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Bayesian analysis using a simple likelihood model outperforms parsimony for estimation of phylogeny from discrete

April M Wright¹, David M Hillis¹

¹Department of Integrative Biology, University of Texas at Austin, Austin, Texas, United States of America.

|October 4, 2014

Summary

Parsimony methods can inaccurately estimate phylogenetic trees from morphological data. Bayesian inference using the Mk model offers a more reliable approach, especially with missing data and varying evolutionary rates common in paleontology.

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Area of Science:

Evolutionary biology
Paleontology
Computational phylogenetics

Background:

Parsimony is widely used for phylogenetic tree estimation from discrete morphological data.
Parsimony's limitations in accurately estimating tree topology are known, particularly in certain solution spaces.
Likelihood-based methods, especially the Mk model, offer an alternative for discrete character evolution.

Purpose of the Study:

To evaluate the performance of Bayesian inference with the Mk model for phylogenetic tree estimation.
To compare the efficacy of Bayesian Mk model inference against parsimony under paleontological conditions.
To assess the impact of missing data and rate heterogeneity on phylogenetic estimation methods.

Main Methods:

Utilized simulated discrete morphological data.
Employed Bayesian inference with the Mk model for phylogenetic analysis.
Simulated scenarios included varying degrees of missing data and rate heterogeneity.

Main Results:

Parsimony methods demonstrated limitations in accurately reconstructing phylogenetic trees under specific conditions.
Bayesian inference using the Mk model showed improved performance in phylogenetic estimation.
The presence of missing data and rate heterogeneity significantly influenced the accuracy of both parsimony and Mk model analyses.

Conclusions:

Bayesian inference with the Mk model is a robust alternative to parsimony for phylogenetic reconstruction from discrete morphological data.
The Mk model, particularly within a Bayesian framework, is effective under realistic paleontological data conditions.
Researchers should consider likelihood-based methods like the Bayesian Mk model for more accurate phylogenetic inference, especially when dealing with incomplete datasets and evolutionary rate variation.