Univ. Grenoble Alpes, CNRS, Univ. Savoie Mont Blanc, LECA, Grenoble, France; Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, Grenoble, France.
Univ. Grenoble Alpes, CNRS, Univ. Savoie Mont Blanc, LECA, Grenoble, France.
Trends Ecol Evol. 2021 May;36(5):391-401. doi: 10.1016/j.tree.2021.01.002. Epub 2021 Feb 19.
Explaining and modeling species communities is more than ever a central goal of ecology. Recently, joint species distribution models (JSDMs), which extend species distribution models (SDMs) by considering correlations among species, have been proposed to improve species community analyses and rare species predictions while potentially inferring species interactions. Here, we illustrate the mathematical links between SDMs and JSDMs and their ecological implications and demonstrate that JSDMs, just like SDMs, cannot separate environmental effects from biotic interactions. We provide a guide to the conditions under which JSDMs are (or are not) preferable to SDMs for species community modeling. More generally, we call for a better uptake and clarification of novel statistical developments in the field of biodiversity modeling.
解释和建模物种群落比以往任何时候都是生态学的核心目标。最近,联合物种分布模型(JSDM)通过考虑物种之间的相关性,扩展了物种分布模型(SDM),以改进物种群落分析和稀有物种预测,同时潜在地推断物种相互作用。在这里,我们说明了 SDM 和 JSDM 之间的数学联系及其生态意义,并证明 JSDM 与 SDM 一样,无法将环境效应与生物相互作用分开。我们为 JSDM 用于物种群落建模的条件(或不适用的条件)提供了一个指南。更一般地说,我们呼吁在生物多样性建模领域更好地采用和澄清新的统计发展。