Premium
FITTING MODELS OF CONTINUOUS TRAIT EVOLUTION TO INCOMPLETELY SAMPLED COMPARATIVE DATA USING APPROXIMATE BAYESIAN COMPUTATION
Author(s) -
Slater Graham J.,
Harmon Luke J.,
Wegmann Daniel,
Joyce Paul,
Revell Liam J.,
Alfaro Michael E.
Publication year - 2012
Publication title -
evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.84
H-Index - 199
eISSN - 1558-5646
pISSN - 0014-3820
DOI - 10.1111/j.1558-5646.2011.01474.x
Subject(s) - approximate bayesian computation , biology , trait , phylogenetic comparative methods , markov chain monte carlo , bayesian probability , phylogenetic tree , tree (set theory) , evolutionary biology , macroevolution , sampling (signal processing) , statistics , computer science , artificial intelligence , mathematics , mathematical analysis , biochemistry , filter (signal processing) , computer vision , gene , programming language , inference
In recent years, a suite of methods has been developed to fit multiple rate models to phylogenetic comparative data. However, most methods have limited utility at broad phylogenetic scales because they typically require complete sampling of both the tree and the associated phenotypic data. Here, we develop and implement a new, tree‐based method called MECCA (Modeling Evolution of Continuous Characters using ABC) that uses a hybrid likelihood/approximate Bayesian computation (ABC)‐Markov‐Chain Monte Carlo approach to simultaneously infer rates of diversification and trait evolution from incompletely sampled phylogenies and trait data. We demonstrate via simulation that MECCA has considerable power to choose among single versus multiple evolutionary rate models, and thus can be used to test hypotheses about changes in the rate of trait evolution across an incomplete tree of life. We finally apply MECCA to an empirical example of body size evolution in carnivores, and show that there is no evidence for an elevated rate of body size evolution in the pinnipeds relative to terrestrial carnivores. ABC approaches can provide a useful alternative set of tools for future macroevolutionary studies where likelihood‐dependent approaches are lacking.