Evaluating the Impact of Anatomical Partitioning on Summary Topologies Obtained with Bayesian Phylogenetic Analyses of Morphological Data.

Morphological data are a fundamental source of evidence to reconstruct the Tree of Life, and Bayesian phylogenetic methods are increasingly being used for this task. Bayesian phylogenetic analyses require the use of evolutionary models, which have been intensively studied in the past few years, with significant improvements to our knowledge. Notwithstanding, a systematic evaluation of the performance of partitioned models for morphological data has never been performed. Here we evaluate the influence of partitioned models, defined by anatomical criteria, on the precision and accuracy of summary tree topologies considering the effects of model misspecification. We simulated datasets using partitioning schemes, trees, and other properties obtained from two empirical datasets, and conducted Bayesian phylogenetic analyses. Additionally, we reanalysed 32 empirical datasets for different groups of vertebrates, applying unpartitioned and partitioned models, and, as a focused study case, we reanalysed a dataset including living and fossil armadillos, testing alternative partitioning hypotheses based on functional and ontogenetic modules. We found that, in general, partitioning by anatomy has little influence on summary topologies analysed under alternative partitioning schemes with a varying number of partitions. Nevertheless, models with unlinked branch lengths, which account for heterotachy across partitions, improve topological precision at the cost of reducing accuracy. In some instances, more complex partitioning schemes led to topological changes, as tested for armadillos, mostly associated with models with unlinked branch lengths. We compare our results with other empirical evaluations of morphological data and those from empirical and simulation studies of partitioning of molecular data, considering the adequacy of anatomical partitioning relative to alternative methods of partitioning morphological datasets.