in

Widespread slowdown in short-term species turnover despite accelerating climate change


Abstract

When the species composition of ecological communities changes over time, environmental drivers are often invoked as the most plausible explanation. Several lines of reasoning, however, suggest that such compositional change, called temporal species turnover, can similarly result from intrinsic ecosystem dynamics, even in a constant environment. The degree to which these two drivers contribute to observed turnover remains unclear. To address this conundrum, we analyse the well-established BioTIME database of surveys. We expect either an acceleration of turnover with accelerating climate change or constant turnover if intrinsic mechanisms dominate. Surprisingly we find instead that species turnover over short time intervals (1-5 years) has decelerated in significantly more communities during the last 100 years than it has accelerated, typically by one third. The observed slowing of turnover, we argue, could be understood—when intrinsic dynamics dominate—as resulting because anthropogenic environmental degradation or declines of regional species pools reduce the number of potential colonisers driving turnover. Our results suggest that observed past changes in species composition were often manifestations of natural, intrinsic ecosystem dynamics. Although one can expect environmental drivers to dominate species turnover eventually as climate change accelerates further, for now such attribution should be done with caution.

Similar content being viewed by others

Intrinsic ecological dynamics drive biodiversity turnover in model metacommunities

Warming and cooling catalyse widespread temporal turnover in biodiversity

Temporal complexity of terrestrial ecosystem functioning and its drivers

Data availability

The data underlying this study were sourced from the BioTIME database28. A file listing for each breakyear, lag, metric and community the corresponding turnover rates before and since the breakyear, has been deposited in the Zenodo database with [https://doi.org/10.5281/zenodo.17791135]. Source data are provided with this paper.

Code availability

The code used for our analyses has been deposited in the Zenodo database with [https://doi.org/10.5281/zenodo.17791135].

References

  1. Magurran, A. E. How ecosystems change. Science 351, 448–449 (2016).

    Google Scholar 

  2. Antão, L. H. et al. Temperature-related biodiversity change across temperate marine and terrestrial systems. Nat. Ecol. Evol. 4, 927–933 (2020).

    Google Scholar 

  3. Daskalova, G. N. et al. Landscape-scale forest loss as a catalyst of population and biodiversity change. Science 368, 1341–1347 (2020).

  4. Buckley, L. B. & Jetz, W. Linking global turnover of species and environments. Proc. Natl. Acad. Sci. USA https://doi.org/10.1073/pnas.0803524105 (2008).

  5. Russell, G. J., Diamond, J. M., Pimm, S. L. & Reed, T. M. A century of turnover: community dynamics at three timescales. J. Animal Ecol. 64, 628–641 (1995).

  6. Pilotto, F. et al. Meta-analysis of multidecadal biodiversity trends in Europe. Nat. Commun. https://doi.org/10.1038/s41467-020-17171-y (2020).

  7. Seymour, M. et al. Environmental DNA provides higher resolution assessment of riverine biodiversity and ecosystem function via spatio-temporal nestedness and turnover partitioning. Commun. Biol. 4, 1–12 (2021).

    Google Scholar 

  8. Malhi, Y. et al. Climate change and ecosystems: threats, opportunities and solutions. Philos. Trans. R. Soc. B 375, 20190104 (2020).

  9. Ockendon, N. et al. Mechanisms underpinning climatic impacts on natural populations: altered species interactions are more important than direct effects. Glob. Chang Biol. 20, 2221–2229 (2014).

    Google Scholar 

  10. MacArthur, R. H. & Wilson, E. O. The Theory of Island Biogeography, Vol. 1 (Princeton University Press, 2001).

  11. Hanski, I. Spatial patterns of coexistence of competing species in patchy habitat. Theor. Ecol. 1, 29–43 (2008).

    Google Scholar 

  12. Emborg, J., Christensen, M. & Heilmann-Clausen, J. The structural dynamics of Suserup Skov, a near-natural temperate deciduous forest in Denmark. Ecol. Manag. 126, 173–189 (2000).

    Google Scholar 

  13. Acevedo-Whitehouse, K. & Duffus, A. L. J. Effects of environmental change on wildlife health. Philos. Trans. R. Soc. B: Biol. Sci. 364, 3429 (2009).

    Google Scholar 

  14. Burrows, M. T. et al. Ocean community warming responses explained by thermal affinities and temperature gradients. Nat. Clim. Change 9, 959–963 (2019).

    Google Scholar 

  15. Devictor, V. et al. Differences in the climatic debts of birds and butterflies at a continental scale. Nat. Clim. Chang 2, 121–124 (2012).

    Google Scholar 

  16. O’Sullivan, J. D., Terry, J. C. D. & Rossberg, A. G. Intrinsic ecological dynamics drive biodiversity turnover in model metacommunities. Nat Commun 12, 3627 (2021).

  17. Blowes, S. A. et al. The geography of biodiversity change in marine and terrestrial assemblages. Science 366, 339–345 (2019).

    Google Scholar 

  18. Pérez, L. et al. Ecological turnover in neotropical freshwater and terrestrial communities during episodes of abrupt climate change. Quat. Res. 101, 26–36 (2021).

    Google Scholar 

  19. Khaliq, I. et al. Warming underpins community turnover in temperate freshwater and terrestrial communities. Nat Commun 15, 1921 (2024).

  20. Bernhardt, E. S., Rosi, E. J. & Gessner, M. O. Synthetic chemicals as agents of global change. Front Ecol Environ 15, 84–90 (2017).

  21. Fitzpatrick, M. C. et al. Environmental and historical imprints on beta diversity: Insights from variation in rates of species turnover along gradients. Proc. R. Soc. B Biol. Sci. 280, 20131201 (2013).

  22. Rossberg, A. G. Food Webs and Biodiversity: Foundations, Models, Data (John Wiley & Sons, 2013).

  23. Reichenbach, T., Mobilia, M. & Frey, E. Mobility promotes and jeopardizes biodiversity in rock–paper–scissors games. Nature 448, 1046–1049 (2007).

    Google Scholar 

  24. Macarthur, R. H. & Wilson, E. O. The Theory of Island Biogeography, 467 (Princeton Univ. Press, 1967).

  25. Botkin, D. B., Janak, J. F. & Wallis, J. R. Some ecological consequences of a computer model of forest growth. J. Ecol. 60, 849 (1972).

    Google Scholar 

  26. Watt, A. S. Pattern and process in the plant community. J. Ecol. 35, 1–22 (1947).

    Google Scholar 

  27. Bormann, F. H. & Likens, G. E. Catastrophic disturbance and the steady state in northern hardwood forests: a new look at the role of disturbance in the development of forest ecosystems suggests important implications for land-use policies. Am. Sci. 67, 660–669 (1979).

    Google Scholar 

  28. Dornelas, M. et al. BioTIME: a database of biodiversity time series for the Anthropocene. Glob. Ecol. Biogeogr. 27, 760–786 (2018).

    Google Scholar 

  29. Arias, P. A. et al. Intergovernmental Panel on Climate Change (IPCC). Technical summary. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 35–144 (Cambridge University Press, 2023).

  30. Stocker, T.F. et al. (eds.) Climate change. The Physical Science Basis. Contribution of Working Group I to the Fifth Assess- Ment Report of the Intergovernmental Panel on Climate Change. https://www.ipcc.ch/site/assets/uploads/2018/03/WG1AR5_SummaryVolume_FINAL.pdf (2013).

  31. Henson, S. A., Cael, B. B., Allen, S. R. & Dutkiewicz, S. Future phytoplankton diversity in a changing climate. Nat. Commun. 12, 5372–5379 (2021).

    Google Scholar 

  32. Gonzalez, A. et al. Estimating local biodiversity change: a critique of papers claiming no net loss of local diversity. Ecology 97, 1949–1960 (2016).

    Google Scholar 

  33. Cardinale, B. J., Gonzalez, A., Allington, G. R. H. & Loreau, M. Is local biodiversity declining or not? A summary of the debate over analysis of species richness time trends. Biol. Conserv 219, 175–183 (2018).

    Google Scholar 

  34. Ochiai, A. Zoogeographical studies on the soleoid fishes found in Japan and its neighbouring regions-III. Environ. Sci. 22, 522–525 (1957).

    Google Scholar 

  35. De Cáceres, M., Font, X. & Oliva, F. Assessing species diagnostic value in large data sets: a comparison between phi-coefficient and Ochiai index. J. Vegetation Sci. 19, 779–788 (2008).

    Google Scholar 

  36. Kalgotra, P., Sharda, R. & Luse, A. Which similarity measure to use in network analysis: Impact of sample size on phi correlation coefficient and Ochiai index. Int J. Inf. Manag. 55, 102229 (2020).

    Google Scholar 

  37. Lynch, J. F. & Johnson, N. K. Turnover and equilibria in insular avifaunas, with special reference to the California Channel Islands. Condor 76, 370–384 (1974).

  38. Nilsson, I. N. & Nilsson, S. G. Experimental estimates of census efficiency and pseudoturnover on islands: error trend and between-observer variation when recording vascular plants. J. Ecol. 73, 65 (1985).

    Google Scholar 

  39. Nilsson, I. N. & Nilsson, S. G. Turnover of vascular plant species on small islands in lake Möckeln, South Sweden 1976-1980. Oecologia 53, 128–133 (1982).

  40. Miller, R. G. Simultaneous Statistical Inference. Simultaneous statistical inference (Springer, 1981).

  41. Krieger, A., Porembski, S. & Barthlott, W. Temporal dynamics of an ephemeral plant community: species turnover in seasonal rock pools on Ivorian inselbergs. Plant Ecol. 167, 283–292 (2003).

  42. Ghosh, S. & Matthews, B. Temporal turnover in species’ ranks can explain variation in Taylor’s slope for ecological timeseries. Ecology 105, e4381 (2024).

    Google Scholar 

  43. Cleland, E. E. et al. Sensitivity of grassland plant community composition to spatial vs. temporal variation in precipitation. Ecology 94, 1687–1696 (2013).

  44. O’Sullivan, J. D., Terry, J. C. D. & Rossberg, A. G. Temporally robust occupancy frequency distributions in riverine metacommunities explained by local biodiversity regulation. Glob. Ecol. Biogeogr. 32, 2230–2243 (2023).

    Google Scholar 

  45. Terry, J. C. D. & Rossberg, A. G. Slower but deeper community change: intrinsic dynamics regulate anthropogenic impacts on species temporal turnover. Ecology 105, e4430 (2024).

    Google Scholar 

  46. Gibbons, J. D. & Chakraborti, S. Nonparametric Statistical Inference. Nonparametric Statistical Inference (Chapman and Hall/CRC, 2010). https://doi.org/10.1201/9781439896129.

  47. Cornford, R., Spooner, F., McRae, L., Purvis, A. & Freeman, R. Ongoing over-exploitation and delayed responses to environmental change highlight the urgency for action to promote vertebrate recoveries by 2030. Proc. R. Soc. B Biol. Sci. 290, 20230464 (2023).

  48. Arnoulx De Pirey, T. & Bunin, G. Many-species ecological fluctuations as a jump process from the brink of extinction. Phys. Rev. X 14, 011037 (2024).

  49. Mallmin, E., Traulsen, A. & De Monte, S. Chaotic turnover of rare and abundant species in a strongly interacting model community. Proc. Natl. Acad. Sci. USA 121, e2312822121 (2024).

  50. Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).

    Google Scholar 

  51. Aulsebrook, L. C. et al. Reproduction in a polluted world: Implications for wildlife. Reproduction 160, R13–R23 (2020).

  52. Tinoco, B. A., Latta, S. C., Astudillo, P. X., Nieto, A. & Graham, C. H. Temporal stability in species richness but reordering in species abundances within avian assemblages of a tropical Andes conservation hot spot. Biotropica 53, 1673–1684 (2021).

    Google Scholar 

  53. Robinson, G. R. & Quinn, J. F. Extinction, turnover and species diversity in an experimentally fragmented California annual grassland. Oecologia 76, 71–82 (1988).

    Google Scholar 

  54. Damschen, E. I. et al. Ongoing accumulation of plant diversity through habitat connectivity in an 18-year experiment. Science 365, 1478–1480 (2019).

    Google Scholar 

  55. Di Cecco, G. J. & Gouhier, T. C. Increased spatial and temporal autocorrelation of temperature under climate change. Sci. Rep. 8, 14850 (2018).

  56. Ummenhofer, C. C. & Meehl, G. A. Extreme weather and climate events with ecological relevance: a review. Philos. Trans. R. Soc. B. 372, https://doi.org/10.1098/rstb.2016.0135 (2017).

  57. Kirchengast, G. & Pichler, M. A traceable global warming record and clarity for the 1.5 °C and well-below-2 °C goals. Commun. Earth Environ. 6, 1–12 (2025).

    Google Scholar 

  58. Cai, W. et al. Anthropogenic impacts on twentieth-century ENSO variability changes. Nat. Rev. Earth Environ. 4, 407–418 (2023).

  59. Ramalho, Q. et al. Evidence of stronger range shift response to ongoing climate change by ectotherms and high-latitude species. Biol. Conserv 279, 109911 (2023).

    Google Scholar 

  60. McCain, C. M., King, S. R. B. & Szewczyk, T. M. Unusually large upward shifts in cold-adapted, montane mammals as temperature warms. Ecology 102, e03300 (2021).

    Google Scholar 

  61. Hastings, R. A. et al. Climate change drives poleward increases and equatorward declines in marine species. Curr. Biol. 30, 1572–1577.e2 (2020).

    Google Scholar 

  62. Dornelas, M. et al. Quantifying temporal change in biodiversity: challenges and opportunities. Proc. R. Soc. B Biol. Sci. 280, 20121931 (2013).

    Google Scholar 

  63. Pinsky, M. L. et al. Warming and cooling catalyse widespread temporal turnover in biodiversity. Nature 638, 995–999 (2025).

    Google Scholar 

  64. Gower, J. C. & Legendre, P. Metric and Euclidean properties of dissimilarity coefficients. J. Classif. 3, 5–48 (1986).

    Google Scholar 

  65. Bolton, H. C. On the mathematical significance of the similarity index of Ochiai as a measure for biogeographical habitats. Aust. J. Zool. 39, 143–156 (1991).

    Google Scholar 

  66. Baselga, A. Partitioning the turnover and nestedness components of beta diversity. Glob. Ecol. Biogeogr. 19, 134–143 (2010).

    Google Scholar 

  67. Hollander, M., Wolfe, D. A. & Chicken, E. Nonparametric Statistical Methods (Wiley, 2014).

  68. Dray, S. & Dufour, A.-B. The ade4 package: implementing the duality diagram for ecologists. J. Stat. Softw. 22, 1–20 (2007).

    Google Scholar 

  69. Cockrell, C. et al. Self-organization of ecosystems to exclude half of all potential invaders. Phys. Rev. Res 6, 013093 (2024).

    Google Scholar 

  70. Arnold Taylor B. leaderCluster: Leader Clustering Algorithm. R package version 1.5. Available at https://CRAN.R-project.org/package=leaderCluster (2023).

  71. Williamson, M. The land-bird community of Skokholm: ordination and turnover. Oikos 41, 378–384 (1983).

Download references

Acknowledgements

The authors acknowledge discussion of this study with Chris Terry. This work was supported by the Natural Environment Research Council (NERC) [grant number NE/T003510/1] (AGR).

Author information

Authors and Affiliations

Authors

Contributions

A.G.R. acquired funding, and designed and conceptualised the study. E.C.N. developed and tested the methods, performed the data analyses, and wrote the original draft. E.C.N. and A.G.R. jointly tested the code, reviewed, and edited the manuscript and developed data visualisation.

Corresponding author

Correspondence to
Axel G. Rossberg.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Communications thanks Simon Ferrier and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

Additional information

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

Supplementary information

Supplementary Information

Description of Additional Supplementary Files

Supplementary Code 1

Reporting Summary

Transparent Peer Review file

Source data

Source data

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Cite this article

Nwankwo, E.C., Rossberg, A.G. Widespread slowdown in short-term species turnover despite accelerating climate change.
Nat Commun (2026). https://doi.org/10.1038/s41467-025-68187-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41467-025-68187-1


Source: Ecology - nature.com

Evaluation of mineral composition and in-vitro nutrient digestibility of macrophytes to assess their potential as sustainable animal feed

Risk sources quantitative identification of heavy metals in coal mining hinterland river sediments, Northern China

Back to Top