Aug 9, 2006

Likelihood methods for detecting temporal shifts in diversification rates

Evolution; International Journal of Organic Evolution
Daniel L Rabosky


Maximum likelihood is a potentially powerful approach for investigating the tempo of diversification using molecular phylogenetic data. Likelihood methods distinguish between rate-constant and rate-variable models of diversification by fitting birth-death models to phylogenetic data. Because model selection in this context is a test of the null hypothesis that diversification rates have been constant over time, strategies for selecting best-fit models must minimize Type I error rates while retaining power to detect rate variation when it is present. Here I examine model selection, parameter estimation, and power to reject the null hypothesis using likelihood models based on the birth-death process. The Akaike information criterion (AIC) has often been used to select among diversification models; however, I find that selecting models based on the lowest AIC score leads to a dramatic inflation of the Type I error rate. When appropriately corrected to reduce Type I error rates, the birth-death likelihood approach performs as well or better than the widely used gamma statistic, at least when diversification rates have shifted abruptly over time. Analyses of datasets simulated under a range of rate-variable diversification scenarios...Continue Reading

  • References22
  • Citations172


  • References22
  • Citations172


Mentioned in this Paper

In Silico
Family Agamidae
Likelihood Functions
Maximum Likelihood Estimates
Internode Region of Axon
Tempo brand of antacid Reformulated Feb 2009

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