Bayesian methods outperform parsimony but at the expense of precision in the estimation of phylogeny from discrete morphological data - PubMed (original) (raw)

Bayesian methods outperform parsimony but at the expense of precision in the estimation of phylogeny from discrete morphological data

Joseph E O'Reilly et al. Biol Lett. 2016 Apr.

Abstract

Different analytical methods can yield competing interpretations of evolutionary history and, currently, there is no definitive method for phylogenetic reconstruction using morphological data. Parsimony has been the primary method for analysing morphological data, but there has been a resurgence of interest in the likelihood-based Mk-model. Here, we test the performance of the Bayesian implementation of the Mk-model relative to both equal and implied-weight implementations of parsimony. Using simulated morphological data, we demonstrate that the Mk-model outperforms equal-weights parsimony in terms of topological accuracy, and implied-weights performs the most poorly. However, the Mk-model produces phylogenies that have less resolution than parsimony methods. This difference in the accuracy and precision of parsimony and Bayesian approaches to topology estimation needs to be considered when selecting a method for phylogeny reconstruction.

Keywords: Bayesian; likelihood; morphology; parsimony; phylogenetics.

© 2016 The Authors.

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Figures

Figure 1.

Figure 1.

Mk tree reconstructions (blue) outperform equal-weights parsimony (grey) and implied-weights parsimony (green) for 100, 350 and 1000 characters (a,c,e,g), and these differences remain in the subset of the simulated data matrices that exhibit realistic levels of homoplasy (b,d,f,h). Bars above the plots mark the 95th percentile range for each method, and dashed vertical lines show the median values. Percentage topology error (g,h) is the Robinson–Foulds value of the reconstructed tree compared with the worst possible value, as shown in [5].

Figure 2.

Figure 2.

The Mk model exhibits higher accuracy with lower precision than parsimony methods; these results are less clear as more characters are added. Contour plots of Robinson–Foulds distances against the number of resolved nodes in each tree; the contours represent the density of the distribution of trees.

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