How to Optimize Multispecies Set Predictions in Presence-Absence Modeling ?
This addresses the need for robust and reproducible tools in ecological inference and conservation planning, though it is incremental as it builds on existing binarization challenges in species distribution modeling.
The paper tackles the problem of converting probabilistic species distribution model predictions into binary presence-absence maps, which can distort ecological estimates, by introducing MaxExp and SSE methods that optimize evaluation metrics or expected richness without calibration data. Results show MaxExp consistently matches or surpasses existing methods, particularly under class imbalance and rarity, with SSE offering a competitive simpler alternative.
Species distribution models (SDMs) commonly produce probabilistic occurrence predictions that must be converted into binary presence-absence maps for ecological inference and conservation planning. However, this binarization step is typically heuristic and can substantially distort estimates of species prevalence and community composition. We present MaxExp, a decision-driven binarization framework that selects the most probable species assemblage by directly maximizing a chosen evaluation metric. MaxExp requires no calibration data and is flexible across several scores. We also introduce the Set Size Expectation (SSE) method, a computationally efficient alternative that predicts assemblages based on expected species richness. Using three case studies spanning diverse taxa, species counts, and performance metrics, we show that MaxExp consistently matches or surpasses widely used thresholding and calibration methods, especially under strong class imbalance and high rarity. SSE offers a simpler yet competitive option. Together, these methods provide robust, reproducible tools for multispecies SDM binarization.