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Prolonged morphological expansion of spiny-rayed fishes following the end-Cretaceous

Nature Ecology & Evolution volume 6, pages 1211–1220 (2022)Cite this article

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Abstract

Spiny-rayed fishes (Acanthomorpha) dominate modern marine habitats and account for more than a quarter of all living vertebrate species. Previous time-calibrated phylogenies and patterns from the fossil record explain this dominance by correlating the origin of major acanthomorph lineages with the Cretaceous–Palaeogene mass extinction. Here we infer a time-calibrated phylogeny using ultraconserved elements that samples 91.4% of all acanthomorph families and investigate patterns of body shape disparity. Our results show that acanthomorph lineages steadily accumulated throughout the Cenozoic and underwent a significant expansion of among-clade morphological disparity several million years after the end-Cretaceous. These acanthomorph lineages radiated into and diversified within distinct regions of morphospace that characterize iconic lineages, including fast-swimming open-ocean predators, laterally compressed reef fishes, bottom-dwelling flatfishes, seahorses and pufferfishes. The evolutionary success of spiny-rayed fishes is the culmination of multiple species-rich and phenotypically disparate lineages independently diversifying across the globe under a wide range of ecological conditions.

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Fig. 1: Time-calibrated phylogeny of Acanthomorpha, continued in Fig. 2.
Fig. 2: Time-calibrated phylogeny and subclade disparity through time for Acanthomorpha, continued from Fig. 1.
Fig. 3: Bayesian concordance factor analyses used to compare alternative phylogenetic hypotheses concerning the sister taxa of three major acanthomorph lineages.
Fig. 4: Robustness of DTT analyses.
Fig. 5: Changes in body elongation and compression.

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Data availability

NCBI BioSample Accession numbers corresponding to sequence data are listed in Supplementary Table 1. New raw sequence data are available for download from the NCBI Sequence Read Archive (SRA), under BioProject ID PRJNA758064. Sequence alignments, partitioning schemes, phylogenetic trees, phenotypic trait data and other related data files are available on the corresponding Dryad Digital Repository: https://datadryad.org/stash/share/-vfd5XqnNuJ1BHG7s2nBDw2nRRyK80Rc4BAtkrAkkoU.

Code availability

Analyses relied on open-source programs, and scripts used for data analysis are available on the Dryad Digital Repository: https://datadryad.org/stash/share/-vfd5XqnNuJ1BHG7s2nBDw2nRRyK80Rc4BAtkrAkkoU.

References

  1. Fricke, R., Eschmeyer, W. N. & Fong, J. D. Eschmeyer’s Catalog of Fishes: species by family/subfamily (1 September 2021) https://researcharchive.calacademy.org/research/ichthyology/catalog/SpeciesByFamily.asp (2021).

  2. Wainwright, P. C. & Longo, S. J. Functional innovations and the conquest of the oceans by acanthomorph fishes. Curr. Biol. 27, R550–R557 (2017).

    Article  CAS  PubMed  Google Scholar 

  3. Near, T. J. et al. Phylogeny and tempo of diversification in the superradiation of spiny-rayed fishes. Proc. Natl Acad. Sci. USA 110, 12738–12743 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Dornburg, A. & Near, T. J. The emerging phylogenetic perspective on the evolution of actinopterygian fishes. Annu. Rev. Ecol., Evolution, Syst. 52, 427–452 (2021).

    Article  Google Scholar 

  5. Chen, W.-J. et al. New insights on early evolution of spiny-rayed fishes (Teleostei: Acanthomorpha). Front. Mar. Sci. 1, 53 (2014).

    Article  Google Scholar 

  6. Friedman, M. Explosive morphological diversification of spiny-finned teleost fishes in the aftermath of the end-Cretaceous extinction. Proc. R. Soc. B 277, 1675–1683 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Alfaro, M. E. et al. Explosive diversification of marine fishes at the Cretaceous–Palaeogene boundary. Nat. Ecol. Evolution 2, 688–696 (2018).

    Article  Google Scholar 

  8. Meredith, R. W. et al. Impacts of the Cretaceous terrestrial revolution and KPg extinction on mammal diversification. Science 334, 521–524 (2011).

    Article  CAS  PubMed  Google Scholar 

  9. Stadler, T. Mammalian phylogeny reveals recent diversification rate shifts. Proc. Natl Acad. Sci. USA 108, 6187–6192 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Venditti, C., Meade, A. & Pagel, M. Multiple routes to mammalian diversity. Nature 479, 393–396 (2011).

    Article  CAS  PubMed  Google Scholar 

  11. Liu, L. et al. Genomic evidence reveals a radiation of placental mammals uninterrupted by the KPg boundary. Proc. Natl Acad. Sci. USA 114, E7282–E7290 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Slater, G. J. Phylogenetic evidence for a shift in the mode of mammalian body size evolution at the Cretaceous–Palaeogene boundary. Methods Ecol. Evol. 4, 734–744 (2013).

    Article  Google Scholar 

  13. Jetz, W. & Pyron, R. A. The interplay of past diversification and evolutionary isolation with present imperilment across the amphibian tree of life. Nat. Ecol. Evolution 2, 850–858 (2018).

    Article  Google Scholar 

  14. Longrich, N. R., Bhullar, B.-A. S. & Gauthier, J. Mass extinction of lizards and snakes at the Cretaceous–Paleogene boundary. Proc. Natl Acad. Sci. USA 109, 21396–21401 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Jarvis, E. D. et al. Whole-genome analyses resolve early branches in the tree of life of modern birds. Science 346, 1320 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Patterson, C. An overview of the early fossil record of acanthomorphs. Bull. Mar. Sci. 52, 29–59 (1993).

    Google Scholar 

  17. Betancur-R, R. et al. The tree of life and a new classification of bony fishes. PLOS Curr. https://doi.org/10.1371/currents.tol.53ba26640df0ccaee75bb165c8c26288 (2013).

  18. Hughes, L. C. et al. Comprehensive phylogeny of ray-finned fishes (Actinopterygii) based on transcriptomic and genomic data. Proc. Natl Acad. Sci. USA 115, 6249–6254 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Johnson, G. D. & Patterson, C. Percomorph phylogeny: a survey of acanthomorphs and a new proposal. Bull. Mar. Sci. 52, 554–626 (1993).

    Google Scholar 

  20. Near, T. J. et al. Resolution of ray-finned fish phylogeny and timing of diversification. Proc. Natl Acad. Sci. USA 109, 13698–13703 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Miya, M. et al. Major patterns of higher teleostean phylogenies: a new perspective based on 100 complete mitochondrial DNA sequences. Mol. Phylogenet. Evol. 26, 121–138 (2003).

    Article  CAS  PubMed  Google Scholar 

  22. Wainwright, P. C. et al. The evolution of pharyngognathy: a phylogenetic and functional appraisal of the pharyngeal jaw key innovation in labroid fishes and beyond. Syst. Biol. 61, 1001–1027 (2012).

    Article  PubMed  Google Scholar 

  23. Ribeiro, E., Davis, A. M., Rivero-Vega, R. A., Ortí, G. & Betancur-R, R. Post-Cretaceous bursts of evolution along the benthic-pelagic axis in marine fishes. Proc. R. Soc. B 285, 20182010 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Price, S. A. et al. Building a body shape morphospace of teleostean fishes. Integ. Comp. Biol. 59, 716–730 (2019).

    Article  CAS  Google Scholar 

  25. Smith, W. L., Stern, J. H., Girard, M. G. & Davis, M. P. Evolution of venomous cartilaginous and ray-finned fishes. Integ. Comp. Biol. 56, 950–961 (2016).

    Article  Google Scholar 

  26. Liem, K. Evolutionary strategies and morphological innovations: cichlid pharyngeal jaws. Syst. Zool. 22, 425–441 (1973).

    Article  Google Scholar 

  27. Chan, K. O., Hutter, C. R., Wood, P. L., Grismer, L. L. & Brown, R. M. Larger, unfiltered datasets are more effective at resolving phylogenetic conflict: Introns, exons, and UCEs resolve ambiguities in Golden-backed frogs (Anura: Ranidae; genus Hylarana). Mol. Phylogenet. Evol. 151, 106899 (2020).

    Article  PubMed  Google Scholar 

  28. Minh, B. Q., Hahn, M. W. & Lanfear, R. New methods to calculate concordance factors for phylogenomic datasets. Mol. Biol. Evol. 37, 2727–2733 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Simion, P., Delsuc, F. & Philippe, H. in Phylogenetics in the genomic era (eds C. Scornavacca, F. Delsuc & N. Galtier) 2.1:1–2.1:34 (no commercial publisher, authors open access book, 2020).

  30. Gilbert, P. S. et al. Genome-wide ultraconserved elements exhibit higher phylogenetic informativeness than traditional gene markers in percomorph fishes. Mol. Phylogenet. Evol. 92, 140–146 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Alda, F., Ludt, W. B., Elías, D. J., McMahan, C. D. & Chakrabarty, P. Comparing ultraconserved elements and exons for phylogenomic analyses of Middle American cichlids: when data agree to disagree. Genome Biol. Evol. https://doi.org/10.1093/gbe/evab161 (2021).

  32. Harmon, L. J., Schulte, J. A., Larson, A. II & Losos, J. B. Tempo and mode of evolutionary radiation in iguanian lizards. Science 301, 961–964 (2003).

    Article  CAS  PubMed  Google Scholar 

  33. Claverie, T. & Wainwright, P. C. A morphospace for reef fishes: elongation Is the dominant axis of body shape evolution. PLoS ONE 9, e112732 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Friedman, S. T. et al. Body shape diversification along the benthic–pelagic axis in marine fishes. Proc. R. Soc. B 287, 20201053 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Schwarzhans, W. & Stringer, G. Fish otoliths from the Late Maastrichtian Kemp Clay (Texas, USA) and the Early Dannian Clayton Formation (Arkansas, USA) and an assessment of extinction and survival of teleost lineages across the K–Pg boundary based on otoliths. Riv. Ital. Paleontol. Strat. 126, 395–446 (2020).

    Google Scholar 

  36. Friedman, M. et al. A phylogenomic framework for pelagiarian fishes (Acanthomorpha: Percomorpha) highlights mosaic radiation in the open ocean. Proc. R. Soc. B 286, 20191502 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Price, S. A., Holzman, R., Near, T. J. & Wainwright, P. C. Coral reefs promote the evolution of morphological diversity and ecological novelty in labrid fishes. Ecol. Lett. 14, 462–469 (2011).

    Article  CAS  PubMed  Google Scholar 

  38. McGee, M. D. et al. The ecological and genomic basis of explosive adaptive radiation. Nature 586, 75–79 (2020).

    Article  CAS  PubMed  Google Scholar 

  39. Daane, J. M. et al. Historical contingency shapes adaptive radiation in Antarctic fishes. Nat. Ecol. Evolution https://doi.org/10.1038/s41559-019-0914-2 (2019).

    Article  Google Scholar 

  40. Faircloth, B. C. et al. Ultraconserved elements anchor thousands of genetic markers spanning multiple evolutionary timescales. Syst. Biol. 61, 717–726 (2012).

    Article  PubMed  Google Scholar 

  41. Glenn, T. C. et al. Adapterama I: universal stubs and primers for 384 unique dual-indexed of 147,456 combinatorially-indexed Illumina libraries (iTru & iNext). PeerJ 7, e7755 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Faircloth, B. C. PHYLUCE is a software package for the analysis of conserved genomic loci. Bioinformatics 32, 786–788 (2016).

    Article  CAS  PubMed  Google Scholar 

  43. Nguyen, L. T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).

    Article  CAS  PubMed  Google Scholar 

  44. Kozlov, A. M., Darriba, D., Flouri, T., Morel, B. & Stamatakis, A. RAxML-NG: a fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference. Bioinformatics 35, 4453–4455 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Puigbò, P., Garcia-Vallvé, S. & McInerney, J. O. TOPD/FMTS: a new software to compare phylogenetic trees. Bioinformatics 23, 1556–1558 (2007).

    Article  PubMed  CAS  Google Scholar 

  46. Mai, U. & Mirarab, S. TreeShrink: fast and accurate detection of outlier long branches in collections of phylogenetic trees. BMC Genomics 19, 23–40 (2018).

    Article  Google Scholar 

  47. 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 

  48. Zhang, C., Rabiee, M., Sayyari, E. & Mirarab, S. ASTRAL-III: polynomial time species tree reconstruction from partially resolved gene trees. BMC Bioinformatics 19, 15–30 (2018).

    Article  CAS  Google Scholar 

  49. Minh, B. Q. et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol. Biol. Evol. 37, 1530–1534 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Ronquist, F. et al. MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space. Syst. Biol. 61, 539–542 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Ane, C., Larget, B., Baum, D. A., Smith, S. D. & Rokas, A. Bayesian estimation of concordance among gene trees. Mol. Biol. Evol. 24, 412–426 (2007).

    Article  CAS  PubMed  Google Scholar 

  52. Bouckaert, R. et al. BEAST 2.5: an advanced software platform for Bayesian evolutionary analysis. PLoS Comput. Biol. 15, e1006650 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Gernhard, T. The conditioned reconstructed process. J. Theor. Biol. 253, 769–778 (2008).

    Article  PubMed  Google Scholar 

  54. Drummond, A. J., Ho, S. Y. W., Phillips, M. J. & Rambaut, A. Relaxed phylogenetics and dating with confidence. PLoS Biol. 4, 699–710 (2006).

    Article  CAS  Google Scholar 

  55. Harrington, R. C. et al. Phylogenomic analysis of carangimorph fishes reveals flatfish asymmetry arose in a blink of the evolutionary eye. BMC Evol. Biol. 16, 224 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  56. 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 (2017).

    CAS  PubMed  Google Scholar 

  57. Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarization in Bayesian phylogenetics using Tracer 1.7. Syst. Biol. 67, 901–904 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Drummond, A. J., Suchard, M. A., Xie, D. & Rambaut, A. Bayesian phylogenetics with BEAUti and the BEAST 1.7. Mol. Biol. Evol. 29, 1969–1973 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Höhna, S., May, M. R. & Moore, B. R. TESS: an R package for efficiently simulating phylogenetic trees and performing Bayesian inference of lineage diversification rates. Bioinformatics 32, 789–791 (2016).

    Article  PubMed  CAS  Google Scholar 

  60. Rabosky, D. L. Automatic detection of key innovations, rate shifts, and diversity-dependence on phylogenetic trees. PLoS ONE 9, e89543 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  61. Rabosky, D. L. et al. BAMMtools: an R package for the analysis of evolutionary dynamics on phylogenetic trees. Methods Ecol. Evol. 5, 701–707 (2014).

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We thank J. Johnson for the fish illustrations in Figs. 1–3 and Extended Data Fig. 3, and the numerous undergraduate and graduate researchers from the University of California, Davis and Clemson University who helped collect morphological data. Portions of this research were conducted with high-performance computational resources provided by Louisiana State University (http://www.hpc.lsu.edu). We are grateful to the ichthyology curators and staff of the following collections for granting access to the tissues and specimens that made this study possible: Smithsonian National Museum of Natural History (Washington, DC), University of Florida Museum of Natural History (Gainesville), Scripps Institution of Oceanography (La Jolla), South African Institute for Aquatic Biodiversity (Grahamstown), Southeastern Louisiana University Museum of Biology (Hammond), American Museum of Natural History (New York), Australian Museum (Sydney), Academy of Natural Sciences (Philadelphia), Field Museum of Natural History (Chicago), California Academy of Sciences (San Francisco), Cornell University Museum of Vertebrates (Ithaca), University of Tennessee David A. Etnier Ichthyological Collection (Knoxville), Burke Museum of Natural History and Culture (Seattle), Academia Sinica (Taipei), Biodiversity Research Museum (Taipei), Natural History Museum and Institute (Chiba), Australian National Fish Collection (Hobart), Kyoto University Museum, Mie University Fish Collection of the Fisheries Research Laboratory (Shima), Hokkaido University Museum (Sapporo), Illinois Natural History Survey (Champaign), Kagoshima University Museum (Korimoto), University of Kansas Biodiversity Institute (Lawrence), Natural History Museum of Los Angeles County, Universidade Estadual Paulista (São Paulo), Louisiana Museum of Natural History (Baton Rouge), Harvard Museum of Comparative Zoology (Cambridge), Museo Nacional de Ciencias Naturales (Madrid), Museo Nacional de Historia Natural (Santiago), North Carolina Museum of Natural Sciences (Raleigh), National Museum of Natural History (New Delhi), Museum of New Zealand Te Papa Tongarewa (Wellington), Museum Victoria (Melbourne), National Museum of Nature and Science (Tokyo), Museums and Art Galleries of the Northern Territory (Darwin), University of Tokyo Ocean Research Institute, Queensland Museum (Brisbane), Royal Ontario Museum (Toronto), Seikai National Fisheries Research Institute (Nagasaki), Universitetsmuseet i Bergen (Hordaland), University of Copenhagen Zoological Museum, and Peabody Museum of Natural History (New Haven). Funding: Authors were independently funded by the National Institute of Health Predoctoral Training Program in Genetics T32 GM 007499 (to A.G.), the Bingham Oceanographic Fund maintained by the Peabody Museum of Natural History, Yale University (to T.J.N.), the Australian Research Council DECRA Fellowship DE170100516 (to P.F.C.), start-up funds from Louisiana State University (to B.C.F.) and the following National Science Foundation grants: NSF DEB-1556953 (to S.A.P. and P.C.W.), NSF DEB-1655624 (to B.C.F.), NSF DEB-1701323 (to P.C. and W.B.L.), NSF DEB-1830127 (to S.A.P.), NSF DEB-1839915 (to P.C.), NSF DEB-2017822 (to M.F.) and NSF IOS-1755242 (to A.D.).

Author information

Author notes

  1. These authors contributed equally: Ava Ghezelayagh, Richard C. Harrington.

Authors and Affiliations

  1. Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA

    Ava Ghezelayagh, Richard C. Harrington, Edward D. Burress, Sarah T. Friedman & Thomas J. Near

  2. Department of Animal Science, University of California, Davis, CA, USA

    Matthew A. Campbell

  3. University of Alaska Museum of the North, Fairbanks, AK, USA

    Matthew A. Campbell

  4. Department of Biology, University of Texas, Arlington, TX, USA

    Janet C. Buckner

  5. Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA, USA

    Janet C. Buckner, W. Tyler McCraney & Michael E. Alfaro

  6. Museum of Natural Science and Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA

    Prosanta Chakrabarty & Brant C. Faircloth

  7. College of Fisheries and Ocean Sciences, University of Alaska Fairbanks, Fairbanks, AK, USA

    Jessica R. Glass

  8. Institute for Applied Ecology, University of Canberra, Canberra, Australian Capital Territory, Australia

    Peter J. Unmack

  9. Santa Barbara Museum of Natural History, Santa Barbara, CA, USA

    Christine E. Thacker

  10. Natural History Museum of Los Angeles County, Los Angeles, CA, USA

    Christine E. Thacker & William B. Ludt

  11. Department of Evolution and Ecology, University of California, Davis, CA, USA

    Sarah T. Friedman & Peter C. Wainwright

  12. Queensland Museum, Townsville, Queensland, Australia

    Peter F. Cowman

  13. Department of Earth and Environmental Sciences, University of Michigan, Ann Arbor, MI, USA

    Matt Friedman

  14. Museum of Paleontology, University of Michigan, Ann Arbor, MI, USA

    Matt Friedman

  15. Department of Biological Sciences, Clemson University, Clemson, SC, USA

    Samantha A. Price

  16. Department of Bioinformatics and Genomics, University of North Carolina, Charlotte, NC, USA

    Alex Dornburg

  17. Peabody Museum of Natural History, Yale University, New Haven, CT, USA

    Thomas J. Near

Authors
  1. Ava Ghezelayagh
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  2. Richard C. Harrington
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Contributions

The project was conceived and designed by T.J.N., A.G., R.C.H., J.R.G., B.C.F. and P.C.W. A.G., R.C.H., J.R.G., M.A.C., J.C.B., W.T.M., M.E.A., W.B.L., B.C.F., P.F.C. and T.J.N. organized and executed the collection of UCE sequence data. S.A.P., S.T.F. and P.C.W. coordinated and collected morphological data. A.G., R.C.H., B.C.F. and A.D. carried out phylogenetic analyses. A.G. and R.C.H. performed phylogenetic divergence dating analyses, and A.G. performed analyses of lineage diversification rates. E.D.B. and P.C.W. performed morphological comparative analyses. C.E.T., P.C., W.B.L., P.F.C., P.J.U., A.D. and B.C.F. aided in the interpretation of certain phylogenetic or taxonomic results, and M.F. contributed palaeontological insights into some results. A.G., R.C.H., A.D. and T.J.N. wrote the first draft of the manuscript. All authors contributed to the writing and editing of the final draft of the manuscript.

Corresponding authors

Correspondence to Ava Ghezelayagh or Richard C. Harrington.

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Nature Ecology & Evolution thanks Ole Seehausen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Relationships among gene concordance factor (gCF) values, site concordance factor values (sCF) and branch lengths (substitutions per sequence site) along all branches of the acanthomorph phylogeny represented in Supplementary Figs. 1–25.

Note that unlike gCF values, site concordance and discordance values sum to 100% because their calculations allow for only three possible resolutions of a branch. Branch lengths have a positive, logarithmic correlation with gCF and sCF. A one percent increase in branch lengths leads to a 0.1413 increase in the natural log of gCF (Standard Error = 0.2925, y-intercept = 108.3323, R-squared = 0.6791) and a 0.0905 change in the natural log of sCF (Standard Error = 0.3416, y-intercept = 109.851, R-squared = 0.3889). These correlation analyses were performed using base R functions after log-transforming branch lengths; all branches were included in these calculations. a, Relationship between gCF and sCF, with points colored by branch lengths. The dashed black line with a slope of 1 demonstrates dissimilar levels of conflict among loci and sites, suggesting that the low gCF values are not just caused by genuine discordance in the gene trees. Eleven gray points reflect extremely long branches with lengths greater than 0.1 nucleotide substitutions per site (greater than the upper 99th percentile). b, Logarithmic relationship between branch lengths and gCF. c, Relationship between branch lengths and sCF values, with points colored by gCF values. Not shown are three points with very long branches (0.176-0.328 substitutions per sequence sites), all of which have sCF and gCF values >94%.

Extended Data Fig. 2 ASTRAL-III summary species tree inferred using individual gene trees.

Collapsed species tree inferred under the multi-species coalescent model (the uncollapsed tree can be found in the study’s Dryad repository). Local posterior probability values at nodes do not measure support for bipartitions, but rather are a function of the frequencies of the represented quartet topologies among all gene trees. ‘Acropomatiformes I’ refers to the clade containing Champsodon, Creediidae, and Hemerocoetidae while ‘Acropomatiformes II’ includes all other acropomatiform taxa according to Supplementary Table 3.

Extended Data Fig. 3 Median stem age estimates and 95% Highest Posterior Density (HPD) credible intervals for 22 major acanthomorph clades, as reported in the following 3 phylogenomic studies: Alfaro et al.7, Hughes et al.18 and this study.

Estimates for this study are the raw node heights reported in the 1,084-taxa time tree represented in Figs. 1 and 2. The 95% HPD credible interval of stem ages for most of the represented clades overlap with previous estimates, but we observe some major discrepancies, likely due to differences in tree topologies and taxon sampling. Fish illustrations by Julie Johnson.

Extended Data Fig. 4 Acanthomorph lineages classified as taxonomic families underwent a steady period of increased origination beginning at the K-Pg boundary.

Bar plot of the estimated number of lineages classified as taxonomic families originating during 5 million year bins. Note that the amounts of time represented by the first two bars are greater than the 5 million years represented by all other bars.

Extended Data Fig. 5 TESS-CoMET analyses suggest constant tree-wide diversification rates through most of the history of Acanthomorpha.

In b, d, and e, horizontal dashed lines and the right-hand y-axis mark statistical support cutoffs for rate shifts, with 2 ≤ Bayes factors (BF) < 6 considered to be low support, 6 ≤ BF < 10 considered to be moderate support and ≥10 considered to be high support. The rate shifts observed in a, c, and e over the last 10 million years are likely an artifact of the CoMET model (see Supplementary Information for further discussion). a, Posterior mean (blue line) and 95% credible interval (blue shading) for acanthomorph lineage origination (that is speciation) rates through time. b, Bayes factor (BF) support for a shift in lineage origination rate at every 1 Myr time period. c, Posterior mean (pink line) and 95% credible interval (pink shading) for acanthomorph lineage extinction rates through time. d, Bayesian support (in BF) for a shift in extinction rate at every 1 Myr time period. e, Posterior mean (violet line) and 95% credible interval (violet shading) for acanthomorph net-diversification (origination minus extinction) rates through time. Though this plot suggests that there is a small shift in net-diversification rate ~50 Mya, there is no statistical support for such a shift (see b and d). f, There is no statistical support (in BF) for a mass extinction event in Acanthomorpha at any 1 Myr time period.

Extended Data Fig. 6 Pairwise comparisons of 8 birth-death (BD) branching process models.

TESS calculated Bayes Factors (2ln(BF)) from the pairwise comparisons of marginal likelihoods for 8 birth-death (BD) branching-process models with the following priors: 1.) constant diversification rate with uniform (random) sampling, 2.) constant diversification rate with diversified sampling (sampling results in even coverage of all clades), 3.) decreasing speciation rate with uniform sampling, 4.) decreasing speciation rate with diversified sampling, 5.) constant diversification rate with a rate shift 50 Mya (‘Episodic BD’) with uniform sampling, 6.) constant diversification rate with a rate shift 50 Mya (‘Episodic BD’) with diversified sampling, 7.) constant speciation rate with a single mass extinction event occurring at any point in time (‘Mass Extinction BD’) with uniform sampling, and 8.) constant speciation rate with a single mass extinction event occurring at any point in time (‘Mass Extinction BD’) with diversified sampling. Observations of the pairwise comparisons note strong preference for a model that assumes uniform (random) sampling and strong support for either a constant rate BD model, or an episodic BD model that assumes a shift 50 mya. There is moderate Bayesian support (2ln(BF) = 6.4) that among the models assuming uniform sampling, the constant rate model is preferred over the model with a shift 50 Mya.

Extended Data Fig. 7 Visual summary of shifts in speciation rates inferred using BAMM.

The configuration presented here is the maximum shift credibility (MSC) configuration for an analysis that expected 15 rate shifts under the prior. Darker red colors depict relatively fast rates, while darker blue colors depict relatively slow rates. Rate shifts along branches are denoted with filled, black circles and assigned identifying numbers. Shifts in speciation rate are estimated to have occurred on the branches leading to the following 27 clades: 1.) Dinematichthyidae, 2.) Apogonidae (to the exclusion of Pseudamia), 3.) Gobiidae and Oxudercidae, 4.) Solenostomus, 5.) Parupeneus and Pseudopeneus (in Mullidae), 6.) Ariomma, Nomeidae and Stromateidae, 7.) Mastacembelidae, 8.) the clade defined by Scophthalmidae and Soleidae, 9.) Carangidae (to the exclusion of Seriola), 10.) Pseudocrenilabrinae (in Cichlidae), 11.) Pomacentridae, 12.) the clade defined by Gobiesocidae and Dactyloscopidae, 13.) Poeciliidae, 14.) the clade defined by Scorpididae and Terapontidae, 15.) Labridae, 16.) Sciaenidae, 17.) the clade defined by Nemipteridae and Sparidae, 18.) Tetraodontidae, 19.) Chaetodontidae, 20.) Acanthuridae (to the exclusion of Prionurus and Naso), 21.) Anthiadinae and Epinephelidae, 22.) darters (Etheostomatinae), 23.) the clade defined by Nototheniidae and Channichthyidae, 24.) Sebastes, 25.) the clade defined by Trichodontidae and Psychrolutidae, 26.) the clade defined by Stichaeidae and Zoarcidae and 27.) the clade defined by Bothrocara and Lycodes concolor (in Zoarcidae). Shifts labelled with blue rather than black numbers are estimated to have occurred by 16 of the 23 BAMM analyses conducted in this study. Although shift number 20 and shift number 26 are labelled in black, the vast majority of BAMM analyses predicted a rate shift in nearby branches leading to slightly more inclusive clades (specifically Acanthuridae to the exclusion of Naso, and all of Lycodinae, respectively).

Extended Data Fig. 8 Mean relative disparity through time (DTT) for all of Acanthomorpha, calculated using the combined data for seven phenotypic measurements and repeated on a sample of 100 trees from the posterior distribution of time-trees.

The gray line and blue shaded region indicate the median and 95% confidence interval (CI) expected under a Brownian motion model (BM) of evolution, respectively, and the solid black line indicates the observed pattern of disparity. This is the same plot visualized in Fig. 2, but note that the earliest portion of the DTT plot that includes the outgroup is not shown in Fig. 2. Acanthomorph body shapes radiated for approximately 15–20 million years in the aftermath of the K-Pg, followed by within-lineage phenotypic diversification. The blue histogram along the x-axis shows the proportion of time-calibrated trees for which the null hypothesis is rejected (P <0.05) and the observed disparity falls outside of the BM model’s 95% CI in each one-million-year interval. The inset, black box-and-whisker plot depicts the mean (±95% CI) of the initial time point at which the observed disparity dropped below that expected from BM following the K-Pg.

Extended Data Fig. 9 Phenograms depicting the evolutionary history of four size-corrected phenotypic traits (body depth and width, head depth, and mouth width) across seven major lineages that arose around the K-Pg.

The vertical dashed line marks the K-Pg boundary. Note that not all major acanthomorph lineages are represented in these plots and that Lophioidei and Tetraodontoidei are major subclades of Acanthuriformes. ‘Squamipinnes’ refers to the acanthuriform clade in Supplementary Fig. 23 defined by Chaetodon kleinii and Luvarus imperialis.

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Supplementary Materials and Methods, fossil calibrations, results, references, Figs. 1–29 and Tables 1–4.

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Ghezelayagh, A., Harrington, R.C., Burress, E.D. et al. Prolonged morphological expansion of spiny-rayed fishes following the end-Cretaceous. Nat Ecol Evol 6, 1211–1220 (2022). https://doi.org/10.1038/s41559-022-01801-3

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