A METHOD FOR ASSESSING PHYLOGENETIC LEAST SQUARES MODELS FOR SHAPE AND OTHER HIGHDIMENSIONAL MULTIVARIATE DATA
Studies of evolutionary correlations commonly use phylogenetic regression (i.e., independent contrasts and phylogenetic generalized least squares) to assess trait covariation in a phylogenetic context. However, while this approach is appropriate for evaluating trends in one or a few traits, it is in... Full description
Journal Title:  Evolution 201409, Vol.68 (9), p.26752688 
Main Author:  Adams, Dean C 
Format:  Electronic Article 
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English 
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Publisher:  United States: Blackwell Publishing Ltd 
ID:  ISSN: 00143820 
Link:  https://www.ncbi.nlm.nih.gov/pubmed/24899536 
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title:  A METHOD FOR ASSESSING PHYLOGENETIC LEAST SQUARES MODELS FOR SHAPE AND OTHER HIGHDIMENSIONAL MULTIVARIATE DATA 
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ispartof:  Evolution, 201409, Vol.68 (9), p.26752688 
description:  Studies of evolutionary correlations commonly use phylogenetic regression (i.e., independent contrasts and phylogenetic generalized least squares) to assess trait covariation in a phylogenetic context. However, while this approach is appropriate for evaluating trends in one or a few traits, it is incapable of assessing patterns in highly multivariate data, as the large number of variables relative to sample size prohibits parametric test statistics from being computed. This poses serious limitations for comparative biologists, who must either simplify how they quantify phenotypic traits, or alter the biological hypotheses they wish to examine. In this article, I propose a new statistical procedure for performing ANOVA and regression models in a phylogenetic context that can accommodate highdimensional datasets. The approach is derived from the statistical equivalency between parametric methods using covariance matrices and methods based on distance matrices. Using simulations under Brownian motion, I show that the method displays appropriate Type I error rates and statistical power, whereas standard parametric procedures have decreasing power as data dimensionality increases. As such, the new procedure provides a useful means of assessing trait covariation across a set of taxa related by a phylogeny, enabling macroevolutionary biologists to test hypotheses of adaptation, and phenotypic change in highdimensional datasets. 
language:  eng 
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identifier:  ISSN: 00143820 
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