MaxExp is a decision-driven framework for binarizing probabilistic species distribution models into presence-absence maps by maximizing evaluation metrics. It requires no calibration data and outperforms thresholding methods, especially under class imbalance. SSE provides a simpler alternative using expected species richness.
Key Points
- 1.Directly maximizes chosen evaluation metric for assemblages
- 2.No calibration data needed, flexible across scores
- 3.SSE as efficient richness-based predictor
- 4.Outperforms heuristics in diverse case studies
Impact Analysis
Enhances accuracy in ecological inference and conservation by reducing distortion in prevalence and composition estimates. Offers robust, reproducible tools for multispecies SDM binarization amid rarity and imbalance.
Technical Details
Selects most probable species set via metric optimization without calibration. SSE approximates via expected richness for efficiency. Validated on taxa-spanning studies with strong performance gains.