Uncertain-tree: discriminating among competing approaches to the phylogenetic analysis of phenotype data

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.

The phylogeny of fossil whip spiders

“…Other relationships are unchanged. We note that discussions regarding the merits of different weighting schemes, and parsimony in comparison to probabilistic methods are ongoing [52, 53]. In this work we opt to focus our discussion on the equally weighted analysis, recognising this is potentially a more accurate form of parsimony analysis than implied weights, but could be overly-precise.

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Parsimony and maximum-likelihood phylogenetic analyses of morphology do not generally integrate uncertainty in inferring evolutionary history: a response to Brown et al.

“…This was a key aspect of our study comparing the primary methods of phylogenetic reconstruction as they are commonly implemented. Our and others previous studies [1,3,4] reject parsimony in favour of a Bayesian MCMC framework in which uncertainty is incorporated, further drawing into question the veracity of Brown and colleagues' assertion that there is equivalent performance amongst methods.…”

“…The principle thrust of the argument presented by Brown and colleagues [2] is that the experiments performed by Puttick et al [1] did not allow for a fair comparison between phylogenetic methods: the Bayesian implementation intrinsically integrates uncertainty, while it is common practise to evaluate uncertainty post hoc for Maximum Likelihood and parsimony inference using bootstrap methodology. In Puttick et al [1], we explicitly addressed this issue in two ways.…”

“…In Puttick et al [1], we explicitly addressed this issue in two ways. The first argument was that bootstrapping is not an intrinsic aspect of Maximum Likelihood estimation or parsimony phylogenetic analysis.…”

Taxonomic reassessment ofClevosaurus latidensFraser, 1993 (Lepidosauria, Rhynchocephalia) and rhynchocephalian phylogeny based on parsimony and Bayesian inference

“…So far, all phylogenetic studies of the Rhynchocephalia have only used parsimony analysis, recovering a few distinct clades. More recently, Bayesian inference methods have been employed for phylogenetic analyses based on morphological characters (e.g., Parry et al, 2016;Wright, 2017), and recent studies suggest that Bayesian methods outperform parsimony for morphological data (O'Reilly et al, 2016;Puttick et al, 2017), recovering more accurate, but less precise results.…”