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Contrasting drought sensitivity of Eurasian and North American grasslands

Nature volume 639, pages 114–118 (2025)Cite this article

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Abstract

Extreme droughts generally decrease productivity in grassland ecosystems1,2,3 with negative consequences for nature’s contribution to people4,5,6,7. The extent to which this negative effect varies among grassland types and over time in response to multi-year extreme drought remains unclear. Here, using a coordinated distributed experiment that simulated four years of growing-season drought (around 66% rainfall reduction), we compared drought sensitivity within and among six representative grasslands spanning broad precipitation gradients in each of Eurasia and North America—two of the Northern Hemisphere’s largest grass-dominated regions. Aboveground plant production declined substantially with drought in the Eurasian grasslands and the effects accumulated over time, while the declines were less severe and more muted over time in the North American grasslands. Drought effects on species richness shifted from positive to negative in Eurasia, but from negative to positive in North America over time. The differing responses of plant production in these grasslands were accompanied by less common (subordinate) plant species declining in Eurasian grasslands but increasing in North American grasslands. Our findings demonstrate the high production sensitivity of Eurasian compared with North American grasslands to extreme drought (43.6% versus 25.2% reduction), and the key role of subordinate species in determining impacts of extreme drought on grassland productivity.

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Fig. 1: The EDGE.
Fig. 2: Effects of four years of extreme drought on plant productivity and richness.
Fig. 3: The relationships between plant species richness response to extreme drought and ANPP response to extreme drought.

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Data availability

Experimental data of this study are available at Zenodo58 (https://doi.org/10.5281/zenodo.14004857). Maps were created using ArcGIS v.10.8.2 (Environmental Systems Research Institute), and the GIS data of distribution of grassland types are available online (https://www.worldwildlife.org/publications/world-grassland-types).

Code availability

R code for statistical analyses and visualization is available at Zenodo58 (https://doi.org/10.5281/zenodo.14004857).

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Acknowledgements

We acknowledge the National Key R&D Program of China (2022YFE0128000, 2022YFF1300603, 2019YFE0117000), the National Natural Science Foundation of China (32171592, 42130515, 31988102, 32061123005) and the Scientific and Technological Innovation Project of China Academy of Chinese Medical Sciences (CI2024C003YN) for funding. Support for North American EDGE was provided by NSF Macrosystems Biology/Emerging Frontiers Programs (DEB-1137378, 1137363 and 1137342), Drought-Net Research Coordination Network (DEB-1354732), the Konza Prairie Long-Term Ecological Research Program (DEB-1440484), with additional support from NSF (DEB-1655499 and DEB-2423861). Logistical support was provided by Inner Mongolia Horqin Grassland Ecosystem National Observation and Research Station, the Colorado State University Agricultural Experiment Station, the US Department of Agriculture Agricultural Research Service High Plains Grasslands Research Station and K. Harmoney.

Author information

Authors and Affiliations

  1. School of Grassland Science, Beijing Forestry University, Beijing, China

    Qiang Yu, Qian Gu & Shikui Dong

  2. State Key Laboratory of Efficient Production of Forest Resources, Beijing Forestry University, Beijing, China

    Qiang Yu

  3. State Key Laboratory of Efficient Utilization of Arid and Semi-Arid Arable Land in Northern China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China

    Chong Xu, Honghui Wu & Xiaoping Xin

  4. Key Laboratory of Arable Land Quality Monitoring and Evaluation, Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China

    Chong Xu & Honghui Wu

  5. National Hulunber Grassland Ecosystem Observation and Research Station, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China

    Yuguang Ke, Hongqiang Wang & Xiaoping Xin

  6. Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Urat Desert-Grassland Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Science, Lanzhou, China

    Xiaoan Zuo

  7. Liaoning Northwest Grassland Ecosystem National Observation and Research Station; Erguna Forest-Steppe Ecotone Research Station, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, China

    Wentao Luo, Wang Ma & Zhengwen Wang

  8. College of Agro-grassland Science, Nanjing Agricultural University, Nanjing, China

    Haiyan Ren

  9. Department of Biology and Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, USA

    Alan K. Knapp & Melinda D. Smith

  10. Department of Biology, University of New Mexico, Albuquerque, NM, USA

    Scott L. Collins & Jennifer A. Rudgers

  11. Soil and Crop Sciences Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, USA

    Yiqi Luo

  12. Ecology and Biodiversity Group, Department of Biology, Utrecht University, Utrecht, The Netherlands

    Yann Hautier

  13. College of Grassland Science, Inner Mongolia Agricultural University, Hohhot, China

    Chengjie Wang & Guodong Han

  14. School of Life Sciences, Hebei University, Baoding, China

    Yong Jiang & Xingguo Han

  15. Institute of Grassland Science, Key Laboratory of Vegetation Ecology, Ministry of Education, State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, Northeast Normal University, Changchun, China

    Yingzhi Gao

  16. Key Laboratory of Grassland Resources and Ecology of Western Arid Desert Area of the Ministry of Education, College of Grassland Science, Xinjiang Agricultural University, Urumqi, China

    Yingzhi Gao

  17. Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, Northeast Forestry University, Harbin, China

    Nianpeng He

  18. Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

    Juntao Zhu & Guirui Yu

  19. State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, China

    Linghao Li & Xingguo Han

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  1. Qiang Yu
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  2. Chong Xu
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Contributions

Q.Y., M.D.S, A.K.K. and X.H. conceived, designed and supervised the project. C.X., Q.Y., M.D.S., A.K.K., S.L.C., J.A.R., W.L., H. Wang, W.M. and X.Z. gathered data. C.X. and Y.K. developed the statistical analyses. Q.Y. and C.X. wrote the paper. H. Wu, Y.K., Q.G., C.W., M.D.S., A.K.K., S.L.C., Y.H., Y.L., X.X., H.R., J.A.R., Z.W., Y.J., G.H., Y.G., N.H., J.Z., S.D., G.Y., L.L. and X.H. revised the manuscript. All of the authors read and revised the final version of the manuscript.

Corresponding authors

Correspondence to Melinda D. Smith or Xingguo Han.

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The authors declare no competing interests.

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Nature thanks Travis Huxman and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 The probability density function of long-term growing season precipitation for the 12 grassland sites.

a, TAR. b, ERG. c, XLL/XLS. d, MUR. e, URA. f, KNZ. g, HYS. h, CHY. i, SGS. j, SBL/ SBK. The probability density function is based on 37-year historical climate data from 1982 to 2018 for each site (see Methods). Mean precipitation of ambient plots and extreme drought plots during the four-year experiment as well as pre-treatment precipitation are overlaid on the estimated probability density function.

Extended Data Fig. 2 Effects of four years of extreme drought on absolute value of ecosystem productivity and species richness, and the relationship between species richness and the response of productivity to extreme drought.

a, the response of aboveground net primary productivity (ANPP). b, the response of plant species richness. c, the relationship between total species richness and the response ratio of ANPP. d, the relationship between subordinate species richness and the response ratio of ANPP. Total and subordinate species richness were the absolute values observed in ambient plots. Values are mean ± s.e.m. of the absolute values of ANPP and species richness in six Eurasian grasslands (n = 36) and six North American grasslands (n = 60). Statistical significance of the values between treatments is depicted as *, p < 0.05, which was assessed by Tukey’s HSD tests (two-sided). Lines are mixed-effects model fits for each trend. The purple, yellow and grey lines indicate the trends averaged across all Eurasian grassland sites (n = 24), averaged across all North American grassland sites (n = 24) and the trends for each site (n = 4), respectively. Solid lines indicate significant associations, while dashed lines indicate non-significant associations (see Supplementary Table 3 for detailed model specification and summary statistics).

Extended Data Fig. 3 Effects of four years of extreme drought on the productivity for each site.

a, the response ratios of aboveground net primary productivity (ANPP) for Eurasian grasslands (n = 6). b, the response ratios of ANPP for North American grasslands (n = 10). c, the absolute value of ANPP for Eurasian grasslands (n = 6). d, the absolute value of ANPP for North American grasslands (n = 10). Values are mean ± s.e.m. Different uppercase letters with the same colour indicate significant differences in response ratio to extreme drought among treatment years, and statistical significance of the values between treatments is depicted as * (p < 0.05). The significances were assessed by Tukey’s HSD tests (two-sided).

Extended Data Fig. 4 The patterns of abiotic factors in Eurasian and North American grasslands.

a, Frequency of historical extreme drought, normal precipitation, and extreme precipitation years (n = 6). b, Ambient precipitation (n = 6). c, Effects of extreme drought on soil moisture in Eurasian (n = 36) and North American grasslands (n = 60). d, Effects of extreme drought on soil moisture for each site in Eurasian grasslands (n = 6). e, Effects of extreme drought on soil moisture for each site in North American grasslands (n = 10). Values are mean ± s.e.m. Significance was assessed by t-tests (two-sided) in a, and no significant difference was found (all p > 0.05). The frequencies and precipitation are based on 37-year historical weather data from 1982 to 2018 for each site (see Methods). Purple and yellow dashed lines represent averaged precipitation across all Eurasian grassland sites and North American grassland sites, with their 95% CI, respectively. Statistical significance of the values between treatments is depicted as *(p < 0.05), which was assessed by Tukey’s HSD tests (two-sided).

Extended Data Fig. 5 Effect of abiotic factors on drought response.

Relationships between the response ratio of aboveground net primary productivity and a, historic drought frequency, b, soil moisture, c, mean annual precipitation (MAP), d, coefficient of variation of annual precipitation (CV of AP), e, natural log of the aridity index (AI), f, mean annual temperature (MAT), and g, the amount of annual precipitation removed by the drought treatment. Historic drought frequency, MAP, CV of AP and MAT were calculated based on 37-year historical climate data from 1982 to 2018 for each site, and soil moisture values were observed under the four-year extreme drought plots (see Methods). AI was calculated as MAP/mean annual potential evapotranspiration. Purple and yellow points represent values of Eurasian and North American grasslands, respectively. Fitted lines are from general linear models. The yellow lines indicate the trends across all North American grassland sites and the grey bands indicate 95% confidence interval (see Supplementary Table 2 for detailed model specification and summary statistics).

Extended Data Fig. 6 Effects of four years of extreme drought on species richness for each site.

a, the absolute value of species richness in Eurasian grasslands (n = 6). b, the absolute value of species richness in North American grasslands (n = 10). c, the response ratio of species richness in Eurasian grasslands (n = 6). d, the response ratio of species richness in North American grasslands (n = 10). Values are mean ± s.e.m. Statistical significance of the values between treatments is depicted as * (p < 0.05), and different uppercase letters with the same colour indicate significant differences in response ratio to extreme drought among treatment years. The significances were assessed by Tukey’s HSD tests (two-sided).

Extended Data Fig. 7 The effect of extreme drought on species stability and species asynchrony.

a, species stability, and b, species asynchrony. Species stability was calculated as average population stability (the interannual mean cover divided by its interannual standard deviation) across species in each plot. Species asynchrony compared the sum of the variance of individual species with the variance of the aggregated community in each plot. Center line, lower and upper limits of box, and whiskers indicated median, lower and upper quartiles, and 1.5 × interquartile range, respectively. Outliers were not shown. In Eurasian grasslands, n = 36, and in North American grasslands, n = 60, for both species stability and species asynchrony. Different uppercase and lowercase letters indicate significant differences between treatments, which were assessed by Tukey’s HSD tests (two-sided).

Extended Data Fig. 8 Effects of four years of extreme drought on dominant and subordinate species cover and richness.

a, the absolute values for dominant species cover. b, subordinate species cover. c, dominant species richness. d, subordinate species richness. e, the response ratio for dominant species cover. f, subordinate species cover. g, dominant species richness. h, subordinate species richness. Values are mean ± s.e.m. In Eurasian grasslands, n = 36, and in North American grasslands, n = 60, for both the absolute values and response ratios of species cover and richness. Statistical significance of the absolute values between treatments is depicted as * (p < 0.05) and different uppercase and lowercase letters indicate significant differences in response ratio to extreme drought among treatment years for Eurasian and North American grasslands, respectively. The significances were assessed by Tukey’s HSD tests (two-sided).

Extended Data Fig. 9 The response ratio relationship between subordinate species richness and dominant species cover (a), and the characters of community composition.

b, relative cover of each subordinate species functional group in ambient plots. c, Relative frequency-relative cover curve in Eurasian grasslands. d, relative frequency-relative cover curve in North American grasslands. Lines are mixed-effects model fits for each trend. The black, purple, and yellow lines indicate the trends averaged across all sites, the trends for each Eurasian grassland site, and the trends for each North American grassland site, respectively (see Supplementary Table 6 for detailed summary statistics). Values are mean ± s.e.m of the relative cover of each functional group in subordinate species in ambient plots across the six Eurasian grasslands (n = 6) and six North American grasslands (n = 6), and significance was assessed by t-tests (two-sided). Relative cover of perennial forb (PF) was significantly higher (t = 2.60, P = 0.04) in Eurasian grasslands compared with North American grasslands. No significant difference in relative cover was found for annuals and biennials (AB, t = −1.23, P = 0.26), perennial grass (PG, t = 0.48, P = 0.64), and shrubs and subshrubs (SS, t = −1.20, P = 0.28) between Eurasian and North American grassland sites. Relative frequency and relative cover are calculated based on the values over four years in ambient plots for each species at each site. Light pink and green represent dominant and subordinate species, respectively. □, ○, △, ◇, ▽, × represent TAR, ERG, XLL, XLS, MUR and URA in Eurasian grasslands, as well as KNZ, HYS, CHY, SGS, SBL and SBK in North American grasslands. Species with a relative frequency greater than 0.8 and a relative cover greater than 12% are dominant species, and the rest are subordinate species.

Extended Data Fig. 10 The relationship between aboveground net primary productivity (ANPP) and cover, and effects of extreme drought on plant community cover.

a, the relationship between ANPP and cover in Eurasian grasslands. b, the relationship between ANPP and cover in North American grasslands. c, the response ratios of plant community cover over the four-year drought period. Displayed are the absolute values of ANPP and cover for each plot during four treatment years across the 12 study sites (see Methods). Fitted lines are from linear mixed-effects models. The purple, yellow and grey lines indicate the trends averaged across all Eurasian grassland sites, averaged across all North American grassland sites and the trends for each site, respectively (see Supplementary Table 7 for detailed model specification and summary statistics). Values are mean ± s.e.m. of the response ratios of plant community cover in six Eurasian grasslands (n = 36) and six North American grasslands (n = 60). Different uppercase letters and lowercase letters indicate significant differences in response ratios among treatment years in Eurasian and North American grasslands, respectively, which were assessed by Tukey’s HSD tests (two-sided).

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Yu, Q., Xu, C., Wu, H. et al. Contrasting drought sensitivity of Eurasian and North American grasslands. Nature 639, 114–118 (2025). https://doi.org/10.1038/s41586-024-08478-7

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