Abstract

Soils harbour some of the most diverse microbiomes on Earth and are essential for both nutrient cycling and carbon storage. To understand soil functioning, it is necessary to model the global distribution patterns and functional gene repertoires of soil microorganisms, as well as the biotic and environmental associations between the diversity and structure of both bacterial and fungal soil communities1,2,3,4. Here we show, by leveraging metagenomics and metabarcoding of global topsoil samples (189 sites, 7,560 subsamples), that bacterial, but not fungal, genetic diversity is highest in temperate habitats and that microbial gene composition varies more strongly with environmental variables than with geographic distance. We demonstrate that fungi and bacteria show global niche differentiation that is associated with contrasting diversity responses to precipitation and soil pH. Furthermore, we provide evidence for strong bacterial–fungal antagonism, inferred from antibiotic-resistance genes, in topsoil and ocean habitats, indicating the substantial role of biotic interactions in shaping microbial communities. Our results suggest that both competition and environmental filtering affect the abundance, composition and encoded gene functions of bacterial and fungal communities, indicating that the relative contributions of these microorganisms to global nutrient cycling varies spatially.

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Acknowledgements

The authors thank I. Liiv for technical and laboratory assistance; S. Waszak for comments on the manuscript; Y. P. Yuan and A. Glazek for bioinformatics support and A. Holm Viborg for help in retrieving the CAZY database. We also thank V. Benes, R. Hercog and other members of the EMBL GeneCore (Heidelberg), who provided assistance and facilities for sequencing. This study was funded by the Estonian Research Council (grants PUT171, PUT1317, PUT1399, IUT20-30, MOBERC, KIK, RMK, ECOLCHANGE), the Swedish Research Council (VR grant 2017-05019), Royal Swedish Academy of Sciences, Helge Axson Johnsons Stiftelse, EU COST Action FP1305 Biolink (STSM grant), Netherlands Organization for Scientific research (vidi grant 016.161.318), EMBL European Union’s Horizon 2020 Research and Innovation Programme (#686070; DD-DeDaF) and Marie Skłodowska-Curie (600375).

Reviewer information

Nature thanks S. Tringe and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Author notes

    • Sofia K. Forslund

    Present address: Experimental and Clinical Research Center, a cooperation of Charité-Universitätsmedizin and the Max-Delbrück Center, Berlin, Germany

    • Sofia K. Forslund

    Present address: Max Delbrück Centre for Molecular Medicine, Berlin, Germany

    • Johan Bengtsson-Palme

    Present address: Wisconsin Institute of Discovery, University of Wisconsin-Madison, Madison, WI, USA

    • Jaime Huerta-Cepas

    Present address: Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Madrid, Spain

    • Shinichi Sunagawa

    Present address: Department of Biology, Institute of Microbiology, ETH Zurich, Zurich, Switzerland

  1. These authors contributed equally: Mohammad Bahram, Falk Hildebrand

Affiliations

  1. Department of Botany, Institute of Ecology and Earth Sciences, University of Tartu, Tartu, Estonia

    • Mohammad Bahram
    • , Sten Anslan
    • , Helery Harend
    • , Mari Pent
    •  & Sergei Põlme
  2. Department of Organismal Biology, Evolutionary Biology Centre, Uppsala University, Uppsala, Sweden

    • Mohammad Bahram
    • , Jennifer L. Anderson
    •  & Martin Ryberg
  3. Department of Ecology, Swedish University of Agricultural Sciences, Uppsala, Sweden

    • Mohammad Bahram
  4. Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany

    • Falk Hildebrand
    • , Sofia K. Forslund
    • , Luis Pedro Coelho
    • , Jaime Huerta-Cepas
    • , Shinichi Sunagawa
    •  & Peer Bork
  5. Environmental Biology Department, Institute of Environmental Sciences, CML, Leiden University, Leiden, The Netherlands

    • Nadejda A. Soudzilovskaia
    •  & Peter M. Bodegom
  6. Department of Infectious Diseases, Institute of Biomedicine, The Sahlgrenska Academy, University of Göteborg, Göteborg, Sweden

    • Johan Bengtsson-Palme
  7. Centre for Antibiotic Resistance research (CARe), University of Göteborg, Göteborg, Sweden

    • Johan Bengtsson-Palme
  8. Braunschweig University of Technology, Zoological Institute, Braunschweig, Germany

    • Sten Anslan
  9. Bioinformatics Group, Wageningen University, Wageningen, The Netherlands

    • Marnix H. Medema
  10. Center for Conservation Biology, University of California, Riverside, Riverside, CA, USA

    • Mia R. Maltz
  11. Section for Genetics and Evolutionary Biology (Evogene), Department of Biosciences, University of Oslo, Oslo, Norway

    • Sunil Mundra
  12. Biodiversity Unit, Department of Biology, Ecology building, Lund University, Lund, Sweden

    • Pål Axel Olsson
  13. Natural History Museum, University of Tartu, Tartu, Estonia

    • Leho Tedersoo
  14. Max Delbrück Centre for Molecular Medicine, Berlin, Germany

    • Peer Bork
  15. Department of Bioinformatics, University of Würzburg, Würzburg, Germany

    • Peer Bork
  16. Experimental and Clinical Research Center, a cooperation of Charité-Universitätsmedizin and the Max-Delbrück Center, Berlin, Germany

    • Sofia K. Forslund
  17. Max Delbrück Centre for Molecular Medicine, Berlin, Germany

    • Sofia K. Forslund

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Contributions

M.B., L.T. and P.B. conceived the project. L.T. supervised DNA extraction and sequencing. M.B., F.H., S.K.F., J.L.A., M.R. and P.M.B. designed and supervised the data analyses. F.H. designed and performed bioinformatics analysis. N.A.S. and P.A.O. performed biomass analysis. S.K.F., S.M., M.P., S.A., H.H., S.P., M.R.M., S.S. and L.T. contributed data. M.B., F.H., S.K.F., J.L.A., P.M.B., S.A., J.B.-P., M.H.M., L.P.C. and J.H.-C. performed the data analyses. M.B. wrote the first draft of the manuscript with input from F.H., S.F., J.L.A., L.T. and P.B. All authors contributed to data interpretation and editing of the paper.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Mohammad Bahram or Leho Tedersoo or Peer Bork.

Extended data figures and tables

  1. Extended Data Fig. 1 Distribution of topsoil samples and diversity patterns of phyla.

    a, A map of samples used for metagenomic and metabarcoding analysis. Colours indicate biomes as shown in the legend. Desert samples were only used in metabarcoding analysis and were excluded in comparative analysis of functional and taxonomic patterns. Black symbols refer to samples from an independent soil dataset (145 topsoil samples; Supplementary Table 1) that were used for validation of our results. b, Relationship between the diversity of major microbial phyla (classes for Proteobacteria) and environmental variables across the global soil samples (n = 197 biologically independent samples). Only regression lines for significant relationships after Bonferroni correction are shown. Diversity was measured using Hellinger-transformed matrices on the basis of inverse Simpson index. Latitude, absolute latitude.

  2. Extended Data Fig. 2 Contrasting microbial structure and function in major terrestrial biomes.

    a–d, The average total biomass normalized to organic carbon (a, n = 152 biologically independent samples) as well as richness (b), diversity (c) and phylogenetic structure including NRI and NTI (d) (n = 188 biologically independent samples) of fungi and bacteria across samples categorized into major terrestrial biomes, including tropical (moist and dry tropical forests and savannahs), temperate (coniferous and deciduous forests, grasslands and shrublands, and Mediterranean biomes) and boreal–arctic ecosystems. ei, Relative abundance of major phyla (n = 188 biologically independent samples) and functional categories (n = 189 biologically independent samples) across biomes: bacterial phyla (classes for Proteobacteria) and archaea (e); fungal classes (f); functional categories of bacteria (g); functional categories of fungi (h); bacterial KEGG metabolic pathways (i). Biomass was measured on the basis of PLFA analysis. Different letters denote significant differences between groups (shown in the legend) at the 0.05 probability level on the basis of Kruskal–Wallis tests corrected for multiple testing. Additional details for these comparisons are presented in Supplementary Table 14. Taxonomic and gene functional diversity indices were calculated on the basis of inverse Simpson index. Data are mean ± s.d.

  3. Extended Data Fig. 3 The significant decrease in the bacterial/fungal biomass ratio with increasing latitude is driven by the joint effect of climate and soil fertility.

    a, The second order polynomial relationship between absolute latitude and the total biomass of bacteria (n = 152 biologically independent samples). b, The relationship between absolute latitude and the total biomass of fungi. c, The relationship between absolute latitude and the bacterial/fungal biomass ratio. d–f, The relationship between bacterial/fungal biomass ratio and MAP, MAT and C/N, as the main correlated environmental variables with bacterial/fungal biomass ratio. Linear regression analysis (Pearson’s correlation) was used in bf (n = 152 biologically independent samples). g, Pairwise Spearman’s correlation matrix of biotic and abiotic variables in soil. h, Direct and indirect relationships and directionality between variables determined from best-fitting structural equation model. Determination coefficients (R2) are given for biomass and diversity factors (see Supplementary Table 5 for more details). Goodness of fit: bacteria, χ2 = 15.37, degrees of freedom  = 11, P = 0.166; RMSEA = 0.041, PCLOSE = 0.573, n = 189; fungi, χ2 = 7.74, degrees of freedom = 12, P = 0.805; RMSEA = 0.00, PCLOSE = 0.970, n = 189). Biomass (nmol g−1) was measured on the basis of PLFA analysis. pH, soil pH representing soil pH and its quadratic term; ∂15N, nitrogen stable isotope signature; ∂13C, carbon stable isotope signature; PET, potential of evapotranspiration; Fire, time from the last fire disturbance; NPP, net primary productivity.

  4. Extended Data Fig. 4 The environment has a stronger effect on bacterial taxa and functions than on those of fungi.

    Correlation and best random forest model for major taxonomic (a, b; n = 188 biologically independent samples) and functional (c, d; n = 189 biologically independent samples) categories of bacteria (a, c) and fungi (b, d) in the global soil samples (n = 189 biologically independent samples). a, Relative abundance of major 16S-based bacterial phyla (class for Proteobacteria). b, Relative abundance of ITS-based fungal classes. c, d, Major orthologous gene categories of bacteria (c) and fungi (d). For variable selection and estimating predictability, the random forest machine-learning algorithm was used. Circle size represents the variable importance (that is, decrease in the prediction accuracy (estimated with out-of-bag cross-validation)) as a result of the permutation of a given variable. Colours represent Spearman correlations. pH, soil pH.

  5. Extended Data Fig. 5 Niche differentiation between bacteria and fungi is probably related to precipitation and soil pH.

    Contrasting effect of pH and MAP on bacterial (16S; left column) and fungal (18S; right column) taxonomic (n = 188 biologically independent samples) and gene functional (n = 189 biologically independent samples) diversity in the global soil samples. a, b, Relationship between soil pH and taxonomic diversity of bacteria and fungi. c, d, Relationship between soil pH and gene functional diversity of bacteria and fungi. e, f, Relationship between MAP and taxonomic diversity of bacteria and fungi. g, h, Relationship between MAP and gene functional diversity of bacteria and fungi. Lines represent regression lines of best fit. The choice of degree of polynomial was determined by a goodness of fit. Colours denote biomes as indicated in the legend. Taxonomic and gene functional diversity indices were calculated on the basis of inverse Simpson index. i–l, NMDS plots of trends in taxonomic (16S and 18S datasets) and gene functional composition (orthologous groups from metagenomes) of bacteria and fungi on the basis of Bray–Curtis dissimilarity. i, Taxonomic composition of bacteria (16S). j, Taxonomic composition of fungi (18S). k, Gene functional composition of bacteria. l, Gene functional composition of fungi. i, Colours denote biomes as indicated in the legend. Vectors are the prominent environmental drivers fitted onto ordination.

  6. Extended Data Fig. 6 Fungal biomass is significantly related to the relative abundance of ARGs.

    a, Increase in fungal biomass is related to ARG relative abundance. b, Bacterial biomass is unrelated to the relative abundance of ARGs. c, ARG relative abundance is inversely correlated with the bacterial/fungal biomass ratio. Biomass (nmol g−1) was measured on the basis of PLFA analysis. Spearman’s correlation was used (n = 152 biologically independent samples).

  7. Extended Data Fig. 7 Topsoil and ocean bacterial phylogenetic diversity is negatively correlated with the abundance of ARGs.

    a, b, Spearman’s correlation between the relative abundance of ARGs and bacterial phylogenetic diversity (Faith’s index) in soil (a, n = 188 biologically independent samples) and the oceans (b, n = 139 biologically independent samples) at the global scale. Similar trends were observed for richness (r = −0.219, P = 0.007 and r = −0.659, P < 10−15 in soil and ocean, respectively). c, Global map of observed bacterial phylogenetic diversity (Faith’s index) at the sampled sites. Note that hotspots of bacterial diversity do not correspond to ARG hotspots (See Extended Data Fig. 8).

  8. Extended Data Fig. 8 Relative abundance of ARGs within and between terrestrial and oceanic ecosystems.

    a, Heat map of the observed relative abundance of ARGs at the global scale. Squares and circles correspond to soil and to ocean samples, respectively. ARG abundance is given on three relative scales for these three datasets. b, Relative abundance of ARGs in ocean samples (across depths) declines with the distance from land (n = 139 biologically independent samples), a pattern that was significant at two water depths, including surface (red) and deep chlorophyll maximum (DCM; green), but not at mesopelagic (blue). Spearman’s correlation statistics for specified comparisons are given in the legends. Dotted lines display Spearman’s correlations across the whole dataset and within the three depth categories, respectively. n, number of biologically independent samples.

  9. Extended Data Fig. 9 Relative abundance of ARGs in both ocean and topsoil samples can be modelled by the relative abundance of fungi and fungus-like protists.

    a, b, Correlation circle indicating the relationships among fungal classes and the relative abundance of ARGs as well as the first two PLS components in soil (a) and ocean (b). Length and direction of vectors indicate the strength and direction of correlations. Percentages show the variation explained by each PLS component. c, d, Linear (Pearson) correlations between observed and modelled ARG relative abundance on the basis of the relative abundance of fungal taxa in soil (c) and ocean (d). The two principal axes were chosen on the basis of leave-one-out cross-validation (LOOCV) and explained 40% (LOOCV: R2 = 0.381) and 71% (LOOCV: r2 = 0.684) of the variation of the relative abundance of ARGs in soil and the oceans, respectively. Only taxa significantly associated with the relative abundance of ARGs are shown. Cross-validation and LASSO regression confirmed this result. Soil dataset: r = 0.619, RMSE =  = 10−9, n = 189 biologically independent samples; ocean dataset, r = 0.832, RMSE = 10−9, n = 139 biologically independent samples.

  10. Extended Data Fig. 10 Fungal classes are among the main taxa associated with the relative abundance, diversity and richness of ARGs in different habitats.

    a, b, Heat map derived from sPLS analysis showing correlation of total relative abundance, richness and diversity of ARGs to that of the main taxonomic classes in soil (a) and ocean (b) metagenomes (see also the Supplementary Discussion for analogous results in previously published soil (from grasslands, deserts agricultural soils) as well as human skin and gut samples). For statistical details and significance, see Supplementary Table 8. c, d, Heat maps showing correlation of total relative abundance of ARGs to that of the main eukaryotic and prokaryotic taxa in soil (c) and the ocean (d) on the basis of sPLS regression analysis. All matrices were normalized to library size and Hellinger transformation. Fungal and fungal-like classes are shown in bold text. See Supplementary Table 15 for ARG gene letter abbreviations.

Supplementary information

  1. Supplementary Information

    This file contains Supplementary Results and Discussion and Supplementary References.

  2. Reporting Summary

  3. Supplementary Tables

    This zipped file contains Supplementary Tables 1-19 and a Supplementary Table Guide.

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https://doi.org/10.1038/s41586-018-0386-6

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