Automatic detection of key innovations, rate shifts, and diversity-dependence on phylogenetic trees

PLoS One. 2014 Feb 26;9(2):e89543. doi: 10.1371/journal.pone.0089543. eCollection 2014.

Abstract

A number of methods have been developed to infer differential rates of species diversification through time and among clades using time-calibrated phylogenetic trees. However, we lack a general framework that can delineate and quantify heterogeneous mixtures of dynamic processes within single phylogenies. I developed a method that can identify arbitrary numbers of time-varying diversification processes on phylogenies without specifying their locations in advance. The method uses reversible-jump Markov Chain Monte Carlo to move between model subspaces that vary in the number of distinct diversification regimes. The model assumes that changes in evolutionary regimes occur across the branches of phylogenetic trees under a compound Poisson process and explicitly accounts for rate variation through time and among lineages. Using simulated datasets, I demonstrate that the method can be used to quantify complex mixtures of time-dependent, diversity-dependent, and constant-rate diversification processes. I compared the performance of the method to the MEDUSA model of rate variation among lineages. As an empirical example, I analyzed the history of speciation and extinction during the radiation of modern whales. The method described here will greatly facilitate the exploration of macroevolutionary dynamics across large phylogenetic trees, which may have been shaped by heterogeneous mixtures of distinct evolutionary processes.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Animals
  • Biodiversity*
  • Biological Evolution*
  • Cetacea / genetics*
  • Extinction, Biological
  • Genetic Speciation*
  • Geography
  • Models, Genetic
  • Phylogeny*

Grant support

Financial support was provided by the Miller Institute for Basic Research in Science at the University of California, Berkeley, by the University of Michigan, and by NSF- DEB-1256330. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.