Disproportionate increase in freshwater methane emissions induced by experimental warming

Abstract

Net emissions of the potent GHG methane from ecosystems represent the balance between microbial methane production (methanogenesis) and oxidation (methanotrophy), each with different sensitivities to temperature. How this balance will be altered by long-term global warming, especially in freshwaters that are major methane sources, remains unknown. Here we show that the experimental warming of artificial ponds over 11 years drives a disproportionate increase in methanogenesis over methanotrophy that increases the warming potential of the gases they emit. The increased methane emissions far exceed temperature-based predictions, driven by shifts in the methanogen community under warming, while the methanotroph community was conserved. Our experimentally induced increase in methane emissions from artificial ponds is, in part, reflected globally as a disproportionate increase in the capacity of naturally warmer ecosystems to emit more methane. Our findings indicate that as Earth warms, natural ecosystems will emit disproportionately more methane in a positive feedback warming loop.

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Fig. 1: Ongoing divergence in methane emissions from the surfaces of our ponds mirrors natural warming.
Fig. 2: Long-term warming increases methane production over methanogen abundance.
Fig. 3: Long-term warming provides a mechanism to selectively alter the methanogen community.
Fig. 4: Methane oxidation is conserved and the growth of methanotrophy is impaired under warming.
Fig. 5: Positive climate warming feedback loop revealed by our long-term experiment.

Data availability

The data that support the findings of this study are available from the corresponding author upon request. The DNA sequences are in the National Center for Biotechnology Information database, under BioProject ID PRJNA484117. Source data are provided with this paper.

References

  1. 1.

    Nisbet, E. G., Dlugokencky, E. J. & Bousquet, P. Methane on the rise—again. Science 343, 493–495 (2014).

    CAS  Google Scholar 

  2. 2.

    Balcombe, P., Speirs, J. F., Brandon, N. P. & Hawkes, A. D. Methane emissions: choosing the right climate metric and time horizon. Environ. Sci. Process. Impacts 20, 1323–1339 (2018).

    CAS  Google Scholar 

  3. 3.

    Holgerson, M. A. & Raymond, P. A. Large contribution to inland water CO2 and CH4 emissions from very small ponds. Nat. Geosci. 9, 222–226 (2016).

    CAS  Google Scholar 

  4. 4.

    Saunois, M. et al. The global methane budget 2000–2012. Earth Syst. Sci. Data 8, 697–751 (2016).

    Google Scholar 

  5. 5.

    Bridgham, S. D., Cadillo-Quiroz, H., Keller, J. K. & Zhuang, Q. Methane emissions from wetlands: biogeochemical, microbial, and modeling perspectives from local to global scales. Glob. Change Biol. 19, 1325–1346 (2013).

    Google Scholar 

  6. 6.

    Gudasz, C. et al. Temperature-controlled organic carbon mineralization in lake sediments. Nature 466, 478–481 (2010).

    CAS  Google Scholar 

  7. 7.

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

    CAS  Google Scholar 

  8. 8.

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

    Google Scholar 

  9. 9.

    Hanson, R. S. & Hanson, T. E. Methanotrophic bacteria. Microbiol. Rev. 60, 439–471 (1996).

    CAS  Google Scholar 

  10. 10.

    Shelley, F., Abdullahi, F., Grey, J. & Trimmer, M. Microbial methane cycling in the bed of a chalk river: oxidation has the potential to match methanogenesis enhanced by warming. Freshw. Biol. 60, 150–160 (2015).

    CAS  Google Scholar 

  11. 11.

    Mohanty, S. R., Bodelier, P. L. E. & Conrad, R. Effect of temperature on composition of the methanotrophic community in rice field and forest soil. FEMS Microbiol. Ecol. 62, 24–31 (2007).

    CAS  Google Scholar 

  12. 12.

    Høj, L., Olsen, R. A. & Torsvik, V. L. Effects of temperature on the diversity and community structure of known methanogenic groups and other archaea in high Arctic peat. ISME J. 2, 37–48 (2008).

    Google Scholar 

  13. 13.

    Hall, E. K. et al. Understanding how microbiomes influence the systems they inhabit. Nat. Microbiol. 3, 977–982 (2018).

    CAS  Google Scholar 

  14. 14.

    Ho, A., Lüke, C. & Frenzel, P. Recovery of methanotrophs from disturbance: population dynamics, evenness and functioning. ISME J. 5, 750–758 (2011).

    CAS  Google Scholar 

  15. 15.

    Rocca, J. D. et al. Relationships between protein-encoding gene abundance and corresponding process are commonly assumed yet rarely observed. ISME J. 9, 1693–1699 (2015).

    Google Scholar 

  16. 16.

    Trimmer, M. et al. Riverbed methanotrophy sustained by high carbon conversion efficiency. ISME J. 9, 2304–2314 (2015).

    CAS  Google Scholar 

  17. 17.

    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).

    CAS  Google Scholar 

  18. 18.

    Ho, A. & Frenzel, P. Heat stress and methane-oxidizing bacteria: effects on activity and population dynamics. Soil Biol. Biochem. 50, 22–25 (2012).

    CAS  Google Scholar 

  19. 19.

    Wilson, R. M. et al. Stability of peatland carbon to rising temperatures. Nat. Commun. 7, 13723 (2016).

    CAS  Google Scholar 

  20. 20.

    Yvon-Durocher, G., Hulatt, C. J., Woodward, G. & Trimmer, M. Long-term warming amplifies shifts in the carbon cycle of experimental ponds. Nat. Clim. Change 7, 209–213 (2017).

    CAS  Google Scholar 

  21. 21.

    Yvon-Durocher, G. et al. Five years of experimental warming increases the biodiversity and productivity of phytoplankton. PLoS Biol. 13, e1002324 (2015).

    Google Scholar 

  22. 22.

    Davidson, T. A. et al. Synergy between nutrients and warming enhances methane ebullition from experimental lakes. Nat. Clim. Change 8, 156–160 (2018).

    CAS  Google Scholar 

  23. 23.

    McCalley, C. K. et al. Methane dynamics regulated by microbial community response to permafrost thaw. Nature 514, 478–481 (2014).

    CAS  Google Scholar 

  24. 24.

    Conrad, R. Contribution of hydrogen to methane production and control of hydrogen concentrations in methanogenic soils and sediments. FEMS Microbiol. Ecol. 28, 193–202 (1999).

    CAS  Google Scholar 

  25. 25.

    Wilson, R. M. et al. Hydrogenation of organic matter as a terminal electron sink sustains high CO2:CH4 production ratios during anaerobic decomposition. Org. Geochem. 112, 22–32 (2017).

    CAS  Google Scholar 

  26. 26.

    Hodgkins, S. B. et al. Changes in peat chemistry associated with permafrost thaw increase greenhouse gas production. Proc. Natl Acad. Sci. USA 111, 5819–5824 (2014).

    CAS  Google Scholar 

  27. 27.

    Glissmann, K., Chin, K. J., Casper, P. & Conrad, R. Methanogenic pathway and archaeal community structure in the sediment of eutrophic Lake Dagow: effect of temperature. Microb. Ecol. 48, 389–399 (2004).

    CAS  Google Scholar 

  28. 28.

    Inglett, K. S., Inglett, P. W., Reddy, K. R. & Osborne, T. Z. Temperature sensitivity of greenhouse gas production in wetland soils of different vegetation. Biogeochemistry 108, 77–90 (2012).

    CAS  Google Scholar 

  29. 29.

    Conrad, R., Klose, M. & Noll, M. Functional and structural response of the methanogenic microbial community in rice field soil to temperature change. Environ. Microbiol. 11, 1844–1853 (2009).

    CAS  Google Scholar 

  30. 30.

    Metje, M. & Frenzel, P. Methanogenesis and methanogenic pathways in a peat from subarctic permafrost. Environ. Microbiol. 9, 954–964 (2007).

    CAS  Google Scholar 

  31. 31.

    Nozhevnikova, A. N. et al. Influence of temperature and high acetate concentrations on methanogenesis in lake sediment slurries. FEMS Microbiol. Ecol. 62, 336–344 (2007).

    CAS  Google Scholar 

  32. 32.

    Wen, X. et al. Global biogeographic analysis of methanogenic archaea identifies community-shaping environmental factors of natural environments. Front. Microbiol. 8, 1339 (2017).

    Google Scholar 

  33. 33.

    Conrad, R. et al. Stable carbon isotope discrimination and microbiology of methane formation in tropical anoxic lake sediments. Biogeosciences 8, 795–814 (2011).

    CAS  Google Scholar 

  34. 34.

    Kotsyurbenko, O. R. Trophic interactions in the methanogenic microbial community of low-temperature terrestrial ecosystems. FEMS Microbiol. Ecol. 53, 3–13 (2005).

    CAS  Google Scholar 

  35. 35.

    Yvon-Durocher, G., Montoya, J. M., Woodward, G., Jones, J. I. & Trimmer, M. Warming increases the proportion of primary production emitted as methane from freshwater mesocosms. Glob. Change Biol. 17, 1225–1234 (2011).

    Google Scholar 

  36. 36.

    Reim, A., Lüke, C., Krause, S., Pratscher, J. & Frenzel, P. One millimetre makes the difference: high-resolution analysis of methane-oxidizing bacteria and their specific activity at the oxic–anoxic interface in a flooded paddy soil. ISME J. 6, 2128–2139 (2012).

    CAS  Google Scholar 

  37. 37.

    Yver Kwok, C. E. et al. Methane emission estimates using chamber and tracer release experiments for a municipal waste water treatment plant. Atmos. Meas. Tech. 8, 2853–2867 (2015).

    Google Scholar 

  38. 38.

    Sanders, I. A. et al. Emission of methane from chalk streams has potential implications for agricultural practices. Freshw. Biol. 52, 1176–1186 (2007).

    CAS  Google Scholar 

  39. 39.

    Neubacher, E. C., Parker, R. E. & Trimmer, M. Short-term hypoxia alters the balance of the nitrogen cycle in coastal sediments. Limnol. Oceanogr. 56, 651–665 (2011).

    CAS  Google Scholar 

  40. 40.

    R: a language and environment for statistical computing v.3.2.5 (R Core Team, 2014).

  41. 41.

    Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. {lmerTest} package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).

    Google Scholar 

  42. 42.

    Lenth, R. emmeans: estimated marginal means, aka least-squares means. R package v.1.4.7 (2019); https://cran.r-project.org/package=emmeans

  43. 43.

    Nicholls, J. C. & Trimmer, M. Widespread occurrence of the anammox reaction in estuarine sediments. Aquat. Microb. Ecol. 55, 105–113 (2009).

    Google Scholar 

  44. 44.

    Lever, M. A. & Teske, A. P. Diversity of methane-cycling archaea in hydrothermal sediment investigated by general and group-specific PCR primers. Appl. Environ. Microbiol. 81, 1426–1441 (2015).

    Google Scholar 

  45. 45.

    Horz, H. P., Rich, V., Avrahami, S. & Bohannan, B. J. M. Methane-oxidizing bacteria in a California upland grassland soil: diversity and response to simulated global change. Appl. Environ. Microbiol. 71, 2642–2652 (2005).

    CAS  Google Scholar 

  46. 46.

    Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).

    CAS  Google Scholar 

  47. 47.

    King, T., Butcher, S. & Zalewski, L. Apocrita—High Performance Computing Cluster for Queen Mary University of London (Queen Mary University of London, 2017); https://doi.org/10.5281/ZENODO.438045

  48. 48.

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

    CAS  Google Scholar 

  49. 49.

    Pester, M., Friedrich, M. W., Schink, B. & Brune, A. pmoA-based analysis of methanotrophs in a littoral lake sediment reveals a diverse and stable community in a dynamic environment. Appl. Environ. Microbiol. 70, 3138–3142 (2004).

    CAS  Google Scholar 

  50. 50.

    Oakley, B. B., Carbonero, F., Dowd, S. E., Hawkins, R. J. & Purdy, K. J. Contrasting patterns of niche partitioning between two anaerobic terminal oxidizers of organic matter. ISME J. 6, 905–914 (2012).

    CAS  Google Scholar 

  51. 51.

    Wilkins, D., Lu, X. Y., Shen, Z., Chen, J. & Lee, P. K. H. Pyrosequencing of mcrA and archaeal 16s rRNA genes reveals diversity and substrate preferences of methanogen communities in anaerobic digesters. Appl. Environ. Microbiol. 81, 604–613 (2015).

    Google Scholar 

  52. 52.

    Yang, S., Wen, X. & Liebner, S. pmoA Gene Reference Database (Fasta-Formatted Sequences and Taxonomy) (GFZ Data Services, 2016).

  53. 53.

    McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).

    CAS  Google Scholar 

  54. 54.

    Anderson, M. J. in Wiley StatsRef: Statistics Reference Online 1–15 (Wiley, 2017).

  55. 55.

    Oksanen, J. et al. vegan: community ecology package. R package v.2.5-6 (2018); https://cran.r-project.org/package=vegan

  56. 56.

    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  Google Scholar 

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Acknowledgements

This study was supported by Queen Mary University of London and the UK Natural Environment Research Council (grant nos NE/M02086X/1 and NE/M020886/1). We thank I. Sanders and F. Shelley for technical and fieldwork assistance, J. Pretty for mesocosm pond maintenance, M. Rouen for designing and installing the Campbell control and data-logging system, H. Prentice for collecting sediments and for DNA extraction, C. Economou and M. Struebig for help with the molecular work, and P. K. H. Lee for providing the mcrA database and related documents for the bioinformatics analysis. We thank the principal investigators of the methane flux data products, including W. Quinton, O. Sonnentag, G. Wohlfahrt, S. Gogo, T. Schuur, K. Krauss, A. Desai, G. Bohrer, R. Vargas, D. Baldocchi, J. Chen, H. Chu, H. Iwata, M. Ueyama and Y. Harazono. We also thank the funding agencies that supported their flux measurements.

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Contributions

M.T., Y.Z. and K.J.P conceived the study. Y.Z. conducted the vast majority of the experiments and analysed the data. Y.Z., M.T., K.J.P., G.Y.-D. and A.J.D. discussed the data. Y.Z., M.T. and K.J.P. wrote the manuscript, and all authors contributed to revisions. Y.Z. and S.F.H. set up the chamber system. Y.Z., O.E. and L.S. performed the molecular analyses.

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Correspondence to Mark Trimmer.

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Extended data

Extended Data Fig. 1 Schematic of experimental pond set-up and dynamic chamber measurements.

Twenty artificial ponds, with 10 warmed (red) by 4 °C above 10 ambient (blue) ponds, were paired in a randomized block design (a) and controlled via two temperature sensors (T1, T2), a thermocouple (T-stat) and a solid-state relay (SSR) (b). Dynamic LI-COR chambers, floating on lifebuoys, were installed on 7 each of the warmed and ambient ponds (c). Each floating chamber was connected to one of the inlet ports on the MIU and the MIU outlet port was connected to the gas inlet port of Ultra-Portable Greenhouse Gas Analyzer (LGR) (d). A dynamic chamber is sequentially triggered to close by a customised Campbell control unit (CCU) for 30 minutes for gas measurements while the other chambers remain open. When a chamber is triggered to close, the MIU switches simultaneously to the inlet connected to the closing chamber to direct its gas flow to the LGR. See Methods and Extended Data Fig. 2 for further details on methane emissions.

Extended Data Fig. 2 Consistent seasonal patterns in daily methane emissions under warming but with ongoing divergence over 10 years (200735, 201320 and 2017 (this study)).

The seasonal patterns in all 3 years are very similar, despite the use of different techniques but the frequent measurements (three times daily) using dynamic chambers in 2017 captured far more details in emissions compared to 2007 and 2013 when static chambers were used to measure methane emission on 7 and 12 occasions over each year, respectively. Note the natural log scale for methane emissions.

Extended Data Fig. 3 Methane emissions at night and during the day.

Methane emissions during the day (a) and at night (b) follow similar seasonal patterns; yet methane emissions at night are significantly greater than during the day (c). Source data

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Supplementary Information

Supplementary Figs. 1–6, Tables 1–10 and discussion.

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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 Fig. 4

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Source Data Extended Data Fig. 3

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Zhu, Y., Purdy, K.J., Eyice, Ö. et al. Disproportionate increase in freshwater methane emissions induced by experimental warming. Nat. Clim. Chang. 10, 685–690 (2020). https://doi.org/10.1038/s41558-020-0824-y

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