Fast and accurate detection of evolutionary shifts in Ornstein–Uhlenbeck models

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 com...

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Bibliographic Details
Published in:Methods in ecology and evolution 2016-07, Vol.7 (7), p.811-824
Main Authors: Khabbazian, Mohammad, Kriebel, Ricardo, Rohe, Karl, Ané, Cécile, Hansen, Thomas
Format: Article
Language:English
Subjects:
adaptation
Anolis
Bayesian analysis
Change detection
Convergence
convergent evolution
Correlation analysis
Criteria
Evolution
Habitat changes
Lacertilia
lasso
Mathematical models
Phenotypes
phylogenetic Bayesian information criterion
phylogenetic comparative method
Phylogenetics
Phylogeny
regularization
ℓ1ou
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Summary: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.
ISSN:2041-210X
2041-210X
DOI:10.1111/2041-210X.12534