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.

翻译：物种分布模型通常生成概率性出现预测，这些预测必须转换为二值化的存在-缺失分布图，以用于生态推断和保护规划。然而，这种二值化步骤通常是启发式的，可能显著扭曲对物种普遍性和群落组成的估计。我们提出了MaxExp，一种决策驱动的二值化框架，通过直接最大化选定的评估指标来选择最可能的物种集合。MaxExp无需校准数据，并可灵活适用于多种评分标准。我们还引入了集合规模期望方法，这是一种基于预期物种丰富度预测物种集合的计算高效替代方案。通过涵盖不同类群、物种数量和性能指标的三个案例研究，我们表明MaxExp始终匹配或超越广泛使用的阈值化和校准方法，尤其是在强烈的类别不平衡和高稀有性条件下。SSE则提供了一种更简单但仍具竞争力的选择。这些方法共同为多物种SDM二值化提供了稳健、可复现的工具。