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INTEGRATING FOSSILS WITH MOLECULAR PHYLOGENIES IMPROVES INFERENCE OF TRAIT EVOLUTION
Authors:Graham J Slater  Luke J Harmon  Michael E Alfaro
Institution:1. Department of Ecology and Evolutionary Biology, University of California, 610 Charles E Young Drive South, Los Angeles, California 90095–1606;2. E‐mail: gslater@ucla.edu;3. Department of Biological Sciences, University of Idaho, Moscow, Idaho 83844;4. Institute for Bioinformatics and Evolutionary Studies (IBEST), University of Idaho, Moscow, Idaho 83844
Abstract:Comparative biologists often attempt to draw inferences about tempo and mode in evolution by comparing the fit of evolutionary models to phylogenetic comparative data consisting of a molecular phylogeny with branch lengths and trait measurements from extant taxa. These kinds of approaches ignore historical evidence for evolutionary pattern and process contained in the fossil record. In this article, we show through simulation that incorporation of fossil information dramatically improves our ability to distinguish among models of quantitative trait evolution using comparative data. We further suggest a novel Bayesian approach that allows fossil information to be integrated even when explicit phylogenetic hypotheses are lacking for extinct representatives of extant clades. By applying this approach to a comparative dataset comprising body sizes for caniform carnivorans, we show that incorporation of fossil information not only improves ancestral state estimates relative to those derived from extant taxa alone, but also results in preference of a model of evolution with trend toward large body size over alternative models such as Brownian motion or Ornstein–Uhlenbeck processes. Our approach highlights the importance of considering fossil information when making macroevolutionary inference, and provides a way to integrate the kind of sparse fossil information that is available to most evolutionary biologists.
Keywords:Ancestral states  Brownian motion  Caniformia  model selection  phylogenetic comparative methods
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