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Disparate parametric branch-support values from ambiguous characters.

Mark P Simmons1, Christopher P Randle2

  • 1Department of Biology, Colorado State University, Fort Collins, CO 80523-1878, USA.

Molecular Phylogenetics and Evolution
|May 14, 2014
PubMed
Summary
This summary is machine-generated.

Parametric phylogenetic methods offer greater resolution but can misinterpret ambiguous data. This study reveals how Bayesian MCMC, maximum likelihood, and parsimony methods handle ambiguous character optimization, impacting phylogenetic tree reliability.

Keywords:
Ambiguous optimizationBootstrapDecisive taxon coverageParsimonyRedundant taxaSH-like aLRT

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Area of Science:

  • Phylogenetics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Parametric phylogenetic methods often outperform parsimony by providing higher resolution and branch support in empirical analyses.
  • Certain factors, sometimes viewed as disadvantages, contribute to the increased power of parametric methods.
  • Understanding how different phylogenetic methods interpret ambiguous character states is crucial for accurate evolutionary inference.

Purpose of the Study:

  • To examine how Bayesian MCMC, maximum likelihood, and parsimony methods interpret ambiguous optimization of character states using empirical and simulated data.
  • To describe the information content in "redundant" terminals.
  • To present a novel approach for identifying clades lacking unequivocal synapomorphic support in empirical matrices.

Main Methods:

  • Analysis of empirical and modified simulated phylogenetic matrices.
  • Comparison of Bayesian MCMC, maximum likelihood, and parsimony methods for phylogenetic inference.
  • Evaluation of branch support metrics, including the SH-like approximate likelihood ratio test and bootstrap values.

Main Results:

  • The SH-like approximate likelihood ratio test is a more reliable indicator of ambiguous branch support than bootstrap values in likelihood analyses presenting a single optimal tree.
  • Bootstrap values from methods producing a single fully resolved tree are less robust to taxon sampling variations compared to conservative methods.
  • Different software implementations (PAUP(∗), GARLI, MrBayes, PhyML, IQ-TREE, RAxML) exhibit varying resilience in resolving ambiguous characters and providing high support.

Conclusions:

  • Methods presenting only a single fully resolved optimal tree can yield discrepancies in resolution and branch support.
  • The SH-like test offers a more dependable assessment of ambiguous support than bootstrap values in specific likelihood contexts.
  • Software choice and branch support quantification methods significantly influence phylogenetic tree outcomes, especially with ambiguous data.