Abstract:
:In recent years, there has been controversy whether multidimensional data such as geometric morphometric data or information on gene expression can be used for estimating phylogenies. This study uses simulations of evolution in multidimensional phenotype spaces to address this question and to identify specific factors that are important for answering it. Most of the simulations use phylogenies with four taxa, so that there are just three possible unrooted trees and the effect of different combinations of branch lengths can be studied systematically. In a comparison of methods, squared-change parsimony performed similarly well as maximum likelihood, and both methods outperformed Wagner and Euclidean parsimony, neighbor-joining and UPGMA. Under an evolutionary model of isotropic Brownian motion, phylogeny can be estimated reliably if dimensionality is high, even with relatively unfavorable combinations of branch lengths. By contrast, if there is phenotypic integration such that most variation is concentrated in one or a few dimensions, the reliability of phylogenetic estimates is severely reduced. Evolutionary models with stabilizing selection also produce highly unreliable estimates, which are little better than picking a phylogenetic tree at random. To examine how these results apply to phylogenies with more than four taxa, we conducted further simulations with up to eight taxa, which indicated that the effects of dimensionality and phenotypic integration extend to more than four taxa, and that convergence among internal nodes may produce additional complications specifically for greater numbers of taxa. Overall, the simulations suggest that multidimensional data, under evolutionary models that are plausible for biological data, do not produce reliable estimates of phylogeny. [Brownian motion; gene expression data; geometric morphometrics; morphological integration; squared-change parsimony; phylogeny; shape; stabilizing selection.].
journal_name
Syst Bioljournal_title
Systematic biologyauthors
Varón-González C,Whelan S,Klingenberg CPdoi
10.1093/sysbio/syaa003subject
Has Abstractpub_date
2020-09-01 00:00:00pages
863-883issue
5eissn
1063-5157issn
1076-836Xpii
5716339journal_volume
69pub_type
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