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Bruce Rannala1

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Summary
This summary is machine-generated.

Estimating species divergence times using Bayesian inference requires both fossil calibrations and sequence data. Conflicts between these data can lead to inaccurate divergence time estimates, even with narrow posterior distributions.

Keywords:
Bayesian inferencedivergence time estimationfossil calibrations

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Area of Science:

  • Evolutionary biology
  • Computational phylogenetics
  • Statistical modeling

Background:

  • Bayesian inference of species divergence times is complex and requires careful consideration of data inputs.
  • Fossil calibrations and molecular sequence data are crucial but can present challenges when integrated.
  • Prior assumptions about divergence times can be influenced by phylogenetic topology and fossil data.

Purpose of the Study:

  • To investigate the identifiability issues in Bayesian inference of species divergence times.
  • To define and illustrate a topology-consistent prior that maintains marginal priors.
  • To highlight potential conflicts between fossil calibrations and sequence data that can lead to erroneous divergence time estimates.

Main Methods:

  • Utilizing Bayesian inference frameworks for phylogenetic analysis.
  • Developing and applying a topology-consistent prior for divergence time estimation.
  • Illustrating potential conflicts and their impact using concrete examples.

Main Results:

  • Divergence time parameters are not identifiable without both fossil calibrations and sequence data.
  • Marginal priors from fossil calibrations can conflict with phylogenetic topology, altering priors.
  • Conflicts between fossil data and sequence data can yield incorrect divergence time estimates with narrow posterior distributions.

Conclusions:

  • Careful scrutiny of overly narrow posterior distributions for divergence times is essential.
  • A topology-consistent prior can help preserve marginal priors in phylogenetic analyses.
  • Accurate species divergence time estimation necessitates robust integration of fossil and molecular data.