Abstract

Independent molecular and morphological phylogenetic analyses have often produced discordant results for certain groups which, for fossil-rich groups, raises the possibility that morphological data might mislead in those groups for which we depend upon morphology the most. Rhynchonellide brachiopods, with more than 500 extinct genera but only 19 extant genera represented today, provide an opportunity to explore the factors that produce contentious phylogenetic signal across datasets, as previous phylogenetic hypotheses generated from molecular sequence data bear little agreement with those constructed using morphological characters. Using a revised matrix of 66 morphological characters, and published ribosomal DNA sequences, we performed a series of combined phylogenetic analyses to identify conflicting phylogenetic signals. We completed a series of parsimony-based and Bayesian analyses, varying the data used, the taxa included, and the models used in the Bayesian analyses. We also performed simulation-based sensitivity analyses to assess whether the small size of the morphological data partition relative to the molecular data influenced the results of the combined analyses. In order to compare and contrast a large number of phylogenetic analyses and their resulting summary trees, we developed a measure for the incongruence between two topologies and simultaneously ignore any differences in phylogenetic resolution. Phylogenetic hypotheses generated using only morphological characters differed among each other, and with previous analyses, whereas molecular-only and combined Bayesian analyses produced extremely similar topologies. Characters historically associated with traditional classification in the Rhynchonellida have very low consistency indices on the topology preferred by the combined Bayesian analyses. Overall, this casts doubt on the use of morphological systematics to resolve relationships among the crown rhynchonellide brachiopods. However, expanding our dataset to a larger number of extinct taxa with intermediate morphologies is necessary to exclude the possibility that the morphology of extant taxa is not dominated by convergence along long branches.

Highlights

  • BAPST ET AL.—COMBINING MORPHOLOGICAL AND MOLECULAR DATA IN BRACHIOPODS and morphological analyses can suffer from issues that can impact any phylogenetic such as poor outgroup selection or long-branch attraction (Felsenstein 1978) among selected taxa

  • A large number of taxa are unshared between the two analyses, out-group taxa: several non-rhynchonellide taxa were used by Cohen and Bitner, and a number of fossil rhynchonellide taxa used as uncertain outgroups by Schreiber et al the molecular data were only available for a portion of the extant rhynchonellide diversity, in contrast to the morphological analyses

  • This artificial dataset is analyzed in MrBayes, using the same settings as analysis Comb-BMaxI-18t

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Summary

Introduction

BAPST ET AL.—COMBINING MORPHOLOGICAL AND MOLECULAR DATA IN BRACHIOPODS and morphological analyses can suffer from issues that can impact any phylogenetic such as poor outgroup selection or long-branch attraction (Felsenstein 1978) among selected taxa. Extant brachiopods of the Order Rhynchonellida (Kuhn 1949) are one such group, where previous morphological (Schreiber et al 2013) and molecular datasets (Cohen and Bitner 2013a) exist but suggest very different patterns of relationship (Fig. 1). A large number of taxa are unshared between the two analyses, out-group taxa: several non-rhynchonellide taxa were used by Cohen and Bitner, and a number of fossil rhynchonellide taxa used as uncertain outgroups by Schreiber et al the molecular data were only available for a portion of the extant rhynchonellide diversity, in contrast to the morphological analyses These differences may have played a large role in the apparent discordance between the analyses. Maximum parsimony analysis is the most commonly used approach for morphological data, Bayesian inference may partially avoid issues related to differences in the size of SYSTEMATIC BIOLOGY

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