Rogelj, J. et al. Paris Agreement climate proposals need a boost to keep warming well below 2 °C. Nature 534, 631–639 (2016).
Google Scholar
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).
Google Scholar
Lal, R. Soil carbon sequestration impacts on global climate change and food security. Science 304, 1623–1627 (2004).
Google Scholar
Houghton, R. A. The contemporary carbon cycle. Treatise Geochem. 8, 473–513 (2003).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Knorr, W., Prentice, I. C., House, J. & Holland, E. Long-term sensitivity of soil carbon turnover to warming. Nature 433, 298–301 (2005).
Google Scholar
Allison, S. D., Wallenstein, M. D. & Bradford, M. A. Soil–carbon response to warming dependent on microbial physiology. Nat. Geosci. 3, 336–340 (2010).
Google Scholar
Kirschbaum, M. U. F. The temperature dependence of organic-matter decomposition—still a topic of debate. Soil Biol. Biochem. 38, 2510–2518 (2006).
Google Scholar
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).
Google Scholar
Pries, C. E. H., Castanha, C., Porras, R. & Torn, M. The whole-soil carbon flux in response to warming. Science 355, 1420–1423 (2017).
Google Scholar
Li, J. et al. Reduced carbon use efficiency and increased microbial turnover with soil warming. Glob. Change Biol. 25, 900–910 (2019).
Google Scholar
Schaphoff, S. et al. Contribution of permafrost soils to the global carbon budget. Environ. Res. Lett. 8, 014026 (2013).
Google Scholar
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).
Google Scholar
Davidson, E. A. & Janssens, I. A. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440, 165–173 (2006).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Schmidt, M. W. et al. Persistence of soil organic matter as an ecosystem property. Nature 478, 49–56 (2011).
Google Scholar
Wieder, W. R. et al. Explicitly representing soil microbial processes in Earth system models. Glob. Biogeochem. Cycles 29, 1782–1800 (2015).
Google Scholar
Gonzalez-Dominguez, B. et al. Temperature and moisture are minor drivers of regional-scale soil organic carbon dynamics. Sci. Rep. 9, 6422 (2019).
Google Scholar
Blankinship, J. C. et al. Improving understanding of soil organic matter dynamics by triangulating theories, measurements, and models. Biogeochemistry 140 (2018).
Koven, C. D. et al. Permafrost carbon–climate feedbacks accelerate global warming. Proc. Natl Acad. Sci. USA 108, 14769–14774 (2011).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Doetterl, S. et al. Links among warming, carbon and microbial dynamics mediated by soil mineral weathering. Nat. Geosci. 11, 589–593 (2018).
Google Scholar
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).
Google Scholar
Hashimoto, S. et al. Global spatiotemporal distribution of soil respiration modeled using a global database. Biogeosciences 12, 4121–4132 (2015).
Google Scholar
Varney, R. M. et al. A spatial emergent constraint on the sensitivity of soil carbon turnover to global warming. Nat. Commun. 11, 5544 (2020).
Google Scholar
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).
Google Scholar
Wieder, W. R. et al. Carbon cycle confidence and uncertainty: exploring variation among soil biogeochemical models. Glob. Change Biol. 24, 1563–1579 (2018).
Google Scholar
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).
Google Scholar
Mahecha, M. D. et al. Global convergence in the temperature sensitivity of respiration at ecosystem level. Science 329, 838–840 (2010).
Google Scholar
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).
Google Scholar
Friedlingstein, P. et al. Climate–carbon cycle feedback analysis: results from the C4MIP model intercomparison. J. Clim. 19, 3337–3353 (2006).
Google Scholar
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).
Google Scholar
Luo, Y. et al. Toward more realistic projections of soil carbon dynamics by Earth system models. Glob. Biogeochem. Cycles 30, 40–56 (2016).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Doetterl, S. et al. Soil carbon storage controlled by interactions between geochemistry and climate. Nat. Geosci. 8, 780–783 (2015).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Todd-Brown, K., Zheng, B. & Crowther, T. W. Field-warmed soil carbon changes imply high 21st-century modeling uncertainty. Biogeosciences 15, 3659–3671 (2018).
Google Scholar
He, Y. et al. Radiocarbon constraints imply reduced carbon uptake by soils during the 21st century. Science 353, 1419–1424 (2016).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Åkerman, H. J. & Johansson, M. Thawing permafrost and thicker active layers in sub‐arctic Sweden. Permafr. Periglac. Process. 19, 279–292 (2008).
Google Scholar
Jilling, A. et al. Minerals in the rhizosphere: overlooked mediators of soil nitrogen availability to plants and microbes. Biogeochemistry 139, 103–122 (2018).
Google Scholar
Jones, M. C. et al. Rapid carbon loss and slow recovery following permafrost thaw in boreal peatlands. Glob. Change Biol. 23, 1109–1127 (2017).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Jansson, J. K. & Hofmockel, K. S. Soil microbiomes and climate change. Nat. Rev. Microbiol. 18, 35–46 (2020).
Crowther, T. et al. The global soil community and its influence on biogeochemistry. Science 365, eaav0550 (2019).
R Core Team. C. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017).
Bond-Lamberty, B. & Thomson, A. Temperature-associated increases in the global soil respiration record. Nature 464, 579–582 (2010).
Google Scholar
Shapiro, S. S. & Wilk, M. B. An analysis of variance test for normality (complete samples). Biometrika 52, 591–611 (1965).
Google Scholar
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).
Google Scholar
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).
Google Scholar
McGuinness, K. A. Of rowing boats, ocean liners and tests of the ANOVA homogeneity of variance assumption. Austral. Ecol. 27, 681–688 (2002).
Google Scholar
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).
Google Scholar
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).
Thornley, J. & Cannell, M. Soil carbon storage response to temperature: an hypothesis. Ann. Bot. 87, 591–598 (2001).
Google Scholar
Lloyd, J. & Taylor, J. On the temperature dependence of soil respiration. Funct. Ecol. 8, 315–323 (1994).
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).
Google Scholar
Kirkby, C. A. et al. Carbon–nutrient stoichiometry to increase soil carbon sequestration. Soil Biol. Biochem. 60, 77–86 (2013).
Google Scholar
Bronick, C. J. & Lal, R. Soil structure and management: a review. Geoderma 124, 3–22 (2005).
Google Scholar
Beer, C. et al. Temporal and among‐site variability of inherent water use efficiency at the ecosystem level. Glob. Biogeochem. Cycles 23, GB2018 (2009).
Google Scholar
Averill, C., Turner, B. L. & Finzi, A. C. Mycorrhiza-mediated competition between plants and decomposers drives soil carbon storage. Nature 505, 543 (2014).
Google Scholar
Bradford, M. A. Thermal adaptation of decomposer communities in warming soils. Front. Microbiol. 4, 333 (2013).
Google Scholar
Friedman, J., Hastie, T. & Tibshirani, R. The Elements of Statistical Learning Vol. 1 (Springer, 2001).
Efron, B., Hastie, T., Johnstone, I. & Tibshirani, R. Least angle regression. Ann. Stat. 32, 407–499 (2004).
Google Scholar
Zou, H. & Hastie, T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67, 301–320 (2005).
Google Scholar
Kuhn, M. & Johnson, K. Applied Predictive Modeling Vol. 26 (Springer, 2013).
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).
Google Scholar
Friedman, J. H. Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001).
Breiman, L. Bagging predictors. Mach. Learn. 24, 123–140 (1996).
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).
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).
Google Scholar
Xu, Q.-S. & Liang, Y.-Z. Monte Carlo cross validation. Chemom. Intell. Lab. Syst. 56, 1–11 (2001).
Google Scholar
Shcherbakov, M. V. et al. A survey of forecast error measures. World Appl. Sci. J. 24, 171–176 (2013).
James, G., Witten, D., Hastie, T. & Tibshirani, R. An Introduction to Statistical Learning Vol. 112 (Springer, 2013).
Kuhn, M. Building predictive models in R using the caret package. J. Stat. Softw. 28 (2008).
Grömping, U. Variable importance assessment in regression: linear regression versus random forest. Am. Statistician 63, 308–319 (2009).
Google Scholar
Wei, P., Lu, Z. & Song, J. Variable importance analysis: a comprehensive review. Reliab. Eng. Syst. Saf. 142, 399–432 (2015).
Google Scholar
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).
Google Scholar
Greenwell, B. M. pdp: an R package for constructing partial dependence plots. R J. 9, 421–436 (2017).
Google Scholar
Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813 (2008).
Google Scholar
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
Hengl, T. et al. SoilGrids250m: global gridded soil information based on machine learning. PLoS ONE 12, e0169748 (2017).
Google Scholar
Moran, P. A. A test for the serial independence of residuals. Biometrika 37, 178–181 (1950).
Google Scholar
Legendre, P. Spatial autocorrelation: trouble or new paradigm? Ecology 74, 1659–1673 (1993).
Google Scholar
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