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

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2018-02-01
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Nason, John
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Ecology, Evolution and Organismal BiologyStatistics
Abstract

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 dualphylogenetic 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.

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This is the peer reviewed version of the following article: Adams, D. C. and Nason, J. D. (2018), A phylogenetic comparative method for evaluating trait coevolution across two phylogenies for sets of interacting species. Evolution, 72: 234–243, which has been published in final form at doi: 10.1111/evo.13415. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving..

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Sun Jan 01 00:00:00 UTC 2017
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