Evolutionary shift detection with ensemble variable selection.

ELPASO Ensemble method Evolutionary shift detection LASSO Ornstein-Uhlenbeck model Phylogenetic comparative methods Trait evolution

Journal

BMC ecology and evolution

ISSN: 2730-7182

Titre abrégé: BMC Ecol Evol

Pays: England

ID NLM: 101775613

Informations de publication

Date de publication:
20 Jan 2024

Historique:

received: 19 01 2023

accepted: 10 01 2024

medline: 21 1 2024

pubmed: 21 1 2024

entrez: 20 1 2024

Statut: epublish

Résumé

Abrupt environmental changes can lead to evolutionary shifts in trait evolution. Identifying these shifts is an important step in understanding the evolutionary history of phenotypes. The detection performances of different methods are influenced by many factors, including different numbers of shifts, shift sizes, where a shift occurs on a tree, and the types of phylogenetic structure. Furthermore, the model assumptions are oversimplified, so are likely to be violated in real data, which could cause the methods to fail. We perform simulations to assess the effect of these factors on the performance of shift detection methods. To make the comparisons more complete, we also propose an ensemble variable selection method (R package ELPASO) and compare it with existing methods (R packages [Formula: see text]1ou and PhylogeneticEM). The performances of methods are highly dependent on the selection criterion. [Formula: see text]1ou+pBIC is usually the most conservative method and it performs well when signal sizes are large. [Formula: see text]1ou+BIC is the least conservative method and it performs well when signal sizes are small. The ensemble method provides more balanced choices between those two methods. Moreover, the performances of all methods are heavily impacted by measurement error, tree reconstruction error and shifts in variance.

Identifiants

DOI: 10.1186/s12862-024-02201-w PMID: 38245667

pubmed: 38245667

doi: 10.1186/s12862-024-02201-w

pii: 10.1186/s12862-024-02201-w

doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

Pagination

Subventions

Organisme : Natural Sciences and Engineering Research Council of Canada

ID : RGPIN/4945-2014

Organisme : Natural Sciences and Engineering Research Council of Canada

ID : RGPIN-2018-05447

Informations de copyright

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