Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Drought resistance enhanced by tree species diversity in global forests

Abstract

Restoring species diversity is proposed as a strategy to improve ecosystem resistance to extreme droughts, but the impact of species diversity on resistance has not been evaluated across global forests. Here we compile a database that contains tree species richness from more than 0.7 million forest plots and satellite-based estimation of drought resistance. Using this database, we provide a spatially explicit map of species diversity effect on drought resistance. We found that higher species diversity could notably enhance drought resistance in about half of global forests but was spatially highly variable. Drought regimes (frequency and intensity) and climatic water deficit were important determinants of differences in the extent that species diversity could enhance forest drought resistance among regions, with such benefits being larger in dry and drought-prone forests. According to a predictive model of species diversity effect, the conversion of current monoculture to mixed-species tree plantations could improve drought resistance, with the large increase in dry forests. Our findings provide evidence that species diversity could buffer global forests against droughts. Restoration of species diversity could then be an effective way to mitigate the impact of extreme droughts on large scales, especially in dry and drought-prone regions.

This is a preview of subscription content, access via your institution

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Distribution of tree species richness and associated drought resistance.
Fig. 2: The relationships between species diversity effect on drought resistance and the main environmental variables across forest ecoregions.
Fig. 3: Predicted species diversity effect on drought resistance across global forest ecoregions.

Data availability

The GFBI species-richness data can be accessed at https://www.gfbinitiative.org/metadata-gfb1. The BIEN species-richness dataset is available from https://doi.org/10.6084/m9.figshare.7436951.v1. The SPEI dataset can be downloaded from https://spei.csic.es/database.html. The MOD13C2 collection 6 NDVI product can be accessed at https://e4ftl01.cr.usgs.gov/MODV6_Cmp_C/MOLT/MOD13C2.006/. The MOD17A2 dataset was derived from http://files.ntsg.umt.edu/data/NTSG_Products/MOD17/GeoTIFF/Monthly_MOD17A2/. The forest-change data are available at https://earthenginepartners.appspot.com/science-2013-global-forest. The distribution of global plantations is freely available at https://www.environmentalgeography.nl/site/. Other datasets supporting the findings of this manuscript are available in the main text or Supplementary Information. The estimated species diversity effect for global forests can be accessed at https://zenodo.org/record/6948912#.YufcT3ZByUk. Source data are provided with this paper.

Code availability

All computer codes used in this study can be provided by the corresponding author upon reasonable request.

References

  1. Beer, C. et al. Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science 329, 834 (2010).

    Article  Google Scholar 

  2. Anderegg, W. R. L. et al. Pervasive drought legacies in forest ecosystems and their implications for carbon cycle models. Science 349, 528–532 (2015).

    Article  Google Scholar 

  3. Ciais, P. et al. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437, 529–533 (2005).

    Article  Google Scholar 

  4. Peñuelas, J. et al. Shifting from a fertilization-dominated to a warming-dominated period. Nat. Ecol. Evol. 1, 1438–1445 (2017).

    Article  Google Scholar 

  5. Morin, X. et al. Temporal stability in forest productivity increases with tree diversity due to asynchrony in species dynamics. Ecol. Lett. 17, 1526–1535 (2014).

    Article  Google Scholar 

  6. Isbell, F. et al. Biodiversity increases the resistance of ecosystem productivity to climate extremes. Nature 526, 574 (2015).

    Article  Google Scholar 

  7. De Boeck, H. J. et al. Patterns and drivers of biodiversity–stability relationships under climate extremes. J. Ecol. 106, 890–902 (2018).

    Article  Google Scholar 

  8. Grossiord, C. Having the right neighbors: how tree species diversity modulates drought impacts on forests. N. Phytol. 228, 42–49 (2020).

    Article  Google Scholar 

  9. O’Brien, M. J. et al. Resistance of tropical seedlings to drought is mediated by neighbourhood diversity. Nat. Ecol. Evol. 1, 1643–1648 (2017).

    Article  Google Scholar 

  10. Gazol, A. & Camarero, J. J. Functional diversity enhances silver fir growth resilience to an extreme drought. J. Ecol. 104, 1063–1075 (2016).

    Article  Google Scholar 

  11. Pretzsch, H., Schütze, G. & Uhl, E. Resistance of European tree species to drought stress in mixed versus pure forests: evidence of stress release by inter-specific facilitation. Plant Biol. 15, 483–495 (2013).

    Article  Google Scholar 

  12. Grossiord, C. et al. Tree diversity does not always improve resistance of forest ecosystems to drought. P. Natl Acad. Sci. USA 111, 14812–14815 (2014).

    Article  Google Scholar 

  13. Grossiord, C. et al. Does drought influence the relationship between biodiversity and ecosystem functioning in boreal forests. Ecosystems 17, 394–404 (2014).

    Article  Google Scholar 

  14. Loreau, M., Mouquet, N. & Gonzalez, A. Biodiversity as spatial insurance in heterogeneous landscapes. P. Natl Acad. Sci. USA 100, 12765 (2003).

    Article  Google Scholar 

  15. Lloret, F. et al. Woody plant richness and NDVI response to drought events in Catalonian (northeastern Spain) forests. Ecology 88, 2270–2279 (2007).

    Article  Google Scholar 

  16. He, Q. & Bertness, M. D. Extreme stresses, niches, and positive species interactions along stress gradients. Ecology 95, 1437–1443 (2014).

    Article  Google Scholar 

  17. Hafner, B. D. et al. Hydraulic redistribution under moderate drought among English oak, European beech and Norway spruce determined by deuterium isotope labeling in a split-root experiment. Tree Physiol. 37, 950–960 (2017).

    Article  Google Scholar 

  18. Forrester, D. I. & Bauhus, J. A review of processes behind diversity–productivity relationships in forests. Curr. For. Rep. 2, 45–61 (2016).

    Article  Google Scholar 

  19. Vitali, V., Forrester, D. I. & Bauhus, J. Know your neighbours: drought response of Norway spruce, silver fir and Douglas fir in mixed forests depends on species identity and diversity of tree neighbourhoods. Ecosystems 21, 1215–1229 (2018).

    Article  Google Scholar 

  20. The State of the World’s Forests 2020: Forests, Biodiversity and People (FAO and UNEP, 2020).

  21. Zhang, J., Fu, B., Stafford-Smith, M., Wang, S. & Zhao, W. Improve forest restoration initiatives to meet Sustainable Development Goal 15. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-020-01332-9 (2020).

  22. Schulze, K., Malek, Ž. & Verburg, P. H. Towards better mapping of forest management patterns: a global allocation approach. For. Ecol. Manage. 432, 776–785 (2019).

    Article  Google Scholar 

  23. Harris, N. L. et al. Global maps of twenty-first century forest carbon fluxes. Nat. Clim. Change https://doi.org/10.1038/s41558-020-00976-6 (2021).

  24. Maxwell, S. L. et al. Area-based conservation in the twenty-first century. Nature 586, 217–227 (2020).

    Article  Google Scholar 

  25. Williams, A. P. et al. Large contribution from anthropogenic warming to an emerging North American megadrought. Science 368, 314–318 (2020).

    Article  Google Scholar 

  26. Blackman, C. et al. Leaf hydraulic vulnerability is related to conduit dimensions and drought resistance across a diverse range of woody angiosperms. N. Phytol. 188, 1113–1123 (2010).

    Article  Google Scholar 

  27. Liang, J. et al. Positive biodiversity–productivity relationship predominant in global forests. Science 354, aaf8957 (2016).

    Article  Google Scholar 

  28. Wieczynski, D. J. et al. Climate shapes and shifts functional biodiversity in forests worldwide. P. Natl Acad. Sci. USA 116, 587–592 (2019).

    Article  Google Scholar 

  29. Tomppo, E. et al. National Forest Inventories: Pathways for Common Reporting (Springer, 2010).

  30. Chirici, G. et al. National Forest Inventories: Contributions to Forest Biodiversity Assessments (Springer, 2011).

  31. Magnussen, S., Smith, B. & Uribe, S. National Forest inventories in North America for monitoring forest tree species diversity. Plant Biosyst. 141, 113–122 (2007).

    Article  Google Scholar 

  32. Lesiv, M. et al. Global forest management data for 2015 at a 100 m resolution. Sci. Data 9, 199 (2022).

    Article  Google Scholar 

  33. Vicente-Serrano, S. M., Beguería, S. & López-Moreno, J. I. A. Multiscalar drought index sensitive to global warming: the Standardized Precipitation Evapotranspiration Index. J. Clim. 23, 1696–1718 (2010).

    Article  Google Scholar 

  34. Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850 (2013).

    Article  Google Scholar 

  35. Forest Resources Assessment 2015 (FAO, 2015).

  36. Lyapustin, A. et al. Scientific impact of MODIS C5 calibration degradation and C6+ improvements. Atmos. Meas. Tech. 7, 4353–4365 (2014).

    Article  Google Scholar 

  37. Didan, K. & Brreto, A. NASA MEaSUREs Vegetation Index and Phenology (VIP) Phenology EVI2 Yearly Global 0.05Deg CMG, NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/MEaSUREs/VIP/VIPPHEN_EVI2.004 (2016).

  38. Olson, D. M. et al. Terrestrial Ecoregions of the World: A New Map of Life on Earth: a new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. Bioscience 51, 933–938 (2001).

    Article  Google Scholar 

  39. Kline, T. J. B. Sample issues, methodological implications, and best practices. Can. J. Behav. Sci. 49, 71–77 (2017).

    Article  Google Scholar 

  40. Gourlet-Fleury, S. et al. Tropical forest recovery from logging: a 24 year silvicultural experiment from Central Africa. Phil. Trans. R. Soc. B 368, 20120302 (2013).

    Article  Google Scholar 

  41. Obiang, N. L. E. et al. Spatial pattern of central African rainforests can be predicted from average tree size. Oikos 119, 1643–1653 (2010).

    Article  Google Scholar 

  42. Plotkin, J. B. et al. Predicting species diversity in tropical forests. P. Natl Acad. Sci. USA 97, 10850–10854 (2000).

    Article  Google Scholar 

  43. Graham, M. H. Confronting multicollinearity in ecological multiple regression. Ecology 84, 2809–2815 (2003).

    Article  Google Scholar 

  44. Tukey, J. W. Exploratory Data Analysis (Addison-Wesley,1977).

Download references

Acknowledgements

This study was supported by the National Natural Science Foundation of China (41922004 and 41871104) (T.W.), Second Tibetan Plateau Scientific Expedition and Research Programme (2019QZKK0606) and the NSFC project Basic Science Centre for Tibetan Plateau Earth System (41988101-04) (D.L., T.W. and S.P.).

Author information

Authors and Affiliations

Authors

Contributions

T.W. designed the study. D.L. performed the analysis and prepared the figures. T.W. and D.L. drafted the manuscript. J.P. and S.P. contributed to the interpretations of the results and to the text.

Corresponding author

Correspondence to Tao Wang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Geoscience thanks Michael O’Brien, Carsten Dormann and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Simon Harold and Xujia Jiang, in collaboration with the Nature Geoscience team.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Spatial pattern along gradient of water availability.

Resistance (a), species richness (b), drought frequency (c) and intensity (d) along spatial gradient of water availability. The annual climatic water deficit (CWD), which considers both water supply and atmospheric water demand was used to describe water availability. The CWD was calculated as precipitation minus potential evapotranspiration, with positive values suggest wet climate and negative values dry climate. The line illustrates the mean value of the response variable for each 100-mm interval of CWD, and the range of the error band shows the associated standard deviation. The significance was based on the t-statistics using two-tailed test (N = 21).

Source data

Extended Data Fig. 2 Comparison between multiple regression model and sequential regression models.

Comparison of θ estimated using the multiple regression model against that based on the sequential regression models. The multiple regression model was constructed as ‘\(R_t\sim \hat S_i + X_1 + X_2 + \ldots + X_n\)’. The first type of sequential regression model was constructed as ‘\(\hat S_i\sim X_1 + X_2 + \ldots + X_n\); \(R_t\sim residual(\hat S_i) + X_1 + X_2 + \ldots + X_n\)’. The second type of sequential regression model was constructed as ‘\(R_t\sim X_1 + X_2 + \ldots + X_n\); \(residual(R_t)\sim \hat S_i\)’. The significance was based on the t-statistics using two-tailed test with sample size of 79 ecoregions.

Source data

Extended Data Fig. 3 Comparison between multiple regression model and structural equation model.

A structural equation model representing the relationships among the 18 environmental variables (X1~X18), species richness (S), a composite variable (ENV), and the response variable (Rt). (a) shows the structure of the model. Arrows depict the linkage among variables. (b) is the histogram of the estimated coefficients from all investigated ecoregions. All variables are standardized, and the coefficients are then comparable between each other. (c) shows the comparison between the estimated θ derived from multiple regression model and that from structural equation model. The significance was based on the t-statistics using two-tailed test with sample size of 79 ecoregions.

Source data

Extended Data Fig. 4 Estimated species diversity effect at the ecoregion level.

Estimated species diversity effect (θ) at the ecoregion level.

Extended Data Fig. 5 Comparison between predicted θ with field-based studies.

The blue bars show the number of sites using δ13C change to measure drought resistance at both tree and ecosystem levels (Δδ13C), using changes in annual tree rings to measure drought resistance at the tree level (Growth, Tree level) and using changes in annual tree rings at the ecosystem scale (Growth, Ecosystem level). The red bars show the number of sites where the sign of predicted θ is consistent with that of documented species diversity effect from field studies. The data used in this plot are listed in Supplementary Table 2.

Extended Data Fig. 6 Increase in drought resistance induced by converting monocultured plantations to forests with four species.

The average change in resistance for each biome is marked with a distinct color, and the associated distribution of forest plantations for the biome is presented in the inset with the same color, and area in grey shows global forest distribution.

Supplementary information

Supplementary Information

Supplementary Figs. 1–6 and Tables 1 and 2.

Source data

Source Data Fig. 2

Estimated species diversity effect for each ecoregion and the associated driving variables.

Source Data Extended Data Fig. 1

The forest drought resistance, species richness, drought frequency and drought intensity changes along CWD gradient.

Source Data Extended Data Fig. 2

Estimated species diversity effect by multiple regression model and two sequential regression models.

Source Data Extended Data Fig. 3

Results from structural equation model and the comparison against multiple regression model.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Liu, D., Wang, T., Peñuelas, J. et al. Drought resistance enhanced by tree species diversity in global forests. Nat. Geosci. (2022). https://doi.org/10.1038/s41561-022-01026-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41561-022-01026-w

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing