Cited by 292Marine Invertebrate Physiology and EcologyGenomics and Phylogenetic StudiesProtist diversity and phylogeny

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

Open access

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 [1-5]. 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., [8-11]). 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 [13-15] 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 [16-20], 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. VIDEO ABSTRACT.

Checked 2026-10-06 · 5 references not checked yet

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

Feuda et al. use posterior predictive tests to show that two phylogenomic datasets previously supporting Ctenophora-sister are poorly described by standard models, especially for site-specific and lineage-specific compositional heterogeneity. When data are recoded and analyzed with CAT-GTR+G, model adequacy improves and Porifera-sister is favored, leading the authors to conclude that Ctenophora-sister is a tree reconstruction artifact.

Key findings

  • All considered models were rejected for site-specific and lineage-specific compositional heterogeneity, but CAT-GTR+G performed best. For WhelanD20-Opistho, PPA-DIV Z scores were 127.35 (WAG), 116.17 (LG), 92.62 (GTR), 104.71 (PF-scheme), and 6.21 (CAT-GTR); for Chang, CAT-GTR gave 6.17. Recoding reduced these values, e.g., Dayhoff-6 + CAT-GTR gave 0.94 for WhelanD20-Opistho and KGB-6 + CAT-GTR gave 0.71 for Chang. For PPA-MAX, all models were strongly rejected, with the smallest absolute Z score being 12.15 (CAT-GTR on Chang).
  • WAG+G, LG+G, GTR+G (with or without recoding), and PF-schemes invariably supported Ctenophora-sister (posterior probabilities [PPs] ≥ 1). CAT-GTR+G on amino acid datasets varied by outgroup: WhelanD20-Opistho supported Ctenophora-sister (PP ≥ 1), whereas WhelanD20-Holo and WhelanD20-Choano supported Porifera-sister (PP = 0.68 and 0.77, respectively); Chang under CAT-GTR+G supported Ctenophora-sister (PP ≥ 1).
  • CAT-GTR+G analyses of recoded datasets increased support for Porifera-sister. Dayhoff-6 PPs for Porifera-sister were 0.89 (WhelanD20-Opistho), 0.99 (WhelanD20-Holo), 0.95 (WhelanD20-Choano), and 0.74 (Chang); S&R-6 PPs were 0.98, 0.99, 0.94, and 0.62; KGB-6 PPs were 0.42, 0.68, 0.37, and 0.78. Ctenophora-sister received lower support in these analyses.
  • Additional tests rejected the claim that Porifera-sister signal is limited to outlier genes: a Shen et al. outlier-excluded dataset still supported Porifera-sister under CAT-GTR+G (PP = 0.98 and 0.99 with and without Dayhoff-6 recoding), and a Dayhoff-6 recoded version of the Whelan et al. 2017 dataset supported Porifera-sister with PP = 1.

Methods

  • Posterior predictive analyses (PPAs) tested model adequacy on WhelanD20 (70 taxa) and Chang (77 taxa, 51,940 amino acid positions) using WAG+G, LG+G, GTR+G, optimal PartitionFinder schemes, and CAT-GTR+G. Site-specific amino acid preferences were assessed with PPA-DIV, PPA-CONV, and PPA-VAR; lineage-specific compositional heterogeneity was assessed with PPA-MAX and PPA-MEAN, with results quantified as Z scores.
  • Data recoding used Dayhoff-6, S&R-6, and KGB-6. Phylogenetic analyses were performed in PhyloBayes MPI v1.7a for GTR+G and CAT-GTR+G (with and without recoding), and RAxML v8.2.9 was used for the Chang PF-scheme analysis; convergence was assessed with bpcomp and tracecomp.
  • Additional analyses applied CAT-GTR+G to a Shen et al. outlier-excluded dataset with and without Dayhoff-6 recoding, and to a Dayhoff-6 recoded version of the Whelan et al. 2017 dataset.

Limitations

  • Stated: none of the considered models explicitly accounts for lineage-specific compositional heterogeneity, and all models—including CAT-GTR+G—were strongly rejected in PPA-MAX and PPA-MEAN tests (smallest absolute Z = 12.15). Models that jointly account for site and lineage heterogeneity are noted as computationally too demanding for phylogenomic datasets.
  • Stated: recoding can cause signal erosion, although the authors argue this would collapse nodes rather than change the supported topology; also, the CAT-GTR+G analysis of the amino acid version of Chang did not converge well, while all other analyses did.
  • Evident: the study’s conclusions depend on a selected set of models and PPA statistics; CAT+G was not directly tested (CAT-GTR+G was used as a representative), and the PartitionFinder protocol excluded some available models such as LG4X.

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Integrity signals

Registry

Crossref · 10.1016/j.cub.2017.11.008

Cites

67 of 72 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Verification

Claims the paper makes

0 of 109 cited sources checked · 0 supported · 0 flagged

Show all 38 claims

  • It may state two separate claims as one. 25
  • Probably not a substantial claim. 25
46 of 46
  • The paper relies on this without support.

    Ctenophora-sister is currently uncorroborated by independent evidence.

    “whereas Ctenophora-sister is currently uncorroborated by independent evidence”
  • The authors state this as an assumption.

    When computing Z-scores, the null distribution is approximately normal, with the Z-score analogous to a standard normal quantile.

    “When computing Z-scores it is assumed that the null distribution is approximately normal, with the Z-score being analogous to a standard normal quantile.”
  • The paper relies on this without support.

    Datasets without outlier genes should not include signal in favor of Porifera-sister.

    “As datasets without ''outlier genes'' should not include signal in favor of Porifera-sister”
  • The paper relies on this without support.

    Current implementations of models that explicitly take into consideration across-taxon compositional heterogeneity are computationally too demanding to allow their application to large phylogenomic datasets.

    “However, current implementations of these models are computationally too demanding to allow their application to large phylogenomic datasets.”
4 of 4

Claims other papers make about this one

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Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Some recoded Chang analyses have higher PPA-MAX Z scores

    Minor

    The Chang GTR+G PPA-MAX Z score is 18.84 without recoding, but rises to 26.99 with Dayhoff-6 recoding and 28.48 with S&R-6 recoding. Those results contradict the claim that all recoded datasets had lower PPA-MAX Z scores.

  • WhelanD20 alignment length differs by 4,000 positions

    Minor

    Table 1 describes WhelanD20-Opistho as having 46,542 amino acid positions, while the methods say WhelanD20 has 42,542 positions. WhelanD20-Opistho is identified as the version retaining all original outgroups, not a different-length alignment.

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Metadata

Authors
Feuda, R., Dohrmann, M., Pett, W., Philippe, H., Rota-Stabelli, O., Lartillot, N., Wörheide, G., & Pisani, D.
Affiliations
  • California Institute of Technology (US)
  • Ludwig-Maximilians-Universität München (DE)
  • Iowa State University (US)
  • Centre National de la Recherche Scientifique (FR)
  • Lyon 1 Université (FR)
  • Bayerische Staatssammlung für Paläontologie und Geologie (DE)
  • University of Bristol (GB)
Corresponding author
  • Wörheide, Gert
  • Pisani, Davide
Venue
Current Biology
Publisher
Elsevier BV
Published
2017-12
Volume
27
Issue
24
Pages
3864-3870.e4
ISSN
0960-9822 (ISSN-L)
OpenAlex
W2775427740
Cited by
291
References
72
Citations by year
2026: 10 · 2025: 22 · 2024: 25 · 2023: 23 · 2022: 29 · 2021: 49 · 2020: 37 · 2019: 56 · 2018: 40
FWCI
25.85
Type
journal article
Language
en
Open access
Locations
  • Current Biology (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Explore Bristol Research (publishedVersion, cc-by)
  • CINECA IRIS Institutional Research Information System (Fondazione Edmund Mach) (submittedVersion, cc-by)
  • CaltechAUTHORS (California Institute of Technology) (submittedVersion, cc-by)
  • HAL (submittedVersion)
  • Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion)
  • Figshare (submittedVersion, cc-by)
  • Iris (University of Trento) (submittedVersion, cc-by)
  • Bristol Research (University of Bristol) (submittedVersion, cc-by)
Funders
  • NERC (NE/P013643/1)
  • Templeton Foundation (60579)
  • French Laboratory of Excellence (ANR-10-LABX- 41, ANR-11-IDEX-0002-02)
  • French National Research Agency (ANR-10-BINF-01-01)
  • LMU Munich’s Institutional Strategy LMUexcellent
  • German Research Foundation (DFG) (Wo896/15-1)
Topics
  • Life Sciences › Agricultural and Biological Sciences › Animal Science and Zoology › Marine Invertebrate Physiology and Ecology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Life Sciences › Agricultural and Biological Sciences › Ecology, Evolution, Behavior and Systematics › Protist diversity and phylogeny
Keywords
animal phylogeny, sponges, comb jellies, Ctenophora, sister group relationships, compositional heterogeneity, data recoding, topology change
MeSH terms
Animals, Biological Evolution, Phylogeny, Porifera, Sequence Analysis, Protein

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