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Climate warming fuels the global antibiotic resistome by altering soil bacterial traits

Nature Ecology & Evolution volume 9, pages 1512–1526 (2025)Cite this article

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Abstract

Understanding the implications of global warming on the spread of antibiotic resistance genes (ARGs) and virulence factor genes (VFGs) within soil ecosystems is crucial for safeguarding human well-being and sustaining ecosystem health. However, there is currently a lack of large-scale, systematic underpinning data needed to examine this issue. Here, using an integrative approach that combines field experiments, extensive global metagenomic data and microbial culturing, we show that warming enriches bacteria with ARGs and VFGs, increases metabolic complexity and adaptability in bacteria, and accelerates genetic alterations related to ARG and VFGs development. Our validation experiments confirm that the warming effect is more pronounced in colder regions. Machine learning predictions further suggest that warming will increase the soil ARG abundance, especially in some areas that rely heavily on fossil fuels. These results suggest another major negative consequence of global warming, highlighting the importance of developing and implementing sustainability policies that simultaneously combat climate change and antibiotic resistance.

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Fig. 1: Schematic diagram of the study.
Fig. 2: Evaluating the effects of various warming methods on soil ARG pollution based on our experiments and global data analysis.
Fig. 3: The impact of warming on alterations in diverse bacterial groups and their life history strategies.
Fig. 4: Changes in bacterial microdiversity, evolutionary forces and gene expression.
Fig. 5: Abundance of ARGs in L. boroniterans, E. quasihormaechei and E. coli incubated at different temperatures.
Fig. 6: Possible mechanism diagram of warming-induced increase in ARGs of bacteria.

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Data availability

Metagenomic sequencing and metatranscriptomics sequencing data produced in this study were deposited in the NCBI Sequence Read Archive database under accession numbers PRJNA1131773 and PRJNA1131776. Supplementary Tables 1–12 in this study are also publicly available via figshare at https://doi.org/10.6084/m9.figshare.28706258 (ref. 95). Source data are provided with this paper.

Code availability

The R script is available via GitHub at https://github.com/DaLin-lab/Warming-ARGs (ref. 96).

References

  1. The Lancet. The antimicrobial crisis: enough advocacy, more action. Lancet 395, 247 (2020).

    Article  CAS  PubMed  Google Scholar 

  2. Murray, C. J. et al. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet 399, 629–655 (2022).

    Article  CAS  Google Scholar 

  3. Allen, J. P., Snitkin, E., Pincus, N. B. & Hauser, A. R. Forest and trees: exploring bacterial virulence with genome-wide association studies and machine learning. Trends Microbiol. 29, 621–633 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Zheng, D. et al. Global biogeography and projection of soil antibiotic resistance genes. Sci. Adv. 8, eabq8015 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Zhang, Q. et al. Metagenomic insight into the global dissemination of the antibiotic resistome. Adv. Sci. 10, 202303925 (2023).

    Article  Google Scholar 

  6. Liu, X. et al. Assessing foodborne health risks from dietary exposure to antibiotic resistance genes and opportunistic pathogens in three types of vegetables: an in vitro simulation of gastrointestinal digestion. J. Hazard. Mater. 484, 136731 (2025).

    Article  CAS  PubMed  Google Scholar 

  7. Banerjee, S. & van der Heijden, M. G. A. Soil microbiomes and one health. Nat. Rev. Microbiol. 21, 6–20 (2023).

    Article  CAS  PubMed  Google Scholar 

  8. Chen, Q. L., Cui, H. L., Su, J. Q., Penuelas, J. & Zhu, Y. G. Antibiotic resistomes in plant microbiomes. Trends Plant Sci. 24, 530–541 (2019).

    Article  CAS  PubMed  Google Scholar 

  9. Singh, B. K., Yan, Z. Z., Whittaker, M., Vargas, R. & Abdelfattah, A. Soil microbiomes must be explicitly included in One Health policy. Nat. Microbiol. 8, 1367–1372 (2023).

    Article  CAS  PubMed  Google Scholar 

  10. Speißer, B., Wilschut, R. A. & van Kleunen, M. Number of simultaneously acting global change factors affects composition, diversity and productivity of grassland plant communities. Nat. Commun. 13, 7811 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Rillig, M. C. et al. The role of multiple global change factors in driving soil functions and microbial biodiversity. Science 366, 886–890 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. The Core Writing Team IPCC. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change 151 (IPCC, 2015).

  13. Tao, X. et al. Experimental warming accelerates positive soil priming in a temperate grassland ecosystem. Nat. Commun. 15, 1178 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Delgado-Baquerizo, M. et al. The proportion of soil-borne pathogens increases with warming at the global scale. Nat. Clim. Chang. 10, 550–554 (2020).

    Article  Google Scholar 

  15. Crowther, T. W. et al. Quantifying global soil carbon losses in response to warming. Nature 540, 104–108 (2016).

    Article  CAS  PubMed  Google Scholar 

  16. Dai, Z. et al. Elevated temperature shifts soil N cycling from microbial immobilization to enhanced mineralization, nitrification and denitrification across global terrestrial ecosystems. Glob. Chang. Biol. 26, 5267–5276 (2020).

    Article  PubMed  Google Scholar 

  17. 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  PubMed  Google Scholar 

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

    Article  Google Scholar 

  19. MacFadden, D. R., McGough, S. F., Fisman, D., Santillana, M. & Brownstein, J. S. Antibiotic resistance increases with local temperature. Nat. Clim. Chang. 8, 510–514 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Li, Z. et al. Climate warming increases the proportions of specific antibiotic resistance genes in natural soil ecosystems. J. Hazard. Mater. 430, 128442 (2022).

    Article  CAS  PubMed  Google Scholar 

  21. Nottingham, A. T. et al. Microbial diversity declines in warmed tropical soil and respiration rise exceed predictions as communities adapt. Nat. Microbiol. 7, 1650–1660 (2022).

    Article  CAS  PubMed  Google Scholar 

  22. Metze, D. et al. Soil warming increases the number of growing bacterial taxa but not their growth rates. Sci. Adv. 10, eadk6295 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Rodríguez-Verdugo, A., Lozano-Huntelman, N., Cruz-Loya, M., Savage, V. & Yeh, P. Compounding effects of climate warming and antibiotic resistance. iScience 23, 101024 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Ruan, Y. et al. Elevated temperature and CO2 strongly affect the growth strategies of soil bacteria. Nat. Commun. 14, 391 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. 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 

  26. Cruz-Loya, M. et al. Stressor interaction networks suggest antibiotic resistance co-opted from stress responses to temperature. ISME J. 13, 12–23 (2019).

    Article  PubMed  Google Scholar 

  27. Rodríguez-Verdugo, A., Gaut, B. S. & Tenaillon, O. Evolution of Escherichia coli rifampicin resistance in an antibiotic-free environment during thermal stress. BMC Evol. Biol. 13, 50 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Zhao, W., Zheng, S., Ye, C., Li, J. & Yu, X. Nonlinear impacts of temperature on antibiotic resistance in Escherichia coli. Environ. Sci. Ecotechnol. 22, 100475 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Zhao, W. et al. High temperatures promote antibiotic resistance genes conjugative transfer under residual chlorine: mechanisms and risks. J. Hazard. Mater. 483, 136675 (2025).

    Article  CAS  PubMed  Google Scholar 

  30. Walsh, T. R., Weeks, J., Livermore, D. M. & Toleman, M. A. Dissemination of NDM-1 positive bacteria in the New Delhi environment and its implications for human health: an environmental point prevalence study. Lancet Infect. Dis. 11, 355–362 (2011).

    Article  PubMed  Google Scholar 

  31. Delgado-Baquerizo, M. et al. The global distribution and environmental drivers of the soil antibiotic resistome. Microbiome 10, 219 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Zhang, Z. et al. Assessment of global health risk of antibiotic resistance genes. Nat. Commun. 13, 1553 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Sáez-Sandino, T. et al. The soil microbiome governs the response of microbial respiration to warming across the globe. Nat. Clim. Chang. 13, 1382–1387 (2023).

    Article  Google Scholar 

  34. Li, Q. et al. Different climate response persistence causes warming trend unevenness at continental scales. Nat. Clim. Chang. 12, 343–349 (2022).

    Article  Google Scholar 

  35. Huang, X. et al. Temperature-dependent trophic associations modulate soil bacterial communities along latitudinal gradients. ISME J. 18, wrae145 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Wang, M. et al. Responses of soil organic carbon to climate extremes under warming across global biomes. Nat. Clim. Chang. 14, 98–105 (2024).

    Article  CAS  Google Scholar 

  37. Kossin, J. P. A global slowdown of tropical-cyclone translation speed. Nature 558, 104–107 (2018).

    Article  CAS  PubMed  Google Scholar 

  38. Wang, M. et al. Global soil profiles indicate depth-dependent soil carbon losses under a warmer climate. Nat. Commun. 13, 5514 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Kang, L. et al. Metagenomic insights into microbial community structure and metabolism in alpine permafrost on the Tibetan Plateau. Nat. Commun. 15, 5920 (2024).

  40. Lin, D., Xu, J. Y., Wang, L., Du, S. & Zhu, D. Long-term application of organic fertilizer prompting the dispersal of antibiotic resistance genes and their health risks in the soil plastisphere. Environ. Int. 183, 108431 (2024).

    Article  PubMed  Google Scholar 

  41. Wood, J. L. et al. Rethinking CSR theory to incorporate microbial metabolic diversity and foraging traits. ISME J. 17, 1793–1797 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Zhang, S. et al. Distribution of ARGs and MGEs among glacial soil, permafrost, and sediment using metagenomic analysis. Environ. Pollut. 234, 339–346 (2018).

    Article  CAS  PubMed  Google Scholar 

  43. Qian, X. et al. Metagenomic analysis reveals the shared and distinct features of the soil resistome across tundra, temperate prairie, and tropical ecosystems. Microbiome 9, 108 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. McDonald, M. D. et al. What is microbial dormancy? Trends Microbiol. 32, 142–150 (2024).

    Article  CAS  PubMed  Google Scholar 

  45. Littlejohn, P. T. et al. Multiple micronutrient deficiencies in early life cause multi-kingdom alterations in the gut microbiome and intrinsic antibiotic resistance genes in mice. Nat. Microbiol. 8, 2392–2405 (2023).

    Article  CAS  PubMed  Google Scholar 

  46. Wang, J. et al. Supercarriers of antibiotic resistome in a world’s large river. Microbiome 10, 111 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Dai, D. et al. Long-read metagenomic sequencing reveals shifts in associations of antibiotic resistance genes with mobile genetic elements from sewage to activated sludge. Microbiome 10, 20 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Johnston, E. R. et al. Responses of tundra soil microbial communities to half a decade of experimental warming at two critical depths. Proc. Natl Acad. Sci. USA 116, 15096–15105 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Yang, S. et al. Effects of a long-term anoxic warming scenario on microbial community structure and functional potential of permafrost-affected soil. Permafr. Periglac. Process. 32, 641–656 (2021).

    Article  Google Scholar 

  50. Wang, C. et al. Bacterial genome size and gene functional diversity negatively correlate with taxonomic diversity along a pH gradient. Nat. Commun. 14, 32 (2023).

    PubMed  PubMed Central  Google Scholar 

  51. Westoby, M. et al. Trait dimensions in bacteria and archaea compared to vascular plants. Ecol. Lett. 24, 1487–1504 (2021).

    Article  PubMed  Google Scholar 

  52. Fierer, N. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat. Rev. Microbiol. 15, 579–590 (2017).

    Article  CAS  PubMed  Google Scholar 

  53. Krause, S. et al. Trait-based approaches for understanding microbial biodiversity and ecosystem functioning. Front. Microbiol. 5, 251 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Zhang, H. Y., Bissett, A., Aguilar-Trigueros, C. A., Liu, H. W. & Powell, J. R. Fungal genome size and composition reflect ecological strategies along soil fertility gradients. Ecol. Lett. 26, 1108–1118 (2023).

    Article  PubMed  Google Scholar 

  55. Darby, E. M. et al. Molecular mechanisms of antibiotic resistance revisited. Nat. Rev. Microbiol. 21, 280–295 (2023).

    Article  CAS  PubMed  Google Scholar 

  56. Cordero, M., Mitarai, N. & Jauffred, L. Motility mediates satellite formation in confined biofilms. ISME J. 17, 1819–1827 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Ciofu, O., Moser, C., Jensen, P. Ø. & Høiby, N. Tolerance and resistance of microbial biofilms. Nat. Rev. Microbiol. 20, 621–635 (2022).

    Article  CAS  PubMed  Google Scholar 

  58. Rosas, N. C. & Lithgow, T. Targeting bacterial outer-membrane remodelling to impact antimicrobial drug resistance. Trends Microbiol. 30, 544–552 (2022).

    Article  CAS  PubMed  Google Scholar 

  59. Andersson, D. I. & Hughes, D. Microbiological effects of sublethal levels of antibiotics. Nat. Rev. Microbiol. 12, 465–478 (2014).

    Article  CAS  PubMed  Google Scholar 

  60. Li, Z. et al. Integrated human skin bacteria genome catalog reveals extensive unexplored habitat-specific microbiome diversity and function. Adv. Sci. 10, 2300050 (2023).

    Article  CAS  Google Scholar 

  61. Du, D. et al. Multidrug efflux pumps: structure, function and regulation. Nat. Rev. Microbiol. 16, 523–539 (2018).

    Article  CAS  PubMed  Google Scholar 

  62. El Meouche, I. & Dunlop, M. J. Heterogeneity in efflux pump expression predisposes antibiotic-resistant cells to mutation. Science 362, 686–690 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Wang, Y. et al. Antidepressants can induce mutation and enhance persistence toward multiple antibiotics. Proc. Natl Acad. Sci. USA 120, e2208344120 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Stalder, T. et al. Evolving populations in biofilms contain more persistent plasmids. Mol. Biol. Evol. 37, 1563–1576 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Yang, Y. et al. Structures of the RNA polymerase-σ54 reveal new and conserved regulatory strategies. Science 349, 882–885 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Whiteley, M., Diggle, S. P. & Greenberg, E. P. Progress in and promise of bacterial quorum sensing research. Nature 551, 313–320 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Wang, X. et al. Hot moment of N2O emissions in seasonally frozen peatlands. ISME J. 17, 792–802 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Bei, Q. et al. Extreme summers impact cropland and grassland soil microbiomes. ISME J. 17, 1589–1600 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Donhauser, J., Qi, W., Bergk-Pinto, B. & Frey, B. High temperatures enhance the microbial genetic potential to recycle C and N from necromass in high-mountain soils. Glob. Chang. Biol. 27, 1365–1386 (2021).

    Article  PubMed  Google Scholar 

  70. Levy-Booth, D. J. et al. Genomics and metatranscriptomics of biogeochemical cycling and degradation of lignin-derived aromatic compounds in thermal swamp sediment. ISME J. 15, 879–893 (2021).

    Article  CAS  PubMed  Google Scholar 

  71. ter Horst, A. M. et al. Minnesota peat viromes reveal terrestrial and aquatic niche partitioning for local and global viral populations. Microbiome 9, 233 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  72. Dove, N. C., Torn, M. S., Hart, S. C. & Taş, N. Metabolic capabilities mute positive response to direct and indirect impacts of warming throughout the soil profile. Nat. Commun. 12, 2089 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Chen, S., Zhou, Y., Chen, Y. & Gu, J. Fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  74. Yin, X. et al. ARGs-OAP v3.0: antibiotic-resistance gene database curation and analysis pipeline optimization. Engineering 27, 234–241 (2023).

    Article  Google Scholar 

  75. Liu, B., Zheng, D., Zhou, S., Chen, L. & Yang, J. VFDB 2022: a general classification scheme for bacterial virulence factors. Nucleic Acids Res. 50, D912–D917 (2022).

    Article  CAS  PubMed  Google Scholar 

  76. Pärnänen, K. et al. Maternal gut and breast milk microbiota affect infant gut antibiotic resistome and mobile genetic elements. Nat. Commun. 9, 3891 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  77. Buchfink, B., Reuter, K. & Drost, H. G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat. Methods 18, 366–368 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Li, D., Liu, C. M., Luo, R., Sadakane, K. & Lam, T. W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).

    Article  CAS  PubMed  Google Scholar 

  79. West, P. T., Probst, A. J., Grigoriev, I. V., Thomas, B. C. & Banfield, J. F. Genome-reconstruction for eukaryotes from complex natural microbial communities. Genome Res. 28, 569–580 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Malik, A. A. et al. Defining trait-based microbial strategies with consequences for soil carbon cycling under climate change. ISME J. 14, 1–9 (2020).

    Article  CAS  PubMed  Google Scholar 

  81. Piton, G. et al. Life history strategies of soil bacterial communities across global terrestrial biomes. Nat. Microbiol. 8, 2093–2102 (2023).

    Article  CAS  PubMed  Google Scholar 

  82. Nayfach, S. & Pollard, K. S. Average genome size estimation improves comparative metagenomics and sheds light on the functional ecology of the human microbiome. Genome Biol. 16, 51 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  83. Mikheenko, A., Prjibelski, A., Saveliev, V., Antipov, D. & Gurevich, A. Versatile genome assembly evaluation with QUAST-LG. Bioinformatics 34, i142–i150 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Pereira-Flores, E., Glöckner, F. O. & Fernandez-Guerra, A. Fast and accurate average genome size and 16S rRNA gene average copy number computation in metagenomic data. BMC Bioinform. 20, 453 (2019).

    Article  Google Scholar 

  85. Kang, D. D., Froula, J., Egan, R. & Wang, Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ 3, e1165 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  86. Wu, Y. W., Simmons, B. A. & Singer, S. W. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 32, 605–607 (2016).

    Article  CAS  PubMed  Google Scholar 

  87. Alneberg, J. et al. Binning metagenomic contigs by coverage and composition. Nat. Methods 11, 1144–1146 (2014).

    Article  CAS  PubMed  Google Scholar 

  88. Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Parks, D. H. et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat. Biotechnol. 36, 996 (2018).

    Article  CAS  PubMed  Google Scholar 

  90. Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 11, 119 (2010).

    Article  Google Scholar 

  91. Olm, M. R. et al. inStrain profiles population microdiversity from metagenomic data and sensitively detects shared microbial strains. Nat. Biotechnol. 39, 727–736 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Weissman, J. L., Hou, S. & Fuhrman, J. A. Estimating maximal microbial growth rates from cultures, metagenomes, and single cells via codon usage patterns. Proc. Natl Acad. Sci. USA 118, e2016810118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Yang, K. et al. Mycorrhizal type regulates trade-offs between plant and soil carbon in forests. Nat. Clim. Chang. 14, 91–97 (2024).

    Article  Google Scholar 

  94. Zhang, Z. et al. Global biogeography of microbes driving ocean ecological status under climate change. Nat. Commun. 15, 4657 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Lin, D. et al. Climate warming fuels the global antibiotic resistome by altering soil bacterial traits. figshare https://doi.org/10.6084/m9.figshare.28706258 (2025).

  96. Lin, D. et al. Climate warming fuels the global antibiotic resistome by altering soil bacterial traits. GitHub https://github.com/DaLin-lab/Warming-ARGs (2025).

  97. Söllinger, A. et al. Down-regulation of the bacterial protein biosynthesis machinery in response to weeks, years, and decades of soil warming. Sci. Adv. 8, eabm3230 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We acknowledge the funds of the National Natural Science Foundation of China (grant nos. 42222701 to D.Z. and 42090063 to Y.-G.Z.), J.P. was supported by the Catalan Government grant no. SGR·2021-1333, Youth Innovation Promotion Association, Chinese Academy of Sciences (grant no. 2023321 to D.Z.), Ningbo Yongjiang Talent Project (grant no. 2022A-163-G to D.Z.) and Ningbo S&T project (grant no. 2021-DST-004 to Y.-G.Z.).

Author information

Author notes

  1. These authors contributed equally: Da Lin, Shuai Du.

Authors and Affiliations

  1. State Key Laboratory of Regional and Urban Ecology, Ningbo Observation and Research Station, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, China

    Da Lin, Shuai Du, Tianlun Zhang, Lu Wang, Dong Zhu & Yong-Guan Zhu

  2. University of Chinese Academy of Sciences, Beijing, China

    Da Lin & Tianlun Zhang

  3. Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control, CAS Haixi Industrial Technology Innovation Center in Beilun, Ningbo, China

    Shuai Du, Lu Wang, Dong Zhu & Yong-Guan Zhu

  4. State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China

    Zhe Zhao & Yong-Guan Zhu

  5. College of Chemistry and Chemical Engineering, Shaoxing University, Shaoxing, China

    Qi Zhang

  6. Guangdong Provincial Key Laboratory of Applied Botany, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, China

    Shu-Yi-Dan Zhou

  7. Department of Biosciences, Durham University, Durham, UK

    David W. Graham

  8. Hawkesbury Institute for the Environment, Western Sydney University, Richmond, Australia

    David T. Tissue

  9. Global Ecology Unit CREAF-CSIC-UAB, CSIC, Barcelona, Spain

    Josep Penuelas

  10. CREAF, Universitat Autònoma de Barcelona, Barcelona, Spain

    Josep Penuelas

  11. Institute for Global Change Biology and School for Environment and Sustainability, University of Michigan, Ann Arbor, MI, USA

    Peter B. Reich

  12. Department of Forest Resources, University of Minnesota, St. Paul, MN, USA

    Peter B. Reich

  13. Hawkesbury Institute for the Environment, Western Sydney University, Sydney, New South Wales, Australia

    Peter B. Reich

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Contributions

D.L., S.-Y.-D.Z. and D.Z. conceived and designed the research. D.L., S.-Y.-D.Z. and D.Z. performed the experiments. D.L., S.D., Z.Z., T.Z. and L.W. analysed the data and prepared the figures. D.L., S.D., Q.Z., S.-Y.-D.Z., D.W.G., D.T.T., D.Z., Y.-G.Z., J.P. and P.B.R. wrote and revised the paper. All authors read and approved the paper.

Corresponding authors

Correspondence to Shu-Yi-Dan Zhou or Dong Zhu.

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Nature Ecology & Evolution thanks Steven Djordjevic, Hang-Wei Hu, Carlos Guerra, Madhav Thakur 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 Global distribution of ARG pollution at different latitudes.

The relationship between (a) ARGs, (b) risk index of ARGs, (c) VFGs and temperature in global soil at different latitudes. Linear regression model with a two-sided test was used for statistical analysis.

Source data

Extended Data Fig. 2 The role of climate variables in shaping the response of soil ARG pollution to warming.

The relative importance of climate variables in influencing the response of soil (a) ARGs, (b) risk index of ARGs and (c) VFGs to warming. The relative importances of climate variable was estimated by calculating the percentage increases in the mean squared error (MSE) of the variables in random forest models (n = 326). Significant differences are denoted as * (P < 0.05) and ** (P < 0.01).

Source data

Extended Data Fig. 3 The overall effect of artificial warming on VFGs and MGEs in soil.

The bars around the mean represent the 95% confidence interval. If the 95% confidence interval (CI) does not overlap with zero, the impact of warming on ARG-related variables is considered significant. Blue, red and gray indicate negative significant, positive significant and insignificant effects of warming on these variables, respectively (n = 150).

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Extended Data Fig. 4 The relationships between the effect of warming and other factors.

(a) Relationships between effect sizes of warming on ARGs and the duration of warming treatment. We excluded three samples from Söllinger et al.97 with warming durations exceeding 56 years, as their treatment durations were ‌considerably different from those of the other samples. (b) Relationships between effect sizes of warming on risk index of ARGs and soil depth. Linear regression model with a two-sided test was used for statistical analysis.

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Extended Data Fig. 5 Features of bacterial community.

Comparison of the abundance of ARGs, MGEs and VFGs between (a) Proteobacteria (n = 77) or (b) bacteria (n = 997) positively affected by increasing temperatures and those negatively affected by warming. The number of ARGs, VFGs and MGEs is normalized using a linear scale, with all raw values standardized within the same range of 0-10. P values were calculated using two-tailed unpaired Student’s t test. The tops of the boxes represent the 75th percentile, the bottoms indicate the 25th percentile, and the center lines denote the median. The whiskers extend to the maximum and minimum non-outlier values.

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Extended Data Fig. 6 Features of ‘high-risk’ bacteria.

Comparison of (a) maximum growth rates and (b) the number of ARGs, MGEs and VFGs between potential ‘high-risk’ bacteria (n = 84) positively affected by increasing temperatures and those negatively affected by warming. The number of ARGs, VFGs and MGEs is normalized using a linear scale, with all raw values standardized within the same range of 0-10. P values were calculated using two-tailed unpaired Student’s t test.

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Extended Data Fig. 7 Composition of ‘high-risk’ bacteria.

Composition of (a) potential “high-risk” bacteria without MGEs and (b) potential ‘high-risk’ bacteria carrying MGEs (n = 84). The red and blue parts in the pie chart represent the proportion of potential “high-risk” bacteria increased and decreased under warming, respectively.

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Extended Data Fig. 8 The co-occurrence patterns of ARGs, MGEs and VFGs on the contigs in the 84 potential ‘high-risk’ bacteria.

Potential ‘high-risk’ bacteria represent bacterial genotypes that undergo substantial changes (P < 0.05) in abundance with warming, as observed in meta-analysis, and also carrying ARGs and VFGs.

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Extended Data Fig. 9 Gene abundance and expression in Escherichia coli.

(a) Abundance of ARGs and MGEs in Escherichia coli incubated at different temperatures (n = 12). Data are presented as mean values ± SD. (b) Comparison of gene expression differences in Escherichia coli incubated at different temperatures. P values were calculated using one-way ANOVA with LSD-test. Different lowercase letters indicate significant differences among the treatments at P < 0.05.

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Extended Data Fig. 10 Future projections of changes in ARG abundance under three future climate scenarios.

‘Fold change in ARG abundance’ represents the change in ARG abundance of future climate scenarios relative to current conditions, with the values normalized using Log2 (n = 512).

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Lin, D., Du, S., Zhao, Z. et al. Climate warming fuels the global antibiotic resistome by altering soil bacterial traits. Nat Ecol Evol 9, 1512–1526 (2025). https://doi.org/10.1038/s41559-025-02740-5

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