Bayesian and parsimony approaches reconstruct informative trees from simulated morphological datasets

Smith, M.R.

Abstract

Phylogenetic analysis aims to establish the true relationships between taxa. Different analytical methods, however, can reach different conclusions. In order to establish which approach best reconstructs true relationships, previous studies have simulated datasets from known tree topologies, and identified the method that reconstructs the generative tree most accurately. On this basis, researchers have argued that morphological datasets should be analysed by Bayesian approaches, which employ an explicit probabilistic model of evolution, rather than parsimony methods—with implied weights parsimony sometimes identified as particularly inaccurate. Accuracy alone, however, is an inadequate measure of a tree's utility: a fully unresolved tree is perfectly accurate, yet contains no phylogenetic information. The highly resolved trees recovered by implied weights parsimony in fact contain as much useful information as the more accurate, but less resolved, trees recovered by Bayesian methods. By collapsing poorly supported groups, this superior resolution can be traded for accuracy, resulting in trees as accurate as those obtained by a Bayesian approach. By contrast, equally weighted parsimony analysis produces trees that are less resolved and less accurate, leading to less reliable evolutionary conclusions.

Citation

Smith, M. (2019). Bayesian and parsimony approaches reconstruct informative trees from simulated morphological datasets. Biology Letters, 15(2), Article 20180632. https://doi.org/10.1098/rsbl.2018.0632

• Journal Article Type: Article
• Acceptance Date: Jan 8, 2019
• Online Publication Date: Feb 6, 2019
• Publication Date: Feb 5, 2019
• Deposit Date: Jan 10, 2019
• Publicly Available Date: Jan 11, 2019
• Journal: Biology Letters
• Print ISSN: 1744-9561
• Electronic ISSN: 1744-957X
• Publisher: The Royal Society
• Peer Reviewed: Peer Reviewed
• Volume: 15
• Issue: 2
• Article Number: 20180632
• DOI: https://doi.org/10.1098/rsbl.2018.0632
• Public URL: https://durham-repository.worktribe.com/output/1310450