AI 中文总结
本研究在空间广义相异度混合模型基础上，扩展提出联合时空广义相异度混合模型stGDMM，用于分析Beta多样性，以南非开普植物区数据集验证其建模价值。
AI 中文摘要
广义相异度模型（GDMs）已成为生物多样性正式统计分析的重要工具。其中，以相异度测度（本文采用Bray-Curtis相异度）衡量的Beta多样性，为不同地点间物种组成的差异提供了统计总结，且因分布于一个时空对与另一个时空对的乘积空间，为空间及时空建模提供了新数据。前期研究中，我们开发了空间广义相异度混合模型（spGDMM），以解决文献中基础GDM的部分随机问题。本研究将其扩展至包含动态情况，发现结合时间与空间变化的Beta多样性可提供更丰富的建模机会，并以南非开普植物区（CFR）的数据集进行了示例说明。
英文摘要
Generalized dissimilarity models (GDMs) have emerged as a valuable tool for formal statistical analysis of biodiversity. In particular, beta diversity, measured using dissimilarity measures, e.g., the Bray-Curtis dissimilarity in our case, provides a statistical summary of the difference in species composition between sites. It also provides novel data for spatial and spatio-temporal modeling as it resides over the product space of one space-time pair and a second space-time pair. In earlier work we developed the spatial generalized dissimilarity mixed model (spGDMM) to remedy some of the stochastic issues concerned with the foundational GDM in the literature. Here, we extend that work to include dynamics. We find much richer modeling opportunities as we consider beta diversity with regard to change in time as well as space. We illustrate with a dataset from the Cape Floristic Region (CFR) in South Africa.
