Measurement Errors Should Always Be Incorporated in Phylogenetic Comparative Analysis

Détails

ID Serval
serval:BIB_A1850873FC48
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Measurement Errors Should Always Be Incorporated in Phylogenetic Comparative Analysis
Périodique
Methods in Ecology and Evolution
Auteur(s)
Silvestro D., Kostikova A., Litsios G., Pearman P.B., Salamin N.
ISSN
2041-2096 (electronic)
ISSN-L
2041-210X
Statut éditorial
Publié
Date de publication
2015
Volume
6
Numéro
3
Pages
340-346
Langue
anglais
Résumé
The evolution of continuous traits is the central component of comparative analyses in phylogenetics, and the comparison of alternative models of trait evolution has greatly improved our understanding of the mechanisms driving phenotypic differentiation. Several factors influence the comparison of models, and we explore the effects of random errors in trait measurement on the accuracy of model selection.
We simulate trait data under a Brownian motion model (BM) and introduce different magnitudes of random measurement error. We then evaluate the resulting statistical support for this model against two alternative models: Ornstein-Uhlenbeck (OU) and accelerating/decelerating rates (ACDC).
Our analyses show that even small measurement errors (10%) consistently bias model selection towards erroneous rejection of BM in favour of more parameter-rich models (most frequently the OU model). Fortunately, methods that explicitly incorporate measurement errors in phylogenetic analyses considerably improve the accuracy of model selection.
Our results call for caution in interpreting the results of model selection in comparative analyses, especially when complex models garner only modest additional support.
Importantly, as measurement errors occur in most trait data sets, we suggest that estimation of measurement errors should always be performed during comparative analysis to reduce chances of misidentification of evolutionary processes.
Mots-clé
Brownian motion, comparative methods, macroevolution, measurement error, model selection, Ornstein-Uhlenbeck
Web of science
Open Access
Oui
Création de la notice
22/01/2015 7:47
Dernière modification de la notice
20/08/2019 15:07
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