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Warming and cooling catalyse widespread temporal turnover in biodiversity

Nature volume 638, pages 995–999 (2025)Cite this article

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An Author Correction to this article was published on 07 March 2025

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Abstract

Turnover in species composition through time is a dominant form of biodiversity change, which has profound effects on the functioning of ecological communities1,2,3,4. Turnover rates differ markedly among communities4, but the drivers of this variation across taxa and realms remain unknown. Here we analyse 42,255 time series of species composition from marine, terrestrial and freshwater assemblages, and show that temporal rates of turnover were consistently faster in locations that experienced faster temperature change, including both warming and cooling. In addition, assemblages with limited access to microclimate refugia or that faced stronger human impacts on land were especially responsive to temperature change, with up to 48% of species replaced per decade. These results reveal a widespread signal of vulnerability to continuing climate change and highlight which ecological communities are most sensitive, raising concerns about ecosystem integrity as climate change and other human impacts accelerate.

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Fig. 1: Records of species composition temporal turnover from around the world.
Fig. 2: Species turnover rates were related to rates of warming and cooling.
Fig. 3: Covariates associated with the sensitivity of turnover rates to temperature change in the marine and terrestrial realms.

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Data availability

Species composition data are available from BioTIME (https://biotime.st-andrews.ac.uk/), human impact data from ref. 66 (pan310071-sup-0003-Supinfo2.7z from https://doi.org/10.1002/pan3.10071), sea surface temperature from ERSST v.5 (ftp://ftp.cdc.noaa.gov/Datasets/noaa.ersst.v5/sst.mnmean.nc), land surface temperature from CRU TS v.4.03 (https://dap.ceda.ac.uk/badc/cru/data/cru_ts/cru_ts_4.03/data/tmp/cru_ts4.03.1901.2018.tmp.dat.nc), terrestrial microclimate data from WorldClim 2.0 (wc2.0_bio_30s_01.tif from https://worldclim.org) and marine microclimate data from Bio-ORACLE 2.2 (https://www.bio-oracle.org/). Further details are available at Zenodo (https://doi.org/10.5281/zenodo.13905417)67.

Code availability

Scripts are available at Zenodo (https://doi.org/10.5281/zenodo.13905417)67.

Change history

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Acknowledgements

We thank K. Lew and J. Hauser for help with data entry; M. Stein for statistical advice; A. Bates, C. Meyer, R. Remelgado and the Global Change Research Group at Rutgers University and the University of California Santa Cruz for useful discussions; Z. Kitchel, A. Maureaud and P. Morin for feedback on earlier drafts; the BioTIME consortium for their commitment to open science; and the Rutgers School of Environmental and Biological Sciences for access to the Annotate and Annotate2 scientific workstations. Views and opinions expressed are those of the authors only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them. The work was supported by the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, funded by the German Research Foundation grant no. FZT 118 (M.L.P., S.A.B., J.M.C., U.B., M.H., B.G, and B.R.); the Helmholtz Institute for Functional Marine Biodiversity (M.L.P. and H.H.); US National Science Foundation grant no. DEB-1616821 (M.L.P.); US National Science Foundation grant no. CBET-2137701 (M.L.P.); US National Science Foundation grant no. DEB-2129351 (M.L.P.); an ERC Advanced Grant (MetaChange) funded by the European Union (J.M.C. and S.A.B.); German Research Foundation grant nos. Hi848/26 and EXC2077 (H.H.); and Academy of Finland grant no. 340280 (L.H.A.).

Author information

Authors and Affiliations

  1. Department of Ecology, Evolution, and Natural Resources, Rutgers University, New Brunswick, NJ, USA

    Malin L. Pinsky

  2. Department of Ecology & Evolutionary Biology, University of California Santa Cruz, Santa Cruz, CA, USA

    Malin L. Pinsky

  3. Institute for Chemistry and Biology of Marine Environments (ICBM), University of Oldenburg, Wilhelmshaven, Germany

    Helmut Hillebrand

  4. Helmholtz Institute for Functional Marine Biodiversity at the University of Oldenburg (HIFMB), Oldenburg, Germany

    Helmut Hillebrand

  5. Alfred Wegener Institute (AWI), Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany

    Helmut Hillebrand

  6. German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany

    Jonathan M. Chase, Myriam R. Hirt, Ulrich Brose, Benoit Gauzens, Benjamin Rosenbaum & Shane A. Blowes

  7. Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany

    Jonathan M. Chase & Shane A. Blowes

  8. Research Centre for Ecological Change, Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland

    Laura H. Antão

  9. Institute of Biodiversity, Friedrich Schiller University Jena, Jena, Germany

    Myriam R. Hirt, Ulrich Brose, Benoit Gauzens & Benjamin Rosenbaum

  10. Scottish Association for Marine Science, Oban, UK

    Michael T. Burrows

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Contributions

Conceptualization: M.L.P. Data curation: S.A.B., M.L.P., B.G., L.H.A., M.T.B., M.R.H. Formal analysis: M.L.P. Funding acquisition: M.L.P., H.H., J.M.C., U.B. Methodology: M.L.P., S.A.B., J.M.C., M.T.B., H.H., B.R. Project administration: M.L.P. Software: M.L.P., S.A.B., L.H.A. Visualization: M.L.P. Writing—original draft: M.L.P., H.H. Writing—review and editing: M.L.P., L.H.A., J.M.C., B.G., S.A.B., U.B., M.R.H., B.R.

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Correspondence to Malin L. Pinsky.

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Extended data figures and tables

Extended Data Fig. 1 Characteristics of the assemblage time series.

a) Start year. b) End year. c) Number of years between the start and end year. d) Number of annual samples in each time series.

Extended Data Fig. 2 The effect of time series duration on turnover rates (change in dissimilarity/yr) and the statistical challenges when time series are compared.

a) Duration affects turnover rates partly because there is a 0-1 constraint on dissimilarity, such that longer duration time series (blue) are constrained to a shallower slope than shorter duration time series (green). b) Turnover rates show strong heteroskedasticity with higher variance and faster rates among shorter time series. The red line shows mean turnover rate estimated from LOESS smoothing. c) Temperature changes (°C/yr) also showed strong heteroskedasticity with higher variance among shorter time series. The red line shows a fit from LOESS smoothing. d) Slopes calculated from Gaussian white noise time series also show strong heteroskedasticity with higher variance among shorter time series. The durations of the white noise time series matched the durations in the species composition dataset. The red line shows a fit from LOESS smoothing. e) A comparison of Type I (false positive) error rates shows that one-stage (i.e. fit directly to dissimilarities) generalized linear mixed models (GLMMs) with ordered beta errors have an acceptably low false positive rate when time series of different durations are analyzed together, while other common analytical methods (Pearson correlations of time series slopes, meta-analysis of time series slopes, or one-stage mixed effect models with Gaussian errors fit to time series data) have unacceptably high false positive rates if time series differ in duration (range of durations > 0). All methods have low false positive rates when time series are all the same duration (range of durations = 0). Data are presented as means with error bars for the 95% binomial confidence bounds. f) Example of a time series with a negative turnover rate. Data are demersal marine taxa from the Northeast Fisheries Science Center Bottom Trawl Survey. Beta regression trend line is shown with shading for +/− one standard error.

Extended Data Fig. 3 The statistical approach was implemented via one-stage generalized linear mixed models (GLMMs) in which the response variable was species composition dissimilarity among years.

a) The simplest model included the relationship between dissimilarity and temporal distance among observations so that, for example, dissimilarity could increase with time. The slope of this relationship is the turnover rate. Random intercepts and slopes helped account for variation among studies and time series (not shown). b) We tested the hypothesis that faster rates of temperature change (Tchange) were associated with faster accumulation of dissimilarity through time (compare red vs. blue line). This hypothesis was statistically tested as an interaction (Tchange × Years). c) We additionally tested the hypothesis that the influence of temperature change on the turnover rate depended on average baseline temperatures. For example, the slope of dissimilarity over time could be steeper in areas with hotter average temperatures and fast rates of temperature change than in areas with colder average temperatures and fast rates of temperature change (compare dashed red vs. solid red line). Statistically, this was tested as a three-way interaction (Tchange × Tave × Years). d) Turnover rates as a function of temperature change rates, showing an increase in turnover rate with increasing rates of temperature change (i.e., the same relationship as panel b but summarized as rates). The slope of this relationship was termed sensitivity (Δturnover rate/Δtemperature change rate). e) Turnover rates as a function of temperature change rates and average baseline temperatures, showing a faster increase in turnover rate with temperature change at hotter average baseline temperatures (i.e., summarizing the same relationship as panel c). f) Sensitivity as a function of average temperatures, showing an increase in sensitivity at hotter average temperatures (i.e., summarizing the same relationship as in panels c and e). The x-axis could also be other environmental covariates, such as microclimates or non-climate human impacts (as in Fig. 3).

Extended Data Fig. 4 Association of turnover rate with taxonomic group and uncertainty of the association with temperature change and average temperature.

a) Turnover rate [proportion of species per year] for studies organized by taxonomic group. Dashed lines are the averages across studies within taxa, and the top horizontal lines indicate the 95% confidence intervals on the averages. The x- and y-axes have been square-root transformed to facilitate visualization. b) Uncertainty in the marginal effects of temperature change on the turnover rate, calculated by downsampling each time series of dissimilarities (see Methods). Plot shows the individual downsampled effects (thin green lines), the average across 1000 downsampling trials (yellow line), the 95% confidence interval from downsampling (green shading), and the mean marginal effects from the full dataset with 95% confidence intervals (black line and shading). c) Uncertainty in the marginal effects of average temperature on the sensitivity of turnover rate to temperature change, calculated by downsampling each time series of dissimilarities (see Methods). Plot shows the individual downsampled effects (thin lines), the average across 1000 downsampling trials (thick lines), and the 95% confidence interval from downsampling (vertical error bars) for warming (orange) and cooling (blue). The mean marginal effects from the full dataset with 95% confidence intervals are also shown (black lines and error bars).

Extended Data Fig. 5 Turnover rate model interactions.

a) Interaction of Tchange (x-axis) with Tave (y-axis) from the Tchange × Tave × Year × Realm model (Table 1). Two average temperature levels (0 °C and 25 °C) from this interaction are plotted in Fig. 2c. b) Marginal effects of temperature change on the turnover rate (lines) as predicted from the best environmental interaction model identified by AIC (Extended Data Table 5). The model included effects of microclimate availability (colors).

Extended Data Table 1 Random effect standard deviations and correlations for the Tchange × Tave × Year × Realm model (see Table 1)
Full size table
Extended Data Table 2 Fixed effect terms for the Tchange × Tave × Year × Realm model (see Table 1)
Full size table
Extended Data Table 3 As for Table 1, but models either 1) included an additional term for the interaction of initial dissimilarity (Dinit), the proportion of species gains vs. losses (propGL), and the duration between observations (Years), and only included random effects for study, or 2) were fit to only time series with at least 7 years of data
Full size table
Extended Data Table 4 Comparison of models of temporal turnover calculated with abundance-based Morisita-Horn dissimilarities with or without the temperature change (Tchange) and average temperature (Tave)
Full size table
Extended Data Table 5 Comparison of models of temporal turnover with or without environmental covariates. Models with microclimate variability (#3, Micro) or human impact (#4, Human) were favored over the simplest Year model (#1) with dissimilarity as a function of year, shown as negative ΔAICnull and were also favored over a model with only temperature (#2), shown as negative ΔAICtemp
Full size table

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Pinsky, M.L., Hillebrand, H., Chase, J.M. et al. Warming and cooling catalyse widespread temporal turnover in biodiversity. Nature 638, 995–999 (2025). https://doi.org/10.1038/s41586-024-08456-z

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