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Assessing the Adequacy of Morphological Models Using Posterior Predictive Simulations.

Laura P A Mulvey1, Michael R May2, Jeremy M Brown3

  • 1GeoZentrum Nordbayern, Department of Geography and Geosciences, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Loewenichstraße 28, 91054 Erlangen, Germany.

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|October 7, 2024
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Summary
This summary is machine-generated.

Choosing the right model for morphological evolution is crucial for accurate phylogenetic trees. This study shows no single model fits all, emphasizing careful selection for better evolutionary insights.

Keywords:
Bayesian phylogenetic analysismodel adequacymodel selectionmorphological datamorphological modelspalaeobiology

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Area of Science:

  • Evolutionary biology
  • Systematics
  • Computational biology

Background:

  • Phylogenetic trees are essential for understanding life's evolutionary history and diversification.
  • Morphological data, alongside molecular data, is vital for phylogenetics, especially for incorporating fossil taxa and uniting extinct and extant species.
  • The Mk Lewis model is commonly used for morphological character evolution, but its adequacy is not fully understood.

Purpose of the Study:

  • To investigate the impact of different morphological models on phylogenetic estimates using tetrapod datasets.
  • To evaluate model adequacy using posterior predictive simulations and compare it with Bayesian model selection.
  • To determine if current Mk model variations adequately describe morphological evolution and if any extensions are universally preferable.

Main Methods:

  • Compared unpartitioned Mk models with partitioned models (by observed states), with and without rate variation and ascertainment bias correction.
  • Utilized posterior predictive simulations for model adequacy assessment.
  • Compared model adequacy with Bayesian model selection, specifically addressing limitations with varying character state spaces.

Main Results:

  • The choice of substitution model significantly impacts phylogenetic tree topology and branch lengths.
  • Posterior predictive simulations were validated as a suitable method for model selection in this context.
  • Model selection based on marginal likelihoods is inappropriate for models with differing character state spaces.
  • Current Mk model variations generally perform adequately, but no single extension showed universal preference across datasets.

Conclusions:

  • Model selection is critical in Bayesian phylogenetics for morphological data, with no "one size fits all" solution.
  • Careful consideration of discrete character evolution models enhances confidence in phylogenetic estimates for both extinct and extant taxa.
  • Posterior predictive simulations are a robust approach for assessing model adequacy in phylogenetics.