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Experimental warming leads to convergent succession of grassland archaeal community

Nature Climate Change volume 13, pages 561–569 (2023)Cite this article

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Abstract

Understanding the temporal succession of ecological communities and the underlying mechanisms in response to climate warming is critical for future climate projections. However, despite its fundamental importance in ecology and evolution, little is known about how the Archaea domain responds to warming. Here we showed that experimental warming of a tallgrass prairie ecosystem significantly altered the community structure of soil archaea and reduced their taxonomic and phylogenetic diversity. In contrast to previous observations in bacteria and fungi, we showed convergent succession of the soil archaeal community between warming and control. Although stochastic processes dominated the archaeal community, their relative importance decreased over time. Furthermore, the warming-induced changes in the archaeal community and soil chemistry had significant impacts on ecosystem functioning. Our results imply that, although the detrimental effects of biodiversity loss on ecosystems could be much severer, the soil archaeal community structure would be more predictable in a warmer world.

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Fig. 1: Effects of experimental warming on archaeal community diversity and succession across 7 yr.
Fig. 2: Ecological processes and community assembly mechanisms associated with the temporal dynamics in the soil archaeal community.
Fig. 3: Variations of ecological processes across different phylogenetic groups.
Fig. 4: Environmental drivers of archaeal community structure and functioning.

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Data availability

The DNA sequences of the archaeal 16S rRNA gene amplicons are available in the National Center for Biotechnology Information (NCBI) Sequence Read Archive under project accession number PRJNA861672. The DNA sequences of the bacterial 16S rRNA gene amplicons are under the 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. The soil physical and chemical attributes, and plant biomass and richness are downloadable online at http://www.ou.edu/ieg/publications/datasets. Silva 132 Ref NR database is available at https://www.arb-silva.de/documentation/release-132/. The Greengene reference dataset is available from the QIIME GitHub repository https://github.com/biocore/qiime-default-reference/blob/master/qiime_default_reference/gg_13_8_otus/rep_set/97_otus.fasta.gz. Source data are provided with this paper.

Code availability

R scripts for statistical analyses and source data are available on GitHub at https://github.com/yazhang2022/OKwarmingsiteArchaea.

References

  1. Woese, C. R., Kandler, O. & Wheelis, M. L. Towards a natural system of organisms: proposal for the domains Archaea, Bacteria, and Eucarya. Proc. Natl Acad. Sci. USA 87, 4576–4579 (1990).

    Article  CAS  Google Scholar 

  2. Hedlund, B. P., Zhang, C., Wang, F., Rinke, C. & Martin, W. F. Editorial: ecology, metabolism and evolution of archaea-perspectives from Proceedings of the International Workshop on Geo-Omics of Archaea. Front. Microbiol. 12, 827229 (2021).

    Article  Google Scholar 

  3. DeLong, E. F. Archaea in coastal marine environments. Proc. Natl Acad. Sci. USA 89, 5685–5689 (1992).

    Article  CAS  Google Scholar 

  4. Fuhrman, J. A., McCallum, K. & Davis, A. A. Novel major archaebacterial group from marine plankton. Nature 356, 148–149 (1992).

    Article  CAS  Google Scholar 

  5. DeLong, E. F. Exploring marine planktonic archaea: then and now. Front. Microbiol. 11, 616086 (2021).

    Article  Google Scholar 

  6. Karimi, B. et al. Biogeography of soil bacteria and archaea across France. Sci. Adv. 4, eaat1808 (2018).

    Article  Google Scholar 

  7. Tahon, G., Geesink, P. & Ettema, T. J. G. Expanding archaeal diversity and phylogeny: past, present, and future. Annu. Rev. Microbiol. 75, 359–381 (2021).

    Article  Google Scholar 

  8. Baker, B. J. et al. Diversity, ecology and evolution of Archaea. Nat. Microbiol. 5, 887–900 (2020).

    Article  CAS  Google Scholar 

  9. Needham, D. M. & Fuhrman, J. A. Pronounced daily succession of phytoplankton, archaea and bacteria following a spring bloom. Nat. Microbiol. 1, 16005 (2016).

    Article  CAS  Google Scholar 

  10. Shu, W. S. & Huang, L. N. Microbial diversity in extreme environments. Nat. Rev. Microbiol. 20, 219–235 (2021).

    Article  Google Scholar 

  11. Adam, P. S., Borrel, G., Brochier-Armanet, C. & Gribaldo, S. The growing tree of Archaea: new perspectives on their diversity, evolution and ecology. ISME J. 11, 2407–2425 (2017).

    Article  Google Scholar 

  12. Zaremba-Niedzwiedzka, K. et al. Asgard archaea illuminate the origin of eukaryotic cellular complexity. Nature 541, 353–358 (2017).

    Article  CAS  Google Scholar 

  13. Imachi, H. et al. Isolation of an archaeon at the prokaryote-eukaryote interface. Nature 577, 519–525 (2020).

    Article  CAS  Google Scholar 

  14. Liu, Y. et al. Expanded diversity of Asgard archaea and their relationships with eukaryotes. Nature 593, 553–557 (2021).

    Article  CAS  Google Scholar 

  15. Angel, R., Soares, M. I., Ungar, E. D. & Gillor, O. Biogeography of soil archaea and bacteria along a steep precipitation gradient. ISME J. 4, 553–563 (2010).

    Article  Google Scholar 

  16. Auguet, J. C., Barberan, A. & Casamayor, E. O. Global ecological patterns in uncultured Archaea. ISME J. 4, 182–190 (2010).

    Article  Google Scholar 

  17. Bates, S. T. et al. Examining the global distribution of dominant archaeal populations in soil. ISME J. 5, 908–917 (2011).

    Article  CAS  Google Scholar 

  18. Bar-On, Y. M., Phillips, R. & Milo, R. The biomass distribution on Earth. Proc. Natl Acad. Sci. USA 115, 6506–6511 (2018).

    Article  CAS  Google Scholar 

  19. Offre, P., Spang, A. & Schleper, C. Archaea in biogeochemical cycles. Annu. Rev. Microbiol. 67, 437–457 (2013).

    Article  CAS  Google Scholar 

  20. Leininger, S. et al. Archaea predominate among ammonia-oxidizing prokaryotes in soils. Nature 442, 806–809 (2006).

    Article  CAS  Google Scholar 

  21. Danovaro, R., Rastelli, E., Corinaldesi, C., Tangherlini, M. & Dell’Anno, A. Marine archaea and archaeal viruses under global change. F1000Research 6, 1241 (2017).

    Article  Google Scholar 

  22. Goberna, M., Garcia, C., Insam, H., Hernandez, M. T. & Verdu, M. Burning fire-prone Mediterranean shrublands: immediate changes in soil microbial community structure and ecosystem functions. Microb. Ecol. 64, 242–255 (2012).

    Article  CAS  Google Scholar 

  23. Gschwendtner, S. et al. Climate change induces shifts in abundance and activity pattern of bacteria and archaea catalyzing major transformation steps in nitrogen turnover in a soil from a mid-European beech forest. PLoS ONE 9, e114278 (2014).

    Article  Google Scholar 

  24. Hayden, H. L. et al. Changes in the microbial community structure of bacteria, archaea and fungi in response to elevated CO2 and warming in an Australian native grassland soil. Environ. Microbiol. 14, 3081–3096 (2012).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  27. Wu, L. et al. Reduction of microbial diversity in grassland soil is driven by long-term climate warming. Nat. Microbiol. 7, 1054–1062 (2022).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  29. Cavicchioli, R. Archaea–timeline of the third domain. Nat. Rev. Microbiol. 9, 51–61 (2011).

    Article  CAS  Google Scholar 

  30. Prach, K. & Walker, L. R. Four opportunities for studies of ecological succession. Trends Ecol. Evol. 26, 119–123 (2011).

    Article  Google Scholar 

  31. Xu, X., Sherry, R. A., Niu, S. L., Li, D. J. & Luo, Y. Q. 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 

  32. Kerou, M., Alves, R. J. E. & Schleper, C. in Bergey’s Manual of Systematics of Archaea and Bacteria (eds Trujillo, M. E. et al.) https://doi.org/10.1002/9781118960608.obm00124 (John Wiley & Sons, 2018).

  33. Nkamga, V. D. & Drancourt, M. in Bergey’s Manual of Systematics of Archaea and Bacteria (eds Trujillo, M. E. et al.) https://doi.org/10.1002/9781118960608.gbm01365 (John Wiley & Sons, 2016).

  34. Zhou, J. et al. High-throughput metagenomic technologies for complex microbial community analysis: open and closed formats. mBio 6, e02288-14 (2015).

    Article  CAS  Google Scholar 

  35. Zhou, J. et al. Random sampling process leads to overestimation of beta-diversity of microbial communities. mBio 4, e00324-13 (2013).

    Article  CAS  Google Scholar 

  36. Xue, K. et al. Tundra soil carbon is vulnerable to rapid microbial decomposition under climate warming. Nat. Clim. Change 6, 595–600 (2016).

    Article  CAS  Google Scholar 

  37. Pecl, G. T. et al. Biodiversity redistribution under climate change: impacts on ecosystems and human well-being. Science 355, eaai9214 (2017).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  39. 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  Google Scholar 

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

    Article  Google Scholar 

  41. 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  Google Scholar 

  42. Kerou, M. et al. Proteomics and comparative genomics of Nitrososphaera viennensis reveal the core genome and adaptations of archaeal ammonia oxidizers. Proc. Natl Acad. Sci. USA 113, E7937–E7946 (2016).

    Article  CAS  Google Scholar 

  43. Taylor, A. E., Giguere, A. T., Zoebelein, C. M., Myrold, D. D. & Bottomley, P. J. Modeling of soil nitrification responses to temperature reveals thermodynamic differences between ammonia-oxidizing activity of archaea and bacteria. ISME J. 11, 896–908 (2017).

    Article  CAS  Google Scholar 

  44. Shi, Z. et al. Functional gene array-based ultrasensitive and quantitative detection of microbial populations in complex communities. mSystems 4, e00296-19 (2019).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  46. Liu, L. et al. Changes in assembly processes of soil microbial communities during secondary succession in two subtropical forests. Soil Biol. Biochem. 154, 108144 (2021).

    Article  CAS  Google Scholar 

  47. Verhamme, D. T., Prosser, J. I. & Nicol, G. W. Ammonia concentration determines differential growth of ammonia-oxidising archaea and bacteria in soil microcosms. ISME J. 5, 1067–1071 (2011).

    Article  CAS  Google Scholar 

  48. Guo, X. et al. Gene-informed decomposition model predicts lower soil carbon loss due to persistent microbial adaptation to warming. Nat. Commun. 11, 4897 (2020).

    Article  CAS  Google Scholar 

  49. Frank, D. A. & McNaughton, S. J. Aboveground biomass estimation with the canopy intercept method: a plant growth form caveat. Oikos 57, 57–60 (1990).

  50. 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 

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

    Article  CAS  Google Scholar 

  52. 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 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  55. Caporaso, J. G. et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 6, 1621–1624 (2012).

    Article  CAS  Google Scholar 

  56. Suzuki, M. T. & Giovannoni, S. J. Bias caused by template annealing in the amplification of mixtures of 16S rRNA genes by PCR. Appl. Environ. Microbiol. 62, 625–630 (1996).

    Article  CAS  Google Scholar 

  57. Takai, K. & Horikoshi, K. Rapid detection and quantification of members of the archaeal community by quantitative PCR using fluorogenic probes. Appl. Environ. Microbiol. 66, 5066–5072 (2000).

    Article  Google Scholar 

  58. Porat, I. et al. Characterization of archaeal community in contaminated and uncontaminated surface stream sediments. Microb. Ecol. 60, 784–795 (2010).

    Article  Google Scholar 

  59. Francis, C. A., Roberts, K. J., Beman, J. M., Santoro, A. E. & Oakley, B. B. Ubiquity and diversity of ammonia-oxidizing archaea in water columns and sediments of the ocean. Proc. Natl Acad. Sci. USA 102, 14683–14688 (2005).

    Article  CAS  Google Scholar 

  60. Peiffer, J. A. et al. Diversity and heritability of the maize rhizosphere microbiome under field conditions. Proc. Natl Acad. Sci. USA 110, 6548–6553 (2013).

    Article  CAS  Google Scholar 

  61. Giardine, B. et al. Galaxy: a platform for interactive large-scale genome analysis. Genome Res. 15, 1451–1455 (2005).

    Article  CAS  Google Scholar 

  62. Kong, Y. Btrim: a fast, lightweight adapter and quality trimming program for next-generation sequencing technologies. Genomics 98, 152–153 (2011).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  64. Edgar, R. C. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998 (2013).

    Article  CAS  Google Scholar 

  65. Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).

    Article  CAS  Google Scholar 

  66. DeSantis, T. Z. et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72, 5069–5072 (2006).

    Article  CAS  Google Scholar 

  67. 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  Google Scholar 

  68. Edgar, R. C. UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing. Preprint available at bioRxiv https://doi.org/10.1101/081257 (2016).

  69. Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).

    Article  CAS  Google Scholar 

  70. Amir, A. et al. Deblur rapidly resolves single-nucleotide community sequence patterns. mSystems 2, e00191-16 (2017).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  74. Munoz, R. et al. Release LTPs104 of the All-Species Living Tree. Syst. Appl. Microbiol. 34, 169–170 (2011).

    Article  Google Scholar 

  75. Oksanen, J. et al. Package ‘vegan’. Community Ecology Package, Version 2.9, 1–295 (The R Project for Statistical Computing, 2013).

  76. Chen, L. X. et al. Comparative metagenomic and metatranscriptomic analyses of microbial communities in acid mine drainage. ISME J. 9, 1579–1592 (2015).

    Article  Google Scholar 

  77. Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4, 133–142 (2013).

    Article  Google Scholar 

  78. Martiny, J. B., Eisen, J. A., Penn, K., Allison, S. D. & Horner-Devine, M. C. Drivers of bacterial beta-diversity depend on spatial scale. Proc. Natl Acad. Sci. USA 108, 7850–7854 (2011).

    Article  CAS  Google Scholar 

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

  80. 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  Google Scholar 

  81. 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  Google Scholar 

  82. Zhou, J. Z. & Ning, D. L. Stochastic community assembly: does it matter in microbial ecology? Microbiol. Mol. Biol. Rev. 81, e00002-17 (2017).

    Article  Google Scholar 

  83. Jaumot, J., Bedia, C. & Tauler, R. Data Analysis for Omic Sciences: Methods and Applications (Elsevier, 2018).

  84. Thevenot, E. A., Roux, A., Xu, Y., Ezan, E. & Junot, C. Analysis of the human adult urinary metabolome variations with age, body mass index, and gender by implementing a comprehensive workflow for univariate and OPLS statistical analyses. J. Proteome Res. 14, 3322–3335 (2015).

    Article  CAS  Google Scholar 

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Acknowledgements

We thank all former and current members of the Institute for Environmental Genomics for their time and energy in maintaining the long-term climate change 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. The data analysis performed by D.N. and N.X. was also partially supported by NSF Grants EF-2025558 and DEB-2129235.

Author information

Authors and Affiliations

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

    Ya Zhang, Daliang Ning, Linwei Wu, Mengting Maggie Yuan, Xishu Zhou, Yuanliang Hu, Siyang Jian, Zhifeng Yang, Shun Han, Jiajie Feng, Jialiang Kuang, Carolyn R. Cornell, Colin T. Bates, Yupeng Fan, Jonathan P. Michael, Yang Ouyang, Jiajing Guo, Zhipeng Gao, Zheng Shi, Naijia Xiao, Ying Fu, Aifen Zhou, Liyou Wu & Jizhong Zhou

  2. 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

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

    Mengting Maggie Yuan

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

    Xishu Zhou & Xueduan Liu

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

    Xue Guo & Yunfeng Yang

  6. Hubei Key Laboratory of Edible Wild Plants Conservation and Utilization, College of Life Sciences, Hubei Normal University, Huangshi, China

    Yuanliang Hu

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

    Jiajing Guo

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

    Zhipeng Gao

  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. School of Computer Science, University of Oklahoma, Norman, OK, USA

    Jizhong Zhou

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

    Jizhong Zhou

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  1. Ya Zhang
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Contributions

All authors contributed intellectual input and assistance to this study. The original concepts were conceived by Y.Z. and J.Z. Field management was carried out by Y.Z., Linwei Wu, M.M.Y., X.Z., X.G., S.J., Z.Y., S.H., J.F., J.K., C.R.C., C.T.B., Y. Fan, J.P.M., Y.O., Y. Fu, D.N., Z.S., N.X., A.Z. and Liyou Wu. Sample collection, soil chemical and microbial characterization were carried out by Y.H., M.M.Y., Linwei Wu, J.G. and Z.G. Data analyses were done by Y.Z. and D.N. with assistance from Linwei Wu and J.Z. All data analysis and integration were guided by J.Z. The manuscript was prepared by Y.Z., D.N., X.L., Y.Y., J.M.T. and J.Z.

Corresponding author

Correspondence to Jizhong Zhou.

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The authors declare no competing interests.

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Nature Climate Change thanks Federica D’Alò and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Effects of experimental warming on archaeal community composition under unwarmed and warmed conditions at the order level.

Cumulative richness is expressed as the number of operational taxonomic units (OTUs).

Extended Data Fig. 2 The succession of soil archaea communities under unwarmed and warmed treatments by detrended correspondence analysis (DCA).

Individual samples from warmed and unwarmed plots within each year are shown in (a) and the centroids of four replicates from each treatment within each year are shown in (b). The analysis was performed based on Sorensen dissimilarity metric. Warmed samples are clustered together with control samples in year 0 (2009) and separated from control samples in the following seven years (2010–2016).

Source data

Extended Data Fig. 3 Temporal changes in community differences between warming and control conditions for archaea and fungi.

The first year is 2009 (year 0). Considering the repeated-measures design, the warming-versus-control dissimilarity values at each block were fitted to the linear mixed-effects (LMMs) models with a fixed effect of time and a random intercept and slope effect among different pairs of plots (blocks). The slopes are presented as a coefficient in fixed effect ± standard error in random effect. The r2 values are calculated (details in Methods), reflecting the variance explained by the whole LMM model. p values were based on permutation tests (two-sided). The lines showed the fixed effects of the LMM.

Source data

Extended Data Fig. 4 Temporal changes in community differences between warming and control conditions for orders Nitrososphaerales and Methanomassiliicoccales.

The analysis was performed based on unweighted UniFrac metrics. Considering the repeated-measures design, the warming-versus-control dissimilarity values at each block were fitted to LMMs with a fixed effect of time and a random intercept and slope effect among different pairs of plots (blocks). The slopes are presented as a coefficient in fixed effect ± standard error in random effect. The r2 values are calculated (details in Methods), reflecting the variance explained by the whole LMM model. p values were based on permutation tests (two-sided). The lines showed the fixed effects of the LMM.

Source data

Extended Data Fig. 5 Constrained ordination analysis of archaeal communities.

(a) Canonical correspondence analyses (CCA) of soil archaea community and environmental attributes. Tested environmental attributes include soil nitrate (NO3−), ammonium (NH4+), total nitrogen (TN), total organic C (TOC), pH, Precipitation of sampling month (Prcp_SM), temperature, moisutre drought index, C3 and C4 aboveground biomass, plant richness, and total biomass. The insert table shows the significance of each environmental variable in explaining the variations of archaeal community (one-way ANOVA test). (b) CCA-based variation partitioning analysis (VPA) showed the relative proportions of archaeal community variations that can be explained by different types of environmental factors. The numbers within the circles showed the variation explained by each group of environmental factors alone. The numbers between the circles showed the interactions of the two factors on either side and number in the center of the interactions of all three factors.

Extended Data Fig. 6 Constrained ordination analysis of the order Nitrososphaerales.

(a) CCA of the Nitrososphaerales group and environmental attributes. The tested environmental attributes and other properties are the same as in Extended Data Fig. 5 (one-way ANOVA test). Significant tests (P < 0.05) are shown in bold red. (b) CCA-based VPA showed the relative proportions of variations in the Nitrososphaerales group that can be explained by different types of environmental factors. The numbers within the circles showed the variation explained by each group of environmental factors alone. The numbers between the circles showed the interactions of the two factors on either side and number in the center of the interactions of all three factors.

Extended Data Fig. 7 Warming-induced changes of different bins.

Warming-induced difference between warming and control is expressed in percentages for the three dominating ecological processes—homogeneous selection (HoS), dispersal limitation (DL), and drift and others (DR).

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Extended Data Fig. 8 Relationships between archaeal community structure and environmental variables and ecosystem processes under control.

Archaeal community structures, which include taxonomical composition by 16 S rRNA genes and functional gene composition by GeoChip and EcoFUN-MAP, were tested against time, soil and plant variables and ecosystem C fluxes. All the other properties are the same as Fig. 4a.

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Zhang, Y., Ning, D., Wu, L. et al. Experimental warming leads to convergent succession of grassland archaeal community. Nat. Clim. Chang. 13, 561–569 (2023). https://doi.org/10.1038/s41558-023-01664-x

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