Joint species distribution models (JSDMs) are an important tool for predicting ecosystem diversity and function under global change. The growing complexity of modern JSDMs necessitates careful model selection tailored to the challenges of community prediction under novel conditions (i.e., transferable models). Common approaches to evaluate the performance of JSDMs for community-level prediction are based on individual species predictions that do not account for the species correlation structures inherent in JSDMs. Here, we formalize a Bayesian model selection approach that accounts for species correlation structures and apply it to compare the community-level predictive performance of alternative JSDMs across broad environmental gradients emulating transferable applications.

中文翻译：

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用于群落水平预测的贝叶斯联合物种分布模型选择

联合物种分布模型（JSDM）是预测全球变化下生态系统多样性和功能的重要工具。现代 JSDM 日益复杂，需要仔细选择适合新条件下社区预测挑战的模型（即可转移模型）。评估 JSDM 在群落水平预测方面的性能的常用方法是基于单个物种的预测，而不考虑 JSDM 固有的物种相关结构。在这里，我们形式化了一种贝叶斯模型选择方法，该方法考虑了物种相关结构，并将其应用于模拟可转移应用的广泛环境梯度中比较替代 JSDM 的群落级预测性能。