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Plant diversity enhances ecosystem resistance to increasing grazing pressure in global drylands

Nature Ecology & Evolution volume 10, pages 258–266 (2026)Cite this article

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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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Fig. 1: Response of vegetation cover to increasing grazing pressure (resistance) across global drylands and latitudinal zones.
Fig. 2: Drivers of changes in vegetation cover to increasing grazing pressure (resistance).
Fig. 3: Relationships between vegetation cover responses to increasing grazing pressure (resistance) and functional dispersion and redundancy.

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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).

Author information

Authors and Affiliations

  1. Environmental Science and Engineering, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia

    Lucio Biancari, Emilio Guirado & Fernando T. Maestre

  2. IFEVA, Facultad de Agronomía, Universidad de Buenos Aires, CONICET, Buenos Aires, Argentina

    Gastón R. Oñatibia, Laura Yahdjian & Martín R. Aguiar

  3. Cátedra de Ecología, Departamento de Recursos Naturales y Ambiente, Facultad de Agronomía, UBA, Buenos Aires, Argentina

    Gastón R. Oñatibia, Laura Yahdjian & Martín R. Aguiar

  4. Aix Marseille Univ, CNRS, Avignon Université, IRD, IMBE, Aix-en-Provence, France

    Yoann Le Bagousse-Pinguet

  5. Université Clermont Auvergne, INRAE, VetAgro Sup, Unité Mixte de Recherche Ecosystème Prairial, Clermont-Ferrand, France

    Nicolas Gross

  6. Departamento de Ciencias Agrarias y Medio Natural, Escuela Politécnica Superior, Instituto Universitario de Investigación en Ciencias Ambientales de Aragón, Universidad de Zaragoza, Huesca, Spain

    Hugo Saiz

  7. Institute of Plant Sciences, University of Bern, Bern, Switzerland

    Hugo Saiz

  8. Centre for Ecosystem Science, School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, New South Wales, Australia

    David J. Eldridge

  9. Departamento de Biodiversidad, Ecología y Evolución, Facultad de Ciencias Biológicas, Universidad Complutense de Madrid, Madrid, Spain

    Enrique Valencia

  10. Departamento de Suelos, Biosistemas y Ecología Agroforestal, Misión Biológica de Galicia, Pontevedra, Spain

    Xoaquín Moreira

  11. Instituto Universitario de Investigación en el Olivar y el Aceite de Oliva-INUO, Universidad de Jaén, Jaén, Spain

    Victoria Ochoa

  12. Instituto Multidisciplinar para el Estudio del Medio ‘Ramón Margalef’, Universidad de Alicante, Alicante, Spain

    Beatriz Gozalo & Jaime Martínez-Valderrama

  13. Grupo de Ecoloxía Animal, Universidade de Vigo, Vigo, Spain

    Sergio Asensio

  14. Instituto de Ciencias Agrarias, Consejo Superior de Investigaciones Científicas, Madrid, Spain

    César Plaza

  15. Department of Agricultural and Food Chemistry, Faculty of Sciences, Universidad Autónoma de Madrid, Madrid, Spain

    César Plaza

  16. Departamento de Ingeniería y Morfología del Terreno, Escuela Técnica Superior de Ingenieros de Caminos, Canales y Puertos, Universidad Politécnica de Madrid, Madrid, Spain

    Miguel García-Gómez

  17. Universidad Nacional de Luján-CONICET, Luján, Argentina

    Juan J. Gaitán

  18. Estación Experimental de Zonas Áridas, CSIC, Almería, Spain

    Jaime Martínez-Valderrama

  19. Departamento de Biología y Geología, Física y Química Inorgánica, Universidad Rey Juan Carlos, Móstoles, Spain

    Betty J. Mendoza

Authors
  1. Lucio Biancari
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  2. Gastón R. Oñatibia
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  3. Yoann Le Bagousse-Pinguet
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  4. Nicolas Gross
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  5. Laura Yahdjian
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  6. Martín R. Aguiar
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  7. Hugo Saiz
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  8. David J. Eldridge
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  9. Enrique Valencia
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  10. Xoaquín Moreira
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  14. César Plaza
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  15. Emilio Guirado
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  20. Fernando T. Maestre
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Contributions

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.

Corresponding author

Correspondence to Lucio Biancari.

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Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Ecology & Evolution thanks Yongfei Bai, Timm Döbert and Dafeng Hui for their contribution to the peer review of this work. Peer reviewer reports are available.

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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 10, 258–266 (2026). https://doi.org/10.1038/s41559-025-02952-9

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