具有空间排他性竞争的百分比覆盖度数据联合物种分布建模
Joint Species Distribution Modeling of Percentage Cover Data with Exclusive Competition for Space
AI总结：
针对现有联合物种分布模型无法处理物种间空间互斥竞争的缺陷，提出一种基于Dirichlet-Multinomial分布的分层JSDM，用于植物百分比覆盖度建模，显著提升了预测性能并减少了总覆盖度估计偏差。
摘要：
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。它们被常规用于推断和各种预测任务，例如构建物种分布图或对空间区域上的生物量进行估计。然而，现有的 JSDM 无法对物种间的互斥性进行建模，这种情况在某些物种群体中可能会发生，例如泥炭地底层中的苔藓。我们在对植物百分比覆盖度数据进行建模的背景下解决了这一缺陷，其中互斥性源于有限的生长空间和对光照的竞争。我们提出了一种分层 JSDM，其中多元潜在高斯变量模型描述物种的生态位偏好，Dirichlet-Multinomial 分布对观测过程以及物种间对空间的排他性竞争进行建模。我们使用平稳和非平稳多元高斯过程来对残差现象进行建模。我们还提出了一种决策论模型比较与验证方法，以评估 JSDM 在四种不同类型预测任务中的优劣。我们将我们的模型和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，忽略种间相互作用和对空间的竞争会显著降低模型的预测性能，并导致对单个物种以及所有物种合计的总百分比覆盖度产生有偏估计。模型的相对预测性能也取决于模型比较方法，这凸显了模型比较和评估应类似于真实的预测任务。我们的结果还表明，所提出的联合物种分布模型可用于同时推断生态位偏好中的种间相关性以及对空间的互斥竞争，从而为生态学研究提供新见解。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
