Joint species distribution models (JSDM) are among the most important statistical tools in community ecology. They are routinely used for inference and various prediction tasks, such as to build species distribution maps or biomass estimation over spatial areas. Existing JSDM's cannot, however, model mutual exclusion between species, which may happen in some species groups, such as mosses in the bottom layer of a peatland site. We tackle this deficiency in the context of modeling plant percentage cover data, where mutual exclusion arises from limited growing space and competition for light. We propose a hierarchical JSDM where multivariate latent Gaussian variable model describes species' niche preferences and Dirichlet-Multinomial distribution models the observation process and exclusive competition for space between species. We use both stationary and non-stationary multivariate Gaussian processes to model residual phenomena. We also propose a decision theoretic model comparison and validation approach to assess the goodness of JSDMs in four different types of predictive tasks. We apply our models and methods to a case study on modeling vegetation cover in a boreal peatland. Our results show that ignoring the interspecific interactions and competition for space significantly reduces models' predictive performance and leads to biased estimates for total percentage cover both for individual species and over all species combined. A model's relative predictive performance also depends on the model comparison methods highlighting that model comparison and assessment should resemble the true predictive task. Our results also demonstrate that the proposed joint species distribution model can be used to simultaneously infer interspecific correlations in niche preference as well as mutual exclusive competition for space and through that provide novel insight into ecological research.

翻译：联合物种分布模型（JSDM）是群落生态学中最重要的统计工具之一，常被用于推断和各类预测任务，例如构建物种分布图或估算空间区域的生物量。然而，现有JSDM无法模拟物种间的互斥关系——这种关系可能出现在某些物种群体中，例如泥炭地底层藓类植物。我们针对植物百分比覆盖数据建模中的这一缺陷展开研究，其中互斥关系源于有限的生长空间和光竞争。我们提出一种分层JSDM：多变量潜在高斯变量模型描述物种生态位偏好，狄利克雷-多项分布模型模拟观测过程及物种间排他性空间竞争。我们采用平稳与非平稳多变量高斯过程对残差现象进行建模。同时提出基于决策理论的模型比较与验证方法，评估JSDM在四类不同预测任务中的拟合优度。我们将所提出的模型与方法应用于北方泥炭地植被覆盖建模的案例研究。结果表明：忽略种间相互作用与空间竞争会显著降低模型预测性能，并导致对个体物种及所有物种总百分比覆盖率的估计产生偏差。模型的相对预测性能取决于模型比较方法，这凸显出模型比较与评估应与真实预测任务相契合。此外，我们的结果证明，所提出的联合物种分布模型可同时推断生态位偏好中的种间相关性以及排他性空间竞争，从而为生态学研究提供新见解。