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Published December 18, 2017 | Supplemental Material + Published
Journal Article Open

Improved Modeling of Compositional Heterogeneity Supports Sponges as Sister to All Other Animals

Abstract

The relationships at the root of the animal tree have proven difficult to resolve, with the current debate focusing on whether sponges (phylum Porifera) or comb jellies (phylum Ctenophora) are the sister group of all other animals [1; 2; 3; 4 ; 5]. The choice of evolutionary models seems to be at the core of the problem because Porifera tends to emerge as the sister group of all other animals ("Porifera-sister") when site-specific amino acid differences are modeled (e.g., [6 ; 7]), whereas Ctenophora emerges as the sister group of all other animals ("Ctenophora-sister") when they are ignored (e.g., [8; 9; 10 ; 11]). We show that two key phylogenomic datasets that previously supported Ctenophora-sister [10 ; 12] display strong heterogeneity in amino acid composition across sites and taxa and that no routinely used evolutionary model can adequately describe both forms of heterogeneity. We show that data-recoding methods [13; 14 ; 15] reduce compositional heterogeneity in these datasets and that models accommodating site-specific amino acid preferences can better describe the recoded datasets. Increased model adequacy is associated with significant topological changes in support of Porifera-sister. Because adequate modeling of the evolutionary process that generated the data is fundamental to recovering an accurate phylogeny [16; 17; 18; 19 ; 20], our results strongly support sponges as the sister group of all other animals and provide further evidence that Ctenophora-sister represents a tree reconstruction artifact.

Additional Information

© 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Received 21 June 2017, Revised 19 September 2017, Accepted 2 November 2017, Available online 30 November 2017. Published: November 30, 2017. We would like to thank the University of Bristol ACRC (Advanced Computing Research Center) and Leibniz-Rechenzentrum der Bayerischen Akademie der Wissenschaften for providing access to supercomputing infrastructure. This work was supported by a NERC grant (NE/P013643/1) and a Templeton Foundation grant (ID 60579) to D.P. H.P. acknowledges the French Laboratory of Excellence project "TULIP" (ANR-10-LABX-41 and ANR-11-IDEX-0002-02). N.L. acknowledges French National Research Agency grant no. ANR-10-BINF-01-01 "Ancestrome." G.W. acknowledges funding by LMU Munich's Institutional Strategy LMU excellent within the framework of the German Excellence Initiative and German Research Foundation (DFG) grant no. Wo896/15-1. The authors would like to thank the reviewers for their helpful suggestions. Author Contributions: Conceptualization, D.P., G.W., R.F., and O.R.-S.; Methodology, D.P., O.R.-S., W.P., N.L., and H.P.; Software, W.P. and N.L.; Validation, W.P., R.F., M.D., D.P., and G.W.; Investigation, R.F., D.P., G.W., and H.P.; Writing – Original Draft, R.F., M.D., D.P., and G.W.; Writing – Review & Editing, M.D., R.F., D.P., G.W., W.P., N.L., H.P., and O.R.-S.; Visualization, R.F.; Supervision, D.P. and G.W.; Funding Acquisition, D.P., G.W., N.L., and H.P.

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Supplemental Material - mmc1.pdf

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Additional details

Created:
August 21, 2023
Modified:
October 17, 2023