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:

Skeletal marine animal biodiversity is built by families with long macroevolutionary lag times

Abstract

The clade dynamics of marine animals have changed markedly over the Phanerozoic. Long-term diversification is associated with decreasing origination and extinction rates, and with increasing taxon longevity. Here we use the diversification trajectories of skeletal non-colonial marine families to infer the mechanisms that generated these trends. Suggested mechanisms behind these trends include stochastic extinction of taxa with high evolutionary volatility and selection for traits that buffer against extinction. We find an increasing predominance of Phanerozoic families with long lag times between first appearance and peak diversity, over those with ‘early burst’ diversification trajectories. Long-lag families persisted for longer and had lower evolutionary volatilities, higher genus-level occupancies and genera with larger niche breadths than early burst families. However, they do not preferentially show ecological modes known to protect against extinction. We interpret the rise of the long-lag families as reflecting an intensification of ecosystem-level mechanisms supporting both long-term coexistence and transient dynamics, which increased the capacity of marine ecosystems to accommodate highly diverse communities.

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

Access options

Buy this article

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

Fig. 1: Distinction of two cohorts of families of skeletal marine metazoans.
Fig. 2: Richness curves of skeletal non-colonial marine metazoans at genus level.
Fig. 3: The increasing abundance of Clong genera in the fossil record.

Similar content being viewed by others

Data availability

The data downloaded from the PaleoBioDB (https://paleobiodb.org) and the URLs for retrieving data used to generate the results are available in the accompanying R code, which is available at https://doi.org/10.5281/zenodo.3901507.

Code availability

The R code for all analyses is available at https://doi.org/10.5281/zenodo.3901507.

References

  1. van Valen, L. M. Resetting the Phanerozoic community evolution. Nature 307, 93–106 (1984).

    Google Scholar 

  2. Alroy, J. Dynamics of origination and extinction in the marine fossil record. Proc. Natl Acad. Sci. USA 105, 11536–11542 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Foote, M. Origination and extinction through the Phanerozoic: a new approach. J. Geol. 111, 125–148 (2003).

    Google Scholar 

  4. Gilinsky, N. L. Volatility and the Phanerozoic decline of background extinction intensity. Paleobiology 20, 445–458 (1994).

    Google Scholar 

  5. Lieberman, B. S. & Melott, A. L. Declining volatility, a general property of disparate systems: from fossils, to stocks, to the stars. Palaeontology 56, 1297–1304 (2013).

    Google Scholar 

  6. Knope, M. L., Bush, A. M., Frishkoff, L. O., Heim, N. A. & Payne, J. L. Ecologically diverse clades dominate the oceans via extinction resistance. Science 367, 1035–1038 (2020).

    CAS  PubMed  Google Scholar 

  7. Janzen, D. H. On ecological fitting. Oikos 45, 308–310 (1985).

    Google Scholar 

  8. Agosta, S. J. & Klemens, J. A. Ecological fitting by phenotypically flexible genotypes: implications for species associations, community assembly and evolution. Ecol. Lett. 11, 1123–1134 (2008).

    PubMed  Google Scholar 

  9. Nielsen, S. N. & Müller, F. in Handbook of Ecosystem Theories and Management (eds Jørgensen, S. E. & Müller, F.) 195–216 (CRC Press, 2000).

  10. Hui, C. et al. Defining invasiveness and invasibility in ecological networks. Biol. Invasions 18, 971–983 (2016).

    Google Scholar 

  11. Foote, M. et al. Rise and fall of species occupancy in Cenozoic fossil mollusks. Science 318, 1131–1134 (2007).

    CAS  PubMed  Google Scholar 

  12. Foote, M. Symmetric waxing and waning of marine invertebrate genera. Paleobiology 33, 517–529 (2007).

    Google Scholar 

  13. Liow, L. H. & Stenseth, N. C. The rise and fall of species: implications for macroevolutionary and macroecological studies. Proc. R. Soc. Lond. B 274, 2745–2752 (2007).

    Google Scholar 

  14. Zliobaite, I., Fortelius, M. & Stenseth, N. C. Reconciling taxon senescence with the Red Queen’s hypothesis. Nature 552, 92–95 (2017).

    CAS  PubMed  Google Scholar 

  15. Gillespie, R. G. et al. Comparing adaptive radiations across space, time, and taxa. J. Hered. 111, 1–20 (2020).

    PubMed  PubMed Central  Google Scholar 

  16. Losos, J. B. Adaptive radiation, ecological opportunity, and evolutionary determinism: American Society of Naturalists EO Wilson Award address. Am. Nat. 175, 623–639 (2010).

    PubMed  Google Scholar 

  17. Yoder, J. B. et al. Ecological opportunity and the origin of adaptive radiations. J. Evol. Biol. 23, 1581–1596 (2010).

    CAS  PubMed  Google Scholar 

  18. Erwin, D. H. Novelty and innovation in the history of life. Curr. Biol. 25, R930–R940 (2015).

    CAS  PubMed  Google Scholar 

  19. Gould, S. J. & Vrba, E. S. Exaptation—a missing term in the science of form. Paleobiology 8, 4–15 (1982).

    Google Scholar 

  20. Cooper, A. & Fortey, R. Evolutionary explosions and the phylogenetic fuse. Trends Ecol. Evol. 13, 151–156 (1998).

    CAS  PubMed  Google Scholar 

  21. Jablonski, D. & Bottjer, D. J. in Major Evolutionary Radiations (eds Taylor, P. D. & Larwood, G. P.) 17–57 (Systematics Association, 1990).

  22. Jablonski, D. Approaches to macroevolution: 1. general concepts and origin of variation. Evol. Biol. 44, 427–450 (2017).

    PubMed  PubMed Central  Google Scholar 

  23. Uyeda, J. C., Hansen, T. F., Arnold, S. J. & Pienaar, J. The million-year wait for macroevolutionary bursts. Proc. Natl Acad. Sci. USA 108, 15908–15913 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Kröger, B., Desrochers, A. & Ernst, A. The reengineering of reef habitats during the Great Ordovician Biodiversification Event. PALAIOS 32, 584–599 (2017).

    Google Scholar 

  25. Robeck, H. E., Maley, C. C. & Donoghue, M. J. Taxonomy and temporal diversity patterns. Paleobiology 26, 171–187 (2000).

    Google Scholar 

  26. Hendricks, J. R., Saupe, E. E., Myers, C. E., Hermsen, E. J. & Allmon, W. D. The generification of the fossil record. Paleobiology 40, 511–528 (2014).

    Google Scholar 

  27. Wagner, P. J., Aberhan, M., Hendy, A. & Kiessling, W. The effects of taxonomic standardization on sampling-standardized estimates of historical diversity. Proc. R. Soc. B 274, 439–444 (2007).

    PubMed  Google Scholar 

  28. Plotnick, R. E. & Wagner, P. J. Roundup of the usual suspects: common genera in the fossil record and the nature of the wastebasket taxa. Paleobiology 32, 126–146 (2006).

    Google Scholar 

  29. Bambach, R. K., Bush, M. A. & Erwin, D. H. Autecology and the filling of ecospace: key metazoan radiations. Palaeontology 50, 1–22 (2007).

    Google Scholar 

  30. Knope, M. L., Heim, N. A., Frishkoff, L. O. & Payne, J. L. Limited role of functional differentiation in early diversification of animals. Nat. Commun. 6, 6455 (2015).

    CAS  PubMed  Google Scholar 

  31. Sepkoski, J. J. A kinetic model of Phanerozoic taxonomic diversity. III Post-Paleozoic families and mass extinctions. Paleobiology 10, 246–267 (1984).

    Google Scholar 

  32. Alroy, J. The shifting balance of diversity among major marine animal groups. Science 329, 1191–1194 (2010).

    CAS  PubMed  Google Scholar 

  33. Liow, L. H. & Nichols, J. D. in The Paleontological Society Short Course, October 30th 2010 (eds Alroy, J. & Hunt, G.) 81–94 (Cambridge Univ. Press, 2010).

  34. Kiessling, W. & Kocsis, Á. T. Adding fossil occupancy trajectories to the assessment of modern extinction risk. Biol. Lett. 12, 20150813 (2016).

    PubMed  PubMed Central  Google Scholar 

  35. Fridley, J. D., Vandermast, D. B., Kuppinger, D. M., Manthey, M. L. & Peet, R. K. Co-occurrence based assessment of habitat generalists and specialists: a new approach for the measurement of niche width. J. Ecol. 95, 707–722 (2007).

    Google Scholar 

  36. Hofmann, R., Tietje, M. & Aberhan, M. Diversity partitioning in Phanerozoic benthic marine communities. Proc. Natl Acad. Sci. USA 116, 79–83 (2019).

    PubMed  Google Scholar 

  37. Bottjer, D. J., Hagadorn, J. W. & Dornbos, S. Q. The Cambrian substrate revolution. GSA Today 10, 1–7 (2000).

    Google Scholar 

  38. Knoll, A. H. & Follows, M. J. A bottom-up perspective on ecosystem change in Mesozoic oceans. Proc. R. Soc. B 283, 20161755 (2016).

    PubMed  PubMed Central  Google Scholar 

  39. Bambach, R. K. Seafood through time: changes in biomass, energetics, and productivity in the marine ecosystem. Paleobiology 19, 372–397 (1993).

    Google Scholar 

  40. Westrop, S. R. The life habits of the Ordovician illaenine trilobite Bumastoides. Lethaia 16, 15–24 (1983).

    Google Scholar 

  41. O’Dea, A. & Jackson, J. Environmental change drove macroevolution in cupuladriid bryozoans. Proc. R. Soc. B 276, 3629–3634 (2009).

    PubMed  PubMed Central  Google Scholar 

  42. Rasmussen, C. M. Ø., Kröger, B., Nielsen, M. L. & Colmenar, J. Cascading trend of Early Paleozoic marine radiations paused by Late Ordovician extinctions. Proc. Natl Acad. Sci. USA 116, 7207 (2019).

    PubMed  PubMed Central  Google Scholar 

  43. Bush, A. M. & Bambach, R. K. Sustained Mesozoic–Cenozoic diversification of marine Metazoa: a consistent signal from the fossil record. Geology 43, 979–982 (2015).

    Google Scholar 

  44. Leibold, M. A. & McPeek, M. A. Coexistence of the niche and neutral perspectives in community ecology. Ecology 87, 1399–1410 (2006).

    PubMed  Google Scholar 

  45. McPeek, M. A. The ecological dynamics of clade diversification and community assembly. Am. Nat. 172, E270–E284 (2008).

    PubMed  Google Scholar 

  46. Chesson, P. Mechanisms of maintenance of species diversity. Annu. Rev. Ecol. Syst. 31, 343–366 (2000).

    Google Scholar 

  47. Wagner, P. J., Aberhan, M., Hendy, A. & W, K. The effects of taxonomic standardization on occurrence-based estimates of diversity. Proc. R. Soc. Lond. B 274, 439–444 (2007).

    Google Scholar 

  48. Cohen, K. M., Harper, D. A. T. & Gibbard, P. L. ICS International Chronostratigraphic Chart 2018/08 (International Commission on Stratigraphy, IUGS, 2018); www.stratigraphy.org

  49. Nichols, J. D. & Pollock, K. H. Estimating taxonomic diversity, extinction rates, and speciation rates from fossil data using capture–recapture models. Paleobiology 9, 150–163 (1983).

    Google Scholar 

  50. Connolly, S. R. & Miller, A. I. Joint estimation of sampling and turnover rates from fossil databases: capture–mark–recapture methods revisited. Paleobiology 27, 767–751 (2001).

    Google Scholar 

  51. Schwarz, C. J. & Arnason, A. N. A general methodology for the analysis of capture–recapture experiments in open populations. Biometrics 52, 860–873 (1996).

    Google Scholar 

  52. Pradel, R. Utilization of capture–mark–recapture for the study of recruitment and population growth rate. Biometrics 52, 703–709 (1996).

    Google Scholar 

  53. Liow, L. H., Reitan, T. & Harnik, P. G. Ecological interactions on macroevolutionary time scales: clams and brachiopods are more than ships that pass in the night. Ecol. Lett. 18, 1030–1039 (2015).

    PubMed  Google Scholar 

  54. Alroy, J. A more precise speciation and extinction rate estimator. Paleobiology 41, 633–639 (2015).

    Google Scholar 

  55. Kocsis, Á. T., Reddin, C. J., Alroy, J. & Kiessling, W. The r package divDyn for quantifying diversity dynamics using fossil sampling data. Methods Ecol. Evol. 10, 735–743 (2019).

    Google Scholar 

  56. Cornette, J. L. & Lieberman, B. S. Random walks in the history of life. Proc. Natl Acad. Sci. USA 101, 187–191 (2004).

    CAS  PubMed  Google Scholar 

  57. Fuller, W. A. Introduction to Statistical Time Series (Wiley, 2009).

  58. Manthey, M. & Fridley, J. D. Beta diversity metrics and the estimation of niche width via species co-occurrence data: reply to Zeleny. J. Ecol. 97, 18–22 (2009).

    Google Scholar 

Download references

Acknowledgements

We are grateful for discussions and commentaries on earlier versions of the manuscript from I. Žliobaitė, M. Fortelius and S. Geritz (all Helsinki, Finland). This is official Paleobiology Database Publication 374.

Author information

Authors and Affiliations

Authors

Contributions

A.P. and B.K. contributed to the design and implementation of the research, to the analysis of the results and to the writing of the manuscript.

Corresponding author

Correspondence to Björn Kröger.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Peer reviewer reports are available.

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

Extended data

Extended Data Fig. 1 Comparison of diversities of the two cohorts with medium lag time cohort, and random sample.

Richness estimates calculated with c-r modelling approach. a, Clong, genera of families with long macroevolutionary lag time, Cshort, genera of families with early burst diversification, Cmedium genera of families with moderate lag-times between 6.05–12.1 Myr. Dataset consists of 8050 genera of Clong, 3879 genera of Cshort, and 1465 genera of Cmedium. The median longevity of the combined families of Clong and Cmedium is 106 Myr compared with 51 Myr in Cshort (Mann-Whitney U test, p << 0.001). b, Csample, mean richness resulting from 50 bootstrap samples of 200 families taken from the total group of 1414 families of skeletal non-colonial animals used for the analysis. Note the difference of the resulting curve from curves of Cshort and Cmedium: the random samples preserve the rising Palaeozoic and Mesozoic-Cenozoic trend of the total group (see Fig. 2). Vertical bars are 95% confidence intervals. Blue hatched lines mark from left to right end-Ordovician, Late Devonian, end-Permian, end-Triassic, and end-Cretaceous mass extinctions. Based on a download from the PaleoBioDB from 16.04.2020.

Extended Data Fig. 2 Waxing and waning of genus richness of families.

All family longevities are standardized ranging from 0-100%. Based on c-r richness estimates of n=1414 non-colonial skeletal marine metazoan families. The pattern predicts that maximum diversification of families occurs within the first two-fifths of the duration of a family regardless of the absolute length of the duration, and it also predicts that in families with longer durations the time from first appearance to peak diversification is longer than in families with shorter durations. The hat-shaped pattern is a common feature of clade evolution12.

Extended Data Fig. 3 Comparison of ecological modes of life of the two cohorts.

Diagrams depict relative frequencies of ecological life modes of genera within the two cohorts. Based on categorisations of 1653 genera of Cshort and 3247 genera of Clong. Categories of ecological modes of life from29, and categorisations of genus life modes from30. Clong, cohort with long macroevolutionary lag; Cshort, cohort with short macroevolutionary lag.

Extended Data Fig. 4 Results of the autocorrelation function (ACF) for the richness curves of Clong, (cohort with long macroevolutionary lag) and Cshort (cohort with short macroevolutionary lag).

Autocorrelations not significantly different from zero are similar to autocorrelation functions of random walks56. Random walk dynamics have been previously identified and analysed for Phanerozoic time series of diversity and evolutionary rates56. Dotted line: 95 % confidence interval.

Extended Data Fig. 5 Richness curves of a reduced set of skeletal marine metazoans at genus level.

Families with maximum diversification during one of the stages with duration twice of maximum short-lag duration (= 12.1 Myr, Emsian, Famennian, Tournaisian, Visean, Norian, Aptian, Albian, Campanian, marked in red) were filtered out. Clong, genera of families with long macroevolutionary lag time (dotted line) and Cshort, genera of families with early burst diversification (solid line). Dataset consists of 1398 families, 16 families less than in the complete analysis. The filtering has a minor effect on the curves but does not change the strongly diverging trend between Cshort and Clong, (comp. Fig. 2). Vertical bars are 95% confidence intervals. Richness estimates calculated with c-r modeling approach. Blue hatched lines mark from left to right end-Ordovician, Late Devonian, end-Permian, end-Triassic, and end-Cretaceous mass extinctions. Apt., Aptian, Alb., Albian, Camp., Campanian, Fam., Famennian. Based on a download from the PaleoBioDB from 16.04.2020.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kröger, B., Penny, A. Skeletal marine animal biodiversity is built by families with long macroevolutionary lag times. Nat Ecol Evol 4, 1410–1415 (2020). https://doi.org/10.1038/s41559-020-1265-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41559-020-1265-8

This article is cited by

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