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Growth form and lifespan of herbaceous species mediate the role of traits in short-term drought response

Nature Ecology & Evolution volume 10, pages 512–522 (2026)Cite this article

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Abstract

Increased climate variability is expected to intensify short-term drought events. Plants have evolved stress tolerance strategies involving trade-offs in resource conservation, mycorrhizal collaboration and plant size, yet how these strategies promote drought resistance across different herbaceous plant groups remains unknown. Leveraging 63 globally distributed grassland and shrubland sites from the International Drought Experiment, we identified plant traits linked to drought resistance in 661 populations of 421 species after 1 year of extreme drought. We assessed how traits, site precipitation and drought severity affected cover change across growth forms and lifespans, and how trait–environment interactions influenced drought resistance. Across all species, leaf N (an acquisitive trait) was associated with drought resistance, whereas in forbs, drought resistance was also associated with a conservative root trait and plant size. In addition, interactions among traits mediated drought resistance; root traits predicted performance only in concert with other traits. Environmental variables influenced trait effects on drought resistance, notably for annuals in wetter sites, suggesting that drought-escape strategies in annuals may be advantageous only under mild stress. Our study highlights variability in traits that predict drought resistance across herbaceous plant groups, emphasizing the importance of species context, environmental stress and the selection of traits in research and management.

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Fig. 1: Map of the study sites.
Fig. 2: Relationships in which traits or environmental variables had a significant effect on cover change.
Fig. 3: Interaction plots showing the predicted effects of significant two-way interactions between traits.
Fig. 4: Interaction plots showing the predicted effects of significant two-way interactions between traits and the environmental variables DSI and MAP.

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Data availability

All data used in this study are openly available via Zenodo at https://doi.org/10.5281/zenodo.17724111 (ref. 84). Source data are provided with this paper.

Code availability

Analyses in this study were conducted using customized scripts in R. The scripts are available via Zenodo at https://doi.org/10.5281/zenodo.17724111 (ref. 84).

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Acknowledgements

Any use of trade, firm or product names is for descriptive purposes only and does not imply endorsement by the US Government. We thank all the landowners who gave access to their lands; without them, this study would not have been possible. N.E. and M.S. acknowledge support from the German Centre for Integrative Biodiversity Research Halle–Jena–Leipzig, funded by the German Research Foundation (DFG; FZT 118, 202548816), as well as by the DFG (Ei 862/29-1). A.S.M. was supported by the Environment Research and Technology Development Fund (JPMEERF15S11420) of the Environmental Restoration and Conservation Agency of Japan, with additional field support from the Teshio Experimental Forest, Hokkaido University. Further support came from the Advanced Studies of Climate Change Projection Grant, Ministry of Education, Culture, Sports, Science and Technology, Japan (JPMXD0722678534). V.V., S.V.H., P.T. and L.G.V. acknowledge support from the Norwegian Research Council (project numbers 255090, 315249). A.S. and M.Z. acknowledge funding from the Swiss National Science Foundation, grants 149862 and 185110 to A.S. C.N.C., A.B., J.F.C., E.W.B. and S.X.C. acknowledge support from the Alberta Livestock and Meat Agency and Emissions Reduction Alberta. F.I. acknowledges funding from the US National Science Foundation (NSF DEB-2224852, NSF DEB-1831944). K.T. and L.v.d.B. acknowledge funding by the German Research Foundation (DFG) Priority Program Earthshape: Earth Surface Shaping by Biota, SPP-1803 (TI 338/14-1&2), with additional support to L.v.d.B. from ANID PIA/ACT 210038. K.M.B. acknowledges support from the US Bureau of Land Management (L16AS00178) and California State University Agricultural Research Institute (18-06-004). E.G.L. and H.A.L.H. acknowledge support from separate Natural Sciences and Engineering Research Council Discovery Grants. U.N.N. acknowledges support from the Australian Research Council (DP150104199, DP190101968, DE210101822). M.C., T.G.W.F. and A.P. acknowledge that their work has benefited from the equipment and framework of the COMP-HUB and COMP-R Initiatives, funded by the ‘Departments of Excellence’ programme of the Italian Ministry for University and Research (MIUR, 2018–2022, and MUR, 2023–2027). A.J. acknowledges funding from the Federal Ministry of Research, Technology and Space of Germany (BMFTR, grant 031B1067C). M.G.L. acknowledges funding from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) and the Universidade Federal da Paraíba, João Pessoa, Paraíba, 58051-900, Brazil. We thank the park rangers from Parque Nacional La Campana and La Comunidad Agricola Quebrada de Talca for their onsite support and access to their lands. M.J.T. acknowledges support from the National Research Foundation (grant number 116262). A.V. acknowledges funding from Generalitat Valenciana, Project R2D–Responses to Desertification (CIPROM/2021/001).

Author information

Authors and Affiliations

  1. Department of Evolution and Ecology, University of California, Davis, CA, USA

    Samantha J. Worthy, Jonathan Aguiñaga, Elise C. Elwood & Regina A. Fairbanks

  2. The Holden Arboretum, Kirtland, OH, USA

    Samantha J. Worthy

  3. Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, USA

    Justin C. Luong

  4. Department of Plant Sciences, University of California, Davis, CA, USA

    Justin C. Luong, Brooke E. Wainwright, Anca C. Barcu, Katherine E. Brafford, Jennifer E. Cribbs, Anjum K. Gujral & Jennifer L. Funk

  5. Center for Population Biology, University of California, Davis, CA, USA

    Jonathan Aguiñaga, Regina A. Fairbanks, Anjum K. Gujral & Dylan J. MacArthur-Waltz

  6. Department of Community Ecology, Helmholtz Centre for Environmental Research—UFZ, Halle, Germany

    Harald Auge

  7. German Centre for Integrative Biodiversity Research (iDiv) Halle–Jenna–Leipzig, Leipzig, Germany

    Harald Auge, Nico Eisenhauer, Christiane Roscher & Marie Sünnemann

  8. Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Alberta, Canada

    Amgaa Batbaatar, Edward W. Bork & Cameron N. Carlyle

  9. Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada

    Amgaa Batbaatar & James F. Cahill

  10. Department of Wildland Resources and the Ecology Center, Utah State University, Logan, UT, USA

    Karen H. Beard & Andrew Kulmatiski

  11. Department of Environmental Science and Management, California State Polytechnic University, Humboldt, Arcata, CA, USA

    Kerry M. Byrne

  12. Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, Parma, Italy

    Michele Carbognani, T’ai G. W. Forte & Alessandro Petraglia

  13. Department of Ecology, Evolution and Behavior, University of Minnesota, St. Paul, MN, USA

    Karen Castillioni, Amber C. Churchill & Forest Isbell

  14. Hawkesbury Institute for the Environment, Western Sydney University, Penrith, New South Wales, Australia

    Manjunatha H. Chandregowda, Jeff Chieppa, Uffe N. Nielsen & Sally A. Power

  15. Department of Renewable Resources, University of Alberta, Edmonton, Alberta, Canada

    Scott X. Chang

  16. Environmental Studies Program, Binghamton University—State University of New York, Binghamton, NY, USA

    Amber C. Churchill

  17. Disturbance Ecology and Vegetation Dynamics, University of Bayreuth, Bayreuth Center of Ecology and Environmental Research (BayCEER), Bayreuth, Germany

    Thomas Deola & Anke Jentsch

  18. Department of Global Ecology, Carnegie Institution for Science, Stanford, CA, USA

    Jeffrey S. Dukes

  19. Department of Forestry and Natural Resources, Purdue University, West Lafayette, IN, USA

    Jeffrey S. Dukes

  20. Institute of Ecology and Evolution, University of Jena, Jena, Germany

    Anne Ebeling

  21. Institute of Biology, Leipzig University, Leipzig, Germany

    Nico Eisenhauer & Marie Sünnemann

  22. Centro de Recursos Naturales Renovables de la Zona Semiárida (CERZOS)—Consejo Nacional de Investigaciones Científicas y Técnicas de la República Argentina (CONICET), Bahía Blanca, Argentina

    Flavia A. Funk, Alejandro Loydi & Clara Milano

  23. Departamento de Biología, Bioquímica y Farmacia, Universidad Nacional del Sur (UNS), Bahía Blanca, Argentina

    Flavia A. Funk & Alejandro Loydi

  24. Department of Biological Sciences, University of Bergen, Bergen, Norway

    Siri V. Haugum & Vigdis Vandvik

  25. The Heathland Centre, Alver, Norway

    Siri V. Haugum

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

    Yann Hautier

  27. Department of Biology, University of Western Ontario, London, Ontario, Canada

    Hugh A. L. Henry

  28. Chelan-Douglas Land Trust, Wenatchee, WA, USA

    Samuel E. Jordan

  29. Department of Biology, University of North Carolina Greensboro, Greensboro, NC, USA

    Sally E. Koerner & Alyssa L. Young

  30. Institute of Botany and Landscape Ecology, Department of Experimental Plant Ecology, University of Greifswald, Greifswald, Germany

    Juergen Kreyling

  31. HUN-REN Centre for Ecological Research, Institute of Ecology and Botany, Vácrátót, Hungary

    György Kröel-Dulay

  32. Department of Plant Sciences, University of Saskatchewan, Saskatoon, Saskatchewan, Canada

    Eric G. Lamb

  33. Department of Environmental Studies, University of California, Santa Cruz, CA, USA

    Michael E. Loik

  34. Licenciatura en Diseño del Paisaje, Centro Universitario Regional Este, Universidad de la República Uruguay, Rocha, Uruguay

    María G. Longo

  35. Department of Entomology and Nematology, University of California, Davis, CA, USA

    Dylan J. MacArthur-Waltz

  36. Department of Ecological, Plant and Animal Sciences, La Trobe University, Melbourne, Victoria, Australia

    John W. Morgan

  37. Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan

    Akira S. Mori

  38. US Geological Survey, Southwest Biological Science Center, Flagstaff, AZ, USA

    Seth M. Munson

  39. School of Biological Sciences, University of Oklahoma, Norman, OK, USA

    Gregory S. Newman & Lara A. Souza

  40. USDA-ARS, Range and Meadow Forage Management Research Unit, Burns, OR, USA

    Rory C. O’Connor

  41. Department of Ecology and Evolutionary Biology, University of Colorado at Boulder, Boulder, CO, USA

    Timothy J. Ohlert

  42. Department of Biology, Colorado State University, Fort Collins, CO, USA

    Timothy J. Ohlert & Melinda D. Smith

  43. Department of Environment and Society, Utah State University, Logan, UT, USA

    Brooke B. Osborne

  44. Department of Biology, The University of Winnipeg, Winnipeg, Manitoba, Canada

    Rafael Otfinowski

  45. Institute of Ecology and Earth Sciences, University of Tartu, Tartu, Estonia

    Meelis Pärtel

  46. Instituto Nacional de Tecnología Agropecuaria (INTA)–Universidad Nacional de la Patagonia Austral–CONICET, Río Gallegos, Argentina

    Pablo L. Peri

  47. Universidad Nacional de Río Negro, Centro de Estudios Ambientales desde la NorPatagonia (CEANPa), Sede Atlántica—CONICET, Viedma, Argentina

    Guadalupe Peter & Juan M. Zeberio

  48. Departamento de Modelización Estadística de Datos e Inteligencia Artificial, Centro Universitario Regional Este, Universidad de la República, Montevideo, Uruguay

    Juan M. Piñeiro-Guerra

  49. Departamento de Sistemática e Ecologia, Universidade Federal da Paraíba, João Pessoa, Brazil

    Juan M. Piñeiro-Guerra & Bráulio A. Santos

  50. Southern Colorado Plateau Network, Inventory and Monitoring Division, National Park Service, Flagstaff, AZ, USA

    Laura W. Ploughe

  51. Department of Biology, Saint Louis University, St. Louis, MO, USA

    Cristy Portales-Reyes

  52. Commonwealth Scientific and Industrial Research Organization (CSIRO) Environment, Canberra, Australian Capital Territory, Australia

    Suzanne M. Prober

  53. Departamento de Biodiversidad y Restauración, Instituto Pirenaico de Ecología (IPE), Consejo Superior de Investigaciones Científicas (CSIC), Zaragoza, Spain

    Yolanda Pueyo

  54. Department of Physiological Diversity, Helmholtz-Centre for Environmental Research—UFZ, Leipzig, Germany

    Christiane Roscher

  55. Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, USA

    Melinda D. Smith

  56. School of Agricultural, Forest and Food Sciences, Bern University of Applied Sciences, Zollikofen, Switzerland

    Andreas Stampfli & Michaela Zeiter

  57. Oeschger Center for Climate Change Research, University of Bern, Bern, Switzerland

    Andreas Stampfli & Michaela Zeiter

  58. School of Environmental and Conservation Sciences, Murdoch University, Perth, Western Australia, Australia

    Rachel J. Standish

  59. School of Agriculture and Science, University of KwaZulu-Natal, Pietermaritzburg, South Africa

    Michelle J. Tedder

  60. Norwegian Institute of Bioeconomy Research (NIBIO), Trondheim, Norway

    Pål Thorvaldsen & Liv G. Velle

  61. Institute of Ecology and Evolution, Plant Ecology Group, University of Tübingen, Tübingen, Germany

    Katja Tielbörger & Liesbeth van den Brink

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

    Alejandro Valdecantos

  63. Instituto Multidisciplinar para el Estudio del Medio Ramón Margalef, Universidad de Alicante, Alicante, Spain

    Alejandro Valdecantos

  64. ECOBIOSIS, Departamento de Botánica, Universidad de Concepción, Concepción, Chile

    Liesbeth van den Brink

  65. Department of Geography, University of British Columbia, Vancouver, British Columbia, Canada

    Jennifer L. Williams

  66. Department of Integrative Biology, University of Texas at Austin, Austin, TX, USA

    Amelia A. Wolf

  67. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA), CONICET, Facultad de Agronomía, Universidad de Buenos Aires, Buenos Aires, Argentina

    Laura Yahdjian

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

    Michaela Zeiter

  69. Department of Biology, Indiana University, Bloomington, IN, USA

    Richard P. Phillips

Authors
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Contributions

S.J.W., R.P.P. and J.L.F. conceived of the study. S.J.W., J.C.L., B.E.W., J.A., A.C.B., K.E.B., J.E.C., E.C.E., R.A.F., A.K.G., D.J.M.-W., R.P.P. and J.L.F contributed to early-stage discussions. S.J.W., J.C.L., B.E.W., J.A., A.C.B., K.E.B., J.E.C., E.C.E., R.A.F., A.K.G., D.J.M.-W. and J.L.F. collected and preprocessed trait data. T.J.O. and M.D.S. collected and preprocessed site data. H.A., A.B., K.H.B., E.W.B., K.M.B, J.F.C., M.C., C.N.C., K.C., M.H.C., S.X.C., J.C., A.C.C., T.D., J.S.D., A.E., N.E., T.G.W.F., F.A.F., S.V.H., Y.H., H.A.L.H., F.I., A.J., S.E.J., S.E.K., J.K., G.K.-D., A.K., E.G.L., M.E.L., M.G.L., A.L., C.M., J.W.M., A.S.M., S.M.M., G.S.N., U.N.N., R.C.O’C., T.J.O., B.B.O., R.O., M.P., P.L.P., G.P., A.P., J.M.P.-G., L.W.P., C.P.-R., S.A.P., S.M.P., Y.P., C.R., B.A.S., M.D.S., L.A.S., A.S., R.J.S., M.S., M.J.T., P.T., K.T., A.V., L.v.d.B., V.V., L.G.V., J.L.W., A.A.W., L.Y., A.L.Y., J.M.Z. and M.Z. contributed plant cover data. S.J.W. performed the analyses. S.J.W., J.C.L., B.E.W., R.P.P. and J.L.F. interpreted the results and drafted the initial paper. All co-authors reviewed the results and contributed to the writing and revision of the paper.

Corresponding authors

Correspondence to Samantha J. Worthy, Richard P. Phillips or Jennifer L. Funk.

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The authors declare no competing interests.

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Nature Ecology & Evolution thanks Zoltán Botta-Dukát and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Model parameter estimates for each of the eight plant groups.

Points represent the mean of the posterior distribution and lines represent the 95% credible intervals. Filled points indicate significant predictors of cover change where the 95% credible interval does not overlap zero. Traits include drought severity index (DSI), height (m), mass-based leaf nitrogen content (Leaf N, mg g−1), mean annual precipitation (Precipitation, mm), rooting depth (m), root diameter (mm), mass-based root nitrogen content (Root N, mg g−1), root mass fraction (RMF, g g−1), root tissue density (RTD, g cm−3), specific leaf area (SLA, m2 kg−1), and specific root length (SRL, m g−1).

Source data

Extended Data Fig. 2 Plots displaying the effects of traits and environmental variables on change in population cover for the all-species group (n = 661 populations, species = 421, R2 = 6%).

Trend lines represent median conditional effects of the trait, dashed lines are nonsignificant relationships and solid lines are significant relationships, colored envelopes represent 95% credible intervals. Opaque gray points are observed data points where darker points indicate overlap among points. Values on the x-axes are back-transformed.

Source data

Extended Data Fig. 3 Plots displaying the effects of traits and environmental variables on change in population cover for the annual species group (n = 178 populations, species = 121, R2 = 15%).

Trend lines represent median conditional effects of the trait, dashed lines are nonsignificant relationships and solid lines are significant relationships, colored envelopes represent 95% credible intervals. Opaque gray points are observed data points where darker points indicate overlap among points. Values on the x-axes are back-transformed.

Source data

Extended Data Fig. 4 Plots displaying the effects of traits and environmental variables on change in population cover for the perennial species group (n = 462 populations, species = 292, R2 = 8%).

Trend lines represent median conditional effects of the trait, dashed lines are nonsignificant relationships and solid lines are significant relationships, colored envelopes represent 95% credible intervals. Opaque gray points are observed data points where darker points indicate overlap among points. Values on the x-axes are back-transformed.

Source data

Extended Data Fig. 5 Plots displaying the effects of traits and environmental variables on change in population cover for the graminoid species group (n = 251 populations, species = 151, R2 = 11%).

Trend lines represent median conditional effects of the trait, dashed lines are nonsignificant relationships and solid lines are significant relationships, colored envelopes represent 95% credible intervals. Opaque gray points are observed data points where darker points indicate overlap among points. Values on the x-axes are back-transformed.

Source data

Extended Data Fig. 6 Plots displaying the effects of traits and environmental variables on change in population cover for the forb species group (n = 410 populations, species = 270, R2 = 11%).

Trend lines represent median conditional effects of the trait, dashed lines are nonsignificant relationships and solid lines are significant relationships, colored envelopes represent 95% credible intervals. Opaque gray points are observed data points where darker points indicate overlap among points. Values on the x-axes are back-transformed.

Source data

Extended Data Fig. 7 Plots displaying the effects of traits and environmental variables on change in population cover for the annual forb species group (n = 134 populations, species = 95, R2 = 23%).

Trend lines represent median conditional effects of the trait, dashed lines are nonsignificant relationships and solid lines are significant relationships, colored envelopes represent 95% credible intervals. Opaque gray points are observed data points where darker points indicate overlap among points. Values on the x-axes are back-transformed.

Source data

Extended Data Fig. 8 Plots displaying the effects of traits and environmental variables on change in population cover for the perennial graminoid species group (n = 205 populations, species = 123, R2 = 15%).

Trend lines represent median conditional effects of the trait, dashed lines are nonsignificant relationships and solid lines are significant relationships, colored envelopes represent 95% credible intervals. Opaque gray points are observed data points where darker points indicate overlap among points. Values on the x-axes are back-transformed.

Source data

Extended Data Fig. 9 Plots displaying the effects of traits and environmental variables on change in population cover for the perennial forb species group (n = 257 populations, species = 169, R2 = 15%).

Trend lines represent median conditional effects of the trait, dashed lines are nonsignificant relationships and solid lines are significant relationships, colored envelopes represent 95% credible intervals. Opaque gray points are observed data points where darker points indicate overlap among points. Values on the x-axes are back-transformed.

Source data

Extended Data Fig. 10 Parameter estimates for models comparing relationships between trait or environment variables and cover change among lifespans, growth forms, or the combinations of lifespans and growth forms.

These models were only fitted with predictors that were previously noted as significant in the group specific models (Extended Data Fig. 1). Points represent the mean of the posterior distribution and lines represent the 95% credible intervals. Filled points indicate significant predictors of cover change where the 95% credible interval does not overlap zero. Reference groups for the models were annual (lifespan model), forb (growth form model), and annual forb (lifespan*growth form model). Traits include height (m), mass-based leaf nitrogen content (Leaf N, mg g−1), mean annual precipitation (MAP, mm), and root tissue density (RTD, g cm−3).

Source data

Supplementary information

Source data

Source Data Figs. 1–4 and Extended Data Figs. 1–10 (download ZIP )

Source Data Fig. 1: Data to add site points on the map. Source Data Figs. 2–4 and Extended Data Figs. 1–10: Statistical source data.

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Worthy, S.J., Luong, J.C., Wainwright, B.E. et al. Growth form and lifespan of herbaceous species mediate the role of traits in short-term drought response. Nat Ecol Evol 10, 512–522 (2026). https://doi.org/10.1038/s41559-026-02989-4

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