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Life history strategies of soil bacterial communities across global terrestrial biomes

Nature Microbiology volume 8, pages 2093–2102 (2023)Cite this article

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Matters Arising to this article was published on 13 May 2024

Abstract

The life history strategies of soil microbes determine their metabolic potential and their response to environmental changes. Yet these strategies remain poorly understood. Here we use shotgun metagenomes from terrestrial biomes to characterize overarching covariations of the genomic traits that capture dominant life history strategies in bacterial communities. The emerging patterns show a triangle of life history strategies shaped by two trait dimensions, supporting previous theoretical and isolate-based studies. The first dimension ranges from streamlined genomes with simple metabolisms to larger genomes and expanded metabolic capacities. As metabolic capacities expand, bacterial communities increasingly differentiate along a second dimension that reflects a trade-off between increasing capacities for environmental responsiveness or for nutrient recycling. Random forest analyses show that soil pH, C:N ratio and precipitation patterns together drive the dominant life history strategy of soil bacterial communities and their biogeographic distribution. Our findings provide a trait-based framework to compare life history strategies of soil bacteria.

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Fig. 1: Global trait dimensions of soil bacteria metagenomes.
Fig. 2: The global life history strategies of soil bacterial communities.
Fig. 3: Hypothesized role of C, R and S traits in shaping the life history strategy observed at the community level and associated environmental gradients.
Fig. 4: Environmental control and global-scale projection of bacterial communities’ coordinates along MCOA dimensions 1 and 2.

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Data availability

The five CAT databases used to build the trait dimensions and the associated environmental variables are available on the Figshare repository at https://doi.org/10.6084/m9.figshare.22620025. All the original sequences are available in the European Bioinformatics Institute Sequence Read Archive database: soil metagenomes, accession numbers PRJEB18701 (ERP020652); 16S metabarcoding sequences, accession numbers PRJEB19856 (ERP021922).

Code availability

Access to the code used in the analyses done for this research is available by request to the corresponding author.

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Acknowledgements

We thank L. Tedersoo and P. Bork who conceived and supervised the acquisition of the global dataset used in this study with M. Bahram and F. Hildebrand; all their collaborators who contributed to this global data acquisition effort; A. Larkin and L. Ustick for guidance in the bioinformatic analysis conducted in this study. G.P., S.D.A., J.B.H.M., K.K.T. and A.C.M. were supported by the US Department of Energy, Office of Science, Office of Biological and Environmental Research grants DE-SC0016410 and DE-SC0020382. F.H. was supported by the European Research Council H2020 StG (erc-stg-948219, EPYC) and by the Biotechnology and Biological Sciences research Council (BBSrC) Institute Strategic Program (ISP) Food Microbiome and Health BB/X011054/1 and its constituent project BBS/E/F/000PR13631; Earlham DECODE ISP BBX011089/1 and its constituent work package BBS/E/ER/230002A.

Author information

Authors and Affiliations

  1. Department of Earth System Science, University of California, Irvine, Irvine, CA, USA

    Gabin Piton, Steven D. Allison & Adam C. Martiny

  2. Eco&Sols, University Montpellier, CIRAD, INRAE, Institut Agro, IRD, Montpellier, France

    Gabin Piton

  3. Department of Ecology and Evolutionary Biology, University of California, Irvine, Irvine, CA, USA

    Steven D. Allison, Jennifer B. H. Martiny, Kathleen K. Treseder & Adam C. Martiny

  4. Department of Ecology, Swedish University of Agricultural Sciences, Uppsala, Sweden

    Mohammad Bahram

  5. Institute of Ecology and Earth Sciences, University of Tartu, Tartu, Estonia

    Mohammad Bahram

  6. Gut Microbes and Health, Quadram Institute Bioscience, Norwich Research Park, Norwich, Norfolk, UK

    Falk Hildebrand

  7. Digital Biology, Earlham Institute, Norwich Research Park, Norwich, Norfolk, UK

    Falk Hildebrand

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  1. Gabin Piton
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Contributions

Data collection was designed and supervised by M.B. Initial bioinformatics analysis to obtain functional genes abundance tables (eggNOG, KEGG, SEED, CAZy) was designed and performed by F.H. The idea of this new analysis was conceived by G.P. with inputs from A.C.M., S.D.A, J.B.H.M. and K.K.T. New quantification of genomic traits, Unifrac and data analyses were performed by G.P. Writing of the first draft and subsequent editing was performed by G.P. with inputs from all co-authors.

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Correspondence to Gabin Piton.

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Extended data

Extended Data Fig. 1

Stress plot representing the % of variation of the global dataset captured by each dimension of the MCOA.

Extended Data Fig. 2 Variable contributions to the third trait dimension of the multiple co-inertia analysis (MCOA).

The MCOA summarizes in a common structure the information shared by 5 community aggregated trait (CAT) databases (Genomic trait, CAZy, eggNOG, SEED and KEGG). Only the most important variables with significant correlation (p < 0.001) with each dimension are reported in this figure.

Extended Data Fig. 3 Correlations between average genome size (a,b) or average rrn gene copy number (c,d) and the coordinates along dimensions 1 and 2 of the MCOA.

The P value indicates the significance of the regression slope obtained using a t-test. Shade represents the estimated 95% confidence interval. Color gradients follow MCOA dimensions and match with Figs. 1 and 3 in the main text.

Extended Data Fig. 4 Correlations between MCOA dimensions (MCOA1 and MCOA2) and mapping coverages on the 3 general databases (eggNOG, KEGG, SEED) used in this study.

The P value indicates the significance of the regression slope obtained using a t-test.

Extended Data Fig. 5 Environmental drivers of the bacterial community trait dimensions.

Environmental variable importances are represented as the mean decrease in mean square error (%MSE) and R squared in random forest models predicting MCOA Dimension 1 (a) and 2 (b). Bar colours indicate which end of the dimension (Figs. 1 and 3) is positively correlated with the variable.

Extended Data Fig. 6 Correlations between local trait dimension observations and global spatial prediction.

Correlations between local observations of bacterial community positions along the first and second trait dimensions from the MCOA (Figs. 1–2) and the predicted value of the global map cell (Fig. 4) corresponding to where the local observations have been done. Dashed line represents a 1:1 correlation. The P value indicates the significance of the regression slope obtained using a t-test. Shade represents the estimated 95% confidence interval. Color gradients follow MCOA dimension and match with Figs. 1,2 and 4 in the main text.

Extended Data Fig. 7 Correlation between phylogenetic distance (Unifrac metric) and functional distance (Euclidian distance in MCOA space using coordinates of the two principal dimensions).

Correlation for all samples (a) and restricted to samples with average genome size below (b) and above (c) its median value in the dataset. The P value indicates the significance of the regression slope obtained using a t-test.

Extended Data Fig. 8 Dataset distribution and environmental coverage.

a. Sample localisations and associated biomes b, c. Comparison between global range of environmental variables from the Atlas of the Biosphere (b) and the environmental coverage of dataset (n = 128) used in this study (c). Boxplot elements: Center line=median; box limits=upper and lower quartiles; whiskers = 1.5x interquartile range; points=outliers. World map was done with rnaturalearth R package (https://github.com/ropensci/rnaturalearth).

Extended Data Fig. 9 Environmental coverage comparison between the database used in this study from Barham et al.2 and databases from the main metagenomes repositories (MG-RAST and IMG:M).

N corresponds to the number of metagenomes available in each database. MAT=Mean Annual Temperature, AP=Annual Precipitation. Boxplot elements: Center line=median; box limits=upper and lower quartiles; whiskers = 1.5× interquartile range; points=outliers.

Extended Data Table 1 Life history traits used in this study
Full size table

Supplementary information

Reporting Summary

Supplementary Table 1

List of substrate specificity of glycoside hydrolases (GHs) and auxiliary activities (AA) enzymes from the CAZy database.

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Piton, G., Allison, S.D., Bahram, M. et al. Life history strategies of soil bacterial communities across global terrestrial biomes. Nat Microbiol 8, 2093–2102 (2023). https://doi.org/10.1038/s41564-023-01465-0

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