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Anatomical partitioning has little influence in topologies from Bayesian phylogenetic analyses of morphological data


Casali, Daniel; Freitas, Felipe; Perini, Fernando (2021), Anatomical partitioning has little influence in topologies from Bayesian phylogenetic analyses of morphological data, Dryad, Dataset,


Morphological data is a fundamental source of evidence to reconstruct the Tree of Life, and Bayesian phylogenetic methods are increasingly being used for this task, along with, or instead of, traditional parsimony approaches. Bayesian phylogenetic analyses require the use of proper evolutionary models and their performance have been intensively studied in the past few years, with significant improvements to our knowledge regarding their performance. Notwithstanding, it was only recently that partitioned models for morphology received attention in studies of empirical data, but a systematic evaluation of its performances using simulations was never performed. Here we evaluate the influence of partitioned models defined by anatomical criterion in the precision and accuracy of consensus tree topologies, evaluating the possible negative effects of under and overpartitioning. For that, we analysed datasets simulated using parameters and properties of two empirical datasets, using Bayesian phylogenetic analyses in MrBayes. Additionally, we reanalysed 32 empirical datasets for diverse groups of vertebrates, applying unpartitioned and partitioned models. We found that in general, partitioning by anatomy has little to no influences in the performance of Bayesian phylogenetic methods in respect to the metrics studied here, with analyses under alternative partitioning schemes presenting very similar tree precision and accuracy. We discuss the possible reasons for the disagreement between the results obtained here and previous studies for empirical morphological data, and with empirical and simulation studies of molecular data, discussing the adequacy of anatomical partitioning relative to alternative methods to partition morphological datasets and how morphological and molecular partitioning are related.


Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Award: 0001

Fundação de Amparo à Pesquisa do Estado de São Paulo, Award: 2018/09666-5