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.
Highlights
Morphology once provided the only means of inferring evolutionary trees, but it was effectively rendered obsolete by molecular sequence data and the development of sophisticated molecular evolutionary models for phylogenetic analysis [1]
Methods for phylogenetic analysis of morphological data remain underdeveloped and though likelihood models are available that may more accurately accommodate the vagaries of morphological datasets [3], including high rates of heterogeneity and a preponderance of missing data [4], parsimony remains the method of choice, principally perhaps as a consequence of tradition
Median Robinson– Foulds distances are lower for the Mk-model compared with both equal-weights and implied-weights parsimony, and for all approaches, accuracy of topology reconstruction increases with increasing dataset size
Summary
Morphology once provided the only means of inferring evolutionary trees, but it was effectively rendered obsolete by molecular sequence data and the development of sophisticated molecular evolutionary models for phylogenetic analysis [1]. A recent simulation-based study by Wright & Hillis [5] demonstrated that a Bayesian implementation of Lewis’s Mk-model [3] strongly outperforms parsimony, especially when rates of character change are high, or when relatively few characters are analysed. The conclusions drawn by Wright & Hillis [5] were based on data effectively simulated using the Mk-model, potentially biasing the test in favour of the Mk-model. They did not consider whether the simulated data exhibited realistic levels of homoplasy, analysed unrealistically large simulated datasets, and evaluated only the relative performance of
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