Approximate Bayesian estimation of coevolutionary arms races.


Journal

PLoS computational biology
ISSN: 1553-7358
Titre abrégé: PLoS Comput Biol
Pays: United States
ID NLM: 101238922

Informations de publication

Date de publication:
04 2019
Historique:
received: 24 10 2018
accepted: 29 03 2019
revised: 25 04 2019
pubmed: 16 4 2019
medline: 22 5 2019
entrez: 16 4 2019
Statut: epublish

Résumé

Exaggerated traits involved in species interactions have long captivated the imagination of evolutionary biologists and inspired the durable metaphor of the coevolutionary arms race. Despite decades of research, however, we have only a handful of examples where reciprocal coevolutionary change has been rigorously established as the cause of trait exaggeration. Support for a coevolutionary mechanism remains elusive because we lack generally applicable tools for quantifying the intensity of coevolutionary selection. Here we develop an approximate Bayesian computation (ABC) approach for estimating the intensity of coevolutionary selection using population mean phenotypes of traits mediating interspecific interactions. Our approach relaxes important assumptions of a previous maximum likelihood approach by allowing gene flow among populations, variable abiotic environments, and strong coevolutionary selection. Using simulated data, we show that our ABC method accurately infers the strength of coevolutionary selection if reliable estimates are available for key background parameters and ten or more populations are sampled. Applying our approach to the putative arms race between the plant Camellia japonica and its seed predatory weevil, Curculio camelliae, provides support for a coevolutionary hypothesis but fails to preclude the possibility of unilateral evolution. Comparing independently estimated selection gradients acting on Camellia pericarp thickness with values simulated by our model reveals a correlation between predicted and observed selection gradients of 0.941. The strong agreement between predicted and observed selection gradients validates our method.

Identifiants

pubmed: 30986245
doi: 10.1371/journal.pcbi.1006988
pii: PCOMPBIOL-D-18-01822
pmc: PMC6483265
doi:

Types de publication

Journal Article Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

e1006988

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

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Auteurs

Scott L Nuismer (SL)

Department of Biological Sciences, University of Idaho, Moscow, Idaho, United States of America.
Bioinformatics and Computational Biology, University of Idaho, Moscow, Idaho, United States of America.

Bob Week (B)

Bioinformatics and Computational Biology, University of Idaho, Moscow, Idaho, United States of America.

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Classifications MeSH