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Heterogeneous selectivity and morphological evolution of marine clades during the Permian–Triassic mass extinction

Abstract

Morphological disparity and taxonomic diversity are distinct measures of biodiversity, typically expected to evolve synergistically. However, evidence from mass extinctions indicates that they can be decoupled, and while mass extinctions lead to a drastic loss of diversity, their impact on disparity remains unclear. Here we evaluate the dynamics of morphological disparity and extinction selectivity across the Permian–Triassic mass extinction. We developed an automated approach, termed DeepMorph, for the extraction of morphological features from fossil images using a deep learning model and applied it to a high-resolution temporal dataset encompassing 599 genera across six marine clades. Ammonoids, brachiopods and ostracods experienced a selective loss of complex and ornamented forms, while bivalves, gastropods and conodonts did not experience morphologically selective extinctions. The presence and intensity of morphological selectivity probably reflect the variations in environmental tolerance thresholds among different clades. In clades affected by selective extinctions, the intensity of diversity loss promoted the loss of morphological disparity. Conversely, under non-selective extinctions, the magnitude of diversity loss had a negligible impact on disparity. Our results highlight that the Permian–Triassic mass extinction had heterogeneous morphological selective impacts across clades, offering new insights into how mass extinctions can reshape biodiversity and ecosystem structure.

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Fig. 1: Schematic illustration of the DeepMorph method and simulations for testing morphological selectivity.
Fig. 2: Evolution of disparity (SOV) and diversity over three time bins and across subsets.
Fig. 3: Morphological evolution of six clades across three intervals.
Fig. 4: Simulation results of marginal selective intensity under varying magnitude losses of diversity and disparity (SOV).
Fig. 5: Bivariate plots illustrating the intensity of morphological extinction selectivity.
Fig. 6: Four distinct patterns of morphological evolution were identified during the PTME.

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Data availability

All data including the taxonomy list, fossil images, codes and trained deep learning model are provided and available via Zenodo at ref. 90. The taxonomy dataset was compiled from ref. 51 and the Paleobiology Database.

Code availability

The DeepMorph was implemented in Python (v. 3.6.5) and R (v. 4.2.0). Other libraries include Pytorch (v. 1.10.2), torchvision (v. 0.11.3+cu113), opencv-python (v. 4.5.2.54), geomorph (v. 4.0.5) and dispRity (v. 1.7.0) was also used for feature extraction and disparity quantifiation. Models and scripts are available at GitHub (https://github.com/XiaokangLiuCUG/DeepMorph).

References

  1. Briggs, D. E., Fortey, R. A. & Wills, M. A. Morphological disparity in the Cambrian. Science 256, 1670–1673 (1992).

    CAS  PubMed  Google Scholar 

  2. Foote, M. Discordance and concordance between morphological and taxonomic diversity. Paleobiology 19, 185–204 (1993).

    Google Scholar 

  3. Gould, S. J. Trends as changes in variance: a new slant on progress and directionality in evolution. J. Paleontol. 62, 319–329 (1988).

    Google Scholar 

  4. Guillerme, T. et al. Disparities in the analysis of morphological disparity. Biol. Lett. 16, 20200199 (2020).

    PubMed  PubMed Central  Google Scholar 

  5. Hopkins, M. J. & Gerber, S. in Evolutionary Developmental Biology: A Reference Guide (eds Nuño de la Rosa, L. & Müller, G. B.) 965–976 (Springer, 2021).

  6. Cole, S. R. & Hopkins, M. J. Selectivity and the effect of mass extinctions on disparity and functional ecology. Sci. Adv. 7, eabf4072 (2021).

    PubMed  PubMed Central  Google Scholar 

  7. Deline, B. & Ausich, W. I. Testing the plateau: a reexamination of disparity and morphologic constraints in early Paleozoic crinoids. Paleobiology 37, 214–236 (2011).

    Google Scholar 

  8. Stubbs, T. L. & Benton, M. J. Ecomorphological diversifications of Mesozoic marine reptiles: the roles of ecological opportunity and extinction. Paleobiology 42, 547–573 (2016).

    Google Scholar 

  9. Erwin, D. H. Disparity: morphological pattern and developmental context. Palaeontology 50, 57–73 (2007).

    Google Scholar 

  10. Carvalho, M. R. et al. Extinction at the end-Cretaceous and the origin of modern neotropical rainforests. Science 372, 63–68 (2021).

    CAS  PubMed  Google Scholar 

  11. Bapst, D. W., Bullock, P. C., Melchin, M. J., Sheets, H. D. & Mitchell, C. E. Graptoloid diversity and disparity became decoupled during the Ordovician mass extinction. Proc. Natl Acad. Sci. USA 109, 3428–3433 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Grossnickle, D. M. & Newham, E. Therian mammals experience an ecomorphological radiation during the Late Cretaceous and selective extinction at the K–Pg boundary. Proc. R. Soc. B 283, 20160256 (2016).

    PubMed Central  Google Scholar 

  13. Pimiento, C. et al. Selective extinction against redundant species buffers functional diversity. Proc. R. Soc. B 287, 20201162 (2020).

    PubMed  PubMed Central  Google Scholar 

  14. Puttick, M. N., Guillerme, T. & Wills, M. A. The complex effects of mass extinctions on morphological disparity. Evolution 74, 2207–2220 (2020).

    PubMed  Google Scholar 

  15. Korn, D., Hopkins, M. J. & Walton, S. A. Extinction space—a method for the quantification and classification of changes in morphospace across extinction boundaries. Evolution 67, 2795–2810 (2013).

    PubMed  Google Scholar 

  16. Raup, D. M. & Sepkoski, J. J. Mass extinctions in the marine fossil record. Science 215, 1501–1503 (1982).

    CAS  PubMed  Google Scholar 

  17. Erwin, D. H. Extinction: How Life on Earth Nearly Ended 250 Million Years Ago (Princeton Univ. Press, 2006).

  18. Song, H. et al. Respiratory protein-driven selectivity during the Permian–Triassic mass extinction. Innovation 5, 100618 (2024).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Song, H., Wignall, P. B., Tong, J. & Yin, H. Two pulses of extinction during the Permian–Triassic crisis. Nat. Geosci. 6, 52–56 (2013).

    CAS  Google Scholar 

  20. Fan, J.-x et al. A high-resolution summary of Cambrian to Early Triassic marine invertebrate biodiversity. Science 367, 272–277 (2020).

    CAS  PubMed  Google Scholar 

  21. Stanley, S. M. Estimates of the magnitudes of major marine mass extinctions in Earth history. Proc. Natl Acad. Sci. USA 113, E6325–E6334 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Luo, M., Shi, G. R., Buatois, L. A. & Chen, Z. Trace fossils as proxy for biotic recovery after the end-Permian mass extinction: a critical review. Earth-Sci. Rev. 203, 103059 (2020).

    Google Scholar 

  23. Villier, L. & Korn, D. Morphological disparity of ammonoids and the mark of Permian mass extinctions. Science 306, 264–266 (2004).

    CAS  PubMed  Google Scholar 

  24. Dai, X., Korn, D. & Song, H. Morphological selectivity of the Permian–Triassic ammonoid mass extinction. Geology 49, 1112–1116 (2021).

    Google Scholar 

  25. Wan, J. et al. Decoupling of morphological disparity and taxonomic diversity during the end-Permian mass extinction. Paleobiology 47, 402–417 (2021).

    Google Scholar 

  26. Smithwick, F. M. & Stubbs, T. L. Phanerozoic survivors: actinopterygian evolution through the Permo–Triassic and Triassic–Jurassic mass extinction events. Evolution 72, 348–362 (2018).

    PubMed  PubMed Central  Google Scholar 

  27. Romano, C. et al. Permian–Triassic Osteichthyes (bony fishes): diversity dynamics and body size evolution. Biol. Rev. 91, 106–147 (2016).

    PubMed  Google Scholar 

  28. Hsiang, A. Y. et al. AutoMorph: accelerating morphometrics with automated 2D and 3D image processing and shape extraction. Methods Ecol. Evol. 9, 605–612 (2018).

    Google Scholar 

  29. Sibert, E., Friedman, M., Hull, P., Hunt, G. & Norris, R. Two pulses of morphological diversification in Pacific pelagic fishes following the Cretaceous–Palaeogene mass extinction. Proc. R. Soc. B 285, 20181194 (2018).

    PubMed  PubMed Central  Google Scholar 

  30. Weeks, B. C. et al. A deep neural network for high‐throughput measurement of functional traits on museum skeletal specimens. Methods Ecol. Evol. 14, 347–359 (2023).

    Google Scholar 

  31. Newell, A., Yang, K. & Deng, J. in Computer Vision – ECCV 2016 (ed. Leibe, B. et al.) 483–499 (Springer, 2016).

  32. Huang, S., Gong, M. & Tao, D. A coarse-fine network for keypoint localization. In Proc. IEEE International Conference on Computer Vision (ed. O’Conner, L.) 3028–3037 (IEEE Computer Society, 2017).

  33. Le, V.-L., Beurton-Aimar, M., Zemmari, A., Marie, A. & Parisey, N. Automated landmarking for insects morphometric analysis using deep neural networks. Ecol. Inform. 60, 101175 (2020).

    Google Scholar 

  34. Nguyen, H. H. et al. A lightweight keypoint matching framework for insect wing morphometric landmark detection. Ecol. Inform. 70, 101694 (2022).

    Google Scholar 

  35. Brayard, A. et al. Good genes and good luck: ammonoid diversity and the end-Permian mass extinction. Science 325, 1118–1121 (2009).

    CAS  PubMed  Google Scholar 

  36. Jattiot, R., Bucher, H. & Brayard, A. Smithian (Early Triassic) ammonoid faunas from Timor: taxonomy and biochronology. Palaeontogr. A 317, 1–137 (2020).

    Google Scholar 

  37. Brosse, M., Brayard, A., Fara, E. & Neige, P. Ammonoid recovery after the Permian–Triassic mass extinction: a re-exploration of morphological and phylogenetic diversity patterns. J. Geol. Soc. 170, 225–236 (2013).

    Google Scholar 

  38. McGowan, A. J. Ammonoid taxonomic and morphologic recovery patterns after the Permian–Triassic. Geology 32, 665–668 (2004).

    Google Scholar 

  39. Jablonski, D. Survival without recovery after mass extinctions. Proc. Natl Acad. Sci. USA 99, 8139–8144 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. He, W., Shi, G. & Bu, J. in Brachiopods Around the Permian–Triassic Boundary of South China (eds He, W. et al.) 51–60 (Springer, 2019).

  41. Zhang, Y. et al. Significant pre-mass extinction animal body-size changes: evidences from the Permian–Triassic boundary brachiopod faunas of South China. Palaeogeogr. Palaeoclimatol. Palaeoecol. 448, 85–95 (2016).

    Google Scholar 

  42. Foster, W., Lehrmann, D., Yu, M., Ji, L. & Martindale, R. Persistent environmental stress delayed the recovery of marine communities in the aftermath of the latest Permian mass extinction. Palaeogeogr. Palaeoclimatol. 33, 338–353 (2018).

    Google Scholar 

  43. Huang, Y., Tong, J., Fraiser, M. L. & Chen, Z.-Q. Extinction patterns among bivalves in South China during the Permian–Triassic crisis. Palaeogeogr. Palaeoclimatol. Palaeoecol. 399, 78–88 (2014).

    Google Scholar 

  44. Tu, C., Chen, Z.-Q. & Harper, D. A. Permian–Triassic evolution of the Bivalvia: extinction-recovery patterns linked to ecologic and taxonomic selectivity. Palaeogeogr. Palaeoclimatol. Palaeoecol. 459, 53–62 (2016).

    Google Scholar 

  45. Foster, W. J. & Twitchett, R. J. Functional diversity of marine ecosystems after the Late Permian mass extinction event. Nat. Geosci. 7, 233–238 (2014).

    CAS  Google Scholar 

  46. Orchard, M. J. Conodont diversity and evolution through the latest Permian and Early Triassic upheavals. Palaeogeogr. Palaeoclimatol. Palaeoecol. 252, 93–117 (2007).

    Google Scholar 

  47. Payne, J. L. & Finnegan, S. The effect of geographic range on extinction risk during background and mass extinction. Proc. Natl Acad. Sci. USA 104, 10506–10511 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Jablonski, D. & Raup, D. M. Selectivity of end-Cretaceous marine bivalve extinctions. Science 268, 389–391 (1995).

    CAS  PubMed  Google Scholar 

  49. Song, H. et al. Anoxia/high temperature double whammy during the Permian–Triassic marine crisis and its aftermath. Sci. Rep. 4, 4132 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Knoll, A. H., Bambach, R. K., Payne, J. L., Pruss, S. & Fischer, W. W. Paleophysiology and end-Permian mass extinction. Earth Planet. Sci. Lett. 256, 295–313 (2007).

    CAS  Google Scholar 

  51. Song, H., Wignall, P. B. & Dunhill, A. M. Decoupled taxonomic and ecological recoveries from the Permo–Triassic extinction. Sci. Adv. 4, eaat5091 (2018).

    PubMed  PubMed Central  Google Scholar 

  52. Dai, X. et al. A Mesozoic fossil lagerstätte from 250.8 million years ago shows a modern-type marine ecosystem. Science 379, 567–572 (2023).

    CAS  PubMed  Google Scholar 

  53. Ciampaglio, C. N., Kemp, M. & McShea, D. W. Detecting changes in morphospace occupation patterns in the fossil record: characterization and analysis of measures of disparity. Paleobiology 27, 695–715 (2001).

    Google Scholar 

  54. Ruta, M., Angielczyk, K. D., Fröbisch, J. & Benton, M. J. Decoupling of morphological disparity and taxic diversity during the adaptive radiation of anomodont therapsids. Proc. R. Soc. B 280, 20131071 (2013).

    PubMed  PubMed Central  Google Scholar 

  55. Bazzi, M., Kear, B. P., Blom, H., Ahlberg, P. E. & Campione, N. E. Static dental disparity and morphological turnover in sharks across the end-Cretaceous mass extinction. Curr. Biol. 28, 2607–2615.e3 (2018).

    CAS  PubMed  Google Scholar 

  56. Khanna, N., Godbold, J. A., Austin, W. E. & Paterson, D. M. The impact of ocean acidification on the functional morphology of foraminifera. PLoS ONE 8, e83118 (2013).

    PubMed  PubMed Central  Google Scholar 

  57. Fox, L., Stukins, S., Hill, T. & Miller, C. G. Quantifying the effect of anthropogenic climate change on calcifying plankton. Sci. Rep. 10, 1620 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Jurikova, H. et al. Permian–Triassic mass extinction pulses driven by major marine carbon cycle perturbations. Nat. Geosci. 13, 745–750 (2020).

    CAS  Google Scholar 

  59. Payne, J. L. et al. Calcium isotope constraints on the end-Permian mass extinction. Proc. Natl Acad. Sci. USA 107, 8543–8548 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Dal Corso, J. et al. Environmental crises at the Permian–Triassic mass extinction. Nat. Rev. Earth Environ. 3, 197–214 (2022).

    CAS  Google Scholar 

  61. Dick, D. G., Darroch, S., Novack-Gottshall, P. & Laflamme, M. Does functional redundancy determine the ecological severity of a mass extinction event? Proc. R. Soc. B 289, 20220440 (2022).

    PubMed  PubMed Central  Google Scholar 

  62. Dunhill, A. M., Foster, W. J., Sciberras, J. & Twitchett, R. J. Impact of the Late Triassic mass extinction on functional diversity and composition of marine ecosystems. Palaeontology 61, 133–148 (2018).

    Google Scholar 

  63. Larson, D. W., Brown, C. M. & Evans, D. C. Dental disparity and ecological stability in bird-like dinosaurs prior to the end-Cretaceous mass extinction. Curr. Biol. 26, 1325–1333 (2016).

    CAS  PubMed  Google Scholar 

  64. Benton, M. J. Vertebrate Palaeontology (John Wiley & Sons, 2014).

  65. Payne, J. L., Bush, A. M., Heim, N. A., Knope, M. L. & McCauley, D. J. Ecological selectivity of the emerging mass extinction in the oceans. Science 353, 1284–1286 (2016).

    CAS  PubMed  Google Scholar 

  66. Pimiento, C. et al. Functional diversity of marine megafauna in the Anthropocene. Sci. Adv. 6, eaay7650 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Woodhouse, A. et al. Adaptive ecological niche migration does not negate extinction susceptibility. Sci. Rep. 11, 15411 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Raja, N. B. et al. Morphological traits of reef corals predict extinction risk but not conservation status. Glob. Ecol. Biogeogr. 30, 1597–1608 (2021).

    Google Scholar 

  69. Malanoski, C. M., Farnsworth, A., Lunt, D. J., Valdes, P. J. & Saupe, E. E. Climate change is an important predictor of extinction risk on macroevolutionary timescales. Science 383, 1130–1134 (2024).

    CAS  PubMed  Google Scholar 

  70. Huang, S., Roy, K. & Jablonski, D. Origins, bottlenecks, and present-day diversity: patterns of morphospace occupation in marine bivalves. Evolution 69, 735–746 (2015).

    PubMed  Google Scholar 

  71. Carlson, S. J. The evolution of Brachiopoda. Annu. Rev. Earth Planet. Sci. 44, 409–438 (2016).

    CAS  Google Scholar 

  72. Ramezani, J. & Bowring, S. A. Advances in numerical calibration of the Permian timescale based on radioisotopic geochronology. Geol. Soc. Lond. Spec. Publ. 450, 51–60 (2018).

    Google Scholar 

  73. Burgess, S. D., Bowring, S. & Shen, S. Z. High-precision timeline for Earth’s most severe extinction. Proc. Natl Acad. Sci. USA 111, 3316–3321 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Yin, H., Zhang, K., Tong, J., Yang, Z. & Wu, S. The global stratotype section and point (GSSP) of the Permian–Triassic boundary. Episodes 24, 102–114 (2001).

    Google Scholar 

  75. Henderson, C. M. Permian conodont biostratigraphy. Geol. Soc. Lond. Spec. Publ. 450, 119–142 (2018).

    Google Scholar 

  76. Yin, H. & Wu, S. Transitional bed—the basal Triassic unit of South China. J. China Univ. Geosci. 10, 163–172 (1985).

    Google Scholar 

  77. Liu, X., Song, H., Bond, D. P. G., Tong, J. & Benton, M. J. Migration controls extinction and survival patterns of foraminifers during the Permian–Triassic crisis in South China. Earth-Sci. Rev. 209, 103329 (2020).

    Google Scholar 

  78. Teichert, C., Kummnel, B. & Kapoor, H. Mixed Permian–Triassic fauna, Guryul Ravine, Kashmir. Science 167, 174–175 (1970).

    CAS  PubMed  Google Scholar 

  79. Chen, Z. Q., Kaiho, K. & George, A. D. Survival strategies of brachiopod faunas from the end-Permian mass extinction. Palaeogeogr. Palaeoclimatol. Palaeoecol. 224, 232–269 (2005).

    Google Scholar 

  80. Widmann, P. et al. Dynamics of the largest carbon isotope excursion during the Early Triassic biotic recovery. Front. Earth Sci. 8, 196 (2020).

    Google Scholar 

  81. Qin, X. et al. U2-Net: going deeper with nested U-structure for salient object detection. Pattern Recognit. 106, 107404 (2020).

    Google Scholar 

  82. Liu, X. et al. Automatic taxonomic identification based on the Fossil Image Dataset (>415,000 images) and deep convolutional neural networks. Paleobiology 49, 1–22 (2023).

    CAS  Google Scholar 

  83. Paszke, A. et al. in Advances in Neural Information Processing Systems 32 (eds Wallach, H. et al.) 8024–8035 (Neural Information Processing Systems Foundation, 2019).

  84. Bradski, G. & Kaehler, A. Learning OpenCV: Computer Vision with the OpenCV Library (O’Reilly Media, 2008).

  85. Gower, J. C. Generalized Procrustes analysis. Psychometrika 40, 33–51 (1975).

    Google Scholar 

  86. Rohlf, F. J. & Slice, D. Extensions of the Procrustes method for the optimal superimposition of landmarks. Syst. Zool. 39, 40–59 (1990).

    Google Scholar 

  87. Kuhl, F. P. & Giardina, C. R. Elliptic Fourier features of a closed contour. Comput. Graph. Image Process. 18, 236–258 (1982).

    Google Scholar 

  88. Grieve, S. spatial-efd: a spatial-aware implementation of elliptical Fourier analysis. J. Open Source Softw. 2, 189 (2017).

    Google Scholar 

  89. Foote, M. Morphological and taxonomic diversity in clade’s history: the blastoid record and stochastic simulations. Contrib. Mus. Paleontol. 28, 101–140 (1991).

    Google Scholar 

  90. Liu, X. Heterogeneous selectivity and morphological evolution of marine clades during the Permian–Triassic mass extinction. Zenodo https://doi.org/10.5281/zenodo.10531896 (2024).

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Acknowledgements

We acknowledge J. Wan, J. Yin, S. Jiang and X. Li for collecting the literature. We thank J. Sun and Y. Sun for helping to collect the fossil images. This study is supported by the National Natural Science Foundation of China (42325202, 92155201, 92255303), the State Key R&D Project of China (2023YFF0804000), the Natural Science Foundation of Hubei (2023AFA006) and the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan). X.L. thanks the financial support from the China Scholarship Council (202206410024). D.S. received funding from the Swiss National Science Foundation (PCEFP3_187012), the Swedish Research Council (VR: 2019-04739) and the Swedish Foundation for Strategic Environmental Research MISTRA within the framework of the research programme BIOPATH (F 2022/1448). We acknowledge the contributors to the Paleobiology Database. This is Paleobiology Database publication number 485.

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H.S. and X.L. conceived this study. X.L., X.D. and F.W. collected data. X.L., H.S. and D.S. contributed to the writing of the manuscript. X.L. and D.S. analysed the data. X.L. and H.S. designed the figures. All authors revised and edited the manuscript.

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Correspondence to Haijun Song.

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Nature Ecology & Evolution thanks Adam Woodhouse and the other, anonymous, reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Percentages of PCA variances of six clades that are explained by the first 10 axes.

a–f, Percentages of PCA variances of ammonoids (a), brachiopods (b), ostracods (c), bivalves (d), gastropods (e), and conodonts (f).

Extended Data Fig. 2 Evolution of disparity (SOR, blue squares) and diversity (orange diamonds) over time and across subsets.

Vertical bars represent 95% of the quantiles, which are calculated from 10,000 bootstrap replicates for each subset. a–f, Disparity and diversity of ammonoids (a), brachiopods (b), ostracods (c), bivalves (d), gastropods (e), and conodonts (f). Abbreviations: SOR = Sum of ranges. Fossil silhouettes adapted from ref. 18 under a Creative Commons licence CC BY 4.0.

Extended Data Fig. 3 Marginal selective intensity simulations under different magnitude losses of diversity and disparity (SOR).

ad, Selectivity intensities among morphologies. Strong selectivity (a) indicates the highest extinction risk is ten times higher than the lowest extinction possibility. Moderate selectivity (b) indicates the highest extinction risk is five times higher than the lowest extinction risk. Weak selectivity (c) represents the highest extinction risk for a taxon that is two times higher than the one with the lowest extinction risk. Random extinction (d) represents a non-selective extinction. The rest of the extinction rates are distributed linearly. e, Disparity loss under different magnitude of diversity loss and selectivity. f, Diversity loss under different magnitude of disparity loss and selectivity.

Extended Data Fig. 4 Frequency distribution of the centroid distance shifts under 10,000 replicates from pre-extinction ammonoids.

Gray histograms indicate the 95% quantile. The red arrows represent the empirical results.

Extended Data Fig. 5 Marginal selective intensity simulations based on empirical morphological occupations under different magnitude losses of diversity.

extinction probabilities were based on the linear distributed model. a–f, Disparity loss rates of ammonoids (a), brachiopods (b), ostracods (c), bivalves (d), gastropods (e), and conodonts (f).

Extended Data Fig. 6 Morphological variations of victims, survivors, and newcomers for six clades during the PTME.

a–f, Morphological variations of ammonoids (a), brachiopods (b), ostracods (c), bivalves (d), gastropods (e), and conodonts (f). All the specimens are not to scale. Taxonomy list for ammonoids, a1–a11: Urartoceras abichianum, Araxoceras latissimum, Changhsingoceras sichuangense, Schizoloboceras vediensis, Araxoceltites sanestapanus, Metotoceras woodwardi, Episageceras dalailamae, Dunedinites pinguis, Anotoceras kama, Tellerites sp., and Pseudovishnuites guidingensis. For brachiopods, b1–b12: Fusispirifer sp., Glyptorhynchia lens, Janiceps peracuta, Paramarginifera japonica, Cathaysia chonetoides, Marginifera ornata, Permianella typica, Paryphella orbicularis, Piarorhynchella selongensis, Lichuanorelloides lichuanensis, and Orbicoelia speciosa. For ostracods, c1–c11: Cooperuna tenuis, Baschkirina ballei, Triplacera sp., Polycope baudi, Coronakirkbya hamori, Acanthoscapha blessi, Fabalicypris parva, Basslerella superarella, Samarella meishanella, Permoyoungiella bogschi, and Hollinella martensiformis. For bivalves, d1–d10: Pteronites pinnaeformis, Parallelodon laochangensis, Dyasmya elegans, Unionites canalensis, Solemya togata, Ensipteria guizhouensis, Entolioides subdemissus, Nucinella taylori, Isognomon ephippium, and Permophorus bregeri. For gastropods, e1–e11: Palaeostylus pupoides, Streptacis whitfieldi, Retispira sinensis, Porcellia paucituberculata, Stachella micra, Tropidodiscus curvilineatus, Anomphalus fusuiensis, Worthenia humilis, Meekospira solenisciforma, Microlampra heshanensis, and Wannerispira shangganensis. For conodonts, f1–f9: Hadrodontina aequabilis, Gondolella constricta, Sweetocristatus arcticus, Cypridodella spengleri, Iranognathus sosioensis, Pachycladina rendona, Isarcicella isarcica, Clarkina meishanensis, and Furnishius triserratus.

Extended Data Fig. 7 Ammonoid morphological occupation across three intervals based on multiple species and specimens.

a, Multiple species from one genus and multiple specimens for the same species. b, The result of interspecific variations of some representatives. Plots based on the raw data, including 191 species and 219 specimens. Notably, genera dating from the Changhsingian and characterized by stronger shell ornamentations exhibited higher levels of interspecific variation, as exemplified by Paratirolites and Alibashites. Conversely, genera from the Induan fauna displayed fewer variations, as observed in Ambites and Mullericeras.

Extended Data Fig. 8 Disparity comparison between genus-based and species rarefied results and selectivity test based on the rarefied results.

a, The square dots represent results based on genus-level, and diamond dots indicate rarefied disparity changes. Vertical bars represent 95% quantiles, calculated from 10,000 bootstrap replicates for each subset (that is, one-side test). b, Frequency distribution of the SOV by subsampling six species (Nsurvivors = 6) from Changhsingian, and the Pquantile < 0.08. c, Frequency distribution of the SOV by subsampling 52 species (NInduan = 52) from Changhsingian, Pquantile < 0.05. d, Frequency distribution of the centroid distance shifts under random replicates from pre-extinction taxa, Pquantile « 0.01 (NInduan = 52).

Extended Data Fig. 9 SOV comparison between the results using two PCA axes and ten PCA axes from ammonoids and conodonts.

a, Sum of variance of ammonoids, first two and ten PCA axes include 89.4% and 98.0% of total variations, respectively. b, Sum of variance of conodonts, first two and ten PCA axes include 66.6% and 7.6% of total variations, respectively. Vertical bars represent 95% quantiles, calculated from 10,000 bootstrap replicates for each subset.

Extended Data Table 1 Generic diversity distribution of clades and its coverage compared to the Paleobiology Database (PBDB)

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Liu, X., Song, H., Chu, D. et al. Heterogeneous selectivity and morphological evolution of marine clades during the Permian–Triassic mass extinction. Nat Ecol Evol 8, 1248–1258 (2024). https://doi.org/10.1038/s41559-024-02438-0

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