Data from: Modelling habitat distributions for multiple species using phylogenetics

In this paper, we describe an empirical approach to model community structure using phylogenetic signals. That approach combines information about the species (i.e. traits and phylogeny) with information about the habitat (i.e. environmental conditions and spatial distribution of sampling sites) and their interactions to predict the species responses (e.g. the local densities). As an application, we use the approach to model fish densities in rivers. In the model, the different species and size classes were described using a functional trait, body length, and phylogenetic eigenvectors maps whereas the sites were described using water velocity, depth, substrate composition, macrophyte cover, degree-days, total phosphorus, and spatial eigenvector maps. The model (estimated using a regularised Poisson-family Generalised Linear Modelling approach) fitted the data well (likelihood-based R2adj=0.512) and showed fair predictive power (likelihood-based cross-validation R2=0.283) to predict the density of fish pertaining to 48 species totalling 143 combinations of species and size classes in 15 unregulated Canadian rivers. Using the model as a baseline to estimate the effect of flow regulation on community composition, we found that, with few exceptions, the densities of most fish species were lower in regulated than in unregulated rivers. Phylogenetics have been proposed to study community structure, but this is, to our knowledge, the first time phylogenetic information is used explicitly for numerical habitat modelling. We expect that models of that type will be in increasing demand now that development projects are routinely assessed through impact studies.

本研究提出一种基于系统发育信号（phylogenetic signals）构建群落结构模型的实证方法。该方法整合物种相关信息（即功能性状与系统发育背景）、栖息地相关信息（即环境条件与采样点空间分布）及其交互作用，以预测物种响应（如种群局部密度）。作为应用案例，本研究利用该方法构建河流鱼类密度模型。模型中，不同物种及体长组以功能性状——体长、系统发育特征向量图（phylogenetic eigenvectors maps）进行表征，而采样位点则通过水流流速、水深、底质组成、大型水生植物（macrophyte）覆盖度、积温（degree-days）、总磷（total phosphorus）含量及空间特征向量图（spatial eigenvector maps）进行描述。本模型采用正则化泊松族广义线性模型（regularised Poisson-family Generalised Linear Modelling）方法进行参数估计，拟合效果优异（基于似然的调整决定系数R²adj=0.512），且具备良好的预测能力：针对加拿大15条未受径流调控河流中的48个物种、总计143种物种-体长组组合的鱼类密度预测中，基于似然的交叉验证决定系数R²=0.283。以该模型为基准估算径流调控对群落组成的影响时，研究发现：除极少数例外，多数鱼类物种在受调控河流中的种群密度均低于未受调控河流。此前已有研究提出利用系统发育学方法开展群落结构研究，但据我们所知，本研究首次将系统发育信息明确应用于量化栖息地建模中。如今开发项目均需通过环境影响评价流程开展常规评估，我们预计此类模型的应用需求将持续增长。