Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Ctenophore relationships and their placement as the sister group to all other animals

Nature Ecology & Evolution volume 1, pages 1737–1746 (2017)Cite this article

Subjects

An Author Correction to this article was published on 18 October 2017

This article has been updated

Abstract

Ctenophora, comprising approximately 200 described species, is an important lineage for understanding metazoan evolution and is of great ecological and economic importance. Ctenophore diversity includes species with unique colloblasts used for prey capture, smooth and striated muscles, benthic and pelagic lifestyles, and locomotion with ciliated paddles or muscular propulsion. However, the ancestral states of traits are debated and relationships among many lineages are unresolved. Here, using 27 newly sequenced ctenophore transcriptomes, publicly available data and methods to control systematic error, we establish the placement of Ctenophora as the sister group to all other animals and refine the phylogenetic relationships within ctenophores. Molecular clock analyses suggest modern ctenophore diversity originated approximately 350 million years ago ± 88 million years, conflicting with previous hypotheses, which suggest it originated approximately 65 million years ago. We recover Euplokamis dunlapae—a species with striated muscles—as the sister lineage to other sampled ctenophores. Ancestral state reconstruction shows that the most recent common ancestor of extant ctenophores was pelagic, possessed tentacles, was bioluminescent and did not have separate sexes. Our results imply at least two transitions from a pelagic to benthic lifestyle within Ctenophora, suggesting that such transitions were more common in animal diversification than previously thought.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Exemplar morphological forms of Ctenophora.
Fig. 2: Relationships among metazoans inferred with the CAT-GTR substitution model and dataset Metazoa_Choano_RCFV_strict.
Fig. 3: Evolutionary relationships among Ctenophora and ancestral character state reconstruction of the general body plan.
Fig. 4: Evolutionary relationships of Ctenophora and ancestral character state reconstruction of benthic versus pelagic lifestyle.
Fig. 5: Evolutionary relationships of Ctenophora and ancestral state reconstruction of the primary feeding mode.

Similar content being viewed by others

Change history

  • 18 October 2017

    In the version of this Article originally published the location of Punta Arenas was incorrect and should have read 'Chile' in Figures 3–5 and in the Supplementary Information. This has been corrected in all versions of the Article.

References

  1. Hyman, L. The Invertebrates Vol. 1 (McGraw-Hill, New York, 1940).

  2. Mackie, G. O., Mills, C. E. & Singla, C. L. Structure and function of the prehensile tentialla of Euplokamis (Ctenophora, Cydippida). Zoomorphology 107, 319–337 (1988).

    Article  Google Scholar 

  3. Moroz, L. L. et al. The ctenophore genome and the evolutionary origins of neural systems. Nature 510, 109–114 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Roohi, A. et al. Changes in biodiversity of phytoplanton, zooplankton, fishes and macrobenthos in the Southern Caspian Sea after the invasion of the ctenophore Mnemiopsis leidyi. Biol. Invasions 12, 2342–2361 (2010).

    Article  Google Scholar 

  5. Ryan, J. F. et al. The genome of the ctenophore Mnemiopsis leidyi and its implications for cell type evolution. Science 342, 1242592 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Harbison, G. R. in The Origins and Relationships of Lower Invertebrates (eds Morris, S. C., George, J. D., Gibson, R. & Platt, H. M.) 78–100 (Oxford Univ. Press, Oxford, 1985).

  7. Dunn, C. W., Leys, S. & Haddock, S. H. D. The hidden biology of sponges and ctenophores. Trends Ecol. Evol. 30, 282–291 (2015).

    Article  PubMed  Google Scholar 

  8. Whelan, N. V., Kocot, K. M. & Halanych, K. M. Employing phylogenomics to resolve the relationships among cnidarians, ctenophores, sponges, placozoans and bilaterians. Integr. Comp. Biol. 55, 1084–1095 (2015).

    Article  PubMed  Google Scholar 

  9. Dunn, C. W. et al. Broad phylogenomic sampling improves resolution of the animal tree of life. Nature 452, 745–749 (2008).

    Article  CAS  PubMed  Google Scholar 

  10. Hejnol, A. et al. Assessing the root of bilaterian animals with scalable phylogenomic models. Proc. Biol. Sci. 276, 4261–4270 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Whelan, N. V., Kocot, K. M., Moroz, L. L. & Halanych, K. M. Error, signal, and the placement of Ctenophora sister to all other animals. Proc. Natl. Acad. Sci. USA 112, 5773–2778 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Podar, M., Haddock, S. H. D., Sogin, M. L. & Harbison, G. R. A molecular phylogenetic framework for the phylum Ctenophora using 18S rRNA genes. Mol. Phylogen. Evol. 21, 218–230 (2001).

    Article  CAS  Google Scholar 

  13. Simion, P., Bekkouche, N., Jager, M., Quéinnec, E. & Manuel, M. Exploring the potential of small RNA subunit and ITS sequences for resolving the phylogenetic relationships within the phylum Ctenophora. Zoology 118, 102–114 (2015).

    Article  PubMed  Google Scholar 

  14. Tang, F., Bengtson, S., Wang, Y., Wang, X.-l. & Yin, C.-Y. Eoandromeda and the origin of Ctenophora. Evol. Dev. 13, 408–414 (2011).

    Article  PubMed  Google Scholar 

  15. Morris, S. C. & Collins, D. H. Middle Cambiran ctenophores from Stephen Formation British Columbia, Canada. Phil. Trans. R. Soc. Lond. B 351, 279–308 (1996).

    Article  Google Scholar 

  16. Chen, J.-Y. et al. Raman spectra of a Lower Cambrian ctenophore embryo from southwestern Shaanxi, China. Proc. Natl Acad. Sci USA 104, 6289–6292 (1997).

    Article  CAS  Google Scholar 

  17. Mills, C. E. Revised classification of the genus Euplokamis Chun, 1880 (Ctenophora: Cydippida: Euplokamidae n. fam.) with a description of the new species Euplokamis dunlapae. Can. J. Zool. 65, 2661–2668 (1987).

    Article  Google Scholar 

  18. Harbison, G. R. & Miller, R. L. Not all ctenophores are hermaphrodites. Studies on the sytematics, distribution, sexuality and development of two species of Ocyropsis. Mar. Biol. 90, 413–424 (1986).

    Article  Google Scholar 

  19. Uyeno, D., Lasley, R. M., Moore, J. M. & Berumen, M. L. New records of Lobatolampea tetragona (Ctenophora: Lobata: Lobatolampeidae) from the Red Sea. Mar. Biodivers. Rec. 8, e33 (2015).

    Article  Google Scholar 

  20. Pisani, D. et al. Genomic data do not support comb jellies as the sister group to all other animals. Proc. Natl Acad. Sci. USA 112, 15402–15407 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Simion, P. et al. A large and consistent phylogenomic dataset supports sponges as the sister group to all other animals. Curr. Biol. 27, 958–967 (2017).

    Article  CAS  PubMed  Google Scholar 

  22. Halanych, K. M., Whelan, N. V., Kocot, K. M., Kohn, A. B. & Moroz, L. L. Miscues misplace sponges. Proc. Natl Acad. Sci. USA 113, E946–E949 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Moroz, L. L. & Halanych, K. M. A sisterly dispute: methodological misconceptions. Nature 529, 286–287 (2016).

    Article  CAS  PubMed  Google Scholar 

  24. Whelan, N. V. & Halanych, K. M. Who let the CAT out of the bag? Accurately dealing with subtitutional heterogeneity in phylogenomics analyses. Syst. Biol. 66, 232–255 (2017).

    CAS  PubMed  Google Scholar 

  25. Arcila, D. et al. Genome-wide interrogation advances resolution of recalcitrant groups in the tree of life. Nat. Ecol. Evol. 1, 0020 (2017).

    Article  Google Scholar 

  26. Shen, X.-X., Hittinger, C. T. & Rokas, A. Contentious relationships in phylogenomic studies can be driven by a handful of genes. Nat. Ecol. Evol. 1, 0126 (2017).

    Article  Google Scholar 

  27. Heath, T. A., Hedtke, S. M. & Hillis, D. M. Taxon sampling and the accuracy of phylogenetic analyses. J. Syst. Evol. 46, 239–257 (2008).

    Google Scholar 

  28. Hedtke, S. M., Townsend, T. M. & Hillis, D. M. Resolution of phylogenetic conflict in large data sets by increased taxon sampling. Syst. Biol. 55, 522–529 (2006).

    Article  PubMed  Google Scholar 

  29. Zwickl, D. J. & Hillis, D. M. Increased taxon sampling greatly reduces phylogenetic error. Syst. Biol. 51, 588–598 (2002).

    Article  PubMed  Google Scholar 

  30. Benton, M. J. & Twitchett, R. J. How to kill (almost all life): the end-Permian extinctoin event. Trends Ecol. Evol. 18, 358–365 (2003).

    Article  Google Scholar 

  31. Lartillot, N. & Philippe, H. A Bayesian mixture model for across-site heterogeneities in the amino-acid replacement process. Mol. Biol. Evol. 21, 1095–1109 (2004).

    Article  CAS  PubMed  Google Scholar 

  32. Steinmetz, P. R. H. et al. Independent evolution of striated muscles in cnidarians and bilaterians. Nature 487, 231–234 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Martindale, M. Q. in Atlas of Marine Invertebrate Larvae (eds Young, C. M., Sewell, M. A. & Rice, M. E.) 109–122 (Academic Press, Cambridge, MA, 2002).

  34. Rigby, S. & Milsom, C. Benthic origins of zooplankton: an environmentaly determined macroevolutionary effect. Geology 24, 52–54 (1996).

    Article  Google Scholar 

  35. Haddock, S. H. D. & Case, J. F. Not all ctenophores are bioluminescent: Pleurobrachia. Biol. Bull. 189, 356–362 (1995).

    Article  CAS  PubMed  Google Scholar 

  36. Widder, E. A. Bioluminescence in the ocean: origins of biological, chemical, and ecological diversity. Science 328, 704–708 (2010).

    Article  CAS  PubMed  Google Scholar 

  37. Gershwin, L.-A., Zeidler, W. & Davie, P. J. F. Ctenophora of Australia. Mem. Queensl. Mus. 54, 1–45 (2010).

    Google Scholar 

  38. Haddock, S. H. D. Comparative feeding behaviour of planktonic ctenophores. Integr. Comp. Biol. 47, 847–853 (2007).

    Article  PubMed  Google Scholar 

  39. Moroz, L. L. Covergent evolution of neural systems in ctenophores. J. Exp. Biol. 218, 598–611 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Moroz, L. L. & Kohn, A. B. Independent origins of neurons and synapses: insights from ctenophores. Phil. Trans. R. Soc. Lond. B 371, 20150041 (2016).

    Article  CAS  Google Scholar 

  41. Borowiec, M. L., Lee, E. K., Chiu, J. C. & Plachetzki, D. C. Extracting phylogenetic signal and accounting for bias in whole-genome data sets supports the Ctenophora as sister to remaining Metazoa. BMC Genomics 16, 987 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Cannon, J. T. et al. Xenacoelomorpha is the sister group to Nephrozoa. Nature 530, 89–93 (2016).

    Article  CAS  PubMed  Google Scholar 

  43. Chang, E. S. et al. Genomic insights into the evolutionary origin of Myxozoa within Cnidaria. Proc. Natl Acad. Sci. USA 112, 14912–14917 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Nabhan, A. R. & Sarkar, I. N. The impact of taxon sampling on phylogenetic inference: a review of two decades of controversy. Brief. Bioinformatics 13, 122–134 (2011).

    Article  PubMed  Google Scholar 

  45. Brown, T., Howe, C., Zhang, A., Pyrkosz, Q. & Brom, A. B. A reference-free algorithm for computational normalization of shotgun sequencing data. Preprint at https://arxiv.org/abs/1203.4802 (2012).

    Google Scholar 

  46. Haas, B. J. et al. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat. Protoc. 8, 1494–1512 (2013).

    Article  CAS  PubMed  Google Scholar 

  47. Ebersberger, I., Strauss, S. & von Haeseler, A. HaMStR: profile hidden Markov model based search for orthologs in ESTs. BMC Evol. Biol. 9, 157 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Altschul, S. F. et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Li, L., Stoeckert, C. J. & Roos, D. S. OrthoMCL: identification of ortholog groups for eukaryotic genomes. Genome Res. 13, 2178–2189 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Kocot, K. M. et al. Phylogenomics of Lophotrochozoa with consideration of systematic error. Syst. Biol. 66, 256–282 (2017).

    CAS  PubMed  Google Scholar 

  51. Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2—approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Kocot, K. M., Citarella, M. R., Moroz, L. L. & Halanych, K. M. PhyloTreePruner: a phylogenetic tree-based approach for selection of orthologous sequences for phylogenomics. Evol. Bioinform. 9, 429–435 (2013).

    Article  CAS  Google Scholar 

  54. Finn, R. D., Clements, J. & Eddy, S. R. HMMER web server: interactive sequence similarity searching. Nucleic Acids Res. 39, W29–W37 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Struck, T. H. TreSpEx—detection of misleading signal in phylogenetic reconstructions based on tree information. Evol. Bioinform. 10, 51–67 (2014).

    Article  CAS  Google Scholar 

  57. R Development Core Team. R: A Language and Environment for Statistical Computing http://www.R-project.org/  (R Foundation for Statistical Computing, Vienna, 2015).

  58. Zhong, M. et al. Detecting the symplesiomorphy trap: a multigene phylogenetic analysis of terebelliform annelids. BMC Evol. Biol. 11, 369 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Kück, P. & Struck, T. H. BaCoCa—a heuristic software tool for the parallel assessment of sequence biases in hundreds of gene and taxon partitions. Mol. Phylogen. Evol. 70, 94–98 (2014).

    Article  CAS  Google Scholar 

  60. Lartillot, N., Rodrigue, N., Stubbs, D. & Richer, J. PhyloBayes MPI: phylogenetic reconstruction with infinite mixtures of profiles in a parallel environment. Syst. Biol. 62, 611–615 (2013).

    Article  CAS  PubMed  Google Scholar 

  61. Nosenko, T. et al. Deep metazoan phylogeny: when different genes tell different stories. Mol. Phylogen. Evol. 67, 223–233 (2013).

    Article  Google Scholar 

  62. Brown, J. M. & Lemmon, A. R. The importance of data partitioning and the utility of Bayes factors in Bayesian phylogenetics. Syst. Biol. 56, 643–655 (2007).

    Article  PubMed  Google Scholar 

  63. Lanfear, R., Frandsen, P. B., Wright, A. M., Senfeld, T. & Calcott, B. PartitionFinder 2: new methods for selecting partitioned models of evolution for molecular and morphological phylogenetic analyses. Mol. Biol. Evol., 34, 772–773 (2016).

    Google Scholar 

  64. Lanfear, R., Calcott, B., Kainer, D., Mayer, C. & Stamatakis, A. Selecting optimal partitioning schemes for phylogenomic datasets. BMC Evol. Biol. 14, 82 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Jékely, G., Paps, J. & Nielsen, C. The phylogenetic position of ctenophores and the origin(s) of nervous systems. EvoDevo 6, 1 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Bouckaert, R. et al. BEAST 2: A software platform for Bayesian evolutionary analysis. PLoS Comput. Biol. 10, e1003537 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Rice, P., Longden, I. & Bleasby, A. EMBOSS: the European Molecular Biology Open Software Suite. Trends Genet. 16, 276–277 (2000).

    Article  CAS  PubMed  Google Scholar 

  68. Duchêne, S., Molak, M. & Ho, S. Y. W. ClockstaR: choosing the number of relaxed-clock models in molecular phylogenetic analysis. Bioinformatics 30, 1017–1019 (2014).

    Article  CAS  PubMed  Google Scholar 

  69. Drummond, A. J., Ho, S. Y. W., Phillips, M. J. & Rambaut, A. Relaxed phylogenetics and dating with confidence. PLoS Biol. 4, e88 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Dos Reis, M. et al. Uncertainty in the timing of origin of animals and the limits of precision in molecular timescales. Curr. Biol. 25, 29392950 (2015).

    Google Scholar 

  71. Brusca, R. C., Moore, W. & Shuster, S. M. in Invertebrates 3rd edn 1104 (Sinauer Associates, Sunderland, MA, 2016).

  72. Eechkaut, I., Flammang, P., Bue, C. L. & Jangoux, M. Functional morphology of the tentacles and tentilla of Coeloplana bannworthi (Ctenophora, Platyctenida), and ectosymbiont of Diadema setosum (Echinodermata, Echinoida). Zoomorphology 117, 165–174 (1997).

    Article  Google Scholar 

  73. Harbison, G. R. & Madin, L. P. in Synopsis and Classification of Living Organisms (ed. Parker, S. P.) 707–715 (McGraw-Hill, New York, 1982).

  74. Horita, T. An undescribed lobate ctenophore, Lobatolampea tetragona gen. nov. & spec. nov., representing a new family, from Japan. Zool. Meded. 73, 457–464 (2000).

    Google Scholar 

  75. Matsumoto, G. I. & Harbison, G. R. In situ observations of foraging, feeding, and escape behavior in three orders of oceanic ctenophores: Lobata, Cestida, and Beroida. Mar. Biol. 117, 279–287 (1993).

    Article  Google Scholar 

  76. Purcell, J. E., Sturdevant, M. V. & Galt, C. P. Response of Marine Ecosystems to Global Change: Ecological Impact of Appendicularians (eds Gorsky, G., Yongbluth, M. J. & Deibel, D.) 359–435 (GB Science Publishers-Editions Scientifiques, Paris, 2005).

  77. Stretch, J. J. Observations on the abundance and feeding behavior of the cestid ctenophore. Velamen Parallelum. Bull. Mar. Sci. 32, 796–799 (1982).

    Google Scholar 

  78. Blomberg, S. P., Garland, T. Jr & Ives, A. R. Testing for phylogenetic signal in comparative data: behavioral traits are more labile. Evolution 57, 717–745 (2003).

    Article  PubMed  Google Scholar 

  79. Revell, L. J. Phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).

    Article  Google Scholar 

  80. Huelsenbeck, J. P., Nielsen, R. & Bollback, J. P. Stochastic mapping of morphological characters. Syst. Biol. 52, 131–158 (2003).

    Article  PubMed  Google Scholar 

  81. Nielsen, R. Mapping mutations on phylogenies. Syst. Biol. 51, 729–739 (2002).

    Article  PubMed  Google Scholar 

  82. Paradis, E., Claude, J. & Strimmer, K. APE: analysis of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).

  83. Harmon, L. J., Weir, J. T., Brock, C. D., Glor, R. E. & Challenger, W. GEIGER: investigating evolutionary radiations. Bioinformatics 24, 129–131 (2008).

    Google Scholar 

Download references

Acknowledgements

This work was made possible in part by a grant of high-performance computing resources and technical support from the Alabama Supercomputer Authority and was supported by the United States National Aeronautics and Space Administration (grant NASA-NNX13AJ31G), the National Science Foundation (grants ANT-1043670, ANT-1043745, 1557923, 1548121 and 1645219) and the Ministry of Education of the Russian Federation (#14W03.31.0015). We thank the International SeaKeepers Society, captains and crew of RV Laurence M Gold, RV Capasetic, RV Penny Mae, RV Defiance, RV Basic Explorer, RV Harle of Fleet Miami and RV Miss Phebe II, as well as J. Jacoby for their help with the collection of ctenophores around the globe. A. Kohn helped with laboratory work and data curation. C. Mills helped with species identifications. The findings and conclusions in this paper are those of the authors and do not necessarily represent the views of the United States Fish and Wildlife Service. This is Molette Biology Laboratory contribution 70 and Auburn University Marine Biology Program contribution 166.

Author information

Authors and Affiliations

  1. Molette Biology Laboratory for Environmental and Climate Change Studies, Department of Biological Sciences, Auburn University, Auburn, AL, 36849, USA

    Nathan V. Whelan & Kenneth M. Halanych

  2. Warm Springs Fish Technology Center, US Fish and Wildlife Service, 5308 Spring St, Warm Springs, GA, 31830, USA

    Nathan V. Whelan

  3. Department of Biological Sciences, The University of Alabama, Box 870344, Tuscaloosa, AL, 35487, USA

    Kevin M. Kocot

  4. The Whitney Laboratory for Marine Biosciences, University of Florida, St. Augustine, FL, 32080, USA

    Tatiana P. Moroz, Krishanu Mukherjee, Peter Williams & Leonid L. Moroz

  5. Florida Museum of Natural History, University of Florida, Gainesville, FL, 32611, USA

    Gustav Paulay

  6. Department of Neuroscience and McKnight Brain Institute, University of Florida, Gainesville, FL, 32611, USA

    Leonid L. Moroz

Authors
  1. Nathan V. Whelan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kevin M. Kocot
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tatiana P. Moroz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Krishanu Mukherjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Peter Williams
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Gustav Paulay
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Leonid L. Moroz
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Kenneth M. Halanych
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

N.V.W., K.M.K., L.L.M. and K.M.H. designed the study. K.M.K. collected and identified the Australian speices. L.L.M. and G.P. collected and identified all other ctenophores. P.W., K.M., T.P.M. and L.L.M. sequenced and assembled the ctenophore transcriptomes. N.V.W. and K.M.K. performed phylogenetic analyses and ancestral state reconstruction. N.V.W., K.M.K., L.L.M. and K.M.H. wrote the manuscript. All authors edited manuscript versions and approved the final version.

Corresponding authors

Correspondence to Nathan V. Whelan, Leonid L. Moroz or Kenneth M. Halanych.

Ethics declarations

Competing interests

The authors declare no competing financial interests

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Supplementary Information

Supplementary Discussion, Tables, and Figures

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Whelan, N.V., Kocot, K.M., Moroz, T.P. et al. Ctenophore relationships and their placement as the sister group to all other animals. Nat Ecol Evol 1, 1737–1746 (2017). https://doi.org/10.1038/s41559-017-0331-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41559-017-0331-3

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing