Uncertain-tree: discriminating among competing approaches to the phylogenetic analysis of phenotype data. Issue 1846 (11th January 2017)

• Record Type: Journal Article
• Authors: Puttick, Mark N.; O'Reilly, Joseph E.; Tanner, Alastair R.; Fleming, James F.; Clark, James; Holloway, Lucy; Lozano-Fernandez, Jesus; Parry, Luke A.; Tarver, James E.; Pisani, Davide; Donoghue, Philip C. J.
• Is Part Of: Proceedings. Volume 284:Issue 1846(2017)
• Publication Date: 2017-01-11
• Subjects: phylogeny -- Bayesian -- parsimony -- cladistics -- morphology -- palaeontology; Biology -- Periodicals; 570.5
• DOI: 10.1098/rspb.2016.2290
• Languages: English
• ISSNs: 0962-8452

Abstract

Abstract : Morphological data provide the only means of classifying the majority of life's history, but the choice between competing phylogenetic methods for the analysis of morphology is unclear. Traditionally, parsimony methods have been favoured but recent studies have shown that these approaches are less accurate than the Bayesian implementation of the Mk model. Here we expand on these findings in several ways: we assess the impact of tree shape and maximum-likelihood estimation using the Mk model, as well as analysing data composed of both binary and multistate characters. We find that all methods struggle to correctly resolve deep clades within asymmetric trees, and when analysing small character matrices. The Bayesian Mk model is the most accurate method for estimating topology, but with lower resolution than other methods. Equal weights parsimony is more accurate than implied weights parsimony, and maximum-likelihood estimation using the Mk model is the least accurate method. We conclude that the Bayesian implementation of the Mk model should be the default method for phylogenetic estimation from phenotype datasets, and we explore the implications of our simulations in reanalysing several empirical morphological character matrices. A consequence of our finding is that high levels of resolution or the ability to classify species or groups with much confidence should not be expected when using small datasets. It is now necessary to depart from the traditional parsimony paradigms of constructing character matrices, towards datasets constructed explicitly for Bayesian methods.