Data from: A phylogenetic comparative method for evaluating trait coevolution across two phylogenies for sets of interacting species

Evaluating trait correlations across species within a lineage via phylogenetic regression is fundamental to comparative evolutionary biology, but when traits of interest are derived from two sets of lineages that co-evolve with one another, methods for evaluating such patterns in a dual-phylogenetic context remain underdeveloped. Here we extend multivariate permutation-based phylogenetic regression to evaluate trait correlations in two sets of interacting species while accounting for their respective phylogenies. This extension is appropriate for both univariate and multivariate response data, and may utilize one or more independent variables, including environmental covariates. Imperfect correspondence between species in the interacting lineages can also be accommodated, such as when species in one lineage associate with multiple species in the other, or when there are unmatched taxa in one or both lineages. For both univariate and multivariate data, the method displays appropriate type I error, and statistical power increases with the strength of the trait covariation and the number of species in the phylogeny. These properties are retained even when there is not a 1:1 correspondence between lineages. Finally, we demonstrate the approach by evaluating the evolutionary correlation between traits in fig species and traits in their agaonid wasp pollinators. R computer code is provided.通过系统发育回归（phylogenetic regression）分析单支谱系内物种种间性状相关性，是比较进化生物学的基础性研究内容。然而，当目标性状来源于两类彼此协同演化的谱系时，双系统发育框架下的此类相关性分析方法仍未得到充分发展。本研究将基于置换的多变量系统发育回归方法进行拓展，以评估两类互作物种的性状相关性，同时兼顾二者各自的系统发育背景。该拓展方法适用于单变量与多变量响应数据，可纳入一个或多个自变量（包括环境协变量），还可兼容互作谱系间物种对应关系不完美的场景，例如一类谱系中的物种与另一类谱系中的多个物种存在关联，或两类谱系中存在未匹配的分类单元。针对单变量与多变量数据，该方法的I型错误（type I error）控制表现合理，且统计效力随性状协变强度与系统发育内物种数量的增加而提升；即使谱系间并非严格1:1对应关系，该方法仍可保留上述统计特性。最后，本研究通过分析榕属物种性状与其传粉榕小蜂（agaonid wasp）的演化相关性，验证了该方法的实用性，并提供了R语言实现代码。