Skip to main content

Thank you for visiting 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.

Extinction risk controlled by interaction of long-term and short-term climate change


Assessing extinction risk from climate drivers is a major goal of conservation science. Few studies, however, include a long-term perspective of climate change. Without explicit integration, such long-term temperature trends and their interactions with short-term climate change may be so dominant that they blur or even reverse the apparent direct relationship between climate change and extinction. Here we evaluate how observed genus-level extinctions of arthropods, bivalves, cnidarians, echinoderms, foraminifera, gastropods, mammals and reptiles in the geological past can be predicted from the interaction of long-term temperature trends with short-term climate change. We compare synergistic palaeoclimate interaction (a short-term change on top of a long-term trend in the same direction) to antagonistic palaeoclimate interaction such as long-term cooling followed by short-term warming. Synergistic palaeoclimate interaction increases extinction risk by up to 40%. The memory of palaeoclimate interaction including the climate history experienced by ancestral lineages can be up to 60 Myr long. The effect size of palaeoclimate interaction is similar to other key factors such as geographic range, abundance or clade membership. Insights arising from this previously unknown driver of extinction risk might attenuate recent predictions of climate-change-induced biodiversity loss.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Schematic of analytical protocol.
Fig. 2: Model comparison.
Fig. 3: Change in extinction risk due to palaeoclimate interaction of all studied fossil clades.
Fig. 4: Change in extinction risk of fossil clades related to median duration.
Fig. 5: Palaeoclimate interactions through the Late Cretaceous and Cenozoic and potential implications for future climate changes.

Data availability

All data used to conduct analyses are available at

Code availability

All scripts used to conduct analyses are available at


  1. 1.

    Molinos, J. G. et al. Climate velocity and the future global redistribution of marine biodiversity. Nat. Clim. Change 6, 83–87 (2016).

    Google Scholar 

  2. 2.

    Pecl, G. T. et al. Biodiversity redistribution under climate change: impacts on ecosystems and human well-being. Science 355, eaai9214 (2017).

    PubMed  Google Scholar 

  3. 3.

    Brook, B. W. & Alroy, J. Pattern, Process, Inference and Prediction in Extinction Biology (Royal Society, 2017).

  4. 4.

    Barnosky, A. D. et al. Has the Earth’s sixth mass extinction already arrived? Nature 471, 51–57 (2011).

    Google Scholar 

  5. 5.

    Thomas, C. D. et al. Extinction risk from climate change. Nature 427, 145–148 (2004).

    Google Scholar 

  6. 6.

    Urban, M. C. Accelerating extinction risk from climate change. Science 348, 571–573 (2015).

    CAS  PubMed  Google Scholar 

  7. 7.

    Collins, K. S., Edie, S. M., Hunt, G., Roy, K. & Jablonski, D. Extinction risk in extant marine species integrating palaeontological and biodistributional data. Proc. Biol. Sci. 285, 20181698 (2018).

    PubMed  PubMed Central  Google Scholar 

  8. 8.

    Finnegan, S. et al. Paleontological baselines for evaluating extinction risk in the modern oceans. Science 348, 567–570 (2015).

    CAS  PubMed  Google Scholar 

  9. 9.

    van Woesik, R. et al. Hosts of the Plio-Pleistocene past reflect modern-day coral vulnerability. Proc. R. Soc. B 279, 2448–2456 (2012).

    PubMed  Google Scholar 

  10. 10.

    Harnik, P. G. et al. Extinctions in ancient and modern seas. Trends Ecol. Evol. 27, 608–617 (2012).

    PubMed  Google Scholar 

  11. 11.

    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 

  12. 12.

    Calosi, P., Putnam, H. M., Twitchett, R. J. & Vermandele, F. Marine metazoan modern mass extinction: improving predictions by integrating fossil, modern, and physiological data. Annu. Rev. Mar. Sci. 11, 369–390 (2019).

    Google Scholar 

  13. 13.

    Wiens, J. J. & Graham, C. H. Niche conservatism: integrating evolution, ecology, and conservation biology. Annu. Rev. Ecol. Evol. Syst. 36, 519–539 (2005).

    Google Scholar 

  14. 14.

    Beaugrand, G. Theoretical basis for predicting climate-induced abrupt shifts in the oceans. Philos. Trans. R. Soc. B 370, 20130264 (2015).

    Google Scholar 

  15. 15.

    Lord, J. P., Barry, J. P. & Graves, D. Impact of climate change on direct and indirect species interactions. Mar. Ecol. Prog. Ser. 571, 1–11 (2017).

    Google Scholar 

  16. 16.

    Bond, D. P. G. & Grasby, S. E. On the causes of mass extinctions. Palaeogeogr. Palaeoclimatol. Palaeoecol. 478, 3–29 (2017).

    Google Scholar 

  17. 17.

    Penn, J. L., Deutsch, C., Payne, J. L. & Sperling, E. A. Temperature-dependent hypoxia explains biogeography and severity of end-Permian marine mass extinction. Science 362, eaat1327 (2018).

    PubMed  Google Scholar 

  18. 18.

    Reddin, C. J., Nätscher, P. S., Kocsis, Á. T., Pörtner, H.-O. & Kiessling, W. Marine clade sensitivities to climate change conform across timescales. Nat. Clim. Change 10, 249–253 (2020).

    Google Scholar 

  19. 19.

    Bolker, B. M. et al. Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol. Evol. 24, 127–135 (2009).

    PubMed  Google Scholar 

  20. 20.

    Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference. A Practical Information-Theoretic Approach 2nd edn (Springer, 2010).

  21. 21.

    McKinney, M. L. Extinction vulnerability and selectivity: combining ecological and paleontological views. Annu. Rev. Ecol. Syst. 28, 495–516 (1997).

    Google Scholar 

  22. 22.

    Foote, M., Crampton, J. S., Beu, A. G. & Cooper, R. A. On the bidirectional relationship between geographic range and taxonomic duration. Paleobiology 34, 421–433 (2008).

    Google Scholar 

  23. 23.

    Payne, J. L., Truebe, S., Nützel, A. & Chang, E. T. Local and global abundance associated with extinction risk in late Paleozoic and early Mesozoic gastropods. Paleobiology 37, 616–632 (2011).

    Google Scholar 

  24. 24.

    Harnik, P. G. Direct and indirect effects of biological factors on extinction risk in fossil bivalves. Proc. Natl Acad. Sci. USA 108, 13594–13599 (2011).

    CAS  PubMed  Google Scholar 

  25. 25.

    Harnik, P. G., Simpson, C. & Payne, J. L. Long-term differences in extinction risk among the seven forms of rarity. Proc. R. Soc. B 279, 4969–4976 (2012).

    PubMed  Google Scholar 

  26. 26.

    Hopkins, M. J., Simpson, C. & Kiessling, W. Differential niche dynamics among major marine invertebrate clades. Ecol. Lett. 17, 314–323 (2014).

    PubMed  Google Scholar 

  27. 27.

    Svenning, J. ‐C. & Skov, F. Limited filling of the potential range in European tree species. Ecol. Lett. 7, 565–573 (2004).

    Google Scholar 

  28. 28.

    Normand, S. et al. Postglacial migration supplements climate in determining plant species ranges in Europe. Proc. R. Soc. B 278, 3644–3653 (2011).

    PubMed  Google Scholar 

  29. 29.

    Stigall, A. L. When and how do species achieve niche stability over long time scales? Ecography 37, 1123–1132 (2014).

    Google Scholar 

  30. 30.

    Steinbauer, M. J. et al. Biogeographic ranges do not support niche theory in radiating Canary Island plant clades. Glob. Ecol. Biogeogr. 25, 792–804 (2016).

    Google Scholar 

  31. 31.

    Foster, G. L., Hull, P., Lunt, D. J. & Zachos, J. C. Placing our Current ‘Hyperthermal’ in the Context of Rapid Climate Change in our Geological Past (The Royal Society Publishing, 2018).

  32. 32.

    Leckie, R. M., Bralower, T. J. & Cashman, R. Oceanic anoxic events and plankton evolution: biotic response to tectonic forcing during the mid‐Cretaceous. Paleoceanography 17, 13-1–13-29 (2002).

    Google Scholar 

  33. 33.

    Coxall, H. K. & Pearson, P. N. in Deep Time Perspectives on Climate Change: Marrying the Signal From Computer Models and Biological Proxies Vol. 2 (eds Williams, M. et al.) 351–387 (Geological Society of London, 2007).

  34. 34.

    Ritterbush, K. A. & Foote, M. Association between geographic range and initial survival of Mesozoic marine animal genera: circumventing the confounding effects of temporal and taxonomic heterogeneity. Paleobiology 43, 209–223 (2017).

    Google Scholar 

  35. 35.

    Stigall, A. L. Analysing links between biogeography, niche stability and speciation: the impact of complex feedbacks on macroevolutionary patterns. Palaeontology 56, 1225–1238 (2013).

    Google Scholar 

  36. 36.

    BouDagher-Fadel, M. K. Evolution and Geological Significance of Larger Benthic Foraminifera (UCL Press, 2018).

  37. 37.

    Reddin, C. J., Kocsis, Á. T. & Kiessling, W. Marine invertebrate migrations trace climate change over 450 million years. Glob. Ecol. Biogeogr. 27, 704–713 (2018).

    Google Scholar 

  38. 38.

    Valentine, J. W. Temporal bias in extinctions among taxonomic categories. J. Paleontol. 48, 549–552 (1974).

  39. 39.

    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 

  40. 40.

    Gradstein, F. M., Ogg, J. G., Schmitz, M. & Ogg, G. The Geologic Time Scale 2012 (Elsevier, 2012).

  41. 41.

    Foote, M. Origination and extinction components of taxonomic diversity: general problems. Paleobiology 26, 74–102 (2000).

    Google Scholar 

  42. 42.

    Veizer, J. & Prokoph, A. Temperatures and oxygen isotopic composition of Phanerozoic oceans. Earth Sci. Rev. 146, 92–104 (2015).

    CAS  Google Scholar 

  43. 43.

    Song, H., Wignall, P. B., Song, H., Dai, X. & Chu, D. Seawater temperature and dissolved oxygen over the past 500 million years. J. Earth Sci. 30, 236–243 (2019).

    Google Scholar 

  44. 44.

    Grossman, E. L. Applying oxygen isotope paleothermometry in deep time. Paleontol. Soc. Pap. 18, 39–68 (2012).

    Google Scholar 

  45. 45.

    Henkes, G. A. et al. Temperature evolution and the oxygen isotope composition of Phanerozoic oceans from carbonate clumped isotope thermometry. Earth Planet. Sci. Lett. 490, 40–50 (2018).

    CAS  Google Scholar 

  46. 46.

    Ryb, U. & Eiler, J. M. Oxygen isotope composition of the Phanerozoic ocean and a possible solution to the dolomite problem. Proc. Natl Acad. Sci. USA 115, 6602–6607 (2018).

    CAS  PubMed  Google Scholar 

  47. 47.

    R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).

  48. 48.

    Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Soft. (2015).

  49. 49.

    Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).

  50. 50.

    Quené, H. & van den Bergh, H. Examples of mixed-effects modeling with crossed random effects and with binomial data. J. Mem. Lang. 59, 413–425 (2008).

    Google Scholar 

  51. 51.

    Malik, W. A., Marco-Llorca, C., Berendzen, K. & Piepho, H.-P. Choice of link and variance function for generalized linear mixed models: a case study with binomial response in proteomics. Commun. Stat. Theory Methods 49, 1–20 (2019).

  52. 52.

    Zuur, A., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer Science & Business Media, 2009).

  53. 53.

    Fox, G. A., Negrete-Yankelevich, S. & Sosa, V. J. Ecological Statistics: Contemporary Theory and Application (Oxford Univ. Press, 2015).

  54. 54.

    Lieberman, M. D. & Cunningham, W. A. Type I and Type II error concerns in fMRI research: re-balancing the scale. Soc. Cogn. Affect. Neurosci. 4, 423–428 (2009).

    PubMed  PubMed Central  Google Scholar 

  55. 55.

    Durbin, J. & Watson, G. S. Testing for serial correlation in least squares regression. Biometrika 58, 1–19 (1971).

    Google Scholar 

  56. 56.

    Nakagawa, S., Johnson, P. C. D. & Schielzeth, H. The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. J. R. Soc. Interface 14, 20170213 (2017).

    PubMed  PubMed Central  Google Scholar 

  57. 57.

    IPCC. Special Report on Global Warming of 1.5 °C (eds Masson-Delmotte, V. et al.) (WMO, 2018).

Download references


This work was supported by the Deutsche Forschungsgemeinschaft (KI 806/16–1 and STE 2360/2-1) and is embedded in the Research Unit TERSANE (FOR 2332: Temperature-related stressors as a unifying principle in ancient extinctions). M.J.S. acknowledges support by European Research Council grant no. 741413 Humans on Planet Earth (HOPE).

Author information




M.J.S. was responsible for conceptualization and supervision. G.H.M. undertook formal analysis, investigation and visualization. J.v.D. designed the methodology. W.K. secured resources. W.K. and M.J.S. were responsible for funding acquisition. G.H.M. wrote the original draft and the other authors were involved in reviewing and editing the final manuscript.

Corresponding author

Correspondence to Gregor H. Mathes.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Ecology & Evolution thanks Michael Benton, Noel Heim and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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 Density distribution of GLMM results.

The difference in distributions were used to assess change in extinction probability of taxa due to paleoclimate interaction. Upper row blue areas show distributions of extinction risk subsequent to cooling–cooling interaction. Upper row red area cooling–warming interaction. Lower row blue area warming-cooling interaction. Lower row red area warming–warming interaction.

Extended Data Fig. 2 Temporal memory of the effect of paleoclimate interaction compared to the intensity of the effect.

The model with the lowest ∆AIC (here representatively shown for cooling–cooling of bivalvia) universally showed the highest effect of past temperature change on extinction risk. Effect intensity decreased with increasing AIC of the remaining models, enabling determination of the temporal memory of the effect. Trends one to ten covered a successively growing time of temperature history: Trend1 ranged one stage back, trend2 two stages, …, trend10 ten stages (methods).

Extended Data Fig. 3 Temporal memory of paleoclimate interaction for fossil clades related to genus duration.

a, Temporal memory versus median durations of fossil clades and (b) mean durations. Grey area depicts the 95 % confidence interval of the regression slope. Trend line and R2 value are based on univariate linear regression and are not significant (p-value for median duration = 0.79 and for mean duration = 0.63).

Extended Data Fig. 4 Results of simulations for null model.

Change of extinction risk of null models based on simulated data for datasets with varying sizes for warming–warming palaeoclimate interaction is shown in (a), and for cooling–cooling interaction in (b). We simulated datasets with increasing number of observations and calculated 100 GLMM’s for each to determine Type I Error rate of models used in our analysis. The shaded area shows the distribution of all 100 model results for warming–warming and cooling–cooling interaction respectively. The mean for each number of observations and its corresponding 95 % Wald Confidence Interval is shown in (c), where the red points and shaded intervals show the simulated response to warming–warming palaeclimate interaction, and blue points and shaded intervals to cooling–cooling.

Extended Data Fig. 5 Number of fossil genera within datasets.

The number of genera is shown for each fossil clade and for each stage. This is based on raw data (before filtering and processing of data).

Extended Data Fig. 6 Model comparison of subsampled data for robustness testing for bivalves and reptiles.

Values show AIC values after shareholder quorum subsampling (SQS) and classical rarefaction (CR). After subsampling, model quality of traditional models (change only) is compared to quality of models taking palaeoclimate interaction into account (change & trend), to test if models improve when long-term temperature trends are included. Red text indicates an improved model performance when palaeoclimate interactions are included. We used a shareholder quorum of 0.4 and classical rarefaction with 50 occurrences.

Extended Data Fig. 7 Simulations for potential autocorrelation bias.

a, Simulations were used to test if autocorrelation between the extinction signal and climate proxy data could bias our results. The results from these simulations show that the simulated extinction risk for both blue (inversely correlated) and red noise (positively correlated) is within the range of the null models, indicating that autocorrelation does not bias our results. b, We additionally tested for serial autocorrelation of the final GLMMs. No model shows indication for a strong serial autocorrelation. Durbin–Watson statistic values below 1 generally indicate a strong positive autocorrelation, values above 3 a strong negative autocorrelation and values around 2 no autocorrelation.

Extended Data Fig. 8 The effect of mass extinctions on the observed effect.

Models fitted on all fossil groups and all stages were compared to models excluding stages where the big five mass extinctions occurred (End-Ordovician, Late Devonian, End-Permian, End-Triassic and End-Cretaceous). For each model, the proportion of the variance using the conditional coefficient for determination (pseudo-R2) was quantified.

Supplementary information

Supplementary Information

Supplementary Figs, 1–4, Tables 1–8 and notes.

Reporting Summary

Peer Review Information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Mathes, G.H., van Dijk, J., Kiessling, W. et al. Extinction risk controlled by interaction of long-term and short-term climate change. Nat Ecol Evol 5, 304–310 (2021).

Download citation


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