Weighted parsimony outperforms other methods of phylogenetic inference under models appropriate for morphology

Abstract

One of the lasting controversies in phylogenetic inference is the degree to which specific evolutionary models should influence the choice of methods. Model‐based approaches to phylogenetic inference (likelihood, Bayesian) are defended on the premise that without explicit statistical models there is no science, and parsimony is defended on the grounds that it provides the best rationalization of the data, while refraining from assigning specific probabilities to trees or character‐state reconstructions. Authors who favour model‐based approaches often focus on the statistical properties of the methods and models themselves, but this is of only limited use in deciding the best method for phylogenetic inference—such decision also requires considering the conditions of evolution that prevail in nature. Another approach is to compare the performance of parsimony and model‐based methods in simulations, which traditionally have been used to defend the use of models of evolution for DNA sequences. Some recent papers, however, have promoted the use of model‐based approaches to phylogenetic inference for discrete morphological data as well. These papers simulated data under models already known to be unfavourable to parsimony, and modelled morphological evolution as if it evolved just like DNA, with probabilities of change for all characters changing in concert along tree branches. The present paper discusses these issues, showing that under reasonable and less restrictive models of evolution for discrete characters, equally weighted parsimony performs as well or better than model‐based methods, and that parsimony under implied weights clearly outperforms all other methods.

Probabilistic methods surpass parsimony when assessing clade support in phylogenetic analyses of discrete morphological data

“…Goloboff et al . () have criticized this approach to simulating morphology‐like datasets on the basis that our generating trees encompass only contemporaneous taxa, assume that evolutionary rates are constant across time and the tree, and that our measure of biological realism, the spread of homoplasy exhibited by datasets, is inadequate. However, our experiments do not attempt to simulate non‐contemporary taxa or address the problem of missing data, qualities of palaeontological data that are of a level of complexity that is beyond the current debate.…”

“…Goloboff et al . (, fig. A) demonstrated that our simulated data broadly achieve their preferred measure of biological realism.…”

“…Our review of their datasets indicates that, while Goloboff et al . () drew characters from an empirically realistic global distribution of homoplasy, their simulated datasets are not individually empirically realistic, with many matrices dominated by characters with very high consistency and an unrealistically small proportion of characters exhibiting high levels of homoplasy. The datasets simulated by Goloboff et al .…”

“…The datasets simulated by Goloboff et al . () have qualities that strongly bias in favour of parsimony phylogenetic inference, and implied‐weights parsimony in particular, as the presence of large numbers of characters that are congruent with the tree allows implied weights to increase the power of these ‘true’ congruent characters. This effect will not be possible when increased levels of homoplasy are present or when the true tree is unknown (as is the case for all empirical datasets).…”

A new species of burnetiid (Therapsida, Burnetiamorpha) from the early Wuchiapingian of South Africa and implications for the evolutionary ecology of the family Burnetiidae

“…Consistent with recent work by Goloboff et al . (), we used a value of 12 for the constant of concavity ( k ) and trees were calculated through implicit enumeration. In order to achieve greater resolution at the base of the tree, the basal synapsid Dimetrodon was added to the matrix and used as the outgroup.…”