemble projection → Model metrics: report performance
- Suitability rasters → MapBiomas cross: cross habitat
- MapBiomas Argentina → MapBiomas cross: cross land use
- MapBiomas cross → Habitat-use totals: summarize areas
- Suitability rasters → Interactive map: visualize suitability
- Interactive map → Leaflet widget binding: render layers
- Leaflet widget binding → Leaflet map engine: use map engine
- Analyst → Interactive map: inspect results