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Bayesian phylogenetic tree inference is compromised because tree parameters include data. This prevents accurate model comparison, impacting evolutionary and epidemiological studies. Solutions are proposed to improve tree model assessment.

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Area of Science:

  • Evolutionary biology
  • Computational biology
  • Biostatistics

Background:

  • Time-calibrated phylogenetic trees are crucial for evolutionary, ecological, and epidemiological research.
  • These trees are typically inferred using Bayesian methods with a 'tree prior' representing the phylogeny.
  • Current methods treat the tree as a parameter, overlooking its data component.

Purpose of the Study:

  • To identify a fundamental flaw in Bayesian phylogenetic tree inference regarding the treatment of tree parameters.
  • To explain how this flaw compromises model comparison techniques like marginal likelihood estimation.
  • To highlight the broad implications for applications relying on accurate time-calibrated phylogenies.

Main Methods:

  • Analysis of the mathematical and conceptual basis of Bayesian tree priors.
  • Examination of the impact of treating taxon samples as part of the tree parameter.
  • Review of standard model comparison techniques (path-sampling, stepping-stone sampling) in this context.

Main Results:

  • The 'tree parameter' in Bayesian inference incorrectly includes data (taxon samples).
  • This data inclusion invalidates standard model comparison methods, such as Bayes factors.
  • Consequently, the accuracy of comparing different tree priors (birth-death models) is compromised.

Conclusions:

  • Accurate comparison of tree priors is essential for reliable phylogenetic inference.
  • The current Bayesian framework requires revision to properly account for data within tree parameters.
  • Recommendations are provided for researchers to assess tree model fit and improve phylogenetic analyses.