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Climate-driven ecological thresholds in China’s drylands modulated by grazing

Nature Sustainability volume 6, pages 1363–1372 (2023)Cite this article

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Abstract

Degradation of ecosystems can occur when certain ecological thresholds are passed below which ecosystem responses remain within ‘safe ecological limits’. Ecosystems such as drylands are sensitive to both aridification and grazing, but the combined effects of such factors on the emergence of ecological thresholds beyond which ecosystem degradation occurs has yet to be quantitatively evaluated. This limits our understanding on ‘safe operating spaces’ for grazing, the main land use in drylands worldwide. Here we assessed how 20 structural and functional ecosystem attributes respond to joint changes in aridity and grazing pressure across China´s drylands. Gradual increases in aridity resulted in abrupt decreases in productivity, soil fertility and plant richness. Rising grazing pressures lowered such aridity thresholds for most ecosystem variables, thus showing how ecological thresholds can be amplified by the joint effects of these two factors. We found that 44.4% of China’s drylands are unsuitable for grazing due to climate change-induced aridification, a percentage that may increase to 50.8% by 2100. Of current dryland grazing areas, 8.9% exceeded their maximum allowable grazing pressure. Our findings provide important insights into the relationship between aridity and optimal grazing pressure and identify safe operating spaces for grazing across China’s drylands.

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Fig. 1: Nonlinear responses of multiple ecosystem variables to the joint effects of aridity and grazing pressure.
Fig. 2: Combined effects of aridity and grazing on ecosystem structure and functioning across China’s drylands.
Fig. 3: Future changes and climate change vulnerability in China’s drylands.

Data availability

The datasets analysed in this study are publicly available, with data sources for each indicator described in the Data collection subsection of Methods in the paper and summarized in Supplementary Table 1. The data that support the findings of this study are available from figshare at https://doi.org/10.6084/m9.figshare.22678999.

Code availability

All data processing and analysis were conducted in ArcGIS (version 10.7), Microsoft Excel (version 2022), Origin (version 2022b), chngpt and gam packages in R (version 4.1.2) and MATLAB (version 2020a). The code used in this study is available from figshare at https://doi.org/10.6084/m9.figshare.22678999.

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Acknowledgements

This research is jointly funded by the National Natural Science Foundation of China Project (grant 41991235), China’s Second Scientific Research Project on the Qinghai–Tibet Plateau (grant 2019QZKK0405-02) and the Fundamental Research Funds for the Central Universities. F.T.M. is supported by Generalitat Valenciana (CIDEGENT/2018/041), by the Spanish Ministry of Science and Innovation (EUR2022-134048) and by the contract between ETH Zurich and University of Alicante ‘Mapping terrestrial ecosystem structure at the global scale’. E.R.-C. was supported by the Ramon y Cajal fellowship (RYC2020-030762-I) and by the CRUST R-Forze (PID2021-127631NA-I00) project founded by FEDER/Ministerio de Ciencia e Inovacion-Agencia Estatal de Investigación. Many thanks to A. W. R. Seddon at University of Bergen (Norway) for sharing the global Vegetation Sensitivity Index dataset.

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Authors and Affiliations

  1. State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, China

    Changjia Li, Bojie Fu, Shuai Wang, Wenxin Zhou & Zhuobing Ren

  2. Institute of Land Surface System and Sustainable Development, Faculty of Geographical Science, Beijing Normal University, Beijing, China

    Changjia Li, Bojie Fu, Shuai Wang, Wenxin Zhou & Zhuobing Ren

  3. Department of Environment and Geography, University of York, York, UK

    Lindsay C. Stringer

  4. York Environmental Sustainability Institute, University of York, York, UK

    Lindsay C. Stringer

  5. School of Mathematical Sciences, Beijing Normal University, Beijing, China

    Mengqi Hu

  6. School of Statistics, Beijing Normal University, Beijing, China

    Yujia Zhang

  7. Departamento de Agronomía, Universidad de Almería, Almería, Spain

    Emilio Rodriguez-Caballero

  8. Multiphase Chemistry Department, Max Planck Institute for Chemistry, Mainz, Germany

    Emilio Rodriguez-Caballero & Bettina Weber

  9. Division of Plant Sciences, Institute for Biology, University of Graz, Graz, Austria

    Bettina Weber

  10. Instituto Multidisciplinar para el Estudio del Medio “Ramón Margalef”, Universidad de Alicante, Alicante, Spain

    Fernando T. Maestre

  11. Departamento de Ecología, Universidad de Alicante, Alicante, Spain

    Fernando T. Maestre

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Contributions

C.L., B.F., S.W., F.T.M. and L.C.S. conceived and designed the study. C.L. carried out the calculations, drafted the figures and wrote the first draft of the paper. W.Z., Z.R., M.H. and Y.Z. undertook data analysis and figure reproduction. B.F., S.W., L.C.S., E.R.-C., B.W. and F.T.M. reviewed and edited the paper before submission. All authors made substantial contributions to the discussion of content.

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Correspondence to Bojie Fu.

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Extended data

Extended Data Fig. 1 Nonlinear responses of multiple ecosystem variables to aridity.

Examples of aridity thresholds observed for NDVI (a), vegetation cover (b), aboveground carbon density (c), root/shoot ratio (d), soil carbon content (e), soil nitrogen content (f), belowground carbon density (g), carbon sequestration (h), biocrust cover (i), interannual variation of precipitation (j), plant species richness (k), and vegetation sensitivity index (l). In (a.1) to (l.1), black dashed lines and black solid lines represent the smoothed trend fitted by a generalized additive model (GAM) and the linear fits at both sides of each threshold, respectively. The vertical grey dashed lines describe the aridity threshold identified. In (a.2) to (l.2), violin diagrams show bootstrapped slopes at the threshold of the two regressions existing at each side of the threshold values (red: regression before the threshold; yellow, after the threshold). Asterisks indicate significant differences when conducting a Mann-Whitney U test (two-sided) between before and after the threshold where: ***= P < 0.001.

Extended Data Fig. 2 Nonlinear responses of multiple ecosystem variables to grazing pressure.

Examples of grazing pressure thresholds observed for NDVI (a), vegetation sensitivity index (b), aboveground carbon density (c), plant species richness (d), belowground carbon density (e), and sensitivity of vegetation to precipitation (f). In (a.1) to (f.1), black dashed lines and black solid lines represent the smoothed trend fitted by a GAM and the linear fits at both sides of each threshold, respectively. The vertical grey dashed lines describe the aridity threshold identified. In (a.2) to (f.2), violin diagrams show bootstrapped slopes at the threshold of the two regressions existing at each side of the threshold values (red: regression before the threshold; yellow, after the threshold). Asterisks indicate significant differences when conducting a Mann-Whitney U test (two-sided) of the data below and above the threshold where: ***= P value < 0.001.

Extended Data Fig. 3 Predicted areas with the difference between maximum allowable grazing pressure and current grazing pressure.

a,b, For the contrasting effect (a) or synergistic effect (b) of aridity and grazing pressure in China’s drylands. The blue and brown shading denotes where the maximum allowable grazing pressure is higher and lower than the current grazing level, respectively. The red lines denote the baseline drylands in 1950–2000 that are not suitable for grazing and thus where grazing is not recommended, as their maximum allowable grazing pressure is equal to zero and the current grazing pressure leads thresholds to be crossed for ecosystem attributes. The grey shading denotes drylands where the land covers are cropland, wetland or urban areas. The unshaded areas are not drylands today and therefore are outside of the range. Credit: basemaps from the Global Aridity Index database42 and China Data Lab62.

Extended Data Fig. 4 Future changes and climate change vulnerability in China’s drylands.

a. Temporal variation in the mean aridity values across Chinese drylands. The thin solid lines and shading are mean values and the 95% confidence intervals of the 20 CMIP5 climate models, respectively. Bold solid lines show the aridity trends by twenty-year running means. b. Projections of changes in the mean areas of four dryland subtypes (units: percentage of China’s drylands) based on CMIP5 two representative concentration pathways (RCPs): RCP8.5 and RCP4.5 relative to the baseline period (1980–2014) for 2020–2060 and 2061–2100, respectively. Results are presented as mean values ± s.d. (n = 40). Error bars reflect the minimum and maximum number of area change with values equal to one standard derivation above and below the mean area change. The center of the error bar corresponds to the result calculated when the mean area change values are used. Changes include any transitions from the rest of three dryland subtypes to a subtype. Increased category includes any transitions to a subtype from the rest of three dryland subtypes (for example, from dry sub-humid, semi-arid and arid to hyper-arid). Decreased category includes any transitions from a subtype to the rest of three dryland subtypes (for example, from hyper-arid to dry sub-humid, semi-arid and arid).

Extended Data Fig. 5 Predicted areas with the difference between maximum allowable grazing pressure and current grazing pressure in China’s drylands under CMIP5 scenarios.

a-b: CMIP5 scenarios RCP4.5 (that is, assuming saturated increase in CO2 emissions); a and b are for 2020–2060 and 2061–2100 relative to the baseline period (1980–2014), respectively. c-d: CMIP5 scenarios RCP8.5 (that is, assuming sustained increase in CO2 emissions); c and d are for 2020–2060 and 2061–2100 relative to the baseline period (1980–2014), respectively. The blue and brown shading denotes where the maximum allowable grazing pressure is higher and lower than the current grazing level, respectively. The red lines denote the baseline drylands in 1950–2000 that are not suitable for grazing and thus where grazing is not recommended, as their maximum allowable grazing pressure is equal to zero and the current grazing pressure leads thresholds to be crossed for ecosystem attributes. The grey shading denotes drylands where the land covers are cropland, wetland or urban areas. The unshaded areas are not drylands today and therefore are outside of the range. Credit: basemaps from the Global Aridity Index database42 and China Data Lab62.

Extended Data Fig. 6 Predicted areas with the difference between maximum allowable grazing pressure and current grazing pressure when aridity and grazing acted synergistically.

a-b: CMIP5 scenarios RCP4.5 (that is, assuming saturated increase in CO2 emissions); a and b are for 2020–2060 and 2061–2100 relative to the baseline period (1980–2014), respectively. c-d: CMIP5 scenarios RCP8.5 (that is, assuming sustained increase in CO2 emissions); c and d are for 2020–2060 and 2061–2100 relative to the baseline period (1980–2014), respectively. The grey shading denotes the baseline drylands in 1950–2000 that are unsuitable for grazing. The blue and brown shading denotes where the maximum allowable grazing pressure is higher and lower than the current grazing level, respectively. The red lines denote the baseline drylands in 1950–2000 that are not suitable for grazing and thus where grazing is not recommended, as their maximum allowable grazing pressure is equal to zero and the current grazing pressure leads thresholds to be crossed for ecosystem attributes. The grey shading denotes drylands where the land covers are cropland, wetland or urban areas. The unshaded areas are not drylands today and therefore are outside of the range. Credit: basemaps from the Global Aridity Index database42 and China Data Lab62.

Extended Data Fig. 7 Predicted areas with the difference between maximum allowable grazing pressure and current grazing pressure when aridity and grazing acted in opposition.

a-b: CMIP5 scenarios RCP4.5 (that is, assuming saturated increase in CO2 emissions); a and b are for 2020–2060 and 2061–2100 relative to the baseline period (1980–2014), respectively. c-d: CMIP5 scenarios RCP8.5 (that is, assuming sustained increase in CO2 emissions); c and d are for 2020–2060 and 2061–2100 relative to the baseline period (1980–2014), respectively. The blue and brown shading denotes where the maximum allowable grazing pressure is higher and lower than the current grazing level, respectively. The red lines denote the baseline drylands in 1950–2000 that are not suitable for grazing and thus where grazing is not recommended, as their maximum allowable grazing pressure is equal to zero and the current grazing pressure leads thresholds to be crossed for ecosystem attributes. The grey shading denotes drylands where the land covers are cropland, wetland or urban areas. The unshaded areas are not drylands today and therefore are outside of the range. Credit: basemaps from the Global Aridity Index database42 and China Data Lab62.

Extended Data Table 1 The linear relationship between aridity (Ar) and maximum allowable grazing pressure (Gr) as determined by the two-dimensional threshold model which considers the combined effects of aridity and grazing pressure
Full size table

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Supplementary Figs. 1–8, Tables 1–9 and Appendix 1.

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Li, C., Fu, B., Wang, S. et al. Climate-driven ecological thresholds in China’s drylands modulated by grazing. Nat Sustain 6, 1363–1372 (2023). https://doi.org/10.1038/s41893-023-01187-5

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