Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Reduction of microbial diversity in grassland soil is driven by long-term climate warming

Nature Microbiology volume 7, pages 1054–1062 (2022)Cite this article

Subjects

Abstract

Anthropogenic climate change threatens ecosystem functioning. Soil biodiversity is essential for maintaining the health of terrestrial systems, but how climate change affects the richness and abundance of soil microbial communities remains unresolved. We examined the effects of warming, altered precipitation and annual biomass removal on grassland soil bacterial, fungal and protistan communities over 7 years to determine how these representative climate changes impact microbial biodiversity and ecosystem functioning. We show that experimental warming and the concomitant reductions in soil moisture play a predominant role in shaping microbial biodiversity by decreasing the richness of bacteria (9.6%), fungi (14.5%) and protists (7.5%). Our results also show positive associations between microbial biodiversity and ecosystem functional processes, such as gross primary productivity and microbial biomass. We conclude that the detrimental effects of biodiversity loss might be more severe in a warmer world.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Effects of experimental warming on soil microbial communities.
Fig. 2: Effects of experimental warming on different microbial taxa.
Fig. 3: Environmental drivers of microbial diversity.

Similar content being viewed by others

Data availability

The DNA sequences of the 16S rRNA gene, 18S rRNA gene and ITS amplicons are deposited in the National Center for Biotechnology Information (NCBI) under project accession number PRJNA331185. Raw shotgun metagenomic sequences are deposited in the European Nucleotide Archive (http://www.ebi.ac.uk/ena) under study no. PRJNA533082. Silva 138.1 Ref NR database is available at https://www.arb-silva.de/documentation/release-138/. Protist Ribosomal Reference database (PR2) databases are available at https://github.com/pr2database/pr2database. The ASV table and ASV representative sequences, soil physical and chemical attributes, and plant biomass and richness are downloadable online at http://www.ou.edu/ieg/publications/datasets. Source data are provided with this paper.

Code availability

R scripts for statistical analyses are available on GitHub at https://github.com/Linwei-Wu/warming_soil_biodiversity.

References

  1. Rands, M. R. et al. Biodiversity conservation: challenges beyond 2010. Science 329, 1298–1303 (2010).

    Article  CAS  PubMed  Google Scholar 

  2. Diaz, S., Fargione, J., Chapin, F. S. III & Tilman, D. Biodiversity loss threatens human well-being. PLoS Biol. 4, e277 (2006).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. Barnosky, A. D. et al. Has the Earth’s sixth mass extinction already arrived? Nature 471, 51–57 (2011).

    Article  CAS  PubMed  Google Scholar 

  4. Pecl, G. T. et al. Biodiversity redistribution under climate change: impacts on ecosystems and human well-being. Science https://doi.org/10.1126/science.aai9214 (2017).

  5. IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).

  6. Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).

    Article  CAS  PubMed  Google Scholar 

  7. Hautier, Y. et al. Eutrophication weakens stabilizing effects of diversity in natural grasslands. Nature 508, 521–525 (2014).

    Article  CAS  PubMed  Google Scholar 

  8. Bascompte, J., García, M. B., Ortega, R., Rezende, E. L. & Pironon, S. Mutualistic interactions reshuffle the effects of climate change on plants across the tree of life. Sci. Adv. 5, eaav2539 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Blois, J. L., Zarnetske, P. L., Fitzpatrick, M. C. & Finnegan, S. Climate change and the past, present, and future of biotic interactions. Science 341, 499–504 (2013).

    Article  CAS  PubMed  Google Scholar 

  10. Tylianakis, J. M., Didham, R. K., Bascompte, J. & Wardle, D. A. Global change and species interactions in terrestrial ecosystems. Ecol. Lett. 11, 1351–1363 (2008).

    Article  PubMed  Google Scholar 

  11. Fei, S. et al. Divergence of species responses to climate change. Sci. Adv. 3, e1603055 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Li, D., Miller, J. E. D. & Harrison, S. Climate drives loss of phylogenetic diversity in a grassland community. Proc. Natl Acad. Sci. USA 116, 19989–19994 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Bay, R. A. et al. Genomic signals of selection predict climate-driven population declines in a migratory bird. Science 359, 83–86 (2018).

    Article  CAS  PubMed  Google Scholar 

  14. Xue, K. et al. Annual removal of aboveground plant biomass alters soil microbial responses to warming. mBio https://doi.org/10.1128/mBio.00976-16 (2016).

  15. Zhou, J. et al. Microbial mediation of carbon-cycle feedbacks to climate warming. Nat. Clim. Change 2, 106–110 (2012).

    Article  CAS  Google Scholar 

  16. Steidinger, B. S. et al. Climatic controls of decomposition drive the global biogeography of forest-tree symbioses. Nature 569, 404–408 (2019).

    Article  CAS  PubMed  Google Scholar 

  17. Blankinship, J. C., Niklaus, P. A. & Hungate, B. A. A meta-analysis of responses of soil biota to global change. Oecologia 165, 553–565 (2011).

    Article  PubMed  Google Scholar 

  18. Guo, X. et al. Climate warming leads to divergent succession of grassland microbial communities. Nat. Clim. Change 8, 813–818 (2018).

    Article  Google Scholar 

  19. Guo, X. et al. Climate warming accelerates temporal scaling of grassland soil microbial biodiversity. Nat. Ecol. Evol. 3, 612–619 (2019).

    Article  PubMed  Google Scholar 

  20. Yuan, M. M. et al. Climate warming enhances microbial network complexity and stability. Nat. Clim. Change 11, 343–348 (2021).

    Article  Google Scholar 

  21. Thakur, M. P. et al. Climate warming promotes species diversity, but with greater taxonomic redundancy, in complex environments. Sci. Adv. 3, e1700866 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Xu, X., Sherry, R. A., Niu, S., Li, D. & Luo, Y. Net primary productivity and rain‐use efficiency as affected by warming, altered precipitation, and clipping in a mixed‐grass prairie. Glob. Change Biol. 19, 2753–2764 (2013).

    Article  Google Scholar 

  23. Luo, Y., Sherry, R., Zhou, X. & Wan, S. Terrestrial carbon‐cycle feedback to climate warming: experimental evidence on plant regulation and impacts of biofuel feedstock harvest. Glob. Change Biol. Bioenergy 1, 62–74 (2009).

    Article  CAS  Google Scholar 

  24. Chen, M.-M. et al. Effects of soil moisture and plant interactions on the soil microbial community structure. Eur. J. Soil Biol. 43, 31–38 (2007).

    Article  CAS  Google Scholar 

  25. Zhou, J. et al. Temperature mediates continental-scale diversity of microbes in forest soils. Nat. Commun. 7, 12083 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Rousk, J. et al. Soil bacterial and fungal communities across a pH gradient in an arable soil. ISME J. 4, 1340–1351 (2010).

    Article  PubMed  Google Scholar 

  27. DeBruyn, J. M., Nixon, L. T., Fawaz, M. N., Johnson, A. M. & Radosevich, M. Global biogeography and quantitative seasonal dynamics of Gemmatimonadetes in soil. Appl. Environ. Microbiol. 77, 6295–6300 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Van Horn, D. J. et al. Soil microbial responses to increased moisture and organic resources along a salinity gradient in a polar desert. Appl. Environ. Microbiol. 80, 3034–3043 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Van Nuland, M. E. et al. Warming and disturbance alter soil microbiome diversity and function in a northern forest ecotone. FEMS Microbiol. Ecol. 96, fiaa108 (2020).

    Article  CAS  PubMed  Google Scholar 

  30. Reimer, L. C. et al. Bac Dive in 2022: the knowledge base for standardized bacterial and archaeal data. Nucleic Acids Res. 50, D741–D746 (2022).

    Article  CAS  PubMed  Google Scholar 

  31. Nguyen, N. H. et al. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 20, 241–248 (2016).

    Article  Google Scholar 

  32. Delgado-Baquerizo, M. et al. Microbial diversity drives multifunctionality in terrestrial ecosystems. Nat. Commun. 7, 10541 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Banerjee, S. et al. Agricultural intensification reduces microbial network complexity and the abundance of keystone taxa in roots. ISME J. 13, 1722–1736 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Ning, D. et al. A quantitative framework reveals ecological drivers of grassland microbial community assembly in respoÿnse to warming. Nat. Commun. 11, 4717 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Tiedje, J. M. et al. Microbes and climate change: a research prospectus for the future. MBio, e00800-22 (2022). doi:10.1128/mbio.00800-22 (2022).

  36. Maestre, F. T. et al. Increasing aridity reduces soil microbial diversity and abundance in global drylands. Proc. Natl Acad. Sci. USA 112, 15684–15689 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Li, D., Zhou, X., Wu, L., Zhou, J. & Luo, Y. Contrasting responses of heterotrophic and autotrophic respiration to experimental warming in a winter annual‐dominated prairie. Glob. Change Biol. 19, 3553–3564 (2013).

    Google Scholar 

  38. Sherry, R. A. et al. Lagged effects of experimental warming and doubled precipitation on annual and seasonal aboveground biomass production in a tallgrass prairie. Glob. Change Biol. 14, 2923–2936 (2008).

    Article  Google Scholar 

  39. Catchpole, W. & Wheeler, C. Estimating plant biomass: a review of techniques. Aust. J. Ecol. 17, 121–131 (1992).

    Article  Google Scholar 

  40. McLean, E. in Methods of Soil Analysis. Part 2. Chemical and Microbiological Properties (ed Page, A. L.) 199–224 (1982).

  41. Buyer, J. S. & Sasser, M. High throughput phospholipid fatty acid analysis of soils. Appl. Soil Ecol. 61, 127–130 (2012).

    Article  Google Scholar 

  42. Zhou, J., Bruns, M. A. & Tiedje, J. M. DNA recovery from soils of diverse composition. Appl. Environ. Microbiol. 62, 316–322 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Wu, L. et al. Phasing amplicon sequencing on Illumina Miseq for robust environmental microbial community analysis. BMC Microbiol. 15, 125 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Zhou, J. et al. Reproducibility and quantitation of amplicon sequencing-based detection. ISME J. 5, 1303–1313 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Magoč, T. & Salzberg, S. L. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Edgar, R. C. Updating the 97% identity threshold for 16S ribosomal RNA OTUs. Bioinformatics 34, 2371–2375 (2018).

    Article  CAS  PubMed  Google Scholar 

  47. Sievers, F. et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol. Syst. Biol. 7, 539 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2 – approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Munoz, R. et al. Release LTPs104 of the all-species living tree. Syst. Appl. Microbiol. 34, 169–170 (2011).

    Article  PubMed  Google Scholar 

  50. Nuccio, E. E. et al. Climate and edaphic controllers influence rhizosphere community assembly for a wild annual grass. Ecology 97, 1307–1318 (2016).

    Article  PubMed  Google Scholar 

  51. Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Nilsson, R. H. et al. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 47, D259–D264 (2018).

    Article  PubMed Central  CAS  Google Scholar 

  53. Guillou, L. et al. The Protist Ribosomal Reference database (PR2): a catalog of unicellular eukaryote small sub-unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 41, D597–D604 (2013).

    Article  CAS  PubMed  Google Scholar 

  54. Oliverio, A. M. et al. The global-scale distributions of soil protists and their contributions to belowground systems. Sci. Adv. 6, eaax8787 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Andrews, S. FastQC: A Quality Control Tool For High Throughput Sequence Data (Babraham Bioinformatics, 2010).

  56. Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).

    Article  CAS  PubMed  Google Scholar 

  57. Patel, R. K. & Jain, M. NGS QC Toolkit: a toolkit for quality control of next generation sequencing data. PLoS ONE 7, e30619 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).

    Article  CAS  PubMed  Google Scholar 

  59. Huson, D. H., Auch, A. F., Qi, J. & Schuster, S. C. MEGAN analysis of metagenomic data. Genome Res. 17, 377–386 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Kembel, S. W. et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2010).

    Article  CAS  PubMed  Google Scholar 

  61. R Core Team. R: A Language And Environment For Statistical Computing (R Foundation for Statistical Computing, 2014).

  62. Oksanen, J. et al. Package ‘vegan’. Community Ecology Package v.2 (2013).

  63. Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).

    Article  Google Scholar 

  64. Fox, J. & Weisberg, S. An R Companion to Applied Regression (Sage Publications, 2018).

  65. Barton, K. & Barton, M. K. Package ‘MuMIn’ v.1.18 (2015).

  66. Carver, R. Practical Data Analysis with JMP (SAS Institute, 2019).

  67. Kuhn, M. Building predictive models in R using the caret package. J. Stat. Softw. 28, 1–26 (2008).

    Article  Google Scholar 

  68. Rosseel, Y. lavaan: An R Package for Structural Equation Modeling and More v.0.4-9 (BETA) (Ghent University, 2011).

Download references

Acknowledgements

We thank the acting director of the KAEFS field site, M. Bomgraars, and the numerous former and current members of the Institute for Environmental Genomics for their help in maintaining the long-term field experiment. This work was supported by the US Department of Energy, Office of Science, Genomic Science Program under Award Number DE-SC0004601 and DE-SC0010715, and the Office of the Vice President for Research at the University of Oklahoma to J.Z. X.G. and X.Z. were generously supported by the China Scholarship Council (CSC) to visit the University of Oklahoma. The data analyses performed by X.G. were also supported by the China Postdoctoral Science Foundation (2018M641327 and 2019T120101).

Author information

Author notes

  1. These authors contributed equally: Linwei Wu, Ya Zhang, Xue Guo.

Authors and Affiliations

  1. Institute of Ecology, Key Laboratory for Earth Surface Processes of the Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing, China

    Linwei Wu

  2. Institute for Environmental Genomics and Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK, USA

    Linwei Wu, Ya Zhang, Daliang Ning, Xishu Zhou, Jiajie Feng, Mengting Maggie Yuan, Jiajing Guo, Zhipeng Gao, Jie Ma, Jialiang Kuang, Siyang Jian, Shun Han, Zhifeng Yang, Yang Ouyang, Ying Fu, Naijia Xiao, Liyou Wu, Aifen Zhou & Jizhong Zhou

  3. State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China

    Xue Guo, Suo Liu & Yunfeng Yang

  4. School of Minerals Processing and Bioengineering, Central South University, Changsha, Hunan, China

    Xishu Zhou & Xueduan Liu

  5. Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, USA

    Mengting Maggie Yuan

  6. Hunan Agriculture Product Processing Institute, Hunan Academy of Agricultural Sciences, Changsha, Hunan, China

    Jiajing Guo

  7. College of Animal Science and Technology, Hunan Agricultural University, Changsha, Hunan, China

    Zhipeng Gao

  8. School of Environmental Studies, China University of Geosciences, Wuhan, Hubei, China

    Jie Ma

  9. Center for Microbial Ecology, Michigan State University, East Lansing, MI, USA

    James M. Tiedje

  10. School of Civil Engineering and Environmental Sciences, University of Oklahoma, Norman, OK, USA

    Jizhong Zhou

  11. Earth and Environmental Sciences, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Jizhong Zhou

Authors
  1. Linwei Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ya Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xue Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Daliang Ning
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xishu Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jiajie Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Mengting Maggie Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Suo Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jiajing Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Zhipeng Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Jie Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Jialiang Kuang
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Siyang Jian
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Shun Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Zhifeng Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Yang Ouyang
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Ying Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Naijia Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Xueduan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Liyou Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Aifen Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Yunfeng Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. James M. Tiedje
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Jizhong Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed intellectual input and assistance to this study. The original concepts were conceived by J.Z. and J.M.T. Field management was carried out by Linwei Wu, Y.Z., X.G., J.F., M.M.Y., J.K., Y.F., A.Z., D.N., J.M., S.J., S.H., Z.Y., Y.O. and Liyou Wu. Sampling collection, soil chemical and microbial characterization were carried out by M.M.Y., X.G., Linwei Wu, J.G., Z.G. and X.Z. Data analysis were done by Linwei Wu, Y.Z., X.G., S.L. and N.X. with assistance provided by D.N. and J.Z. All data analysis and integration were guided by J.Z. The manuscript was prepared by J.Z., Linwei Wu, Y.Z. and X.G., with significant input from J.M.T., Y.Y. and X.L. Considering their contributions in terms of site management, data collection, analyses and/or integration, Linwei Wu, Y. Z. and X.G. are listed as co-first authors.

Corresponding author

Correspondence to Jizhong Zhou.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Microbiology thanks Pankaj Trivedi, Kirsten Hofmockel and Noah Fierer for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 A schematic map of the field experimental treatments.

In the long-term climate change experiment, warming (+3 °C), half precipitation (−50% precipitation), and double precipitation (+100% precipitation) are primary factors, which are nested with clipping as the secondary factor (all the 24 southern subplots are under clipping treatment). Thus, this experiment has twelve single and combined treatments as follows: Control (N), Warming (W), Half precipitation (H), Double precipitation (D), Clipping (C), Warming & Half precipitation (WH), Warming & Double precipitation (WD), Warming & Clipping (WC), Half precipitation & Clipping (HC), Double precipitation & Clipping (DC), Warming & Half precipitation & Clipping (WHC), and Warming & Double precipitation & Clipping (WDC). Each of these treatments has four replicates in four different blocks. The site was established in July, 2009. Surface (0-15 cm) soil samples were collected annually from all plots at approximately the date of peak plant biomass in fall (September or October) from 2009 to 2016.

Extended Data Fig. 2 Effects of experimental treatments on soil and plant variables by linear mixed-effects models (LMMs).

a, Soil temperature; b, Soil moisture; c, Soil pH; d, Soil NO3−N; e, Soil NH4-N; f, Total plant biomass; and g, Plant richness. Data are presented as mean values ± standard errors of the estimated effect sizes. Statistical significance is based on Wald type II χ² tests (n = 360 independent soil samples). Significant effects are denoted by asterisks: *** P < 0.001, ** P < 0.01, * P < 0.05. W: Warming; P: Precipitation level; C: Clipping. Since the precipitation level is considered as a continuous variable in the LMM (0.5 for half precipitation, 1 for normal and 2 for double precipitation), only one regression coefficient of precipitation treatment would be derived by the LMM. The effect size of half precipitation (as compared to ambient precipitation) can be derived by multiplying the regression coefficient by −0.5, while the effect size of double precipitation (as compared to ambient precipitation) can be derived by multiplying the regression coefficient by 1.

Source data

Extended Data Fig. 3 Sequencing depth among different treatments for the bacterial community (a), fungal community (b), and protistan community (c).

A total of 360 soil samples over 8 years were analyzed with 16S ribosomal RNA (rRNA) gene for bacteria and archaea, the internal transcribed spacer (ITS) between 5.8S and 28S rRNA genes for fungi, and the 18S rRNA gene for protists. For 18S sequences, only those annotated as protists were selected for the subsequent analyses. An average of 56,182 ± 27,613, 23,569 ± 16,323, and 11,146 ± 10,528 sequence reads were obtained for bacteria, fungi, and protists, respectively. There was no significant difference between treatments in the number of sequences (that is, sequencing depth) for the bacteria, fungal, and protistan communities except half precipitation (H), double precipitation (D), clipping (C), and double precipitation & clipping (DC) for protists (p = 0.002-0.021). Groups: Control (N), Warming (W), Half precipitation (H), Double precipitation (D), Clipping (C), Warming & Half precipitation (WH), Warming & Double precipitation (WD), Warming & Clipping (WC), Half precipitation & Clipping (HC), Double precipitation & Clipping (DC), Warming & Half precipitation & Clipping (WHC), and Warming & Double precipitation & Clipping (WDC). In the box plots, hinges show the 25, 50, and 75 percentiles. The upper whisker extends to the largest value no further than 1.5 * IQR from the upper hinge, where IQR is the inter-quartile range between the 25% and 75% quartiles; the lower whisker extends to the smallest value at most 1.5 * IQR from the lower hinge.

Source data

Extended Data Fig. 4 Rarefaction curves.

The number of ASVs with an increasing number of sequences (a, c, e) and accumulation curves of the number of ASVs with an increasing number of samples (b, d, f) for the bacterial community (a, b), fungal community (c, d), and protistan community (e, f). The observed number of ASVs with warming treatment was lower compared with all those without warming treatment except warming & double precipitation & clipping (WDC) versus double precipitation & clipping (DC) for fungi and warming & clipping (WC) versus clipping (C) for protists in (a, c, e) (Paired t test, p < 0.0001). The number of samples did not have a substantial influence on the differences between warming and non-warming control as shown in (b, d, f). After removing global singletons and resampling, the rarefaction curves approached asymptotes for all treatments, indicating that the sequencing depth was sufficient for assessing the effects of various climate change factors on the diversity of these soil microbial communities.

Source data

Extended Data Fig. 5 Yearly differences of bacterial (a), fungal (b), and protistan (c) richness between warmed and unwarmed samples.

Data are presented as mean values ± SEM of the mean differences (warmed -unwarmed). For each year, the treatment effects are tested with linear mixed-effects models, and the significant treatment effects (p < 0.05, Wald type II χ² tests, n = 48 soils each year) are listed in the table. W: Warming; P: Precipitation level; C: Clipping.

Source data

Extended Data Fig. 6 Effects of experimental warming across microbial groups based on linear mixed-effects models.

a, Effect sizes of experimental warming on the (rescaled) phylogenetic diversity of major microbial groups based on linear mixed-effects models. b, Effect sizes of experimental warming on the (rescaled) relative abundance of major microbial groups based on linear mixed-effects models. Data are presented as mean values ± standard errors of the estimated effect sizes. Statistical significance is based on Wald type II χ² tests (n = 360 independent soil samples), which is denoted by asterisks: *** p < 0.001, ** p < 0.01, * p < 0.05.

Source data

Extended Data Fig. 7 Effects of experimental warming on sporulation families or genes of Firmicutes and Actinobacteria.

a, Number of Firmicutes families whose relative abundances increased, decreased or unchanged under warming. b, Number of Actinobacteria families whose relative abundances increased, decreased or unchanged under warming. Significant changes (p < 0.05) are based on Wald type II χ² tests (n = 360 independent soil samples) of the warming effects in linear mixed-effects models (relative abundance ~ warming × precipitation level × clipping + (1|Block) + (1 | year)). ‘Yspore’, ‘Nspore’, and ‘NAspore’ refer to known spore-formers, known non-spore-formers, and information not available on the ability to form spores, respectively. The spore-forming capability or different families within Firmicutes and Actinobacteria were mainly based on databases and literature. c, Effect sizes of experimental warming on the (rescaled) relative abundance of major sporulation genes in Firmicutes (spo0A gene) and Actinobacteria (bldD gene) based on linear mixed-effects models. The sporulation genes were retrieved from shotgun sequencing data. Data are presented as mean values ± standard errors of the estimated effect sizes. Statistical significance is based on Wald type II χ² tests (n = 64 independent soil metagenome samples).

Source data

Extended Data Fig. 8 Hypothesized conceptual models on the relationships between experimental treatments, environmental variables, and microbial diversity.

a, Bacteria and protists; b, Fungi. The environmental variables were selected based on their biological inference and collinearity, as detailed in the Methods and Supplementary Tables 3–7. We hypothesized that each experimental treatment would influence each environmental variable, and the environmental variables would all influence microbial diversity. We also assumed that microbial diversity would influence plant richness and biomass. In addition, we assumed interactions between bacteria and protists since there could be prey-predator relationships between them. In fact, the consumers, which include potential predators of bacteria, account for 84.6% of the total protist abundance. The richness of protistan consumers also highly correlated with the total protistan richness (Pearson’s r = 0.98). Protists was not included in the fungal model because the relative abundance of fungivorous protists is very low.

Extended Data Fig. 9 The structural equation model (SEM) showing the relationships among treatments, soil and plant variables, and fungal richness.

Blue and red arrows indicate positive and negative relationships, respectively. Solid or dashed lines indicate significant (p < 0.05) or nonsignificant relationships. Numbers near the pathway arrow indicate the standard path coefficients. R2 represents the proportion of variance explained for every dependent variable. χ2 = 28.70, df = 23, p = 0.19 (large p value indicates that the predicted model and observed data are equal, that is, good model fitting), CFI = 0.974. n = 48 biologically independent plots.

Source data

Extended Data Fig. 10 Effects of experimental warming on different ecosystem functions.

Data are presented as mean values ± standard errors of the estimated effect sizes. Statistical significance is based on Wald type II χ² tests (n = 360), which is indicated in the plot. GPP: gross primary productivity; ER: ecosystem respiration; NEE: net ecosystem exchange; Rh: heterotrophic respiration; Rs: soil total respiration.

Source data

Supplementary information

Supplementary Information

Supplementary Fig. 1, Tables 1–7 and Notes A–F.

Reporting Summary

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 4

Statistical source data.

Source Data Extended Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 6

Statistical source data.

Source Data Extended Data Fig. 7

Statistical source data.

Source Data Extended Data Fig. 9

Statistical source data.

Source Data Extended Data Fig. 10

Statistical source data.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, L., Zhang, Y., Guo, X. et al. Reduction of microbial diversity in grassland soil is driven by long-term climate warming. Nat Microbiol 7, 1054–1062 (2022). https://doi.org/10.1038/s41564-022-01147-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41564-022-01147-3

This article is cited by

Search

Advanced search

Quick links

Nature Briefing Microbiology

Sign up for the Nature Briefing: Microbiology newsletter — what matters in microbiology research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: Microbiology