in

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

  • 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.

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

    PubMed  Article  CAS  Google Scholar 

  • 3.

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

  • 4.

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

    Article  CAS  Google Scholar 

  • 5.

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

    Google Scholar 

  • 6.

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

    CAS  Article  Google Scholar 

  • 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.

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

    CAS  PubMed  Article  Google Scholar 

  • 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  Article  Google Scholar 

  • 10.

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

    PubMed  Article  Google Scholar 

  • 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  Article  Google Scholar 

  • 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).

    Article  Google Scholar 

  • 13.

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

    Article  Google Scholar 

  • 14.

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

    Article  Google Scholar 

  • 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).

    Article  Google Scholar 

  • 16.

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

    Article  Google Scholar 

  • 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  Article  CAS  Google Scholar 

  • 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).

    Article  Google Scholar 

  • 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  PubMed Central  Article  Google Scholar 

  • 20.

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

  • 21.

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

    Article  Google Scholar 

  • 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).

    Article  Google Scholar 

  • 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).

    Article  Google Scholar 

  • 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  Article  Google Scholar 

  • 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  Article  Google Scholar 

  • 26.

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

    PubMed  Article  Google Scholar 

  • 27.

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

    Article  Google Scholar 

  • 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  Article  Google Scholar 

  • 29.

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

    Google Scholar 

  • 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).

    Article  Google Scholar 

  • 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.

    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).

    Article  Google Scholar 

  • 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.

    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).

    Article  Google Scholar 

  • 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).

    Article  Google Scholar 

  • 36.

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

  • 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).

    Article  Google Scholar 

  • 38.

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

  • 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).

    Article  Google Scholar 

  • 40.

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

  • 41.

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

    Article  Google Scholar 

  • 42.

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

    CAS  Article  Google Scholar 

  • 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).

    Article  CAS  Google Scholar 

  • 44.

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

    Article  Google Scholar 

  • 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  Article  Google Scholar 

  • 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  Article  Google Scholar 

  • 47.

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

  • 48.

    Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Soft. https://doi.org/10.18637/jss.v067.i01 (2015).

  • 49.

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

  • 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).

    Article  Google Scholar 

  • 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.

    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.

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

  • 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  Article  Google Scholar 

  • 55.

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

    Google Scholar 

  • 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  Article  Google Scholar 

  • 57.

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


  • Source: Ecology - nature.com

    The sources of variation for individual prey-to-predator size ratios

    Alteration of coastal productivity and artisanal fisheries interact to affect a marine food web