Campus Units

Ecology, Evolution and Organismal Biology, Statistics

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

1-2018

Journal or Book Title

Systematic Biology

Volume

67

Issue

1

First Page

14

Last Page

31

DOI

10.1093/sysbio/syx055

Abstract

Recent years have seen increased interest in phylogenetic comparative analyses of multivariate datasets, but to date the varied proposed approaches have not been extensively examined. Here we review the mathematical properties required of any multivariate method, and specifically evaluate existing multivariate phylogenetic comparative methods in this context. Phylogenetic comparative methods based on the full multivariate likelihood are robust to levels of covariation among trait dimensions and are insensitive to the orientation of the dataset, but display increasing model misspecification as the number of trait dimensions increases. This is because the expected evolutionary covariance matrix (V) used in the likelihood calculations becomes more illconditioned as trait dimensionality increases, and as evolutionary models become more complex. Thus, these approaches are only appropriate for datasets with few traits and many species. Methods that summarize patterns across trait dimensions treated separately (e.g., SURFACE) incorrectly assume independence among trait dimensions, resulting in nearly a 100% model misspecification rate. Methods using pairwise composite likelihood are highly sensitive to levels of trait covariation, the orientation of the dataset, and the number of trait dimensions. The consequences of these debilitating deficiencies is that a user can arrive at differing statistical conclusions, and therefore biological inferences, simply from a dataspace rotation, like principal component analysis. By contrast, algebraic generalizations of the standard phylogenetic comparative toolkit that use the trace of covariance matrices are insensitive to levels of trait covariation, the number of trait dimensions, and the orientation of the dataset. Further, when appropriate permutation tests are used, these approaches display acceptable Type I error and statistical power. We conclude that methods summarizing information across trait dimensions, as well as pairwise composite likelihood methods should be avoided, while algebraic generalizations of the phylogenetic comparative toolkit provide a useful means of assessing macroevolutionary patterns in multivariate data. Finally, we discuss areas in which multivariate phylogenetic comparative methods are still in need of future development; namely highly multivariate Ornstein-Uhlenbeck models and approaches for multivariate evolutionary model comparisons.

Comments

This is a pre-copyedited, author-produced version of an article accepted for publication in Systematic Biology following peer review. The version of record, Dean C. Adams, Michael L. Collyer; Multivariate Phylogenetic Comparative Methods: Evaluations, Comparisons, and Recommendations, Systematic Biology, Volume 67, Issue 1, January 2018, Pages 14–31 is available online at: doi: 10.1093/sysbio/syx055. Posted with permission.

Copyright Owner

The Authors

Language

en

File Format

application/pdf

Available for download on Tuesday, January 01, 2019

Published Version

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