QMaxent
Plugin ID: 5174
Maxent species distribution modeling (SDM) in QGIS via the elapid Python library — full workflow from data preparation through post-prediction survey planning.
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QMaxent runs the full Maxent SDM workflow inside QGIS, integrating
the elapid Python library so the user never has to leave the GIS.
Modeling:
- Auto / Manual feature types (LQPHT) with the maxnet auto-rule
- Categorical variable support via one-hot encoding
- Optional distance-weight bias correction (Phillips 2009)
Data preparation:
- Check + Harmonize raster workflow (CRS, extent, resolution)
- One-click example datasets — Bradypus and Ariolimax
Evaluation:
- Cross-validation: Geographic K-Fold (default), Random K-Fold,
Checkerboard, Buffered LOO
- Jackknife variable importance averaged across CV folds
(train + held-out test AUCs)
- ROC and per-variable response curves
Projection:
- Cloglog / logistic / raw output transforms
- Auto-styled habitat-suitability layer added to the project
- Save / load (.pkl) with guided variable-mapping dialog
Priority Sites for Survey:
- Discovery mode: random or top-N sampling within a high-suitability
band (auto-set to raster max × 0.9), with spacing constraints
relative to existing presences and between candidate sites
- Validation mode: stratified sampling across four suitability
quartiles (Rhoden et al. 2017), with MTP / T10 / MaxSSS / Custom
threshold methods defining the lower bound
- OpenStreetMap Nominatim reverse geocoding (no API key)
Results export:
- Multi-sheet styled XLSX (Times New Roman, academic-paper
Supplementary Table convention) covering experimental setup,
variable inventory, cross-validation, jackknife importance,
and Priority Sites thresholds — ready to paste into a
manuscript supplement.
Bilingual UI (English / Korean). Dependencies install into a
per-plugin virtual environment that does not affect QGIS.
