Abstract
Understanding the mechanisms that shape ecosystem resistance to increasing livestock grazing pressure, a major driver of land degradation, is essential for predicting its impacts and informing sustainable land management strategies. This issue is particularly relevant in drylands, which host half of the world’s livestock production and are highly vulnerable to desertification caused by overgrazing. Here we conduct a standardized field survey across 73 dryland sites in 25 countries to simultaneously evaluate how climatic, edaphic, vegetation and grazing-related factors influence ecosystem resistance—defined here as the capacity to maintain vegetation cover under increasing grazing pressure. We found that increasing grazing pressure reduced vegetation cover in 80% of sites, with an average decline of 35%. Plant species richness emerged as the strongest predictor of ecosystem resistance, with higher richness associated with lower vegetation cover loss. Functional trait data indicated that this positive effect was mainly explained by complementarity in trait values among plants, rather than by functional redundancy. Our results indicate that conserving plant diversity is key to strengthening ecosystem resistance and sustaining dryland functioning under intensifying grazing pressure.
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Unforeseen plant phenotypic diversity in a dry and grazed world
Climate-driven ecological thresholds in China’s drylands modulated by grazing
Hotspots of biogeochemical activity linked to aridity and plant traits across global drylands
Data availability
The dataset needed to reproduce our results are available via Figshare at https://doi.org/10.6084/m9.figshare.29132654 (ref. 90).
Code availability
The R script used is available via Figshare at https://doi.org/10.6084/m9.figshare.29132654 (ref. 90).
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Acknowledgements
We thank all participants of the BIODESERT global field survey. This survey was funded by the European Research Council (ERC grant agreement 647038), awarded to F.T.M. F.T.M., L.B. and E.G. acknowledge support by the King Abdullah University of Science and Technology (KAUST). M.R.A., G.R.O. and L.Y. acknowledge support by the University of Buenos Aires and CONICET. E.V. was supported by the Spanish Ministry of Science, Innovation and Universities (grant nos. PID2022-140398NA-I00 and CNS2024-154579).
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Conceptualization: L.B., F.T.M., Y.L.B.P., N.G., G.R.O., L.Y. and M.R.A. Methodology: F.T.M., N.G., Y.L.B.P., D.J.E. and H.S. Investigation: F.T.M., Y.L.B.P., N.G., H.S., D.J.E., E.V., X.M., V.O., B.G., S.A., C.P., E.G., M.G.G., J.J.G., J.M.V., B.J.M., G.R.O. and L.Y. Formal analysis: L.B., G.R.O., H.S., Y.L.B.P. and N.G. Writing—original draft: L.B., F.T.M., G.R.O., L.Y. and M.R.A. Writing—review and editing: L.B., F.T.M., G.R.O., M.R.A., L.Y., X.M., E.V., H.S., D.J.E., C.P., N.G., Y.L.B.P., J.M.V., V.O., B.G., S.A., E.G., M.G.G., J.J.G. and B.J.M. Supervision: F.T.M.
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Extended data
Extended Data Fig. 1 Location of the 73 experimental sites surveyed.
Background colors represent aridity index for drylands (areas with an aridity index [mean annual precipitation/potential evapotranspiration] lower than 0.65). Pictures illustrate examples of low grazing plots (left) and high grazing plots (right) across four sites. Photo credits: Matthew Bowker (USA), Juan J. Gaitan (Argentina), Alice Nunes (Portugal), David J. Eldridge (Australia).
Extended Data Fig. 2 Effects of grazing pressure on species richness (A), Shannon diversity index (B), and Pielou evenness index (C).
Mean and 95% confidence intervals are shown (n = 73 sites, each representing a pair of low- and high-grazing plots). Differences between grazing pressures were evaluated using paired t tests (two-sided). For panel A (richness): t = 1.19, P = 0.238, mean difference = −0.99 [95 % CI = −2.64 to 0.67]. For panel B (diversity): t = 1.34, P = 0.183, mean difference = −0.096 [95 % CI = −0.237 to 0.046]. For panel C (evenness): t = 0.90, P = 0.373, mean difference = −0.022 [95 % CI = −0.070 to 0.027]. None of the tests were statistically significant (P > 0.05).
Extended Data Fig. 3 Importance of predictor variables to explain the response of vegetation cover to increasing grazing pressure (resistance) including (A) Shannon’s diversity index, and (B) Pielou’s evenness index.
Importance is based on the sum of Akaike weights of all models where each predictor is present using a multimodel inference approach. MAP = mean annual precipitation, MAT = mean annual temperature, SF = soil fertility, SAC = soil sand content, DIV = Shannon’s diversity index, EVE = Pielou’s evenness index, RWC = relative woody cover, FQ = forage quality, HR = herbivore richness, and LS = dominant livestock species.
Extended Data Fig. 4 Effects of key predictors on the response to increasing grazing pressure (resistance).
Structural equation model showing the relationships among aridity (estimated as 1-Aridity Index), herbivore richness, soil sand content, relative woody cover, latitude, plant species richness, and resistance (estimated as a log response ratio: ln[vegetation cover high grazing pressure/vegetation cover low grazing pressure]). Numbers on arrows are fully standardized path coefficients. Blue and red arrows indicate positive and negative relationships, respectively. Asterisks indicate significance level: ** p<0.01 and *** p<0.001.
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Biancari, L., Oñatibia, G.R., Le Bagousse-Pinguet, Y. et al. Plant diversity enhances ecosystem resistance to increasing grazing pressure in global drylands.
Nat Ecol Evol (2026). https://doi.org/10.1038/s41559-025-02952-9
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DOI: https://doi.org/10.1038/s41559-025-02952-9
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