Model Selection and Model Averaging in Phylogenetics: Advantages of Akaike Information Criterion and Bayesian Approaches Over Likelihood Ratio Tests

• Published in: Systematic biology Vol. 53; no. 5; pp. 793 - 808
• Main Authors: Posada, David, Buckley, Thomas R., Thorne, Jeffrey
• Format: Journal Article
• Language: English
• Published: England Society of Systematic Zoology 01-10-2004; Taylor and Francis; Oxford University Press
• Subjects: AIC; Animals; Bayes factors; Bayes Theorem; Bayesian analysis; BIC; Carabus; Classification - methods; Coleoptera - genetics; DNA, Mitochondrial - genetics; Evolutionary biology; Inference; Likelihood Functions; likelihood ratio tests; Maximum likelihood estimation; model averaging; model selection; model uncertainty; Modeling; Models, Genetic; Molecules; multimodel inference; Parametric models; Phylogenetics; Phylogeny; Posadas; Ratio analysis; Ratio test; Sample size; Topology; model averaging; model uncertainty; Bayes factors; likelihood ratio tests; model selection; multimodel inference; AIC; BIC
• ISSN: 1063-5157; 1076-836X
• DOI: 10.1080/10635150490522304

Model selection is a topic of special relevance in molecular phylogenetics that affects many, if not all, stages of phylogenetic inference. Here we discuss some fundamental concepts and techniques of model selection in the context of phylogenetics. We start by reviewing different aspects of the selection of substitution models in phylogenetics from a theoretical, philosophical and practical point of view, and summarize this comparison in table format. We argue that the most commonly implemented model selection approach, the hierarchical likelihood ratio test, is not the optimal strategy for model selection in phylogenetics, and that approaches like the Akaike Information Criterion (AIC) and Bayesian methods offer important advantages. In particular, the latter two methods are able to simultaneously compare multiple nested or nonnested models, assess model selection uncertainty, and allow for the estimation of phylogenies and model parameters using all available models (model-averaged inference or multimodel inference). We also describe how the relative importance of the different parameters included in substitution models can be depicted. To illustrate some of these points, we have applied AIC-based model averaging to 37 mitochondrial DNA sequences from the subgenus Ohomopterus (genus Carabus) ground beetles described by Sota and Vogler (2001).