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）分析单类群内物种间的性状相关性，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个彼此协同进化的类群集合时，在双系统发育框架下评估此类关联的方法仍未得到充分开发。本研究将基于置换的多变量系统发育回归方法进行拓展，以在纳入两个互作类群各自系统发育信息的前提下，评估其物种间的性状相关性。该拓展方法适用于单变量与多变量响应数据，且可引入一个或多个自变量，包括环境协变量。该方法还可适配互作类群间物种匹配不完全的场景，例如一类群中的物种与另一类群中的多个物种存在关联，或两类群之一或全部存在未匹配的类群。针对单变量与多变量数据，该方法均能控制合理的第一类错误（type I error）率，且统计功效随性状协变强度与系统发育中物种数量的增加而提升。即使类群间不存在1:1的匹配关系，该方法仍可保留上述优良特性。最后，本研究通过分析榕属物种性状与其传粉榕小蜂（agaonid wasp）的性状间进化相关性，对该方法进行了实例验证。本研究附带提供R语言代码。