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.

Microbial diversity declines in warmed tropical soil and respiration rise exceed predictions as communities adapt

Abstract

Perturbation of soil microbial communities by rising temperatures could have important consequences for biodiversity and future climate, particularly in tropical forests where high biological diversity coincides with a vast store of soil carbon. We carried out a 2-year in situ soil warming experiment in a tropical forest in Panama and found large changes in the soil microbial community and its growth sensitivity, which did not fully explain observed large increases in CO2 emission. Microbial diversity, especially of bacteria, declined markedly with 3 to 8 °C warming, demonstrating a breakdown in the positive temperature-diversity relationship observed elsewhere. The microbial community composition shifted with warming, with many taxa no longer detected and others enriched, including thermophilic taxa. This community shift resulted in community adaptation of growth to warmer temperatures, which we used to predict changes in soil CO2 emissions. However, the in situ CO2 emissions exceeded our model predictions threefold, potentially driven by abiotic acceleration of enzymatic activity. Our results suggest that warming of tropical forests will have rapid, detrimental consequences both for soil microbial biodiversity and future climate.

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

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Microbial diversity decline and community change under in situ soil warming in lowland tropical forest.
Fig. 2: Response of microbial growth and enzyme activity to soil warming, and the relationship between this temperature response and microbial community changes.
Fig. 3: The response of soil CO2 efflux to in situ warming is greater than the increase predicted by the temperature response of microbial respiration and growth.

Data availability

Trimmed (primers removed) sequence data generated in this study are deposited in the European Nucleotide Archive (ENA) under Project Accession number PRJEB45074 (ERP129199), sample accession numbers ERS6485270ERS6485284 (16S rRNA) and sample accession numbers ERS6485285ERS6485299 (ITS). Raw fastq files can be accessed through the Smithsonian figshare at https://doi.org/10.25573/data.14686665 (16S rRNA) and https://doi.org/10.25573/data.14686755 (ITS). Related data and data products for individual analysis workflows are available through the Smithsonian figshare under the collection https://doi.org/10.25573/data.c.5667571.

Code availability

All code, reproducible workflows and further information on data availability can be found on the project website at https://sweltr.github.io/high-temp/. The code embedded in the website is available on GitHub (https://github.com/sweltr/high-temp/) in R Markdown format. The version of code used in this study is archived under SWELTR Workflows v1.0 (https://github.com/sweltr/high-temp), DOI identifier https://zenodo.org/badge/latestdoi/368915237.

References

  1. Cavicchioli, R. et al. Scientists’ warning to humanity: microorganisms and climate change. Nat. Rev. Microbiol. 17, 569–586 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Jackson, R. B. et al. The ecology of soil carbon: pools, vulnerabilities, and biotic and abiotic controls. Annu. Rev. Ecol. Evol. Syst. 48, 419–445 (2017).

    Article  Google Scholar 

  3. Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).

    Article  CAS  PubMed  Google Scholar 

  4. Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).

    Article  CAS  PubMed  Google Scholar 

  5. IPCC. Climate Change 2021: The Physical Science Basis. (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, in press).

  6. Mora, C. et al. The projected timing of climate departure from recent variability. Nature 502, 183–187 (2013).

  7. Wood, T. E. et al. in Ecosystem Consequences of Soil Warming: Microbes, Vegetation, Fauna and Soil Biogeochemistry (ed. Mohan, J.) Ch. 14 (Academic Press, 2019).

  8. Davidson, E. A. & Janssens, I. A. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440, 165–173 (2006).

    Article  CAS  PubMed  Google Scholar 

  9. van Gestel, N. et al. Predicting soil carbon loss with warming. Nature 554, E4–E5 (2018).

    Article  PubMed  Google Scholar 

  10. Melillo, J. M. et al. Long-term pattern and magnitude of soil carbon feedback to the climate system in a warming world. Science 358, 101–104 (2017).

    Article  CAS  PubMed  Google Scholar 

  11. Romero-Olivares, A. L., Allison, S. D. & Treseder, K. K. Soil microbes and their response to experimental warming over time: a meta-analysis of field studies. Soil Biol. Biochem. 107, 32–40 (2017).

    Article  CAS  Google Scholar 

  12. Anderson-Teixeira, K. J., Wang, M. M. H., McGarvey, J. C. & LeBauer, D. S. Carbon dynamics of mature and regrowth tropical forests derived from a pantropical database (TropForC-db). Glob. Change Biol. 22, 1690–1709 (2016).

    Article  Google Scholar 

  13. Nottingham, A. T., Meir, P., Velasquez, E. & Turner, B. L. Soil carbon loss by experimental warming in a tropical forest. Nature 584, 234–237 (2020).

    Article  CAS  PubMed  Google Scholar 

  14. Kimball, B. A. et al. Infrared heater system for warming tropical forest understory plants and soils. Ecol. Evol. 8, 1932–1944 (2018).

  15. DeAngelis, K. M. et al. Long-term forest soil warming alters microbial communities in temperate forest soils. Front. Microbiol. https://doi.org/10.3389/fmicb.2015.00104 (2015)

  16. Bååth, E. Temperature sensitivity of soil microbial activity modeled by the square root equation as a unifying model to differentiate between direct temperature effects and microbial community adaptation. Glob. Change Biol. 24, 2850–2861 (2018).

    Article  Google Scholar 

  17. Wieder, W. R., Bonan, G. B. & Allison, S. D. Global soil carbon projections are improved by modelling microbial processes. Nat. Clim. Change 3, 909–912 (2013).

    Article  CAS  Google Scholar 

  18. Ratkowsky, D. A., Olley, J., Mcmeekin, T. A. & Ball, A. Relationship between temperature and growth-rate of bacterial cultures. J. Bacteriol. 149, 1–5 (1982).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Rinnan, R., Rousk, J., Yergeau, E., Kowalchuk, G. A. & Bååth, E. Temperature adaptation of soil bacterial communities along an Antarctic climate gradient: predicting responses to climate warming. Glob. Change Biol. 15, 2615–2625 (2009).

    Article  Google Scholar 

  20. Nottingham, A. T., Bååth, E., Reischke, S., Salinas, N. & Meir, P. Adaptation of soil microbial growth to temperature: using a tropical elevation gradient to predict future changes. Glob. Change Biol. https://doi.org/10.1111/gcb.14502 (2019).

  21. Li, J. Q., Bååth, E., Pei, J. M., Fang, C. M. & Nie, M. Temperature adaptation of soil microbial respiration in alpine, boreal and tropical soils: an application of the square root (Ratkowsky) model. Glob. Change Biol. 27, 1281–1292 (2021).

    Article  CAS  Google Scholar 

  22. Rousk, J., Frey, S. D. & Bååth, E. Temperature adaptation of bacterial communities in experimentally warmed forest soils. Glob. Change Biol. 18, 3252–3258 (2012).

    Article  Google Scholar 

  23. Nottingham, A. T. et al. Annual to decadal temperature adaptation of the soil bacterial community after translocation across an elevation gradient in the Andes. Soil Biol. Biochem. 158, 108217 (2021).

    Article  CAS  Google Scholar 

  24. Nottingham, A. T. et al. Microbial responses to warming enhance soil carbon loss following translocation across a tropical forest elevation gradient. Ecol. Lett. 22, 1889–1899 (2019).

    Article  PubMed  Google Scholar 

  25. Donhauser, J., Niklaus, P. A., Rousk, J., Larose, C. & Frey, B. Temperatures beyond the community optimum promote the dominance of heat-adapted, fast growing and stress resistant bacteria in alpine soils. Soil Biol. Biochem. 148, 107873 (2020).

    Article  CAS  Google Scholar 

  26. Mangan, S. A. et al. Negative plant–soil feedback predicts tree-species relative abundance in a tropical forest. Nature 466, 752–755 (2010).

  27. Pold, G., Melillo, J. M. & DeAngelis, K. M. Two decades of warming increases diversity of a potentially lignolytic bacterial community. Front. Microbiol. https://doi.org/10.3389/fmicb.2015.00480 (2015).

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

  29. Tedersoo, L. et al. Global diversity and geography of soil fungi. Science 346, 1256688 (2014).

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

  31. Oliverio, A. M., Bradford, M. A. & Fierer, N. Identifying the microbial taxa that consistently respond to soil warming across time and space. Glob. Change Biol. 23, 2117–2129 (2017).

    Article  Google Scholar 

  32. Bahram, M. et al. Structure and function of the global topsoil microbiome. Nature 560, 233–237 (2018).

    Article  CAS  PubMed  Google Scholar 

  33. Spracklen, D. V., Baker, J. C. A., Garcia-Carreras, L. & Marsham, J. H. The effects of tropical vegetation on rainfall. Annu. Rev. Env. Resour. 43, 193–218 (2018).

    Article  Google Scholar 

  34. Bradford, M. A. Thermal adaptation of decomposer communities in warming soils. Front. Microbiol. https://doi.org/10.3389/Fmicb.2013.00333 (2013).

  35. Pietikäinen, J., Pettersson, M. & Bååth, E. Comparison of temperature effects on soil respiration and bacterial and fungal growth rates. FEMS Microbiol. Ecol. 52, 49–58 (2005).

    Article  PubMed  Google Scholar 

  36. Mori, A. S. et al. Biodiversity–productivity relationships are key to nature-based climate solutions. Nat. Clim. Change 11, 543–550 (2021).

    Article  Google Scholar 

  37. Delgado-Baquerizo, M. et al. Multiple elements of soil biodiversity drive ecosystem functions across biomes. Nat. Ecol. Evol. 4, 210–220 (2020).

    Article  PubMed  Google Scholar 

  38. Wagg, C., Bender, S. F., Widmer, F. & van der Heijden, M. G. A. Soil biodiversity and soil community composition determine ecosystem multifunctionality. Proc. Natl Acad. Sci. USA 111, 5266–5270 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Nottingham, A. T. et al. Microbes follow Humboldt: temperature drives plant and soil microbial diversity patterns from the Amazon to the Andes. Ecology 99, 2455–2466 (2018).

    Article  PubMed  Google Scholar 

  40. Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).

    Article  Google Scholar 

  41. Brown, J. H. Why are there so many species in the tropics? J. Biogeogr. 41, 8–22 (2014).

    Article  PubMed  Google Scholar 

  42. LaManna, J. A. et al. Plant diversity increases with the strength of negative density dependence at the global scale. Science 356, 1389–1392 (2017).

    Article  CAS  PubMed  Google Scholar 

  43. Bagchi, R. et al. Pathogens and insect herbivores drive rainforest plant diversity and composition. Nature 506, 85–88 (2014).

    Article  CAS  PubMed  Google Scholar 

  44. Lapebie, P., Lombard, V., Drula, E., Terrapon, N. & Henrissat, B. Bacteroidetes use thousands of enzyme combinations to break down glycans. Nat. Commun. https://doi.org/10.1038/s41467-019-10068-5 (2019).

  45. Makhalanyane, T. P. et al. Microbial ecology of hot desert edaphic systems. FEMS Microbiol. Rev. 39, 203–221 (2015).

    Article  CAS  PubMed  Google Scholar 

  46. Aydogan, E. L., Moser, G., Muller, C., Kampfer, P. & Glaeser, S. P. Long-term warming shifts the composition of bacterial communities in the phyllosphere of Galium album in a permanent grassland field-experiment. Front. Microbiol. https://doi.org/10.3389/fmicb.2018.00144 (2018).

  47. Hu, D. Y., Zang, Y., Mao, Y. J. & Gao, B. L. Identification of molecular markers that are specific to the class thermoleophilia. Front. Microbiol. https://doi.org/10.3389/fmicb.2019.01185 (2019).

  48. Mohan, J. E. et al. Mycorrhizal fungi mediation of terrestrial ecosystem responses to global change: mini-review. Fungal Ecol. 10, 3–19 (2014).

    Article  Google Scholar 

  49. Manzoni, S., Taylor, P., Richter, A., Porporato, A. & Agren, G. I. Environmental and stoichiometric controls on microbial carbon-use efficiency in soils. New Phytol. 196, 79–91 (2012).

    Article  CAS  PubMed  Google Scholar 

  50. Allison, S. D., Wallenstein, M. D. & Bradford, M. A. Soil-carbon response to warming dependent on microbial physiology. Nat. Geosci. 3, 336–340 (2010).

    Article  CAS  Google Scholar 

  51. Reed, S. C. et al. Soil biogeochemical responses of a tropical forest to warming and hurricane disturbance. Adv. Ecol. Res. 62, 225–252 (2020).

    Article  Google Scholar 

  52. Nottingham, A. T., Turner, B. L., Stott, A. W. & Tanner, E. V. J. Nitrogen and phosphorus constrain labile and stable carbon turnover in lowland tropical forest soils. Soil Biol. Biochem. 80, 26–33 (2015).

    Article  CAS  Google Scholar 

  53. Walker, T. W. N. et al. Microbial temperature sensitivity and biomass change explain soil carbon loss with warming. Nat. Clim. Change 8, 885–889 (2018).

  54. Kemmitt, S. J. et al. Mineralization of native soil organic matter is not regulated by the size, activity or composition of the soil microbial biomass—a new perspective. Soil Biol. Biochem. 40, 61–73 (2008).

    Article  CAS  Google Scholar 

  55. Nannipieri, P., Trasar-Cepeda, C. & Dick, R. P. Soil enzyme activity: a brief history and biochemistry as a basis for appropriate interpretations and meta-analysis. Biol. Fert. Soils 54, 11–19 (2018).

    Article  CAS  Google Scholar 

  56. Wallenstein, M., Allison, S., Ernakovich, J., Steinweg, J. M. & Sinsabaugh, R. in Soil Enzymology. Soil Biology Vol. 22 (eds Shukla, G. & Varma, A.) Ch. 13 (Springer, 2011).

  57. Zhou, X. Y., Chen, L., Xu, J. M. & Brookes, P. C. Soil biochemical properties and bacteria community in a repeatedly fumigated-incubated soil. Biol. Fert. Soils 56, 619–631 (2020).

    Article  CAS  Google Scholar 

  58. Sanchez-Julia, M. & Turner, B. L. Abiotic contribution to phenol oxidase activity across a manganese gradient in tropical forest soils. Biogeochemistry https://doi.org/10.1007/s10533-021-00764-0 (2021).

  59. Razavi, B. S., Liu, S. B. & Kuzyakov, Y. Hot experience for cold-adapted microorganisms: temperature sensitivity of soil enzymes. Soil Biol. Biochem. 105, 236–243 (2017).

    Article  CAS  Google Scholar 

  60. Pinney, M. M. et al. Parallel molecular mechanisms for enzyme temperature adaptation. Science 371, eaay2784 (2021).

    Article  CAS  PubMed  Google Scholar 

  61. Fanin, N. et al. Soil enzymes in response to climate warming: mechanisms and feedbacks. Funct. Ecol. https://doi.org/10.1111/1365-2435.14027 (2022).

  62. Hall, S. J. & Silver, W. L. Iron oxidation stimulates organic matter decomposition in humid tropical forest soils. Glob. Change Biol. 19, 2804–2813 (2013).

    Article  Google Scholar 

  63. Freeman, C., Ostle, N. & Kang, H. An enzymic ‘latch’ on a global carbon store. Nature 409, 149 (2001).

    Article  CAS  PubMed  Google Scholar 

  64. Sarmiento, C. et al. Soilborne fungi have host affinity and host-specific effects on seed germination and survival in a lowland tropical forest. Proc. Natl Acad. Sci. USA 114, 11458–11463 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Condit, R., Perez, R., Lao, S., Aguilar, S. & Hubbell, S. P. Demographic trends and climate over 35 years in the Barro Colorado 50 ha plot. For. Ecosyst. https://doi.org/10.1186/s40663-017-0103-1 (2017).

  66. Woodring, W. P. Geology of Barro Colorado Island. Smithson. Misc. Collect. 135, 1–39 (1958).

    Google Scholar 

  67. Sanchez, P. A. & Logan, T. J. Myths and science about the chemistry and fertility of soils in the tropics. SSSA Spec. Publ. 29, 35–46 (1992).

    CAS  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Brookes, P. C., Landman, A., Pruden, G. & Jenkinson, D. S. Chloroform fumigation and the release of soil nitrogen: a rapid direct extraction method to measure microbial biomass nitrogen in soil. Soil Biol. Biochem. 17, 837–842 (1985).

    Article  CAS  Google Scholar 

  70. Vance, E. D., Brookes, P. C. & Jenkinson, D. S. An extraction method for measuring soil microbial biomass C. Soil Biol. Biochem. 19, 703–707 (1987).

    Article  CAS  Google Scholar 

  71. Jenkinson, D. S., Brookes, P. C. & Powlson, D. S. Measuring soil microbial biomass. Soil Biol. Biochem. 36, 5–7 (2004).

    Article  CAS  Google Scholar 

  72. Kouno, K., Tuchiya, Y. & Ando, T. Measurement of soil microbial biomass phosphorus by an anion-exchange membrane method. Soil Biol. Biochem. 27, 1353–1357 (1995).

    Article  CAS  Google Scholar 

  73. Tabatabai, M. A. in Methods of Soil Analysis. Part 2. Microbiological and Biochemical Properties (ed. Page, A.L.) 778–833 (SSSA, 1994).

  74. Marx, M. C., Wood, M. & Jarvis, S. C. A microplate fluorimetric assay for the study of enzyme diversity in soils. Soil Biol. Biochem. 33, 1633–1640 (2001).

    Article  CAS  Google Scholar 

  75. Price, N. & Stevens, L. Fundamentals of Enzymology: Cell and Molecular Biology of Catalytic Proteins (Oxford Univ. Press, 1999).

  76. Hagerty, S. B., Allison, S. D. & Schimel, J. P. Evaluating soil microbial carbon use efficiency explicitly as a function of cellular processes: implications for measurements and models. Biogeochemistry 140, 269–283 (2018).

    Article  CAS  Google Scholar 

  77. Frey, S. D., Lee, J., Melillo, J. M. & Six, J. The temperature response of soil microbial efficiency and its feedback to climate. Nat. Clim. Change 3, 395–398 (2013).

    Article  CAS  Google Scholar 

  78. Spohn, M. et al. Soil microbial carbon use efficiency and biomass turnover in a long-term fertilization experiment in a temperate grassland. Soil Biol. Biochem. 97, 168–175 (2016).

    Article  CAS  Google Scholar 

  79. Sinsabaugh, R. L. et al. Stoichiometry of microbial carbon use efficiency in soils. Ecol. Monogr. 86, 172–189 (2016).

    Article  Google Scholar 

  80. Geyer, K. M., Dijkstra, P., Sinsabaugh, R. & Frey, S. D. Clarifying the interpretation of carbon use efficiency in soil through methods comparison. Soil Biol. Biochem. 128, 79–88 (2019).

    Article  CAS  Google Scholar 

  81. Bååth, E., Pettersson, M. & Söderberg, K. H. Adaptation of a rapid and economical microcentrifugation method to measure thymidine and leucine incorporation by soil bacteria. Soil Biol. Biochem. 33, 1571–1574 (2001).

    Article  Google Scholar 

  82. Bárcenas-Moreno, G., Gomez-Brandon, M., Rousk, J. & Bååth, E. Adaptation of soil microbial communities to temperature: comparison of fungi and bacteria in a laboratory experiment. Glob. Change Biol. 15, 2950–2957 (2009).

    Article  Google Scholar 

  83. Smirnova, E., Huzurbazar, S. & Jafari, F. PERFect: PERmutation Filtering test for microbiome data. Biostatistics 20, 615–631 (2019).

    Article  PubMed  Google Scholar 

  84. Alberdi, A. & Gilbert, M. T. P. hilldiv: an R package for the integral analysis of diversity based on Hill numbers. Preprint at bioRxiv https://doi.org/10.1101/545665 (2019).

  85. Lozupone, C., Lladser, M. E., Knights, D., Stombaugh, J. & Knight, R. UniFrac: an effective distance metric for microbial community comparison. ISME J. 5, 169–172 (2011).

    Article  PubMed  Google Scholar 

  86. Oksanen, J. et al. vegan: Community ecology package, R Package version 2 https://cran.r-project.org/web/packages/vegan/ (2018).

  87. Dufrene, M. & Legendre, P. Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecol. Monogr. 67, 345–366 (1997).

    Google Scholar 

  88. Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. https://doi.org/10.1186/gb-2011-12-6-r60 (2011).

  89. Roesch, L. F. W. et al. PIME: a package for discovery of novel differences among microbial communities. Mol. Ecol. Resour. 20, 415–428 (2020).

    Article  CAS  PubMed  Google Scholar 

  90. Roberts, D.W. labdsv: Ordination and multivariate analysis for ecology. R package version 2.0-1 https://cran.r-project.org/web/packages/labdsv/ (2019).

  91. Cao, Y. et al. microbiomeMarker: an R/Bioconductor package for microbiome marker identification and visualization. Bioinformatics 38, 4027–4029 (2022).

  92. Eren, A. M. et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. Peerj 3, e1319 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  93. Peterson, R. A. & Cavanaugh, J. E. Ordered quantile normalization: a semiparametric transformation built for the cross-validation era. J. Appl. Stat. 47, 2312–2327 (2020).

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

This study was supported by three fellowships to A.T.N.: UK NERC grant NE/T012226, European Union Marie-Curie Fellowship FP7-2012-329360 and an STRI Tupper Fellowship. Further support came from UK NERC grant NE/K01627X/1 to P.M., an ANU Biology Innovation grant to P.M. and Simons Foundation grant No. 429440 to W. Wcislo, STRI, and support from the US Department of Agriculture (USDA), Agricultural Research Service to K.B. We thank O. Acevado, D. Agudo, A. Bielnicka, G. Broders, M. Cano, D. Dominguez, M. Garcia, M. Larsen, J. Rodriguez, H. Szczygiel, I. Torres, E. Velasquez, W. Wcislo, K. Winter and J. Wright for support. We thank B. Turner for his contribution to SWELTR, especially during its initial phase of operation. Sequencing analyses were conducted on the Smithsonian High-Performance Cluster (SI/HPC), Smithsonian Institution (https://doi.org/10.25572/SIHPC). For the purpose of open access, the authors have applied a Creative Commons Attribution (CC BY) licence to any author-accepted manuscript version arising from this submission. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the USDA. The USDA is an equal opportunity provider and employer.

Author information

Authors and Affiliations

Authors

Contributions

A.T.N. conceived the study. A.T.N., J.J.S., M.M.-S., J.P., E.B., K.B. and K.S. performed the study. A.T.N. and J.J.S. analysed the data. A.T.N. wrote the paper with input from J.J.S., E.B., K.S., K.B. and P.M.

Corresponding author

Correspondence to Andrew T. Nottingham.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Microbiology thanks Nadia Maaroufi, Ashish Malik and the other, anonymous, reviewer(s) 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 One of five warmed plots at SWELTR.

The images show the soil surface temperature shortly after the warming structure was switched on (a and c) and after a period of thermal equilibration‘ (b and d). The circular heating structure was 3.5 m in diameter and extended to 1.2 m depth, which resulted in an effective heated plot of approximately 5 m diameter x > 1.5 m depth (that is to the bedrock, situated at around 1.5–2.0 m across the study site). The experiment consisted of five warmed and control plot-pairs in total. For this study we had three treatment levels, +3 °C warming (within the warmed plots), +8 °C (within a high-temperature buffer zone close [~10 cm] to the heating source for each warmed plot) and ambient temperature controls (within the control plots). Therefore, all analyses are for n = 5 independent sampling locations for each treatment level. Image credit: J. Bujan and E. Velasquez.

Extended Data Fig. 2 Diversity response of soil bacteria (a–c) and fungi (d–f) to two years of warming by +3 °C and +8 °C.

Shapiro-Wilk Normality and Bartlett tests indicated all alpha diversity estimates (following PERfect filtering) were normally distributed and differences were assessed for (a) bacteria and (d) fungi using analysis of variance (ANOVA) followed by Tukey HSD post-hoc tests. Compositional similarity of microbial communities (beta-diversity) represented as PCoA ordination plots of PERfect filtered data for (b) bacteria—estimated using Unweighted (left) and Weighted Unifrac (right) distance matrices; and (e) fungi estimated—using Jensen–Shannon divergence (left) and Bray-Curtis (right) distance matrices. Within group distances for the (c) bacteria and (f) fungi datasets. The centre line of each box plot represents the median, the lower and upper hinges represent the first and third quartiles and whiskers represent + 1.5 the interquartile range. For panels (a), (c), (d), and (f), only significant differences between treatments are shown.

Extended Data Fig. 3 The response of select soil bacteria taxa to two years of warming by +3 °C and +8 °C.

Differences assessed for multiple-group pair-wise comparisons using ANOVA followed by Tukey HSD post hoc tests. PERfect filtered read count data was log10 transformed and normalized using total sum scaling (TSS). The centre line of each box plot represents the median, the lower and upper hinges represent the first and third quartiles and whiskers represent + 1.5 the interquartile range. Only significant differences between treatments are shown.

Extended Data Fig. 4 The response of select soil fungal taxa to two years of warming by +3 °C and +8 °C.

Differences assessed for multiple-group pair-wise comparisons using ANOVA followed by Tukey HSD post hoc tests. PERfect filtered read count data was log10 transformed and normalized using total sum scaling (TSS). The centre line of each box plot represents the median, the lower and upper hinges represent the first and third quartiles and whiskers represent + 1.5 the interquartile range. Only significant differences between treatments are shown.

Extended Data Fig. 5 Distance-based Redundancy Analysis (db-RDA).

Distance-based Redundancy Analysis (db-RDA) of PIME filtered data based on Bray-Curtis dissimilarity showing the relationships between community composition change for (a) bacteria and (b) fungi versus edaphic properties (left) and microbial functional response (right). All analyses are for soil collected from n = 5 independent sampling locations for each treatment level.

Extended Data Fig. 6 Soil, enzyme, and microbial responses to +3 °C and +8 °C in situ soil warming.

Data are grouped by (a) soil properties, (b) microbial functional responses, and (c) microbial temperature adaptive responses; we used the same grouping to test three hypotheses on how each of these responses were correlated to changes in microbial diversity and community composition (Fig. 2; Extended Data Table 2, Fig. 5). All properties were determined for soil samples collected during the 2018 wet season (June and November); see methods. Units for enzyme Vmax are nmol MU g−1 min−1, except Phenol oxidase in μmol g−1 h−1 and Leucine aminopeptidase in nmol AMC g−1 min−1. The centre line of each box plot represents the median, the lower and upper hinges represent the first and third quartiles and whiskers represent + 1.5 the interquartile range. Significant differences between treatments and controls are highlighted by asterisks (ANOVA; * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001). All analyses are for soil collected from n = 5 independent sampling locations for each treatment level.

Extended Data Fig. 7 Soil enzyme activities in response to incubation temperature (that is instantaneous temperature response determined in laboratory assays).

Data are maximum potential enzyme activity (Vmax), determined by activity under saturating substrate conditions. Enzymes are: α-glucosidase (AGase), β-glucosidase (BGase), phospho-diesterase (BPase), cellolbiohydrolase (CEase), leucine aminopeptidase (LPase), phosphomonoesterase (Pase), N-acetyl β-glucosaminidase (Nase), phenol oxidase (PXase), sulfatase (Sase) and β-xylanase (XYase). Units for enzyme Vmax are nmol MU g−1 min−1, except Phenol oxidase in μmol g−1 h−1 and Leucine aminopeptidase in nmol AMC g−1 min−1. The error bars represent mean ± one standard error, for n = 5 plots. Fitted lines depict quadratic functions with 95% confidence intervals. All analyses are for soil collected from n = 5 independent sampling locations for each treatment level. Activity was determined during the wet season 2018 for the following sampling periods: controls include 4 sampling periods (June, Sept, Oct, Dec 2018); +3 °C include 3 sampling periods (June, Sept, Dec 2018); +8 °C include 1 sampling period (Sept 2018).

Extended Data Table 1 Relationship of bacterial and fungal richness with (a) environmental drivers, (b) microbial functional responses and (c) microbial temperature adaptive responses
Extended Data Table 2 The relationship between (a) bacterial and (b) fungal beta-diversity and edaphic environment (i), soil process rates (ii) and microbial temperature adaptive responses (iii) following 2 years of soil warming by +3 °C to +8 °C
Extended Data Table 3 The influence of soil abiotic environment on soil CO2 efflux (a), and the effect of in situ warming levels (by +3 °C and +8 °C) on soil CO2 efflux (b) and soil moisture (c)

Supplementary information

Supplementary Information

Supplementary Methods, Results, Figs. 1–14 and Tables 1–15.

Reporting Summary

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Nottingham, A.T., Scott, J.J., Saltonstall, K. et al. Microbial diversity declines in warmed tropical soil and respiration rise exceed predictions as communities adapt. Nat Microbiol 7, 1650–1660 (2022). https://doi.org/10.1038/s41564-022-01200-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41564-022-01200-1

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

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