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Relative increases in CH4 and CO2 emissions from wetlands under global warming dependent on soil carbon substrates

Nature Geoscience volume 17, pages 26–31 (2024)Cite this article

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Abstract

Compelling evidence has shown that wetland methane emissions are more temperature dependent than carbon dioxide emissions across diverse hydrologic conditions. However, the availability of carbon substrates, which ultimately determines microbial carbon metabolism, has not been adequately accounted for. By combining a global database and a continental-scale experimental study, we showed that differences in the temperature dependence of global wetland methane and carbon dioxide emissions (EM/C) were dependent on soil carbon-to-nitrogen stoichiometry. This can be explained mainly by the positive relationship between soil organic matter decomposability and EM/C. Our study indicates that only 23% of global wetlands will decrease methane relative to carbon dioxide emissions under future warming scenarios when soil organic matter decomposability is considered. Our findings highlight the importance of incorporating soil organic matter biodegradability into model predictions of wetland carbon–climate feedback.

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Fig. 1: Temperature dependence of the CH4/CO2 emissions ratio (EM/C).
Fig. 2: Relationships between the temperature dependence of the CH4/CO2 emissions ratio (EM/C) and SOM decomposability in our incubation experiments.
Fig. 3: Global variations in the temperature dependence of EM/C.

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Data availability

All data needed to evaluate the conclusions are available at Zenodo (https://zenodo.org/records/10044208). The GeoChip data are available in the repository Figshare (https://doi.org/10.6084/m9.figshare.9746303). Source data are provided with this paper.

Code availability

The code used in this study is archived in Supplementary Code 1 and is available at Zenodo (https://zenodo.org/records/10044208).

References

  1. Lal, R. Carbon sequestration. Phil. Trans. R. Soc. B 363, 815–830 (2008).

    Article  Google Scholar 

  2. Nahlik, A. M. & Fennessy, M. S. Carbon storage in US wetlands. Nat. Commun. 7, 13835 (2016).

    Article  Google Scholar 

  3. Chen, H., Xu, X., Fang, C., Li, B. & Nie, M. Differences in the temperature dependence of wetland CO2 and CH4 emissions vary with water table depth. Nat. Clim. Change 11, 766–771 (2021).

    Article  Google Scholar 

  4. IPCC. Climate Change 2022: Mitigation of Climate Change (eds Shukla, P. R. et al.) (Cambbridge Univ. Press, 2022).

  5. Mao, S. et al. Aerobic oxidation of methane significantly reduces global diffusive methane emissions from shallow marine waters. Nat. Commun. 13, 7309 (2022).

    Article  Google Scholar 

  6. Knoblauch, C., Beer, C., Liebner, S., Grigoriev, M. N. & Pfeiffer, E. M. Methane production as key to the greenhouse gas budget of thawing permafrost. Nat. Clim. Change 8, 309–312 (2018).

    Article  Google Scholar 

  7. Segers, R. Methane production and methane consumption: a review of processes underlying wetland methane fluxes. Biogeochemistry 41, 23–51 (1998).

    Article  Google Scholar 

  8. Yvon-Durocher, G. et al. Methane fluxes show consistent temperature dependence across microbial to ecosystem scales. Nature 507, 488–491 (2014).

    Article  Google Scholar 

  9. Zhao, Q. et al. Soil organic carbon content and stock in wetlands with different hydrologic conditions in the Yellow River Delta, China. Ecohydrol. Hydrobiol. 20, 537–547 (2020).

    Article  Google Scholar 

  10. Vicca, S., Janssens, I. A., Flessa, H., Fiedler, S. & Jungkunst, H. F. Temperature dependence of greenhouse gas emissions from three hydromorphic soils at different groundwater levels. Geobiology 7, 465–476 (2009).

    Article  Google Scholar 

  11. Lu, B., Song, L., Zang, S. & Wang, H. Warming promotes soil CO2 and CH4 emissions but decreasing moisture inhibits CH4 emissions in the permafrost peatland of the Great Xing’an Mountains. Sci. Total Environ. 829, 154725 (2022).

    Article  Google Scholar 

  12. Liu, X. et al. Dynamics and controls of CO2 and CH4 emissions in the wetland of a montane permafrost region, northeast China. Atmos. Environ. 122, 454–462 (2015).

    Article  Google Scholar 

  13. Matysek, M. et al. Impact of fertiliser, water table, and warming on celery yield and CO2 and CH4 emissions from fenland agricultural peat. Sci. Total Environ. 667, 179–190 (2019).

    Article  Google Scholar 

  14. Zhao, M. et al. Responses of soil CO2 and CH4 emissions to changing water table level in a coastal wetland. J. Clean. Prod. 269, 122316 (2020).

    Article  Google Scholar 

  15. Li, Y. et al. Oxygen availability regulates the quality of soil dissolved organic matter by mediating microbial metabolism and iron oxidation. Glob. Change Biol. 28, 7410–7427 (2022).

    Article  Google Scholar 

  16. Goncharova, O. Y., Matyshak, G. V., Bobrik, A. A., Timofeeva, M. V. & Sefilyan, A. R. Assessment of the contribution of root and microbial respiration to the total efflux of CO2 from peat soils and Podzols in the north of Western Siberia by the method of component integration. Eurasia. Soil Sci. 52, 206–217 (2019).

    Article  Google Scholar 

  17. Biasi, C., Pitkamaki, A. S., Tavi, N. M., Koponen, H. T. & Martikainen, P. J. An isotope approach based on 13C pulse-chase labelling vs. the root trenching method to separate heterotrophic and autotrophic respiration in cultivated peatlands. Boreal Environ. Res. 17, 184–192 (2012).

    Google Scholar 

  18. Maucieri, C., Barbera, A. C., Vymazal, J. & Borin, M. A review on the main affecting factors of greenhouse gases emission in constructed wetlands. Agric. For. Meteorol. 236, 175–193 (2017).

    Article  Google Scholar 

  19. Mooshammer, M. et al. Adjustment of microbial nitrogen use efficiency to carbon: nitrogen imbalances regulates soil nitrogen cycling. Nat. Commun. 5, 3694 (2014).

    Article  Google Scholar 

  20. Sinsabaugh, R. L., Hill, B. H. & Shah, J. J. F. Ecoenzymatic stoichiometry of microbial organic nutrient acquisition in soil and sediment. Nature 462, 795–798 (2009).

    Article  Google Scholar 

  21. 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  Google Scholar 

  22. Chanton, J. P. et al. Radiocarbon evidence for the substrates supporting methane formation within northern Minnesota peatlands. Geochim. Cosmochim. Acta 59, 3663–3668 (1995).

    Article  Google Scholar 

  23. Yuan, F. et al. An integrative model for soil biogeochemistry and methane processes. II: warming and elevated CO2 effects on peatland CH4 emissions. J. Geophys. Res. Biogeosci. 126, 2020JG005963 (2021).

    Article  Google Scholar 

  24. Wang, Q., Liu, S. & Tian, P. Carbon quality and soil microbial property control the latitudinal pattern in temperature sensitivity of soil microbial respiration across Chinese forest ecosystems. Glob. Change Biol. 24, 2841–2849 (2018).

    Article  Google Scholar 

  25. Allen, A. P., Gillooly, J. F. & Brown, J. H. Linking the global carbon cycle to individual metabolism. Func. Ecol. 19, 202–213 (2005).

    Article  Google Scholar 

  26. Enquist, B. J. et al. Scaling metabolism from organisms to ecosystems. Nature 423, 639–642 (2003).

    Article  Google Scholar 

  27. Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer, 2009).

  28. Yvon-Durocher, G. et al. Reconciling the temperature dependence of respiration across timescales and ecosystem types. Nature 487, 472–476 (2012).

    Article  Google Scholar 

  29. Xiang, D., Wang, G., Tian, J. & Li, W. Global patterns and edaphic-climatic controls of soil carbon decomposition kinetics predicted from incubation experiments. Nat. Commun. 14, 2171 (2023).

    Article  Google Scholar 

  30. Bosatta, E. & Agren, G. I. Soil organic matter quality interpreted thermodynamically. Soil Biol. Biochem. 31, 1889–1891 (1999).

    Article  Google Scholar 

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

    Article  Google Scholar 

  32. Hall, S. J., Ye, C. L., Weintraub, S. R. & Hockaday, W. C. Molecular trade-offs in soil organic carbon composition at continental scale. Nat. Geosci. 13, 687–692 (2020).

    Article  Google Scholar 

  33. Evans, P. N. et al. An evolving view of methane metabolism in the Archaea. Nat. Rev. Microbiol. 17, 219–232 (2019).

    Article  Google Scholar 

  34. Temmink, R. J. M. et al. Recovering wetland biogeomorphic feedbacks to restore the world’s biotic carbon hotspots. Science 376, eabn1479 (2022).

    Article  Google Scholar 

  35. Wang, Z. G. et al. Human-induced erosion has offset one-third of carbon emissions from land cover change. Nat. Clim. Change 7, 345–349 (2017).

    Article  Google Scholar 

  36. Liu, L. et al. Water table drawdown reshapes soil physicochemical characteristics in Zoige peatlands. Catena 170, 119–128 (2018).

    Article  Google Scholar 

  37. Leifeld, J., Klein, K. & Wust-Galley, C. Soil organic matter stoichiometry as indicator for peatland degradation. Sci. Rep. 10, 7634 (2020).

    Article  Google Scholar 

  38. Leifeld, J. & Menichetti, L. The underappreciated potential of peatlands in global climate change mitigation strategies. Nat. Commun. 9, 1071 (2018).

    Article  Google Scholar 

  39. Evans, C. D. et al. Overriding water table control on managed peatland greenhouse gas emissions. Nature 593, 548–552 (2021).

    Article  Google Scholar 

  40. Hoffland, E., Kuyper, T. W., Comans, R. N. J. & Creamer, R. E. Eco-functionality of organic matter in soils. Plant Soil https://doi.org/10.1007/s11104-020-04651-9 (2020).

  41. Billings, S. A. & Ballantyne, F. How interactions between microbial resource demands, soil organic matter stoichiometry, and substrate reactivity determine the direction and magnitude of soil respiratory responses to warming. Glob. Change Biol. 19, 90–102 (2013).

    Article  Google Scholar 

  42. Cotrufo, M. F. et al. Formation of soil organic matter via biochemical and physical pathways of litter mass loss. Nat. Geosci. 8, 776–779 (2015).

    Article  Google Scholar 

  43. Koven, C. D., Lawrence, D. M. & Riley, W. J. Permafrost carbon-climate feedback is sensitive to deep soil carbon decomposability but not deep soil nitrogen dynamics. Proc. Natl Acad. Sci. USA 112, 3752–3757 (2015).

    Article  Google Scholar 

  44. Noyce, G. L., Smith, A. J., Kirwan, M. L., Rich, R. L. & Megonigal, J. P. Oxygen priming induced by elevated CO2 reduces carbon accumulation and methane emissions in coastal wetlands. Nat. Geosci. 16, 63–68 (2023).

    Article  Google Scholar 

  45. Leroy, F. et al. Vegetation composition controls temperature sensitivity of CO2 and CH4 emissions and DOC concentration in peatlands. Soil Biol. Biochem. 107, 164–167 (2017).

    Article  Google Scholar 

  46. Fey, A. & Conrad, R. Effect of temperature on carbon and electron flow and on the archaeal community in methanogenic rice field soil. Appl. Environ. Microbiol. 66, 4790–4797 (2000).

    Article  Google Scholar 

  47. Bradford, M. A. et al. Thermal adaptation of soil microbial respiration to elevated temperature. Ecol. Lett. 11, 1316–1327 (2008).

    Article  Google Scholar 

  48. Matthews, G. V. T. The Ramsar Convention on Wetlands: Its History and Development (Ramsar Convention Bureau, 1993).

  49. Shangguan, W., Dai, Y. J., Duan, Q. Y., Liu, B. Y. & Yuan, H. A global soil data set for Earth system modeling. J. Adv. Model. Earth Syst. 6, 249–263 (2014).

    Article  Google Scholar 

  50. Fan, Y., Li, H. & Miguez-Macho, G. Global patterns of groundwater table depth. Science 339, 940–943 (2013).

    Article  Google Scholar 

  51. Sun, Y. et al. A global meta-analysis on the responses of C and N concentrations to warming in terrestrial ecosystems. Catena 208, 105762 (2022).

    Article  Google Scholar 

  52. Liu, Y., Jiang, M., Lu, X., Lou, Y. & Liu, B. Carbon, nitrogen and phosphorus contents of wetland soils in relation to environment factors in Northeast China. Wetlands 37, 153–161 (2017).

    Article  Google Scholar 

  53. R Core Team. R: A Language and Environment for Statistical Computing (Version 3.6.3) (R Foundation for Statistical Computing, 2020); http://www.r-project.org

  54. Payton, M. E., Greenstone, M. H. & Schenker, N. Overlapping confidence intervals or standard error intervals: what do they mean in terms of statistical significance? J. Insect Sci. 3, 34 (2003).

    Article  Google Scholar 

  55. Zhong, Z. et al. Adaptive pathways of soil microorganisms to stoichiometric imbalances regulate microbial respiration following afforestation in the Loess Plateau, China. Soil Biol. Biochem. 151, 108048 (2020).

    Article  Google Scholar 

  56. Gelinas, Y., Baldock, J. A. & Hedges, J. I. Demineralization of marine and freshwater sediments for CP/MAS 13C NMR analysis. Org. Geochem. 32, 677–693 (2001).

    Article  Google Scholar 

  57. Liang, Y. et al. Development of a common oligonucleotide reference standard for microarray data normalization and comparison across different microbial communities. Appl. Environ. Microbiol. 76, 1088–1094 (2010).

    Article  Google Scholar 

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

    Article  Google Scholar 

Download references

Acknowledgements

The authors received funding from Strategic Priority Research Program of the Chinese Academy of Sciences (XDA28030102 to Y.L.), National Natural Scientific Foundation of China (92251305 to M.N., 41622104 to Y.L.), Innovation Program of the Institute of Soil Science (ISSASIP2201 to Y.L.) and Youth Innovation Promotion Association of the Chinese Academy of Sciences (2016284 to Y.L.).

Author information

Author notes

  1. These authors contributed equally: Han Hu, Ji Chen.

Authors and Affiliations

  1. State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, China

    Han Hu, Haowei Ni, Weigen Huang, Bo Sun, Jiabao Zhang & Yuting Liang

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

    Han Hu, Haowei Ni, Weigen Huang & Yuting Liang

  3. State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an, China

    Ji Chen

  4. Department of Agroecology, Aarhus University, Tjele, Denmark

    Ji Chen

  5. iCLIMATE Interdisciplinary Centre for Climate Change, Aarhus University, Roskilde, Denmark

    Ji Chen

  6. College of Urban and Environmental Sciences, Peking University, Beijing, China

    Feng Zhou

  7. School of Life Sciences, Fudan University, Shanghai, China

    Ming Nie

  8. State Key Joint Laboratory of ESPC, School of the Environment, Tsinghua University, Beijing, China

    Deyi Hou & Huan Liu

  9. Laboratorio de Biodiversidad y Funcionamiento Ecosistémico, Instituto de Recursos Naturales y Agrobiología de Sevilla (IRNAS), CSIC, Sevilla, Spain

    Manuel Delgado-Baquerizo

  10. School of Biological Sciences, University of Oklahoma, Norman, OK, USA

    Jizhong Zhou

  11. State Key Laboratory of Plant Genomics and National Center for Plant Gene Research (Beijing), Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China

    Xianwei Song & Xiaofeng Cao

  12. Department of Environmental Systems Science, Institute of Integrative Biology, ETH, Zurich, Switzerland

    Thomas W. Crowther

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Contributions

Y.L., H.H., M.N., J.C. and J. Zhang conceptualized the research project. H.H., M.N., M.D.-B., H.N., F.Z., J. Zhou and Y.L. developed the methodology. H.H., J.C., M.D.-B., H.N., W.H., X.S., H.L. and Y.L. were responsible for data collection and analysis. Y.L., J. Zhang, X.C. and B.S. provided supervision throughout the project. Y.L., H.H., J.C., M.N., D.H. and T.W.C. wrote the original draft. H.H., J.C., F.Z., M.N., D.H., H.L., M.D.-B., H.N., W.H., J. Zhou, X.S., X.C., B.S., J. Zhang, T.W.C. and Y.L. contributed to the review and editing process.

Corresponding author

Correspondence to Yuting Liang.

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Nature Geoscience thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editor: Xujia Jiang, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Fig. 1 Geographical distribution of the sites in the global database (a) and frequency distribution of carbon emission observations at the site level (b).

In panel a, there might be overlaps among these sites. The size of the circle indicates the number of observations.

Source data

Extended Data Fig. 2 Temperature dependence of CO2 (EC) and CH4 (EM) emissions on a global scale.

a, The temperature dependence of the greenhouse gas (GHG) emissions was characterized using the linear mixed effects (LME) model after fitting the Boltzmann-Arrhenius function to the data of the GHG emissions and the standardized temperature (Methods). b, The dashed lines show the average.

Source data

Extended Data Fig. 3 Temperature dependence of the CH4:CO2 emission ratio (EM:C) at different water table depth intervals and soil C:N intervals.

The temperature dependence was characterized using the linear mixed effects (LME) model after fitting the Boltzmann-Arrhenius function to the data of the CH4:CO2 emission ratio and the standardized temperature (Methods). Different soil C:N intervals were classified using the results of the sliding window-LME model. Different water table depth intervals are classified according to the results of Chen et al. (ref. 3, 10.1038/s41558-021-01108-4).

Source data

Extended Data Fig. 4 Geographical distribution of the sites (a) and sampling strategy (b) in the incubation experiment.

Samples were taken from 39 paddy soil sites located in 13 regions of China. The numbers in parentheses represent the mean annual air temperature (°C) in the sampling region. At each site, 11 nested samples were collected at distances of 1, 6, 16, 36 and 76 m. CC = Changchun; CT = Changting; FQ = Fengqiu; HK = Haikou; HL = Hailun; HY = Hengyang; JO = Jian’ou; LA = Lin’an; OM = organic matter; QX = Qingxin; QZ = Quzhou; SY = Shenyang; TN = total nitrogen; YY = Yuanyang; ZX = Zixi.

Source data

Extended Data Fig. 5 Linear regression analysis between the temperature dependences of CH4 emissions (EM) and CO2 emissions (EC) with soil organic matter decomposability in incubation experiments.

The Ea of SOM decomposition represents the apparent activation energy of soil organic matter decomposition. DOC, dissolved organic carbon; SOC, soil organic carbon. Error bands are 95% confidence intervals of the regression lines.

Source data

Extended Data Fig. 6 The distribution of the difference in the temperature dependence of CH4 and CO2 emissions (EM:C), soil C:N, water table depth, and mean annual temperature in different climate zones.

a, EM:C distribution in different climate zones. The climatic zones are classified by the Köppen climate classification. A, B, C, D, and E represent tropical, arid, temperate, cold, and polar climates, respectively. Centre lines are medians, and diamonds represent the averages. Box limits are upper and lower quartiles. whiskers are 1.5× the interquartile ranges. The numbers in brackets represent wetland area (km2). b, Relative area of wetlands with positive and negative EM:C values. C, The distribution of soil, water table depth, and mean annual temperature of wetlands in different climate zones. The red dotted lines are the global mean values.

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–8 and Tables 1–4.

Supplementary Data 1

Including (1) the dataset of meta-analysis, (2) the detail information of the soil samples and (3) the exact value of each point in the global map.

Supplementary Code 1

Including code for the machine-learning and the linear mixed-effects models.

Source data

Source Data Fig. 1

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Source Data Fig. 2

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Source Data Fig. 3

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Source Data Extended Data Fig. 1

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Hu, H., Chen, J., Zhou, F. et al. Relative increases in CH4 and CO2 emissions from wetlands under global warming dependent on soil carbon substrates. Nat. Geosci. 17, 26–31 (2024). https://doi.org/10.1038/s41561-023-01345-6

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