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:

Global patterns of geo-ecological controls on the response of soil respiration to warming

Abstract

While soil respiration is known to be controlled by a range of biotic and abiotic factors, its temperature sensitivity in global models is largely related to climate parameters. Here, we show that temperature sensitivity of soil respiration is primarily controlled by interacting soil properties and only secondarily by vegetation traits and plant growth conditions. Temperature was not identified as a primary driver for the response of soil respiration to warming. In contrast, the nonlinearity and large spatial variability of identified controls stress the importance of the interplay among soil, vegetation and climate parameters in controlling warming responses. Global models might predict current soil respiration but not future rates because they neglect the controls exerted by soil development. To accurately predict the response of soil respiration to warming at the global scale, more observational studies across pedogenetically diverse soils are needed rather than focusing on the isolated effect of warming alone.

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

Access options

Buy this article

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

Fig. 1: Prediction performance and partial dependence plots of soil Q10 and controlling factors.
Fig. 2: Global soil Q10 prediction and uncertainty map.

Similar content being viewed by others

Data availability

Datasets used in this study are deposited in a permanent open access online repository at ETH Zurich’s Research Collection under the following DOI: https://doi.org/10.3929/ethz-b-000479158.

Code availability

The R code used and produced in this study is deposited in a permanent open-access online repository at ETH Zurich’s Research Collection at https://doi.org/10.3929/ethz-b-000479158.

References

  1. Rogelj, J. et al. Paris Agreement climate proposals need a boost to keep warming well below 2 °C. Nature 534, 631–639 (2016).

    Article  CAS  Google Scholar 

  2. Song, J. et al. A meta-analysis of 1,119 manipulative experiments on terrestrial carbon-cycling responses to global change. Nat. Ecol. Evol. 3, 1309–1320 (2019).

    Article  Google Scholar 

  3. Lal, R. Soil carbon sequestration impacts on global climate change and food security. Science 304, 1623–1627 (2004).

    Article  CAS  Google Scholar 

  4. Houghton, R. A. The contemporary carbon cycle. Treatise Geochem. 8, 473–513 (2003).

    Article  Google Scholar 

  5. Paterson, E., Midwood, A. J. & Millard, P. Through the eye of the needle: a review of isotope approaches to quantify microbial processes mediating soil carbon balance. New Phytol. 184, 19–33 (2009).

    Article  CAS  Google Scholar 

  6. Bader, M. K. F. & Körner, C. No overall stimulation of soil respiration under mature deciduous forest trees after 7 years of CO2 enrichment. Glob. Change Biol. 16, 2830–2843 (2010).

    Article  Google Scholar 

  7. Reynolds, L. L., Lajtha, K., Bowden, R. D., Johnson, B. R. & Bridgham, S. D. The carbon quality–temperature hypothesis does not consistently predict temperature sensitivity of soil organic matter mineralization in soils from two manipulative ecosystem experiments. Biogeochemistry 136, 249–260 (2017).

    Article  CAS  Google Scholar 

  8. Knorr, W., Prentice, I. C., House, J. & Holland, E. Long-term sensitivity of soil carbon turnover to warming. Nature 433, 298–301 (2005).

    Article  CAS  Google Scholar 

  9. 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 

  10. Kirschbaum, M. U. F. The temperature dependence of organic-matter decomposition—still a topic of debate. Soil Biol. Biochem. 38, 2510–2518 (2006).

    Article  CAS  Google Scholar 

  11. Feng, X., Simpson, A. J., Wilson, K. P., Williams, D. D. & Simpson, M. J. Increased cuticular carbon sequestration and lignin oxidation in response to soil warming. Nat. Geosci. 1, 836–839 (2008).

    Article  CAS  Google Scholar 

  12. Pries, C. E. H., Castanha, C., Porras, R. & Torn, M. The whole-soil carbon flux in response to warming. Science 355, 1420–1423 (2017).

    Article  CAS  Google Scholar 

  13. Li, J. et al. Reduced carbon use efficiency and increased microbial turnover with soil warming. Glob. Change Biol. 25, 900–910 (2019).

    Article  Google Scholar 

  14. Schaphoff, S. et al. Contribution of permafrost soils to the global carbon budget. Environ. Res. Lett. 8, 014026 (2013).

    Article  CAS  Google Scholar 

  15. 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  Google Scholar 

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

    Article  CAS  Google Scholar 

  17. Koven, C. D. et al. The effect of vertically resolved soil biogeochemistry and alternate soil C and N models on C dynamics of CLM4. Biogeosciences 10, 7109–7131 (2013).

    Article  CAS  Google Scholar 

  18. Sulman, B. N., Phillips, R. P., Oishi, A. C., Shevliakova, E. & Pacala, S. W. Microbe-driven turnover offsets mineral-mediated storage of soil carbon under elevated CO2. Nat. Clim. Change 4, 1099–1102 (2014).

    Article  CAS  Google Scholar 

  19. Schmidt, M. W. et al. Persistence of soil organic matter as an ecosystem property. Nature 478, 49–56 (2011).

    Article  CAS  Google Scholar 

  20. Wieder, W. R. et al. Explicitly representing soil microbial processes in Earth system models. Glob. Biogeochem. Cycles 29, 1782–1800 (2015).

    Article  CAS  Google Scholar 

  21. Gonzalez-Dominguez, B. et al. Temperature and moisture are minor drivers of regional-scale soil organic carbon dynamics. Sci. Rep. 9, 6422 (2019).

    Article  CAS  Google Scholar 

  22. Blankinship, J. C. et al. Improving understanding of soil organic matter dynamics by triangulating theories, measurements, and models. Biogeochemistry 140 (2018).

  23. Koven, C. D. et al. Permafrost carbon–climate feedbacks accelerate global warming. Proc. Natl Acad. Sci. USA 108, 14769–14774 (2011).

    Article  CAS  Google Scholar 

  24. Angst, G. et al. Soil organic carbon stocks in topsoil and subsoil controlled by parent material, carbon input in the rhizosphere, and microbial-derived compounds. Soil Biol. Biochem. 122, 19–30 (2018).

    Article  CAS  Google Scholar 

  25. Abramoff, R. et al. The Millennial model: in search of measurable pools and transformations for modeling soil carbon in the new century. Biogeochemistry 137, 51–71 (2017).

    Article  Google Scholar 

  26. Doetterl, S. et al. Links among warming, carbon and microbial dynamics mediated by soil mineral weathering. Nat. Geosci. 11, 589–593 (2018).

    Article  CAS  Google Scholar 

  27. Hamdi, S., Moyano, F., Sall, S., Bernoux, M. & Chevallier, T. Synthesis analysis of the temperature sensitivity of soil respiration from laboratory studies in relation to incubation methods and soil conditions. Soil Biol. Biochem. 58, 115–126 (2013).

    Article  CAS  Google Scholar 

  28. Hashimoto, S. et al. Global spatiotemporal distribution of soil respiration modeled using a global database. Biogeosciences 12, 4121–4132 (2015).

    Article  Google Scholar 

  29. Varney, R. M. et al. A spatial emergent constraint on the sensitivity of soil carbon turnover to global warming. Nat. Commun. 11, 5544 (2020).

    Article  CAS  Google Scholar 

  30. Wu, D., Piao, S., Liu, Y., Ciais, P. & Yao, Y. Evaluation of CMIP5 Earth System Models for the spatial patterns of biomass and soil carbon turnover times and their linkage with climate. J. Clim. 31, 5947–5960 (2018).

    Article  Google Scholar 

  31. Wieder, W. R. et al. Carbon cycle confidence and uncertainty: exploring variation among soil biogeochemical models. Glob. Change Biol. 24, 1563–1579 (2018).

    Article  Google Scholar 

  32. Koven, C. D., Hugelius, G., Lawrence, D. M. & Wieder, W. R. Higher climatological temperature sensitivity of soil carbon in cold than warm climates. Nat. Clim. Change 7, 817–822 (2017).

    Article  CAS  Google Scholar 

  33. Mahecha, M. D. et al. Global convergence in the temperature sensitivity of respiration at ecosystem level. Science 329, 838–840 (2010).

    Article  CAS  Google Scholar 

  34. Foereid, B., Ward, D., Mahowald, N., Paterson, E. & Lehmann, J. The sensitivity of carbon turnover in the Community Land Model to modified assumptions about soil processes. Earth Syst. Dynam. 5, 211–221 (2014).

    Article  Google Scholar 

  35. Friedlingstein, P. et al. Climate–carbon cycle feedback analysis: results from the C4MIP model intercomparison. J. Clim. 19, 3337–3353 (2006).

    Article  Google Scholar 

  36. Post, H., Vrugt, J. A., Fox, A., Vereecken, H. & Hendricks Franssen, H. J. Estimation of Community Land Model parameters for an improved assessment of net carbon fluxes at European sites. J. Geophys. Res. Biogeosci. 122, 661–689 (2017).

    Article  CAS  Google Scholar 

  37. Luo, Y. et al. Toward more realistic projections of soil carbon dynamics by Earth system models. Glob. Biogeochem. Cycles 30, 40–56 (2016).

    Article  CAS  Google Scholar 

  38. Bailey, V. L. et al. Soil carbon cycling proxies: understanding their critical role in predicting climate change feedbacks. Glob. Change Biol. 24, 895–905 (2018).

    Article  Google Scholar 

  39. Conant, R. T. et al. Temperature and soil organic matter decomposition rates—synthesis of current knowledge and a way forward. Glob. Change Biol. 17, 3392–3404 (2011).

    Article  Google Scholar 

  40. Meyer, N., Welp, G. & Amelung, W. The temperature sensitivity (Q10) of soil respiration: controlling factors and spatial prediction at regional scale based on environmental soil classes. Glob. Biogeochem. Cycles 32, 306–323 (2018).

    Article  CAS  Google Scholar 

  41. Doetterl, S. et al. Soil carbon storage controlled by interactions between geochemistry and climate. Nat. Geosci. 8, 780–783 (2015).

    Article  CAS  Google Scholar 

  42. Melillo, J. M. et al. Long-term pattern and magnitude of soil carbon feedback to the climate system in a warming world. Science 358, 101–105 (2017).

    Article  CAS  Google Scholar 

  43. Kramer, M. G. & Chadwick, O. A. Climate-driven thresholds in reactive mineral retention of soil carbon at the global scale. Nat. Clim. Change 8, 1104–1108 (2018).

    Article  CAS  Google Scholar 

  44. Cusack, D. F. et al. Decadal-scale litter manipulation alters the biochemical and physical character of tropical forest soil carbon. Soil Biol. Biochem. 124, 199–209 (2018).

    Article  CAS  Google Scholar 

  45. Wang, X. et al. Are ecological gradients in seasonal Q10 of soil respiration explained by climate or by vegetation seasonality? Soil Biol. Biochem. 42, 1728–1734 (2010).

    Article  CAS  Google Scholar 

  46. Warner, D. L., Bond‐Lamberty, B., Jian, J., Stell, E. & Vargas, R. Spatial predictions and associated uncertainty of annual soil respiration at the global scale. Glob. Biogeochem. Cycles 33, 1733–1745 (2019).

    Article  CAS  Google Scholar 

  47. Todd-Brown, K., Zheng, B. & Crowther, T. W. Field-warmed soil carbon changes imply high 21st-century modeling uncertainty. Biogeosciences 15, 3659–3671 (2018).

    Article  CAS  Google Scholar 

  48. He, Y. et al. Radiocarbon constraints imply reduced carbon uptake by soils during the 21st century. Science 353, 1419–1424 (2016).

    Article  CAS  Google Scholar 

  49. Haddix, M. L. et al. The role of soil characteristics on temperature sensitivity of soil organic matter. Soil Sci. Soc. Am. J. 75, 56–68 (2011).

    Article  CAS  Google Scholar 

  50. Lara, M. J., Lin, D. H., Andresen, C., Lougheed, V. L. & Tweedie, C. E. Nutrient release from permafrost thaw enhances CH4 emissions from Arctic tundra wetlands. J. Geophys. Res. Biogeosci. 124, 1560–1573 (2019).

    Article  CAS  Google Scholar 

  51. Prater, I. et al. From fibrous plant residues to mineral-associated organic carbon–the fate of organic matter in Arctic permafrost soils. Biogeosciences 17, 3367–3383 (2020).

    Article  CAS  Google Scholar 

  52. Åkerman, H. J. & Johansson, M. Thawing permafrost and thicker active layers in sub‐arctic Sweden. Permafr. Periglac. Process. 19, 279–292 (2008).

    Article  Google Scholar 

  53. Jilling, A. et al. Minerals in the rhizosphere: overlooked mediators of soil nitrogen availability to plants and microbes. Biogeochemistry 139, 103–122 (2018).

    Article  CAS  Google Scholar 

  54. Jones, M. C. et al. Rapid carbon loss and slow recovery following permafrost thaw in boreal peatlands. Glob. Change Biol. 23, 1109–1127 (2017).

    Article  Google Scholar 

  55. Korell, L., Auge, H., Chase, J. M., Harpole, W. S. & Knight, T. M. We need more realistic climate change experiments for understanding ecosystems of the future. Glob. Change Biol. 26, 325–327 (2019).

    Article  Google Scholar 

  56. Raich, J. W. & Schlesinger, W. H. The global carbon dioxide flux in soil respiration and its relationship to vegetation and climate. Tellus B 44, 81–99 (1992).

    Article  Google Scholar 

  57. Jansson, J. K. & Hofmockel, K. S. Soil microbiomes and climate change. Nat. Rev. Microbiol. 18, 35–46 (2020).

  58. Crowther, T. et al. The global soil community and its influence on biogeochemistry. Science 365, eaav0550 (2019).

  59. R Core Team. C. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017).

  60. Bond-Lamberty, B. & Thomson, A. Temperature-associated increases in the global soil respiration record. Nature 464, 579–582 (2010).

    Article  CAS  Google Scholar 

  61. Shapiro, S. S. & Wilk, M. B. An analysis of variance test for normality (complete samples). Biometrika 52, 591–611 (1965).

    Article  Google Scholar 

  62. Conover, W. J., Johnson, M. E. & Johnson, M. M. A comparative study of tests for homogeneity of variances, with applications to the outer continental shelf bidding data. Technometrics 23, 351–361 (1981).

    Article  Google Scholar 

  63. Chen, X., Zhao, P. L. & Zhang, J. A note on ANOVA assumptions and robust analysis for a cross‐over study. Stat. Med. 21, 1377–1386 (2002).

    Article  Google Scholar 

  64. McGuinness, K. A. Of rowing boats, ocean liners and tests of the ANOVA homogeneity of variance assumption. Austral. Ecol. 27, 681–688 (2002).

    Article  Google Scholar 

  65. Zimmerman, D. W. & Zumbo, B. D. Relative power of the Wilcoxon test, the Friedman test, and repeated-measures ANOVA on ranks. J. Exp. Educ. 62, 75–86 (1993).

    Article  Google Scholar 

  66. Tomczak, M. & Tomczak, E. The need to report effect size estimates revisited. An overview of some recommended measures of effect size. Trends Sport Sci. 1, 19–25 (2014).

    Google Scholar 

  67. Thornley, J. & Cannell, M. Soil carbon storage response to temperature: an hypothesis. Ann. Bot. 87, 591–598 (2001).

    Article  CAS  Google Scholar 

  68. Lloyd, J. & Taylor, J. On the temperature dependence of soil respiration. Funct. Ecol. 8, 315–323 (1994).

  69. Libohova, Z. et al. The anatomy of uncertainty for soil pH measurements and predictions: implications for modellers and practitioners. Eur. J. Soil Sci. 70, 185–199 (2019).

    Article  Google Scholar 

  70. Kirkby, C. A. et al. Carbon–nutrient stoichiometry to increase soil carbon sequestration. Soil Biol. Biochem. 60, 77–86 (2013).

    Article  CAS  Google Scholar 

  71. Bronick, C. J. & Lal, R. Soil structure and management: a review. Geoderma 124, 3–22 (2005).

    Article  CAS  Google Scholar 

  72. Beer, C. et al. Temporal and among‐site variability of inherent water use efficiency at the ecosystem level. Glob. Biogeochem. Cycles 23, GB2018 (2009).

    Article  CAS  Google Scholar 

  73. Averill, C., Turner, B. L. & Finzi, A. C. Mycorrhiza-mediated competition between plants and decomposers drives soil carbon storage. Nature 505, 543 (2014).

    Article  CAS  Google Scholar 

  74. Bradford, M. A. Thermal adaptation of decomposer communities in warming soils. Front. Microbiol. 4, 333 (2013).

    Article  Google Scholar 

  75. Friedman, J., Hastie, T. & Tibshirani, R. The Elements of Statistical Learning Vol. 1 (Springer, 2001).

  76. Efron, B., Hastie, T., Johnstone, I. & Tibshirani, R. Least angle regression. Ann. Stat. 32, 407–499 (2004).

    Article  Google Scholar 

  77. Zou, H. & Hastie, T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67, 301–320 (2005).

    Article  Google Scholar 

  78. Kuhn, M. & Johnson, K. Applied Predictive Modeling Vol. 26 (Springer, 2013).

  79. Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).

    Article  Google Scholar 

  80. Friedman, J. H. Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001).

  81. Breiman, L. Bagging predictors. Mach. Learn. 24, 123–140 (1996).

    Article  Google Scholar 

  82. Quinlan, J. R. Learning with Continuous Classes in Proceedings of the 5th Australian Joint Conference on Artificial Intelligence (eds Adams, A. & Sterling, L.) 343–348 (World Scientific, 1992).

  83. Boulesteix, A. L., Janitza, S., Kruppa, J. & König, I. R. Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics. WIRES Data Mining Knowl. Discov. 2, 493–507 (2012).

    Article  Google Scholar 

  84. Xu, Q.-S. & Liang, Y.-Z. Monte Carlo cross validation. Chemom. Intell. Lab. Syst. 56, 1–11 (2001).

    Article  CAS  Google Scholar 

  85. Shcherbakov, M. V. et al. A survey of forecast error measures. World Appl. Sci. J. 24, 171–176 (2013).

    Google Scholar 

  86. James, G., Witten, D., Hastie, T. & Tibshirani, R. An Introduction to Statistical Learning Vol. 112 (Springer, 2013).

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

  88. Grömping, U. Variable importance assessment in regression: linear regression versus random forest. Am. Statistician 63, 308–319 (2009).

    Article  Google Scholar 

  89. Wei, P., Lu, Z. & Song, J. Variable importance analysis: a comprehensive review. Reliab. Eng. Syst. Saf. 142, 399–432 (2015).

    Article  Google Scholar 

  90. Yang, R.-M. et al. Comparison of boosted regression tree and random forest models for mapping topsoil organic carbon concentration in an alpine ecosystem. Ecol. Indic. 60, 870–878 (2016).

    Article  CAS  Google Scholar 

  91. Greenwell, B. M. pdp: an R package for constructing partial dependence plots. R J. 9, 421–436 (2017).

    Article  Google Scholar 

  92. Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813 (2008).

    Article  CAS  Google Scholar 

  93. Land Cover CCI Product User Guide Version 2 (ESA, 2017); maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf

  94. Hengl, T. et al. SoilGrids250m: global gridded soil information based on machine learning. PLoS ONE 12, e0169748 (2017).

    Article  CAS  Google Scholar 

  95. Moran, P. A. A test for the serial independence of residuals. Biometrika 37, 178–181 (1950).

    Article  CAS  Google Scholar 

  96. Legendre, P. Spatial autocorrelation: trouble or new paradigm? Ecology 74, 1659–1673 (1993).

    Article  Google Scholar 

Download references

Acknowledgements

Financial support has been given by ETH Zurich and the German Research Foundation (DFG, project no. 387472333 to S.D.). We especially thank H. Maclean for language proofreading.

Author information

Authors and Affiliations

Authors

Contributions

S.D. designed the research. D.H. conducted the data assembly and statistical analyses. D.H. and S.D. processed the data. D.H., J.S. and D.S. interpreted the data and contributed to the writing of the paper with D.S. being the supervising author.

Corresponding author

Correspondence to Sebastian Doetterl.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Climate Change thanks Ben Bond-Lamberty, Jeroen Meersmans and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Supplementary information

Supplementary Information

Supplementary Figs. 1–4, Tables 1–9 and references.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Haaf, D., Six, J. & Doetterl, S. Global patterns of geo-ecological controls on the response of soil respiration to warming. Nat. Clim. Chang. 11, 623–627 (2021). https://doi.org/10.1038/s41558-021-01068-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41558-021-01068-9

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