Improving spatial predictions of taxonomic, functional and phylogenetic diversity

1. In this study, we compare two community modelling approaches to determine their ability to predict the taxonomic, functional and phylogenetic properties of plant assemblages along a broad elevation gradient and at a fine resolution. The first method is the standard stacking individual species distribution modelling (SSDM) approach, which applies a simple environmental filter to predict species assemblages. The second method couples the SSDM and macroecological modelling (MEM - SSDM-MEM) approaches to impose a limit on the number of species co-occurring at each site. Because the detection of diversity patterns can be influenced by different levels of phylogenetic or functional trees, we also examine whether performing our analyses from broad to more exact structures in the trees influences the performance of the two modelling approaches when calculating diversity indices. 2. We found that coupling the SSDM with the MEM improves the predictions for the diversity facets compared with t...1. 本研究对比了两种群落建模方法，以评估其在宽海拔梯度下、以精细分辨率预测植物群落的分类学、功能学与系统发育特征的能力。第一种方法为标准堆叠个体物种分布模型（stacking individual species distribution modelling, SSDM），该方法通过简单的环境过滤来预测物种群落。第二种方法则将SSDM与宏生态建模（macroecological modelling, MEM）相结合（即SSDM-MEM），对每个样地内共存的物种数量施加限制。由于多样性格局的检测结果可能受不同层级的系统发育或功能树的影响，本研究还探究了：在计算多样性指数时，从树的宽泛结构逐步细化至精确结构的分析流程，是否会对两种建模方法的性能产生影响。 2. 本研究发现，相较于（原文此处内容未完整给出），将SSDM与MEM相结合可提升各多样性维度的预测效果。