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Fitting models of continuous trait evolution to incompletely sampled comparative data using approximate Bayesian

Graham J Slater1, Luke J Harmon1, Daniel Wegmann1

  • 1Department of Ecology and Evolutionary Biology, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, California 90095-1606 E-mail: gslater@ucla.eduDepartment of Biological Sciences, University of Idaho, Moscow, Idaho 83844Department of Mathematics, University of Idaho, Moscow, Idaho 83844-1103Initiative in Bioinformatics and Evolutionary Studies (IBEST), University of Idaho, Moscow, Idaho 83844National Evolutionary Synthesis Center (NESCent), 2024 West Main Street, Suite A200 Erwin Mills Building, Durham, North Carolina 27705.

Evolution; International Journal of Organic Evolution
|March 3, 2012
PubMed
Summary
This summary is machine-generated.

A new method, MECCA (Modeling Evolution of Continuous Characters using ABC), analyzes trait evolution on incomplete evolutionary trees. This approach helps test hypotheses about evolutionary rates across diverse life, offering an alternative to traditional methods.

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Area of Science:

  • Macroevolutionary biology
  • Phylogenetics
  • Computational biology

Background:

  • Existing methods for analyzing phylogenetic comparative data often require complete sampling of both evolutionary trees and trait data.
  • This limitation restricts their utility at broad phylogenetic scales, hindering macroevolutionary studies.
  • There is a need for methods that can handle incompletely sampled phylogenies and trait data.

Purpose of the Study:

  • To develop and implement a novel tree-based method, MECCA (Modeling Evolution of Continuous Characters using ABC), for analyzing trait evolution.
  • To infer rates of diversification and trait evolution from incompletely sampled phylogenies and trait data.
  • To test hypotheses about changes in the rate of trait evolution across incomplete trees of life.

Main Methods:

  • Developed MECCA, a hybrid likelihood/approximate Bayesian computation (ABC)-Markov-Chain Monte Carlo approach.
  • Utilized MECCA to simultaneously infer diversification and trait evolution rates.
  • Applied the method to both simulated and empirical datasets, including carnivore body size evolution.

Main Results:

  • MECCA demonstrated significant power in distinguishing between single and multiple evolutionary rate models using simulations.
  • The empirical application to carnivore body size evolution found no evidence for an elevated rate in pinnipeds compared to terrestrial carnivores.
  • Approximate Bayesian computation (ABC) approaches offer a viable alternative for macroevolutionary studies when likelihood-dependent methods are insufficient.

Conclusions:

  • MECCA provides a powerful new tool for macroevolutionary research, particularly for analyzing trait evolution on incompletely sampled phylogenies.
  • The method facilitates hypothesis testing regarding evolutionary rate changes across diverse taxa.
  • ABC methods represent a valuable addition to the toolkit for macroevolutionary studies facing data limitations.