Resistance of ecosystem services to global change weakened by increasing number of environmental stressors

Resistance of ecosystem services to global change weakened by increasing number of environmental stressors | Nature Geoscience

Abstract

Terrestrial ecosystems are subjected to multiple global changes simultaneously. Yet, how an increasing number of global changes impact the resistance of ecosystems to global change remains virtually unknown. Here we present a global synthesis including 14,000 observations from seven ecosystem services (functions and biodiversity), as well as data from a 15-year field experiment. We found that the resistance of multiple ecosystem services to global change declines with an increasing number of global change factors, particularly after long-term exposure to these factors. Biodiversity had a higher resistance to multiple global changes compared with ecosystem functions. Our work suggests that we need to consider the combined effects of multiple global changes on the magnitude and resistance of ecosystem services worldwide, as ecosystem responses will be enhanced by the number of environmental stressors and time of exposure.

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Data availability

The data supporting the findings of this study are available from figshare at https://doi.org/10.6084/m9.figshare.26200925(ref. 47).

Code availability

The code associated with this study is available from figshare at https://doi.org/10.6084/m9.figshare.26202050(ref. 48).

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Acknowledgements

We acknowledge B. A. Hungate, C. B. Field and N. R. Chiariello for use of the plant biomass data from the Jasper Ridge Global Change Experiment. We thank all the scientists whose data and work were included in the global analysis. G.Z. and N.E. acknowledge support by iDiv (German Research Foundation [DFG]–FZT 118, 202548816) and the Gottfried Wilhelm Leibniz Prize (DFG; Ei 862/29-1), both granted by the German Research Foundation. The Jena Experiment is funded by the DFG (FOR 5000). G.Z. acknowledges funding from Spanish Ministry of Science and Innovation through a Ramón y Cajal Fellowship (RYC2022-035226-I), the NextGenerationEU programme of the European Union (MICIU/AEI/10.13039/501100011033y el FSE+) and the AYUDAS DE EXCELENCIA RYC-MAX 2023 project from the Spanish National Research Council. M.D.-B. acknowledges support from the Spanish Ministry of Science and Innovation for the I + D + i project PID2020-115813RA-I00 funded by MCIN/AEI/10.13039/501100011033. M.D.-B. is also supported by a project of the Fondo Europeo de Desarrollo Regional (FEDER) and the Consejería de Transformación Económica, Industria, Conocimiento y Universidades of the Junta de Andalucía (FEDER Andalucía 2014-2020 Objetivo temático ‘01 – Refuerzo de la investigación, el desarrollo tecnológico y la innovación’) associated with the research project P20_00879 (ANDABIOMA). M.B. acknowledges funding from Spanish Ministry of Science and Innovation through a Ramón y Cajal Fellowship (RYC2021-031797-I). D.J.E. is supported by the Hermon Slade Foundation. L.Z. acknowledges support by the National Natural Science Foundation of China (grant no. 32071593). X.Z. acknowledges support by the National Natural Science Foundation of China (grants nos. 31930072, 32241032 and 42261144688).

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Nature Geoscience thanks Masahiro Ryo, Jens-Arne Subke and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Xujia Jiang, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Fig. 1 The multiservice resistance of plant diversity (A) and microbial diversity (B) to single and multiple (>1) global change factors.

Data are presented with mean±bootstrap 95% confidence interval (CI). Kruskal-Wallis test was performed to examine the significant difference in multiservice resistance among two levels of global change factors. Number values with red and green color indicate the number of studies and samples, respectively.

Extended Data Fig. 2 Heat map showing the two-sided spearman correlation between between the resistance of multiple ecosystem services and environmental factors including climate, soil properties and duration of experiments.

(P < 0.05). GCFs, global change factors.

Extended Data Fig. 3 Maps of uncertainty in resistance for one and two and three global change factors.

A, B, and C respectively based on Mahalanobis distance. Areas of high uncertainty are spatially represented in red, while areas of low uncertainty are represented in blue. Gray areas represent areas with no data.

Extended Data Fig. 4 Predicted distribution of multiservice resistance in response to an increasing number of global change factors based on machine learning modelling.

This figure shows the distribution of multiservice resistance under three global change factors (25 km / pixel). The R2 value determined as predicted vs. observed multiservice resistance is 0.64. The gray color corresponds to areas without data, and the white color to areas masked as outliers. A map including information on model uncertainties is available in Extended Data Fig. 3. Due to the relatively lower number of samples this map needs to be considered with caution.

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Zhou, G., Eisenhauer, N., Terrer, C. et al. Resistance of ecosystem services to global change weakened by increasing number of environmental stressors. Nat. Geosci. (2024). https://doi.org/10.1038/s41561-024-01518-x

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DOI: https://doi.org/10.1038/s41561-024-01518-x