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Drought resistance enhanced by tree species diversity in global forests

Nature Geoscience volume 15, pages 800–804 (2022)Cite this article

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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.

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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.

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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.

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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

  1. State Key Laboratory of Tibetan Plateau Earth System, Resources and Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China

    Dan Liu, Tao Wang & Shilong Piao

  2. CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Spain

    Josep Peñuelas

  3. CREAF, Cerdanyola del Vallѐs, Spain

    Josep Peñuelas

  4. Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China

    Shilong Piao

Authors
  1. Dan Liu
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  4. Shilong Piao
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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.

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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.

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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.

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Liu, D., Wang, T., Peñuelas, J. et al. Drought resistance enhanced by tree species diversity in global forests. Nat. Geosci. 15, 800–804 (2022). https://doi.org/10.1038/s41561-022-01026-w

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