Abstract
Earth harbours an extraordinary plant phenotypic diversity1 that is at risk from ongoing global changes2,3. However, it remains unknown how increasing aridity and livestock grazing pressure—two major drivers of global change4,5,6—shape the trait covariation that underlies plant phenotypic diversity1,7. Here we assessed how covariation among 20 chemical and morphological traits responds to aridity and grazing pressure within global drylands. Our analysis involved 133,769 trait measurements spanning 1,347 observations of 301 perennial plant species surveyed across 326 plots from 6 continents. Crossing an aridity threshold of approximately 0.7 (close to the transition between semi-arid and arid zones) led to an unexpected 88% increase in trait diversity. This threshold appeared in the presence of grazers, and moved toward lower aridity levels with increasing grazing pressure. Moreover, 57% of observed trait diversity occurred only in the most arid and grazed drylands, highlighting the phenotypic uniqueness of these extreme environments. Our work indicates that drylands act as a global reservoir of plant phenotypic diversity and challenge the pervasive view that harsh environmental conditions reduce plant trait diversity8,9,10. They also highlight that many alternative strategies may enable plants to cope with increases in environmental stress induced by climate change and land-use intensification.
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Data availability
All processed datasets generated during the current study are available in the open source repository at https://doi.org/10.57745/SFCXOO.
Code availability
The R code used to analyse the data is available in the open source repository at https://doi.org/10.57745/SFCXOO.
Change history
12 August 2024
In the version of this article initially published, an incorrect email address was listed for Fernando Maestre, which is now updated in the HTML and PDF versions of the article.
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Acknowledgements
We acknowledge S. Undrakhbold, M. Uuganbayar, B. Byambatsogt, S. Khaliun, S. Solongo, B. Batchuluun, M. Sloan, S. Spann, J. Spence, E. Geiger, I. Souza, R. Onoo, T. Araújo, M. Mabaso, P. M. Lunga, L. Eloff, J. Sebei, J. J. Jordaan, E. Mudongo, V. Mokoka, B. Mokhou, T. Maphanga, D. Thompson, A. S. K. Frank, R. Matjea, F. Hoffmann, C. Goebel, B. Semple, B. Tamayo, R. Peters, A. L. Piña, R. Ledezma, E. Vidal, F. Perona, J. M. Alcántara, A. Howell, R. Reibold, N. Melone, M. Starbuck, E. Geiger, Bush Heritage Australia, the University of Limpopo, Comunidad Agricola Quebrada de Talca, Conaf Chile and South African Environmental Observation Network (SAEON) for assistance with field work and plant identification, the South African Military for assistance with field work and/or granting access to their properties, and the Scientific Services Kruger National Park. This research was funded by the European Research Council (ERC Grant agreement 647038 1004 [BIODESERT]) and Generalitat Valenciana (CIDEGENT/2018/041). N.G. was supported by CAP 20–25 (16-IDEX-0001) and the AgreenSkills+ fellowship programme which has received funding from the European Union’s Seventh Framework Programme under grant agreement FP7-609398 (AgreenSkills+ contract). F.T.M. acknowledges support from the King Abdullah University of Science and Technology (KAUST), the KAUST Climate and Livability Initiative, the University of Alicante (UADIF22-74 and VIGROB22-350), the Spanish Ministry of Science and Innovation (PID2020-116578RB-I00), and the Synthesis Center (sDiv) of the German Centre for Integrative Biodiversity Research Halle–Jena–Leipzig (iDiv). Y.L.B.-P. was supported by a Marie Sklodowska-Curie Actions Individual Fellowship (MSCA-1018 IF) within the European Program Horizon 2020 (DRYFUN Project 656035). H.S. is supported by a María Zambrano fellowship funded by the Ministry of Universities and European Union-Next Generation plan. L.W. acknowledges support from the US National Science Foundation (EAR 1554894). G.M.W. acknowledges support from the Australian Research Council (DP210102593) and TERN. M.B is supported by a Ramón y Cajal grant from Spanish Ministry of Science (RYC2021-031797-I). L.v.d.B. and K.T. were supported by the German Research Foundation (DFG) Priority Program SPP-1803 (TI388/14-1). A.F. acknowledges the financial support from ANID PIA/BASAL FB210006 and Millenium Science Initiative Program NCN2021-050. A.J. was supported by the Bavarian Research Alliance for travel and field work (BayIntAn UBT 2017 61). A.L. and L.K. acknowledge support from the German Research Foundation, DFG (grant CRC TRR228) and German Federal Government for Science and Education, BMBF (grants 01LL1802C and 01LC1821A). B.B. and S.U. were supported by the Taylor Family-Asia Foundation Endowed Chair in Ecology and Conservation Biology. P.J.R. and A.J.M. acknowledge support from Fondo Europeo de Desarrollo Regional through the FEDER Andalucía operative programme, FEDER-UJA 1261180 project. E.M.-J. and C.P. acknowledge support from the Spanish Ministry of Science and Innovation (PID2020-116578RB-I00). D.J.E. was supported by the Hermon Slade Foundation. J.D. and A.Rodríguez acknowledge support from the FCT (2020.03670.CEECIND and SFRH/BDP/108913/2015, respectively), as well as from the MCTES, FSE, UE and the CFE (UIDB/04004/2021) research unit financed by FCT/MCTES through national funds (PIDDAC). S.C.R. acknowledges support from the US Department of Energy (DE-SC-0008168), US Department of Defense (RC18-1322), and the US Geological Survey Ecosystems Mission Area. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US government. E.H.-S. acknowledges support from Mexican National Science and Technology Council (CONACYT PN 5036 and 319059). A.N. and C. Branquinho. acknowledge the support from FCT—Fundação para a Ciência e a Tecnologia (CEECIND/02453/2018/CP1534/CT0001, PTDC/ASP-SIL/7743/ 2020, UIDB/00329/2020), from AdaptForGrazing project (PRR-C05-i03-I-000035) and from LTsER Montado platform (LTER_EU_PT_001). Field work of G.P. and J.M.Z. was supported by UNRN (PI 40-C-873).
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N.G., F.T.M. and Y.L.B.-P. conceived this study. F.T.M., N.G. and Y.L.B.-P. designed and coordinated the global field survey. N.G., P.L. and Y.L.B.-P. developed the original idea of the analyses presented in the manuscript, with inputs from F.T.M., M.B., R.M., M.D.-B., V.M., E.M.-J., H.S., S.S. and E.V. F. Jabot. developed the theoretical model on plant cover. Fieldwork was done by all co-authors with the assistance of M.G.-G. for field site assessments. Laboratory analyses were done by V.O., B.G., S.A., C.P., M.G.-G. and I.S.P. The trait database was built by N.G., R.M. and Y.L.B.-P. Data and code handling, curation and verification were done by N.G., R.M., V.O., B.G., I.S.P. and Y.L.B.-P. Statistical analyses were performed by N.G., M.B., and R.M. N.G., Y.L.B.-P. and F.T.M. wrote the first manuscript draft and all authors worked on the final version.
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Extended data figures and tables
Extended Data Fig. 1 The trait space of global dryland rangelands.
a-c represent the probabilistic species distributions in the space defined by a Principal Component Analysis (PCA) on whole-plant and leaf size, and on leaf chemical traits. a shows the dimensions related to plant size and leaf C-economy. b-c show the additional, but independent dimensions related to the plant elementome characterized by the concentration of 14 elements in plant leaves: C, N, P, Mg, Mn, Ca, Cu, Al, Ba, Fe, K, Na, S, and Zn. The dryland trait space displayed five major dimensions (Principal Components PC1 to PC5), accounting for 66.7 % of the total trait variation. In a, Leaf traits related to leaf C-economy (PC1) and plant size (PC3) varied along two orthogonal dimensions and accounted for a total of 28.2% of trait variation. In b-c, the plant elementome accounted for 55.5% of trait variation. While a dimension of the plant elementome covaried with the leaf C-economy dimension27 (N-P-K on PC1), it also added three other orthogonal dimensions that were associated with important macro- and micronutrients (PC2, PC4, PC5). These findings show that a large fraction of trait diversity found across global drylands is not captured by plant size and leaf C-economy alone, but by the plant elementome (see Supplementary Fig. 5 for an additional description of the elementome; Supplementary Fig. 8 for the PCA ran without the gap-filling of the data; Supplementary Fig. 7 for pictures of dryland plant species). The color gradient depicts the different species densities in the trait space (high and low density in red and fading yellow, respectively). The arrow length is proportional to the trait loadings. Each point represents the location of a species within the five-dimensional trait space for all the species surveyed (n = 1347). Abbreviations: maximum plant height, H; Lateral spread, LS; Leaf length, LL; leaf area, LA; specific leaf area, SLA; leaf dry matter content, LDMC. See also Supplementary Table 4 for detailed results.
Extended Data Fig. 2 Aridity reshuffles the trait space of global dryland rangelands.
We show how trait covariation changes along the aridity gradient using Principal Component Analysis (PCA) conducted for sites with aridity values located below and above the aridity threshold of ~0.7 (Low aridity n = 338; high aridity n = 1009). The arrow length is proportional to the loadings of the traits considered. In a-b, four principal components were selected at aridity values < 0.7 while in c-e five components were selected at aridity values > 0.7. See Extended Data Fig. 1 for trait abbreviations and Supplementary Table 4 for detailed results.
Extended Data Fig. 3 Presence of grazers modulates the trait space of global dryland rangelands.
We show how trait covariation changes with increasing grazing pressure using Principal Component analysis (High Grazing n = 382; Medium Grazing n = 410; Low Grazing n = 389; Ungrazed n = 166). The arrow length is proportional to the loadings of the traits considered. In a-i, five principal components were significantly selected in low, medium, and high grazing pressures. In j-k, four principal components were significantly selected in ungrazed plots. See Extended Data Fig. 1 for trait abbreviations and Supplementary Table 6 for detailed results. Low = low grazing pressure, Med = medium grazing pressure, and High = high grazing pressure.
Extended Data Fig. 4 Representation of the trait hypervolume before and after crossing the ~ 0.7 aridity threshold.
We show the 2D projection of the hypervolume for each pair of PCA dimensions shown in Extended Data Fig. 1 (n-dimensions = 5, from PC1 to PC5). Colored dots represent the locations of each measured species within the trait space. The blue and the red large bright dots represented the centroids of each hypervolume before and after an aridity value of 0.7 (low aridity n = 189; high aridity n = 696). Colored lines show the 0.95 confidence intervals of the hypervolume before and after this aridity value.
Extended Data Fig. 5 Response of elemental concentration in soils (the soil elementome) to aridity.
Soil elements covary across the 326 sampled plots along a unique Principal Component axis (PC1) that account for 65.8 % of soil total variation (see Methods). a shows responses of the soil elementome, illustrated using the soil PC 1, to aridity. PC1 shows a quadratic response to aridity with non-linear decrease occurring only in the most arid areas, i.e., those with aridity values > 0.8. Grazing did not modify this response. b shows how the soil elementome responded to aridity using a sliding windows analysis (see methods). We first ordered the 326 plots according to their aridity level. We then defined an aridity window that represented 10% of the global aridity gradient and selected all plots within this aridity range (n > 30 plots in each window). We finally examined how the bootstrapped covariation of soil elements across plots changed as aridity increased. We found that aridity further increased the covariation of soil elements in the most arid rangelands surveyed. See Supplementary Table 7 for detailed results of model selections evaluating the response of the soil elementome to aridity. Error band shows the 0.95 confidence interval in a and b.
Extended Data Fig. 6 Global decrease in plant cover driven by aridity and grazing.
a shows the averaged model parameters (± 0.95 confidence interval) for different predictors (i.e. aridity, grazing, soil, and geographical variables) on plant cover (n = 326 plots). Significant predictors do not cross the vertical dotted line. Aridity and grazing were the main drivers of plant cover. b illustrates the effects of aridity on plant cover. Vertical dashed and dotted lines represent the mean location of the threshold and its 0.95 confidence interval, respectively. Error band shows the 0.95 confidence interval. c shows grazing effect on plant cover (High Grazing n = 98; Medium Grazing n = 97; Low Grazing n = 88; Ungrazed n = 43). Data are represented as boxplots where the middle line is the median, the lower and upper hinges correspond to the first and third quartiles, the upper and lower lines show the 0.95 confidence interval. Data beyond the confidence interval are outlying points that are plotted individually. We tested whether different grazing pressure levels showed significant differences using a generalized least squares model (p < 0.001). Letters show results of a post-hoc test based on bootstrapped pairwise comparisons between grazing pressure levels. Different letters indicate significant differences among grazing pressure levels. Plant cover decreased non-linearly at aridity ~0.7 and was the lowest under high grazing pressure.
Extended Data Fig. 7 Plant cover mediates the effect of aridity and grazing pressure on trait diversity across global dryland rangelands.
a-b show the response of trait diversity (hypervolume and trait covariation respectively) to plant cover using a sliding window procedure (see Methods). Increasing plant cover decreased hypervolume and increased trait covariations, with a significant threshold value occurring at a plant cover value close to 50% ± CI (vertical dashed lines, the dotted lines show its 0.95 percentile Confidence Interval, CI). See Supplementary Table 8 for detailed results of model selection evaluating the response of the plant elementome to plant cover. Error band shows the 0.95 confidence interval in a and b.
Supplementary information
Supplementary Information
Supplementary Figs. 1–16, Supplementary Table 1 and 3–9 and Supplementary Text 1–3.
Supplementary Table 2
Role of the elementome for plant development.
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Gross, N., Maestre, F.T., Liancourt, P. et al. Unforeseen plant phenotypic diversity in a dry and grazed world. Nature 632, 808–814 (2024). https://doi.org/10.1038/s41586-024-07731-3
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DOI: https://doi.org/10.1038/s41586-024-07731-3
