CALL FOR PROPOSALS:

ORGANIZERS

  • Harvey Thorleifson, Chair
    Minnesota Geological Survey
  • Carrie Jennings, Vice Chair
    Minnesota Geological Survey
  • David Bush, Technical Program Chair
    University of West Georgia
  • Jim Miller, Field Trip Chair
    University of Minnesota Duluth
  • Curtis M. Hudak, Sponsorship Chair
    Foth Infrastructure & Environment, LLC

 

Paper No. 10
Presentation Time: 4:00 PM

FOSSILS, MOLECULAR PHYLOGENIES, AND MODELS OF TRAIT EVOLUTION


SLATER, Graham J.1, HARMON, Luke J.2 and ALFARO, Michael E.1, (1)Department of Ecology and Evolutionary Biology, University of California, Los Angeles, 621 Charles E Young Drive South, Los Angeles, CA 90095-1606, (2)Biological Sciences, University of Idaho, Life Sciences South 252, P.O. Box 443051, Moscow, ID 83843, gslater@ucla.edu

Evolutionary biologists are increasingly interested in assessing the fit of evolutionary models to phenotypic data. By identifying the best-fitting model of trait evolution, such as an early burst or constrained random walk, evolutionary biologists attempt to draw conclusions about the tempo and mode of evolution in their clade of interest. Several empirical studies have demonstrated the significance of including data from fossil taxa for parameter estimates, such as ancestral states. However, because phylogenies including both fossil and extant taxa are rare, these methods are typically only applied to time-calibrated molecular phylogenies of extant taxa with associated phenotypic data. Here we introduce a Bayesian model fitting approach that treats fossil taxa as nodes and their traits as prior distributions. We show that even a small number (~5%) of nodes with associated fossils can dramatically alter and improve both model selection and parameter estimation from macroevolutionary models. Simulations suggest that this finding is robust to both phylogenetic and phenotypic uncertainty in the calibrations. We end by applying our approach to an example of body size evolution in caniform carnivores. Incorporating a few fossil calibration points dramatically alters both parameter estimates and identification of the best-fitting model for this dataset.
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