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Water availability creates global thresholds in multidimensional soil biodiversity and functions

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Abstract

Soils support an immense portion of Earth’s biodiversity and maintain multiple ecosystem functions which are essential for human well-being. Environmental thresholds are known to govern global vegetation patterns, but it is still unknown whether they can be used to predict the distribution of soil organisms and functions across global biomes. Using a global field survey of 383 sites across contrasting climatic and vegetation conditions, here we showed that soil biodiversity and functions exhibited pervasive nonlinear patterns worldwide and are mainly governed by water availability (precipitation and potential evapotranspiration). Changes in water availability resulted in drastic shifts in soil biodiversity (bacteria, fungi, protists and invertebrates) and soil functions including plant–microbe interactions, plant productivity, soil biogeochemical cycles and soil carbon sequestration. Our findings highlight that crossing specific water availability thresholds can have critical consequences for the provision of essential ecosystem services needed to sustain our planet.

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Fig. 1: Map showing locations of the 383 soil sampling sites included in this study.
Fig. 2: Nonlinear relationships between water availability and the diversity of soil taxa as well as other environmental factors.
Fig. 3: Relationships between soil functions and water availability.

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Data availability

All the materials, raw data and protocols used in the article are available upon request and without restriction and all data that support the main findings of this study will be made publicly available in Figshare76 upon publication.

Code availability

The data in this study were analysed with publicly available tool packages in R and the figures were produced with R. The R code used in the analysis presented in this paper is available in Figshare76.

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Acknowledgements

Y.F. is supported by National Natural Science Foundation of China (42177297) and CAS Strategic Priority Research Program (XDA28010302). M.D.-B. acknowledges support from the Spanish Ministry of Science and Innovation for the I+D+i project PID2020-115813RA-I00 funded by MCIN/AEI/10.13039/501100011033. M.D.-B. is also supported by a project of the Fondo Europeo de Desarrollo Regional (FEDER) and the Consejería de Transformación Económica, Industria, Conocimiento y Universidades of the Junta de Andalucía (FEDER Andalucía 2014-2020 Objetivo temático ‘01—Refuerzo de la investigación, el desarrollo tecnológico y la innovación’) associated with the research project P20_00879 (ANDABIOMA). F.T.M. is supported by Generalitat Valenciana grant CIDEGENT/2018/041 and the Horizon Europe programme of the European Union (SOILGUARD, grant agreement no. 101000371). M.B. acknowledges funding from Spanish Ministry of Science and Innovation through a Ramón y Cajal Fellowship (no. RYC2021-031797-I). C.C. is supported by the European Commission under the Marie Sklodowska-Curie grant agreement no. 702057 (DRYLIFE). The survey of dryland areas was supported by the European Research Council (BIODESERT project, grant agreement no. 647038).

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Authors and Affiliations

  1. College of Chemical Engineering, Nanjing Forestry University, Nanjing, China

    Jianwei Zhang & Youzhi Feng

  2. State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, China

    Jianwei Zhang & Youzhi Feng

  3. Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, Nanjing, China

    Youzhi Feng

  4. Instituto Multidisciplinar para el Estudio del Medio ‘Ramón Margalef’, Universidad de Alicante, Alicante, Spain

    Fernando T. Maestre

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

    Fernando T. Maestre

  6. Department of Environment Systems Science, Institute of Integrative Biology, ETH Zürich, Zürich, Switzerland

    Miguel Berdugo

  7. Depatamento de Biodiversidad, Ecología y Evolución, Universidad Complutense de Madrid, Madrid, Spain

    Miguel Berdugo

  8. Global Centre for Land-Based Innovation, Western Sydney University, Penrith, New South Wales, Australia

    Juntao Wang & Brajesh K. Singh

  9. Hawkesbury Institute for the Environment, Western Sydney University, Richmond, New South Wales, Australia

    Juntao Wang & Brajesh K. Singh

  10. State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China

    Juntao Wang

  11. Department of Ecological and Biological Sciences, University of Tuscia, Viterbo, Italy

    Claudia Coleine

  12. Departamento de Sistemas Físicos, Químicos y Naturales, Universidad Pablo de Olavide, Sevilla, Spain

    Tadeo Sáez-Sandino & Laura García-Velázquez

  13. Laboratorio de Biodiversidad y Funcionamiento Ecosistémico, Instituto de Recursos Naturales y Agrobiología de Sevilla (IRNAS), CSIC, Sevilla, Spain

    Manuel Delgado-Baquerizo

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Contributions

M.D.-B., M.B. and Y.F. developed the original ideas. F.T.M., M.D.-B. and B.K.S. provided the original data. M.D.-B., F.T.M., B.K.S., T.S.-S., L.G.-V. and J.W. contributed to laboratory analyses. J.Z., Y.F., M.B., M.D.-B. and C.C. conducted statistical analyses. J.Z., Y.F. and M.D.-B. wrote the first draft of the manuscript and all authors contributed substantially to revisions.

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Correspondence to Youzhi Feng or Manuel Delgado-Baquerizo.

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Extended data

Extended Data Fig. 1 Response patterns of the diversity of soil taxa to environmental variables.

Regression models represent the linear regressions before and after the average threshold if a statistical threshold exists, or linear regression if otherwise. The vertical lines and numbers in the plot denoted the detected thresholds. MAT mean annual temperature; PSEA and TSEA, seasonality of precipitation and temperature. Latitude uses its absolute values to denote the distance from the equator.

Extended Data Fig. 2 Differential slope at both sides of the environmental threshold.

Violin diagrams showing the bootstrapped (200 times) slope values of the regressions existing each side of environmental threshold. The data was standardized so that slope values are scaled to the same extent when displayed as above. Asterisks indicate a significant difference in regression slopes when conducting an unpaired two-sided Mann–Whitney U test between before and after the threshold where: * = P value < 0.05; ** = P value < 0.01; *** = P value < 0.001; **** = P value < 0.0001, ns = not significant.

Extended Data Fig. 3 The importance of environmental variables that are associated with nonlinear shifts in soil biodiversity.

Average variation degree (AVD) estimates the variation of the detected threshold values in each environmental among multiple soil organisms. A lower AVD suggests a more conservative environmental factor, and thus are assumed to be more important, in determining the nonlinear behaviour of soil biodiversity.

Extended Data Fig. 4 Importance of environmental factors in driving the distribution of soil biodiversity.

We used a random forest algorithm to estimate the importance of environmental factors and presented their importance value as an increase in mean squared error (IncMSE, %). The numbers in the boxes represent the variation explained by the model, and asterisk above bars denotes a significant influential factor at the significance level of P value < 0.05.

Extended Data Fig. 5 Nonlinear relationships between water availability and climatic seasonality.

The rest of the legend is the same as in Fig. 2 in the main text.

Extended Data Fig. 6 Response patterns of soil functions to environmental variables.

Regression models represent the linear regressions before and after the average threshold if a statistical threshold exists, or linear regression if otherwise. The vertical lines and numbers in the plot denoted the detected thresholds. Soil WHC, soil water-holding capacity; Soil AP, soil available phosphorus. Some variables, such as soil ammonium, nitrate and AP, chitin degradation, soil C, soil saprobes, mutualism, and pathogen control, are log-transformed to increase data normality.

Extended Data Fig. 7 Differences in the slope at both sides of the threshold identified.

Violin diagrams showing bootstrapped values of the slope of the two regressions at each side of every environmental threshold found in the study. Rest of legend is the same as in Extended Data Fig. 2.

Extended Data Fig. 8 Importance of environmental factors in driving the distribution of soil functions.

The legend is the same as Extended Data Fig. 4.

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Zhang, J., Feng, Y., Maestre, F.T. et al. Water availability creates global thresholds in multidimensional soil biodiversity and functions. Nat Ecol Evol 7, 1002–1011 (2023). https://doi.org/10.1038/s41559-023-02071-3

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