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Fast and accurate detection of evolutionary shifts in Ornstein–Uhlenbeck models
Author(s) -
Khabbazian Mohammad,
Kriebel Ricardo,
Rohe Karl,
Ané Cécile
Publication year - 2016
Publication title -
methods in ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.425
H-Index - 105
ISSN - 2041-210X
DOI - 10.1111/2041-210x.12534
Subject(s) - overfitting , trait , phylogenetic tree , phylogenetic comparative methods , bayesian probability , phylogenetics , anolis , evolutionary biology , biology , convergent evolution , adaptation (eye) , lizard , artificial intelligence , computer science , ecology , artificial neural network , genetics , neuroscience , gene , programming language
Summary The detection of evolutionary shifts in trait evolution from extant taxa is motivated by the study of convergent evolution, or to correlate shifts in traits with habitat changes or with changes in other phenotypes. We propose here a phylogenetic lasso method to study trait evolution from comparative data and detect past changes in the expected mean trait values. We use the Ornstein–Uhlenbeck process, which can model a changing adaptive landscape over time and over lineages. Our method is very fast, running in minutes for hundreds of species, and can handle multiple traits. We also propose a phylogenetic Bayesian information criterion that accounts for the phylogenetic correlation between species, as well as for the complexity of estimating an unknown number of shifts at unknown locations in the phylogeny. This criterion does not suffer model overfitting and has high precision, so it offers a conservative alternative to other information criteria. Our re‐analysis of Anolis lizard data suggests a more conservative scenario of morphological adaptation and convergence than previously proposed. Software is available on GitHub.