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Global hotspots for soil nature conservation

Abstract

Soils are the foundation of all terrestrial ecosystems1. However, unlike for plants and animals, a global assessment of hotspots for soil nature conservation is still lacking2. This hampers our ability to establish nature conservation priorities for the multiple dimensions that support the soil system: from soil biodiversity to ecosystem services. Here, to identify global hotspots for soil nature conservation, we performed a global field survey that includes observations of biodiversity (archaea, bacteria, fungi, protists and invertebrates) and functions (critical for six ecosystem services) in 615 composite samples of topsoil from a standardized survey in all continents. We found that each of the different ecological dimensions of soils—that is, species richness (alpha diversity, measured as amplicon sequence variants), community dissimilarity and ecosystem services—peaked in contrasting regions of the planet, and were associated with different environmental factors. Temperate ecosystems showed the highest species richness, whereas community dissimilarity peaked in the tropics, and colder high-latitudinal ecosystems were identified as hotspots of ecosystem services. These findings highlight the complexities that are involved in simultaneously protecting multiple ecological dimensions of soil. We further show that most of these hotspots are not adequately covered by protected areas (more than 70%), and are vulnerable in the context of several scenarios of global change. Our global estimation of priorities for soil nature conservation highlights the importance of accounting for the multidimensionality of soil biodiversity and ecosystem services to conserve soils for future generations.

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Fig. 1: Current distribution of global soil ecological hotspots.
Fig. 2: Current distribution of the priority areas for global soil nature conservation.
Fig. 3: Predicted changes in the total area of soil nature conservation priorities under four different future shared socio-economic pathways.

Data availability

All of the materials, raw data and protocols used in this Article are available upon request and without restriction, and all data are publicly available at https://doi.org/10.6084/m9.figshare.20221713.

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Acknowledgements

We thank all of the researchers who were involved in the collection of field data. This project received funding from the British Ecological Society (agreement LRA17\1193; MUSGONET). C.A.G. and N.E. were funded by DFG–FZT 118, 202548816; C.A.G. was supported by FCT-PTDC/BIA-CBI/2340/2020; M.D.-B. was supported by RYC2018-025483-I, PID2020-115813RA-I00\MCIN/AEI/10.13039/501100011033 and P20_00879. M.A.M.-M. and S.A. were funded by FONDECYT 1181034 and ANID-PIA-Anillo INACH ACT192057. J.D. and A.R. acknowledge support from IF/00950/2014, 2020.03670.CEECIND, SFRH/BDP/108913/2015 and UIDB/04004/2020. Y.-R.L. was supported by 2662019PY010 from the FRFCU. L.T. was supported by the ESF grant PRG632. F.B. and J.L.M. were supported by i-LINK+2018 (LINKA20069) funded by CSIC. C.T.-D. was supported by the Grupo de Biodibersidad & Cambio Global UBB–GI 170509/EF. C.P. was supported by the EU H2020 grant agreement 101000224. H.C. was supported by NSFC32101335, FRFCU2412021QD014 and CPSF2021M690589. J.P.V. was supported by DST (DST/INT/SL/P-31/2021) SERB (EEQ/2021/001083) and BHU-IoE (6031).

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Authors

Contributions

C.A.G. and M.D.-B. developed the original idea of the analyses presented in the manuscript. M.D.-B. designed the field study and wrote the grant that funded the work. Field data were collected by M.B., S.A., F.D.A., A.R.B., J.L.B.-P., A.d.l.R., J.D., T.G., J.G.I., Y.-R.L., T.P.M., S.M., M.A.M.-M., A.M., T.U.N., G.F.P.-B., C.P., J.P.V., A. Rey, A. Rodríguez, A.L.T., C.T.-D., P.T., L.W., Jianyong Wang, E.Z., X.Z., X.-Q. Z. and M.D.-B. Laboratory analyses were done by M.D.-B., H.C., F.B., J.L.M., S.P. and L.T. Statistical analyses, mapping and ecological modelling were done by C.A.G., M.D.-B. and M.B. Bioinformatic analyses were done by B.K.S. and Juntao Wang. The manuscript was written by C.A.G. and M.D-B. and edited by N.E. and D.J.E., with contributions from all co-authors.

Corresponding authors

Correspondence to Carlos A. Guerra or Manuel Delgado-Baquerizo.

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Nature thanks Peter de Ruiter, Ruhollah Taghizadeh and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Results of the random forest analysis to identify the main environmental factors associated with soil biodiversity and ecosystem services.

Random forest analyses were done using the rfPermute function of the R package with the same name. MSE, mean square error.

Extended Data Fig. 2 Spearman correlations between environmental factors and soil biodiversity and ecosystem services.

N in Supplementary Table 1.

Extended Data Fig. 3 Spearman correlations between soil biodiversity and ecosystem services.

Total n-values in Supplementary Table 1.

Extended Data Fig. 4 Hotspot and coldspot maps for alpha diversity (left) and community dissimilarity (right).

The Getis-Ord Gi* statistic was calculated for each location (0.25x0.25 deg pixel size) in the dataset 1–3. The resulting z-scores were used to estimate if a given location has statistically high or low values and if these values are spatially clustered. This is done by assessing each location within the context of neighbouring locations. Statistically significant positive z-scores indicate clustering of high values (hotspot) and statistically significant negative z-scores the clustering of low values (coldspot). Values are plotted for both positive (hotspots) and negative (coldspots) 99%, 95%, and 90% confidence levels.

Extended Data Fig. 5 Hotspot and coldspot maps for ecosystem services: soil carbon, fertility, organic matter decomposition, pest control, mutualism and water retention.

The Getis-Ord Gi* statistic was calculated for each location (0.25x0.25 deg pixel size) in the dataset 1–3. The resulting z-scores were used to estimate if a given location has statistically high or low values and if these values are spatially clustered. This is done by assessing each location within the context of neighbouring locations. Statistically significant positive z-scores indicate clustering of high values (hotspot) and statistically significant negative z-scores the clustering of low values (coldspot). Values are plotted for both positive (hotspots) and negative (coldspots) 99%, 95%, and 90% confidence levels.

Supplementary information

Supplementary Information

This file includes all supplementary figures (S1–S17) and supplementary tables (S1–S15) that support our paper.

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Guerra, C.A., Berdugo, M., Eldridge, D.J. et al. Global hotspots for soil nature conservation. Nature 610, 693–698 (2022). https://doi.org/10.1038/s41586-022-05292-x

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