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The paper introduces MaxExp, a decision-driven binarization framework for species distribution models (SDMs) that selects the most probable species assemblage by directly maximizing a chosen evaluation metric without requiring calibration data. They also propose Set Size Expectation (SSE), a computationally efficient alternative predicting assemblages based on expected species richness. Empirical evaluations across three case studies demonstrate that MaxExp consistently matches or surpasses widely used thresholding and calibration methods, particularly under class imbalance and high rarity, while SSE provides a simpler, competitive option.
Stop relying on heuristic thresholding for species distribution models: MaxExp directly maximizes the probability of the entire species assemblage, outperforming traditional methods, especially with rare species.
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.