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Related Experiment Videos

Likelihood methods for detecting temporal shifts in diversification rates.

Daniel L Rabosky1

  • 1Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, New York 14853-2701, USA. DLR32@cornell.edu

Evolution; International Journal of Organic Evolution
|August 9, 2006
PubMed
Summary

Maximum likelihood birth-death models effectively detect diversification rate changes in phylogenetics. Corrected models outperform AIC and gamma statistics, especially with extinction present.

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Area of Science:

  • Phylogenetics and Evolutionary Biology
  • Computational Biology and Bioinformatics

Background:

  • Maximum likelihood methods are crucial for analyzing molecular phylogenetics and understanding diversification tempos.
  • Distinguishing between rate-constant and rate-variable diversification models is key, often using birth-death models.
  • Model selection strategies must balance minimizing Type I errors with retaining power to detect rate variation.

Purpose of the Study:

  • To evaluate model selection, parameter estimation, and statistical power of likelihood-based birth-death models.
  • To compare the performance of birth-death likelihood models against the Akaike information criterion (AIC) and gamma statistic.
  • To assess the power of these models to detect rate variation, particularly in the presence of extinction.

Main Methods:

Related Experiment Videos

  • Utilized likelihood models based on the birth-death process to analyze phylogenetic data.
  • Examined model selection strategies, including the Akaike information criterion (AIC), and proposed corrections to reduce Type I error rates.
  • Simulated datasets under various rate-variable diversification scenarios to assess statistical power.

    • Main Results:

    • Selecting diversification models using the lowest AIC score significantly inflates Type I error rates.
    • Corrected birth-death likelihood models demonstrate comparable or superior performance to the gamma statistic, especially for abrupt rate shifts.
    • The birth-death likelihood method shows enhanced power to detect diversification rate variation when extinction is present.
    • This method can differentiate between temporal increases in diversification rates and rate-constant models with non-zero extinction.

    Conclusions:

    • Standard AIC-based model selection inflates Type I errors in diversification analyses.
    • Corrected birth-death likelihood models offer a robust approach for inferring diversification dynamics from phylogenetic data.
    • This method provides a powerful tool for detecting rate variation and distinguishing complex evolutionary scenarios, as demonstrated with Australian agamid lizards.