A METHOD FOR ASSESSING PHYLOGENETIC LEAST SQUARES MODELS FOR SHAPE AND OTHER HIGH-DIMENSIONAL MULTIVARIATE DATA

2014/06/04 by Dean C. Adams · 304 citations
Earth and Planetary Sciences · Environmental Science · Mathematics · #Biology #Computer science #Context (archaeology) #Covariance #Ecology and Vegetation Dynamics Studies #Evolution and Paleontology Studies #Evolutionary biology #Generalized least squares #Genetics #Mathematics #Morphological variations and asymmetry #Multivariate statistics #Parametric statistics #Phylogenetic comparative methods #Phylogenetic tree #Regression #Sample size determination #Statistical hypothesis testing #Statistical power #Statistics #Trait #Type I and type II errors

paper · doi:10.1111/evo.12463

published in Evolution 68(9), 2675-2688 (Oxford University Press)

openalex publication_date 2014/06/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

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 high-dimensional 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 high-dimensional datasets.

Citations

• TESTING FOR PHYLOGENETIC SIGNAL IN COMPARATIVE DATA: BEHAVIORAL TRAITS ARE MORE LABILE
• Some distance properties of latent root and vector methods used in multivariate analysis
• Are rates of species diversification correlated with rates of morphological evolution?

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