Skip to content

Probabilistic methods outperform parsimony in the phylogenetic analysis of data simulated without a probabilistic model

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)1-17
Number of pages17
JournalPalaeontology
Volume62
Issue number1
Early online date19 Aug 2018
DOIs
DateAccepted/In press - 10 Jun 2018
DateE-pub ahead of print - 19 Aug 2018
DatePublished (current) - Jan 2019

Abstract

To understand patterns and processes of the diversification of life, we require an accurate understanding of taxon interrelationships. Recent studies have suggested that analyses of morphological character data using the Bayesian and maximum likelihood Mk model provide phylogenies of higher accuracy compared to parsimony methods. This has proved controversial, particularly studies simulating morphology-data under Markov models that assume shared branch lengths for characters, as it is claimed this leads to bias favouring the Bayesian or maximum likelihood Mk model over parsimony models which do not explicitly make this assumption. We avoid these potential issues by employing a simulation protocol in which character states are randomly assigned to tips, but datasets are constrained to an empirically realistic distribution of homoplasy as measured by the consistency index. Datasets were analysed with equal weights and implied weights parsimony, and the maximum likelihood and Bayesian Mk model. We find that consistent (low homoplasy) datasets render method choice largely irrelevant, as all methods perform well with high consistency (low homoplasy) datasets, but the largest discrepancies in accuracy occur with low consistency datasets (high homoplasy). In such cases, the Bayesian Mk model is significantly more accurate than alternative models and implied weights parsimony never significantly outperforms the Bayesian Mk model. When poorly supported branches are collapsed, the Bayesian Mk model recovers trees with higher resolution compared to other methods. As it is not possible to assess homoplasy independently of a tree estimate, the Bayesian Mk model emerges as the most reliable approach for categorical morphological analyses.

    Research areas

  • Bayesian, likelihood, morphology, parsimony, phylogenetics, simulation

Download statistics

No data available

Documents

Documents

  • Full-text PDF (final published version)

    Rights statement: This is the final published version of the article (version of record). It first appeared online via Wiley at https://onlinelibrary.wiley.com/doi/abs/10.1111/pala.12388?af=R . Please refer to any applicable terms of use of the publisher.

    Final published version, 3 MB, PDF document

    Licence: CC BY

DOI

View research connections

Related faculties, schools or groups