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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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).
References
Sheffield, J. & Wood, E. F. Projected changes in drought occurrence under future global warming from multi-model, multi-scenario, IPCC AR4 simulations. Clim. Dyn. 31, 79–105 (2008).
Google Scholar
IPCC Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge Univ. Press, 2021).
Cook, B. I., Ault, T. R. & Smerdon, J. E. Unprecedented 21st century drought risk in the American Southwest and Central Plains. Sci. Adv. 1, e1400082 (2015).
Google Scholar
AghaKouchak, A. et al. Climate extremes and compound hazards in a warming world. Annu. Rev. Earth Planet Sci. 48, 519–548 (2020).
Google Scholar
Smith, M. D. et al. Extreme drought impacts have been underestimated in grasslands and shrublands globally. Proc. Natl Acad. Sci. USA 121, e2309881120 (2024).
Google Scholar
Volaire, F. A unified framework of plant adaptive strategies to drought: crossing scales and disciplines. Glob. Chang. Biol. 24, 2929–2938 (2018).
Google Scholar
Kimball, S. et al. Can functional traits predict plant community response to global change? Ecosphere 7, e01602 (2016).
Google Scholar
Harrison, S. & LaForgia, M. Seedling traits predict drought-induced mortality linked to diversity loss. Proc. Natl Acad. Sci. USA 116, 5576–5581 (2019).
Google Scholar
Jentsch, A. & White, P. A theory of pulse dynamics and disturbance in ecology. Ecology 100, e02734 (2019).
Google Scholar
Funk, J. L., Larson, J. E., Blair, M. D., Nguyen, M. A. & Rivera, B. J. Drought response in herbaceous plants: a test of the integrated framework of plant form and function. Funct. Ecol. 38, 679–691 (2024).
Google Scholar
Reich, P. B. The world-wide ‘fast–slow’ plant economics spectrum: a traits manifesto. J. Ecol. 102, 275–301 (2014).
Google Scholar
Weigelt, A. et al. An integrated framework of plant form and function: the belowground perspective. New Phytol. 232, 42–59 (2021).
Google Scholar
Wright, I. J. et al. The worldwide leaf economics spectrum. Nature 428, 821–827 (2004).
Google Scholar
Weemstra, M. et al. Towards a multidimensional root trait framework: a tree root review. New Phytol. 211, 1159–1169 (2016).
Google Scholar
Bergmann, J. et al. The fungal collaboration gradient dominates the root economics space in plants. Sci. Adv. 6, eaba3756 (2020).
Google Scholar
Fort, F. et al. Root traits are related to plant water-use among rangeland Mediterranean species. Funct. Ecol. 31, 1700–1709 (2017).
Google Scholar
Balachowski, J. A. & Volaire, F. A. Implications of plant functional traits and drought survival strategies for ecological restoration. J. Appl. Ecol. 55, 631–640 (2018).
Google Scholar
Poorter, H. et al. Biomass allocation to leaves, stems, and roots: meta-analyses of interspecific variation and environmental control. New Phytol. 193, 30–50 (2012).
Google Scholar
Eziz, A. et al. Drought effect on plant biomass allocation: a meta-analysis. Ecol. Evol. 7, 11002–11010 (2017).
Google Scholar
Garbowski, M. et al. Getting to the root of restoration: considering root traits for restoration outcomes under drought and competition. Restor. Ecol. 28, 1384–1395 (2020).
Google Scholar
Welles, S. R. & Funk, J. L. Patterns of intraspecific trait variation along an aridity gradient suggest both drought escape and drought tolerance strategies in an invasive herb. Ann. Bot. 127, 461–471 (2021).
Google Scholar
Pistón, N. et al. Multidimensional ecological analyses demonstrate how interactions between functional traits shape fitness and life history strategies. J. Ecol. 107, 2317–2328 (2019).
Google Scholar
Umaña, M. N., Arellano, G., Swenson, N. G. & Zambrano, J. Tree seedling trait optimization and growth in response to local-scale soil and light variability. Ecology 102, e03252 (2021).
Google Scholar
Worthy, S. J. et al. Alternative designs and tropical tree seedling growth performance landscapes. Ecology 101, e03007 (2020).
Google Scholar
Li, Y. et al. The complexity of trait–environment performance landscapes in a local subtropical forest. New Phytol. 229, 1388–1397 (2021).
Google Scholar
Kühn, N., Tovar, C., Willis, K. J. & Macias-Fauria, M. Root trait variation along water gradients in the Cape Floristic Region. J. Veg. Sci. 34, e13194 (2023).
Google Scholar
Marks, C. O. & Lechowicz, M. J. Alternative designs and the evolution of functional diversity. Am. Nat. 167, 55–66 (2006).
Google Scholar
Dias, A. T. C., Rosado, B. H. P., de Bello, F., Pistón, N. & de Mattos, E. A. Alternative plant designs: consequences for community assembly and ecosystem functioning. Ann. Bot. 125, 391–398 (2020).
Google Scholar
Wright, I. J. et al. Modulation of leaf economic traits and trait relationships with climate. Glob. Ecol. Biogeogr. 14, 411–421 (2005).
Google Scholar
Laughlin, D. C., Strahan, R. T., Adler, P. B. & Moore, M. M. Survival rates indicate that correlations between community-weighted mean traits and environments can be unreliable estimates of the adaptive value of traits. Ecol. Lett. 21, 411–421 (2018).
Google Scholar
Nippert, J. B. & Knapp, A. K. Soil water partitioning contributes to species coexistence in tallgrass prairie. Oikos 116, 1017–1029 (2007).
Google Scholar
Roumet, C., Lafont, F., Sari, M., Warembourg, F. & Garnier, E. Root traits and taxonomic affiliation of nine herbaceous species grown in glasshouse conditions. Plant Soil 312, 69–83 (2008).
Google Scholar
Mackie, K. A., Zeiter, M., Bloor, J. M. G. & Stampfli, A. Plant functional groups mediate drought resistance and recovery in a multisite grassland experiment. J. Ecol. 107, 937–949 (2019).
Google Scholar
Zwicke, M., Picon-Cochard, C., Morvan-Bertrand, A., Prud’homme, P. & Volaire, F. What functional strategies drive drought survival and recovery of perennial species from upland grassland? Ann. Bot. 116, 1001–1015 (2015).
Google Scholar
Blumenthal, D. M. et al. Traits link drought resistance with herbivore defence and plant economics in semi-arid grasslands: the central roles of phenology and leaf dry matter content. J. Ecol. 108, 2336–2351 (2020).
Google Scholar
Kooyers, N. J. The evolution of drought escape and avoidance in natural herbaceous populations. Plant Sci. 234, 155–162 (2015).
Google Scholar
Wright, I. J., Reich, P. B. & Westoby, M. Strategy shifts in leaf physiology, structure and nutrient content between species of high- and low-rainfall and high- and low-nutrient habitats. Funct. Ecol. 15, 423–434 (2001).
Google Scholar
Yan, P. et al. Plant acquisitive strategies promote resistance and temporal stability of semiarid grasslands. Ecol. Lett. 28, e70110 (2025).
Google Scholar
Sandel, B. et al. Contrasting trait responses in plant communities to experimental and geographic variation in precipitation. New Phytol. 188, 565–575 (2010).
Google Scholar
Griffin-Nolan, R. J. et al. Shifts in plant functional composition following long-term drought in grasslands. J. Ecol. 107, 2133–2148 (2019).
Google Scholar
Guillemot, J. et al. Small and slow is safe: on the drought tolerance of tropical tree species. Glob. Chang. Biol. 28, 2622–2638 (2022).
Google Scholar
Kramp, R. E. et al. Functional traits and their plasticity shift from tolerant to avoidant under extreme drought. Ecology 103, e3826 (2022).
Google Scholar
Volaire, F. et al. Is a seasonally reduced growth potential a convergent strategy to survive drought and frost in plants? Ann. Bot. 131, 245–254 (2023).
Google Scholar
Yang, Y. et al. Changes in mass allocation play a more prominent role than morphology in resource acquisition of the rhizomatous Leymus chinensis under drought stress. Ann. Bot. 132, 121–132 (2023).
Google Scholar
Künzi, Y., Zeiter, M., Fischer, M. & Stampfli, A. Rooting depth and specific leaf area modify the impact of experimental drought duration on temperate grassland species. J. Ecol. 113, 445–458 (2025).
Google Scholar
Nippert, J. B. & Holdo, R. M. Challenging the maximum rooting depth paradigm in grasslands and savannas. Funct. Ecol. 29, 739–745 (2015).
Google Scholar
Tucker, S. S., Craine, J. M. & Nippert, J. B. Physiological drought tolerance and the structuring of tallgrass prairie assemblages. Ecosphere 2, art48 (2011).
Google Scholar
Luong, J. C. & Loik, M. E. Adjustments in physiological and morphological traits suggest drought-induced competitive release of some California plants. Ecol. Evol. 12, e8773 (2022).
Google Scholar
Dawson, W. et al. Root traits vary as much as leaf traits and have consistent phenotypic plasticity among 14 populations of a globally widespread herb. Funct. Ecol. 38, 926–941 (2024).
Google Scholar
Ryser, P. & Eek, L. Consequences of phenotypic plasticity vs. interspecific differences in leaf and root traits for acquisition of aboveground and belowground resources. Am. J. Bot. 87, 402–411 (2000).
Google Scholar
Rowland, L., Ramírez-Valiente, J.-A., Hartley, I. P. & Mencuccini, M. How woody plants adjust above- and below-ground traits in response to sustained drought. New Phytol. 239, 1173–1189 (2023).
Google Scholar
Zirbel, C. R. & Brudvig, L. A. Trait–environment interactions affect plant establishment success during restoration. Ecology 101, e02971 (2020).
Google Scholar
Klimešová, J., Martínková, J. & Ottaviani, G. Belowground plant functional ecology: towards an integrated perspective. Funct. Ecol. 32, 2115–2126 (2018).
Google Scholar
Funk, J. L., Larson, J. E. & Ricks-Oddie, J. Plant traits are differentially linked to performance in a semiarid ecosystem. Ecology 102, e03318 (2021).
Google Scholar
Weigelt, A. et al. The importance of trait selection in ecology. Nature 618, E29–E30 (2023).
Google Scholar
Bueno, C. G. et al. Reply to: The importance of trait selection in ecology. Nature 618, E31–E34 (2023).
Google Scholar
Lemoine, N. P., Sheffield, J., Dukes, J. S., Knapp, A. K. & Smith, M. D. Terrestrial precipitation analysis (TPA): a resource for characterizing long-term precipitation regimes and extremes. Methods Ecol. Evol. 7, 1396–1401 (2016).
Google Scholar
Yahdjian, L. & Sala, O. E. A rainout shelter design for intercepting different amounts of rainfall. Oecologia 133, 95–101 (2002).
Google Scholar
Visser, M. D. et al. Functional traits as predictors of vital rates across the life cycle of tropical trees. Funct. Ecol. 30, 168–180 (2016).
Google Scholar
Umaña, M. N., Needham, J. & Fortunel, C. From seedlings to adults: linking survival and leaf functional traits over ontogeny. Ecology 106, e4469 (2025).
Google Scholar
Balk, M. A. et al. A solution to the challenges of interdisciplinary aggregation and use of specimen-level trait data. iScience 25, 105101 (2022).
Google Scholar
Keller, A. et al. Ten (mostly) simple rules to future-proof trait data in ecological and evolutionary sciences. Methods Ecol. Evol. 14, 444–458 (2023).
Google Scholar
Ross, K. M. & Loik, M. E. Photosynthetic sensitivity to historic meteorological variability for conifers in the eastern Sierra Nevada. Int. J. Biometeorol. 65, 851–863 (2021).
Google Scholar
Mendivelso, H. A., Camarero, J. J., Gutiérrez, E. & Zuidema, P. A. Time dependent effects of climate and drought on tree growth in a Neotropical dry forest: short-term tolerance vs. long-term sensitivity. Agric. For. Meteorol. 188, 13–23 (2014).
Google Scholar
Green, R. H. Sampling Design and Statistical Methods for Environmental Biologists (Wiley, 1979).
Yelenik, S., Rose, E., Cordell, S., Victoria, M. & Kellner, J. R. The role of microtopography and resident species in post-disturbance recovery of arid habitats in Hawai’i. Ecol. Appl. 32, e2690 (2022).
Google Scholar
Pérez-Harguindeguy, N. et al. New handbook for standardised measurement of plant functional traits worldwide. Austr. J. Bot. 61, 167–234 (2013).
Google Scholar
Kattge, J. et al. TRY plant trait database—enhanced coverage and open access. Glob. Chang. Biol. 26, 119–188 (2020).
Google Scholar
Falster, D. et al. AusTraits, a curated plant trait database for the Australian flora. Sci. Data 8, 254 (2021).
Google Scholar
Guerrero-Ramírez, N. R. et al. Global root traits (GRooT) database. Glob. Ecol. Biogeogr. 30, 25–37 (2021).
Google Scholar
Komatsu, K. J. et al. CoRRE trait data: a dataset of 17 categorical and continuous traits for 4079 grassland species worldwide. Sci. Data 11, 795 (2024).
Google Scholar
Enquist, B. J., Condit, R., Peet, R. K., Schildhauer, M. & Thiers, B. M. Cyberinfrastructure for an integrated botanical information network to investigate the ecological impacts of global climate change on plant biodiversity. PeerJ 4, e2615v2612 (2016).
Jin, Y. et al. TiP-Leaf: a dataset of leaf traits across vegetation types on the Tibetan Plateau. Earth Syst. Sci. Data 15, 25–39 (2023).
Google Scholar
Wang, H. et al. The China plant trait database version 2. Sci. Data 9, 769 (2022).
Google Scholar
Schrodt, F. et al. BHPMF—a hierarchical Bayesian approach to gap-filling and trait prediction for macroecology and functional biogeography: a gap-filling in trait databases. Glob. Ecol. Biogeogr. 24, 1510–1521 (2015).
Google Scholar
Matos, I. S. et al. Leaf venation network architecture coordinates functional trade-offs across vein spatial scales: evidence from multiple alternative designs. New Phytol. 244, 407–425 (2024).
Google Scholar
Fazayeli, F., Banerjee, A., Schrodt, F., Kattge, J. & Reich, P. BHPMF: uncertainty quantified matrix completion using bayesian hierarchical matrix factorization. R package version 1.1 (2017).
R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2023).
Cayuela, L., Macarro, I., Stein, A. & Oksanen, J. Taxonstand: taxonomic standardization of plant species names. R package version 2.4 (2021).
Bürkner, P.-C. brms: an R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).
Google Scholar
Lenth, R. emmeans: estimated marginal means, aka least-squares means. R package version 1.8.7 (2023).
Lüdecke, D., Ben-Shachar, M. S., Patil, I., Waggoner, P. & Makowski, D. performance: an R package for assessment, comparison and testing of statistical models. J. Open Source Softw. 60, 3139 (2021).
Google Scholar
James, G., Witten, D., Hastie, T. & Tibshirani, R. An Introduction to Statistical Learning with Applications in R (Springer, 2021).
Worthy, S. jworthy/IDE.Traits: data and code for publication. Zenodo https://doi.org/10.5281/zenodo.17724111 (2025).
Beck, H. E. et al. Daily evaluation of 26 precipitation datasets using stage-IV gauge-radar data for the CONUS. Hydrol. Earth Syst. Sci. 23, 207–224 (2019).
Google Scholar
Vargas Godoy, M. R. & Markonis, Y. pRecipe: a global precipitation climatology toolbox and database. Environ. Model. Softw. 165, 105711 (2023).
Google Scholar
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).
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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.
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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
Supplementary Information
Supplementary Figs. 1–4 and Tables 1–8.
Reporting Summary
Source data
Source Data Figs. 1–4 and Extended Data Figs. 1–10
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 (2026). https://doi.org/10.1038/s41559-026-02989-4
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DOI: https://doi.org/10.1038/s41559-026-02989-4
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