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

Abstract

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.

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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 https://github.com/Ischi94/pal-int-extinction.

Code availability

All scripts used to conduct analyses are available at https://github.com/Ischi94/pal-int-extinction.

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Acknowledgements

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).

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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.

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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.

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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). https://doi.org/10.1038/s41559-020-01377-w

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