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Biogeographic patterns and drivers of soil viromes

Nature Ecology & Evolution volume 8, pages 717–728 (2024)Cite this article

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Abstract

Viruses are crucial in shaping soil microbial functions and ecosystems. However, studies on soil viromes have been limited in both spatial scale and biome coverage. Here we present a comprehensive synthesis of soil virome biogeographic patterns using the Global Soil Virome dataset (GSV) wherein we analysed 1,824 soil metagenomes worldwide, uncovering 80,750 partial genomes of DNA viruses, 96.7% of which are taxonomically unassigned. The biogeography of soil viral diversity and community structure varies across different biomes. Interestingly, the diversity of viruses does not align with microbial diversity and contrasts with it by showing low diversity in forest and shrubland soils. Soil texture and moisture conditions are further corroborated as key factors affecting diversity by our predicted soil viral diversity atlas, revealing higher diversity in humid and subhumid regions. In addition, the binomial degree distribution pattern suggests a random co-occurrence pattern of soil viruses. These findings are essential for elucidating soil viral ecology and for the comprehensive incorporation of viruses into soil ecosystem models.

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Fig. 1: Overview of the global soil virome database.
Fig. 2: Viral community properties across biomes and geography.
Fig. 3: Drivers of viral community assembly.
Fig. 4: Random co-occurrence pattern.
Fig. 5: Map of viral α-diversity indices (Shannon index) at 0.01° resolution as modelled using the random-forest model.

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Data availability

All GSV sequences, GSV database viral information and map TIFF files can be downloaded from Zenodo at https://zenodo.org/records/10463783. The interactive GSV map is available at https://bmalab.shinyapps.io/global_soil_viromes.

Code availability

Scripts used in this manuscript are available on microbma GitHub under project ‘global soil viromes’ (https://microbma.github.io/project/gsv.html).

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Acknowledgements

We thank C. Kelly, C. Averill, D. Buckley, D. Goodheart, D. Duncan, D. Myrold, E. Eloe-Fadrosh, E. Brodie, E. Högfors-Rönnholm, H. Cadillo-Quiroz, J. Tiedje, J. Jansson, J. Norton, J. Blanchard, J. Schweitzer, J. Banfield, J. Gladden, J. Raff, K. Peay, K. Gravuer, K. M. DeAngelis, L. Meredith, M. Kalyuzhnaya, M. Waldrop, N. Fierer, P. Dijkstra, P. Baldrian, S. Theroux, S. Tringe, T. Woyke, T. Whitman, W. Mohn and San Diego State University for permission to use their metagenome data. We also thank Amazon Web Services for providing computing resources. This work was supported by the National Natural Science Foundation of China (grants 41721001, 42090060, 42277283 and 41991334), the Key R&D Program of Zhejiang Province (2023C02004, 2023C02015) and the Fundamental Research Funds for the Central Universities (226-2022-00139).

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Author notes

  1. These authors contributed equally: Bin Ma, Yiling Wang.

Authors and Affiliations

  1. Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China

    Bin Ma, Yiling Wang, Kankan Zhao, Lingfei Hu, Ran Xue, Hengyi Dai & Jianming Xu

  2. Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Zhejiang University, Hangzhou, China

    Bin Ma, Yiling Wang, Kankan Zhao, Lingfei Hu, Ran Xue, Hengyi Dai & Jianming Xu

  3. ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, China

    Bin Ma & Ran Xue

  4. Agriculture and Food, CSIRO, Adelaide, South Australia, Australia

    Erinne Stirling

  5. Acid Sulfate Soils Centre, School of Biological Sciences, The University of Adelaide, Adelaide, South Australia, Australia

    Erinne Stirling

  6. Department of Environmental Engineering, China Jiliang University, Hangzhou, China

    Xiaofei Lv

  7. Arable Soil Quality and Fertilizer Administration Bureau of Zhejiang Province, Hangzhou, China

    Yijun Yu

  8. Institute of Applied Remote Sensing and Information Technology, Zhejiang University, Hangzhou, China

    Chao Tang & Baiyu Dong

  9. School of Earth Sciences, Zhejiang University, Hangzhou, China

    Chuyi Wu

  10. Department of Land, Air and Water Resources, University of California, Davis, CA, USA

    Randy A. Dahlgren

  11. Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou, China

    Xiangfeng Tan

  12. Research Center for Eco-environmental Sciences, Chinese Academy of Sciences, Beijing, China

    Yong-Guan Zhu

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

    Haiyan Chu

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  1. Bin Ma
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Contributions

B.M. and J.X. created the study design. Y.W., K.Z., X.T., H.D. and R.X. collected all datasets. B.M., Y.W., K.Z., C.T., C.W. and B.D. performed the data analysis and visualization. J.X., B.M., Y.W., E.S., K.Z., X.L., R.X., X.T., R.A.D., Y.-G.Z., Y.Y., L.H. and H.C. contributed to scientific discussion and wrote the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Jianming Xu.

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Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Ecology & Evolution thanks Kyle Meyer 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

Extended Data Fig. 1 Flow diagram of sample identification.

The arrow delineates sequential steps. There are three main stages: identification, screening and inclusion. The number in each box represents the total number of samples involved in the step.

Extended Data Fig. 2 Bioinformatic Workflow.

The red background highlights the software used along with version specifics. The blue background outlines information on data volumes. Arrows illustrate the order of computational procedures, encompassing (A) prediction of viral contigs from metagenome-assembled contigs, (B) creation of OTU tables and conducting biogeography analyses, (C) clustering of genomes for database comparison (a) and detailing phylogenetic levels (b), (D) assignment of viral taxonomy, (E) identification of temperate phages and (F) determination of host assignment.

Extended Data Fig. 3 Viral information.

Virus validation (a) Density plot of the number of BUSCO hits divided by the total number of genes (BUSCO ratio) for all viruses in GSV dataset. (b) Histogram of the number of GSV vOTUs with different numbers of viral protein family (VFP) hits. Histograms of the number of (c) vOTUs, (d) viral genus-level vOTUs and (e) viral family-level vOTUs present in different percentages of GSV samples. (f) The proportion of genome populations that are putative prophages for this study (GSV), IMG/VR v3 ‘soil only’ metagenomes (IMGsoil), Phages and Integrated Genomes Encapsidated Or Not database (PIGEON), Global Oceans Viromes 2.0 database (GOV2), Gut Virome Database (GVD), Gut Phage Database (GPD) and Viral Refseq v201 (Refseq). (g) Distribution of sequence quality determined by CheckV. (h) Viral contigs sorted by relative abundance and contig length, and those identified at Family level (blue).

Extended Data Fig. 4 Host-virus linkages.

Host-virus network wherein nodes indicate species (hosts; blue) or vOTUs (viruses; bronze); edges indicate a host-virus relationship. A small number of viral nodes were responsible for a large number of host-viral relationships in the virus-host network. Microbial interaction networks often follow a scale-free format in which the majority of connections belong to a small number of nodes. As such, keystone (or hub) nodes enact substantial leverage over the community as a whole.

Extended Data Fig. 5 Assessing the Impact of Sequencing Depth on Diversity Results.

(a & b) Correlations between Shannon index obtained from subsampled reads and those obtained from all reads. Each dot represents a soil metagenome sample that colored by the biome type. The lines denote the predicted values based on the linear mixed model and the shaded areas flanking the lines indicate the upper and lower 95% confidence intervals. The numbers in the lower right corner are the spearman correlation results. (c) Viral Shannon index across varying sequencing depths, with second-order fit for total samples (left upper corner) and for subsamples separated by biomes (upper) and continents (bottom). The lines in the graph represent the predicted values as calculated by the linear mixed model. Surrounding these lines, the shaded regions illustrate the upper and lower bounds of the 95% confidence intervals. (d) Correlation between microbial diversity and viral Shannon index normalized by sample read number (Shannon per Read Count), and each dot represents a soil metagenome sample that colored by the biome type. (e) Median and interquartile ranges for Shannon per Read Count, with whiskers extending to ≤1.5× interquartile range. Significance differences were assessed using one-way ANOVA with LSD test; biomes with different lowercase letters are significantly different at α=0.05; (n = 620 (Agricultural Land), n = 42 (Artificial Surfaces), n = 40 (Bare Land), n = 310 (Wetland), n = 293 (Grassland), n = 56 (Tundra), n = 417 (Forest), n = 21 (Shrubland)). (f) Correlation between microbial diversity and viral Shannon index for samples with sequencing depths ≥100 million reads. (g) Median and interquartile ranges for viral Shannon index at species level for samples with sequencing depths ≥100 million reads, with whiskers extending to ≤1.5× interquartile range. Significance was assessed using one-way ANOVA and LSD tests, with varying lowercase letters marking significant differences at α = 0.05 (n = Same as (e)).

Extended Data Fig. 6 Expanded viral diversity across biomes (including paddy soil and coastal soil).

Median and interquartile ranges for viral Shannon index at species level, with whiskers extending to ≤1.5× interquartile range. Significance differences were assessed using one-way ANOVA with LSD test; biomes with different lowercase letters are significantly different at α = 0.05. The numbers in the figure represent sample sizes (n).

Extended Data Fig. 7 Model validation, accuracy assessment and extent of interpolation across all terrestrial pixels for the 10 environmental covariate layers.

(a) Clustering tree of covariates (main effects circled with a red box). (b) Leave-One-Out cross validation result of the models forecasting viral alpha diversity (Shannon index). Linear regression was used to analyze the relationship between observed and predicted Shannon indices, assuming a two-sided test. (c) Percentage of pixels falling within the convex hulls of the first 5 principal component spaces (covering >80% of the sample space variation collectively). Prediction outliers occurred at latitudinal extremes. The limited sample footprint in equatorial sites, Sahara Desert area, middle Asia and Australia resulted in lower forecast confidence for these regions. (d) Bootstrapped (100 iterations) coefficient of variation (standard deviation divided by the mean predicted value) results represent prediction accuracy of Shannon index. Sampling was stratified by biome. The Shannon predictions had low certainty in Sahara Desert area, middle Asia and areas between the Tropic of Capricorn and the Equator.

Extended Data Fig. 8 Accumulation curves.

Accumulation curves for total samples (left upper corner) and for subsamples separated by biomes (upper) and continents (bottom). The curves depict mean values, and the shaded regions around these curves represent the standard deviation (SD).

Supplementary information

Reporting Summary

Peer Review File

Supplementary Tables 1–7

Supplementary Table 1. Metagenomes used in the GSV dataset. Sample ID (NCBI number, JGI ID), longitude, latitude, biome, sequencing size, continent, data contributor and library strategy and so on. Table 2. Host–virus linkage information. Table 3. The 84 associated environmental factors used to analyse viral biogeography. Table 4. A total of 84 global covariate layers used in model establishment. The 7 Nadir Reflectance Band layers (that is, MCD43A4.005 BRDF-Adjusted Reflectance 16-Day Global 500 m) are grouped as one item in the table. Table 5. Effect size results of 84 global covariate layers, biome, latitude and longitude on ⍺-diversity and β-diversity. Table 6. Overview of metagenomic data size from previous studies (Sheet 1). Analysis of read count thresholds and their implications on viral diversity (Sheet 2). Table 7. Spearman’s correlation results after spatial regression for environmental factors and for viral and microbial community diversity.

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Ma, B., Wang, Y., Zhao, K. et al. Biogeographic patterns and drivers of soil viromes. Nat Ecol Evol 8, 717–728 (2024). https://doi.org/10.1038/s41559-024-02347-2

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