FITTING MODELS OF CONTINUOUS TRAIT EVOLUTION TO INCOMPLETELY SAMPLED COMPARATIVE DATA USING APPROXIMATE BAYESIAN COMPUTATION

Authors

  • Graham J. Slater,

    1. Department of Ecology and Evolutionary Biology, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, California 90095–1606
    2. E-mail: gslater@ucla.edu
    Search for more papers by this author
  • Luke J. Harmon,

    1. Department of Biological Sciences, University of Idaho, Moscow, Idaho 83844
    2. Initiative in Bioinformatics and Evolutionary Studies (IBEST), University of Idaho, Moscow, Idaho 83844
    Search for more papers by this author
  • Daniel Wegmann,

    1. Department of Ecology and Evolutionary Biology, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, California 90095–1606
    Search for more papers by this author
  • Paul Joyce,

    1. Department of Mathematics, University of Idaho, Moscow, Idaho 83844–1103
    2. Initiative in Bioinformatics and Evolutionary Studies (IBEST), University of Idaho, Moscow, Idaho 83844
    Search for more papers by this author
  • Liam J. Revell,

    1. National Evolutionary Synthesis Center (NESCent), 2024 West Main Street, Suite A200 Erwin Mills Building, Durham, North Carolina 27705
    Search for more papers by this author
  • Michael E. Alfaro

    1. Department of Ecology and Evolutionary Biology, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, California 90095–1606
    Search for more papers by this author

Abstract

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.

Ancillary