Tundra soil carbon is vulnerable to rapid microbial decomposition under climate warming

Journal name:
Nature Climate Change
Year published:
Published online

Microbial decomposition of soil carbon in high-latitude tundra underlain with permafrost is one of the most important, but poorly understood, potential positive feedbacks of greenhouse gas emissions from terrestrial ecosystems into the atmosphere in a warmer world1, 2, 3, 4. Using integrated metagenomic technologies, we showed that the microbial functional community structure in the active layer of tundra soil was significantly altered after only 1.5 years of warming, a rapid response demonstrating the high sensitivity of this ecosystem to warming. The abundances of microbial functional genes involved in both aerobic and anaerobic carbon decomposition were also markedly increased by this short-term warming. Consistent with this, ecosystem respiration (Reco) increased up to 38%. In addition, warming enhanced genes involved in nutrient cycling, which very likely contributed to an observed increase (30%) in gross primary productivity (GPP). However, the GPP increase did not offset the extra Reco, resulting in significantly more net carbon loss in warmed plots compared with control plots. Altogether, our results demonstrate the vulnerability of active-layer soil carbon in this permafrost-based tundra ecosystem to climate warming and the importance of microbial communities in mediating such vulnerability.

At a glance


  1. Warming effects on soil variables and ecosystem C fluxes.
    Figure 1: Warming effects on soil variables and ecosystem C fluxes.

    Grey bars represent control plots and black bars represent warmed treatment plots. a, Soil temperature in both growing season (May to September 2010) and wintertime (December 2009 to March 2010) averaged across 5, 10, 20 and 40cm. b, Soil moisture. c, Maximum thaw depth. d, Standard cellulose filter paper decomposition rate (mass loss) in the field. e, Proportion of soil C pools in total organic C, including labile C pool 1 (LCP1, mainly polysaccharides) and 2 (LCP2, mostly cellulose), and recalcitrant C pool (RCP). f, Growing season (May to September 2010), wintertime (October 2009 to April 2010) and annual ecosystem C fluxes, which were estimated on the basis of the C amount from CO2 emissions. GPP, gross primary productivity; Reco, ecosystem respiration; NEE, net ecosystem C exchange. Positive values indicate C sink, and negative values represent C source. Error bars represent standard error of the mean. The differences between warmed and control plots were tested using two-tailed t tests, indicated by when p < 0.05, or when p < 0.10. Panels ad,f were reanalysed from previously published data12, 23, 31.

  2. Warming effects on functional genes involved in biogeochemical cycling processes.
    Figure 2: Warming effects on functional genes involved in biogeochemical cycling processes.

    a, C degradation from GeoChip data. The targeted substrates were arranged in order from labile to recalcitrant C. GeoChip data are presented as the signal difference between warmed and control plots (warmed–control). Error bars represent standard error. Significance is indicated by when p < 0.05. b, Anaerobic processes from GeoChip data. c, N processes from GeoChip data. The percentage change in N gene abundance in response to warming is indicated in parenthesis. Genes where change in abundance was significant (p < 0.05) are labelled in red. Grey-coloured genes were not targeted by the version of GeoChip used here, not detected or not applicable. d, Abundance of subsystems involved in C, N, phosphorus and sulfur cycling from metagenomic shotgun sequence data. Changes in subsystems are indicated as fold change (log2 (warmed/control)) in abundance. Significant differences between warmed and control plots are highlighted with green squares. e, Response ratios showing significant changes in abundance of gene clusters involved in C and N cycling from the metagenomic shotgun sequence data. These gene clusters were identified by searching the shotgun sequence data sets using GeoChip genes as queries. Each cluster on the x axis represents a group of sequences among which the similarities are ≤95%. The GenBank GI numbers of the representative sequences for the gene clusters are listed in Supplementary Table 8. Error bars indicate 95% confidence intervals of abundance differences between warmed and control groups. The full names of the genes in this figure are listed in Supplementary Table 7.

  3. A conceptual model of the impact of warming on the active layer of tundra ecosystem processes.
    Figure 3: A conceptual model of the impact of warming on the active layer of tundra ecosystem processes.

    Greenhouse gas pools are represented by green square frames, material pools by yellow square frames, and biological processes by frames in the shape of blue punched tape. Material flows are indicated by thicker black arrows. Impacts of environmental attributes (for example, soil temperature) and microbial community are marked by narrow arrows in black and red, respectively, and labelled with a ‘+ if increases in gene abundance were observed in this study.


  1. Schuur, E. A. G. et al. Vulnerability of permafrost carbon to climate change: implications for the global carbon cycle. BioScience 58, 701714 (2008).
  2. Schuur, E. et al. Expert assessment of vulnerability of permafrost carbon to climate change. Climatic Change 119, 359374 (2013).
  3. Zhou, J. et al. Microbial mediation of carbon-cycle feedbacks to climate warming. Nature Clim. Change 2, 106110 (2012).
  4. Graham, D. E. et al. Microbes in thawing permafrost: the unknown variable in the climate change equation. ISME J. 6, 709712 (2012).
  5. Lee, H., Schuur, E. A. G., Inglett, K. S., Lavoie, M. & Chanton, J. P. The rate of permafrost carbon release under aerobic and anaerobic conditions and its potential effects on climate. Glob. Change Biol. 18, 515527 (2012).
  6. Hicks Pries, C. E., Schuur, E. A. G. & Crummer, K. G. Holocene carbon stocks and carbon accumulation rates altered in soils undergoing permafrost thaw. Ecosystems 15, 162173 (2012).
  7. Tarnocai, C. et al. Soil organic carbon pools in the northern circumpolar permafrost region. Glob. Biogeochem. Cycles 23, GB2023 (2009).
  8. Grosse, G. et al. Vulnerability of high-latitude soil organic carbon in North America to disturbance. J. Geophys. Res. 116, G00K06 (2011).
  9. Hassol, S. J. Impacts of A Warming Arctic–Arctic Climate Impact Assessment (Cambridge Univ. Press, 2004).
  10. Schuur, E. A. G. & Abbott, B. Climate change: high risk of permafrost thaw. Nature 480, 3233 (2011).
  11. Lawrence, D. M., Slater, A. G. & Swenson, S. C. Simulation of present-day and future permafrost and seasonally frozen ground conditions in CCSM4. J. Clim. 25, 22072225 (2012).
  12. Natali, S. M., Schuur, E. A. G. & Rubin, R. L. Increased plant productivity in Alaskan tundra as a result of experimental warming of soil and permafrost. J. Ecol. 100, 488498 (2012).
  13. Walker, M. D. et al. Plant community responses to experimental warming across the tundra biome. Proc. Natl Acad. Sci. USA 103, 13421346 (2006).
  14. Natali, S. M. et al. Effects of experimental warming of air, soil and permafrost on carbon balance in Alaskan tundra. Glob. Change Biol. 17, 13941407 (2011).
  15. Yergeau, E. et al. Shifts in soil microorganisms in response to warming are consistent across a range of Antarctic environments. ISME J. 6, 692702 (2012).
  16. Mackelprang, R. et al. Metagenomic analysis of a permafrost microbial community reveals a rapid response to thaw. Nature 480, 368371 (2011).
  17. Hultman, J. et al. Multi-omics of permafrost, active layer and thermokarst bog soil microbiomes. Nature 521, 208212 (2015).
  18. Coolen, M. J. L. & Orsi, W. D. The transcriptional response of microbial communities in thawing Alaskan permafrost soils. Front. Microbiol. 6, 197 (2015).
  19. Sturm, M., Racine, C. & Tape, K. Climate change: increasing shrub abundance in the Arctic. Nature 411, 546547 (2001).
  20. Walker, D. A. et al. The circumpolar arctic vegetation map. J. Veg. Sci. 16, 267282 (2005).
  21. Schuur, E. A. G. et al. The effect of permafrost thaw on old carbon release and net carbon exchange from tundra. Nature 459, 556559 (2009).
  22. Zhao, M. et al. Microbial mediation of biogeochemical cycles revealed by simulation of global changes with soil transplant and cropping. ISME J. 8, 20452055 (2014).
  23. Natali, S. M., Schuur, E. A. G., Webb, E. E., Hicks Pries, C. E. & Crummer, K. G. Permafrost degradation stimulates carbon loss from experimentally warmed tundra. Ecology 95, 602608 (2014).
  24. Rovira, P. & Vallejo, V. R. Labile and recalcitrant pools of carbon and nitrogen in organic matter decomposing at different depths in soil: an acid hydrolysis approach. Geoderma 107, 109141 (2002).
  25. Zhou, J. et al. High-throughput metagenomic technologies for complex microbial community analysis: open and closed formats. mBio 6, e02288-14 (2015).
  26. Lau, M. C. Y. et al. An active atmospheric methane sink in high Arctic mineral cryosols. ISME J. 9, 18801891 (2015).
  27. Natali, S. M. et al. Permafrost thaw and soil moisture driving CO2 and CH4 release from upland tundra. J. Geophys. Res. 120, 525537 (2015).
  28. IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).
  29. Liebner, S. & Wagner, D. Abundance, distribution and potential activity of methane oxidizing bacteria in permafrost soils from the Lena Delta, Siberia. Environ. Microbiol. 9, 107117 (2007).
  30. Friedlingstein, P. et al. Climate-carbon cycle feedback analysis: results from the C4MIP model intercomparison. J. Clim. 19, 33373353 (2006).
  31. Hicks Pries, C. E., Schuur, E. A. G., Vogel, J. G. & Natali, S. M. Moisture drives surface decomposition in thawing tundra. J. Geophys. Res. 118, 11331143 (2013).
  32. Avramidis, P., Nikolaou, K. & Bekiari, V. Total organic carbon and total nitrogen in sediments and soils: a comparison of the wet oxidation–titration method with the combustion-infrared method. Agric. Agric. Sci. Procedia 4, 425430 (2015).
  33. Walker, M. Community baseline measurements for ITEX studies. ITEX Manual 2, 3941 (1996).
  34. Schuur, E. A. G., Crummer, K., Vogel, J. & Mack, M. Plant species composition and productivity following permafrost thaw and thermokarst in Alaskan tundra. Ecosystems 10, 280292 (2007).
  35. Shaver, G. R. et al. Species composition interacts with fertilizer to control long-term change in tundra productivity. Ecology 82, 31633181 (2001).
  36. Clymo, R. S. The growth of Sphagnum: methods of measurement. J. Ecol. 58, 1349 (1970).
  37. Tu, Q. et al. GeoChip 4: a functional gene-array-based high-throughput environmental technology for microbial community analysis. Mol. Ecol. Resour. 14, 1755-0998.12239 (2014).
  38. Wu, L. et al. Phasing amplicon sequencing on Illumina MiSeq for robust environmental microbial community analysis. BMC Microbiol. 15, s12866-015-0450-4 (2015).
  39. He, Z. et al. GeoChip: a comprehensive microarray for investigating biogeochemical, ecological and environmental processes. ISME J. 1, 6777 (2007).
  40. Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 21942200 (2011).
  41. Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 24602461 (2010).
  42. Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 52615267 (2007).
  43. Huse, S. M., Huber, J. A., Morrison, H. G., Sogin, M. L. & Welch, D. M. Accuracy and quality of massively parallel DNA pyrosequencing. Genome Biol. 8, R143 (2007).
  44. Meyer, M., Stenzel, U. & Hofreiter, M. Parallel tagged sequencing on the 454 platform. Nature Protoc. 3, 267278 (2008).
  45. Ronaghi, M., Uhlén, M. & Nyren, P. A sequencing method based on real-time pyrophosphate. Science 281, 363365 (1998).
  46. Chou, H. H. & Holmes, M. H. DNA sequence quality trimming and vector removal. Bioinformatics 17, 10931104 (2001).
  47. Wang, Q. et al. Ecological patterns of nifH genes in four terrestrial climatic zones explored with targeted metagenomics using FrameBot, a new informatics tool. mBio 4, e00592-13 (2013).
  48. Zehr, J. P., Jenkins, B. D., Short, S. M. & Steward, G. F. Nitrogenase gene diversity and microbial community structure: a cross-system comparison. Environ. Microbiol. 5, 539554 (2003).
  49. Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 16581659 (2006).
  50. Palmer, K., Drake, H. L. & Horn, M. A. Genome-derived criteria for assigning environmental narG and nosZ sequences to operational taxonomic units of nitrate reducers. Appl. Environ. Microbiol. 75, 51705174 (2009).
  51. Mao, Y., Yannarell, A. C. & Mackie, R. I. Changes in N-transforming archaea and bacteria in soil during the establishment of bioenergy crops. PLoS ONE 6, e24750 (2011).
  52. Pereira e Silva, M. C., Schloter-Hai, B., Schloter, M., van Elsas, J. D. & Salles, J. F. Temporal dynamics of abundance and composition of nitrogen-fixing communities across agricultural soils. PLoS ONE 8, e74500 (2013).
  53. Luo, C., Tsementzi, D., Kyrpides, N. C. & Konstantinidis, K. T. Individual genome assembly from complex community short-read metagenomic datasets. ISME J. 6, 898901 (2011).
  54. DeSantis, T. Z. et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72, 50695072 (2006).
  55. Kent, W. J. BLAT—the BLAST-like alignment tool. Genome Res. 12, 656664 (2002).
  56. Rho, M., Tang, H. & Ye, Y. FragGeneScan: predicting genes in short and error-prone reads. Nucleic Acids Res. 38, e191 (2010).
  57. Wilke, A. et al. The M5nr: a novel non-redundant database containing protein sequences and annotations from multiple sources and associated tools. BMC Bioinformatics 13, 1471-2105-13-141 (2012).
  58. Overbeek, R. et al. The subsystems approach to genome annotation and its use in the project to annotate 1000 genomes. Nucleic Acids Res. 33, 56915702 (2005).
  59. Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).
  60. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 289300 (1995).
  61. Luo, C., Rodriguez, R. L. & Konstantinidis, K. T. A users guide to quantitative and comparative analysis of metagenomic datasets. Methods Enzymol. 531, 525547 (2013).
  62. Geer, L. Y. et al. The NCBI BioSystems database. Nucleic Acids Res. 38, D492D496 (2010).
  63. Larkin, M. A. et al. Clustal W and Clustal X version 2.0. Bioinformatics 23, 29472948 (2007).
  64. Eddy, S. R. Profile hidden Markov models. Bioinformatics 14, 755763 (1998).
  65. Camacho, C. et al. BLAST + : architecture and applications. BMC Bioinformatics 10, 1471-2105-10-421 (2009).
  66. Kong, Y. Btrim: a fast, lightweight adapter and quality trimming program for next-generation sequencing technologies. Genomics 98, 152153 (2011).
  67. Oksanen, J. et al. vegan: Community Ecology Package R package version 2.3-2 (R Foundation, 2015).
  68. Oksanen, J. & Minchin, P. R. Instability of ordination results under changes in input data order: explanations and remedies. J. Veg. Sci. 8, 447454 (1997).
  69. Zapala, M. A. & Schork, N. J. Multivariate regression analysis of distance matrices for testing associations between gene expression patterns and related variables. Proc. Natl Acad. Sci. USA 103, 1943019435 (2006).
  70. Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Aust. J. Ecol. 18, 117143 (1993).
  71. Sickle, J. V. Using mean similarity dendrograms to evaluate classifications. J. Agric. Biol. Environ. Stat. 2, 370388 (1997).
  72. Hotelling, H. in Breakthroughs in Statistics (eds Kotz, S. & Johnson, N.) 162190 (Springer, 1992).
  73. Chambers, J., Freeny, A. & Heiberger, R. in Statistical Models in S (eds Chambers, J. M. & Hastie, T. J.) 145193 (Wadsworth Brooks/Cole, 1992).
  74. Luo, Y., Hui, D. & Zhang, D. Elevated CO2 stimulates net accumulations of carbon and nitrogen in land ecosystems: a meta-analysis. Ecology 87, 5363 (2006).

Download references

Author information

  1. These authors contributed equally to this work.

    • Kai Xue &
    • Mengting M. Yuan


  1. State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China

    • Kai Xue &
    • Jizhong Zhou
  2. Institute for Environmental Genomics, University of Oklahoma, Norman, Oklahoma 73019, USA

    • Kai Xue,
    • Mengting M. Yuan,
    • Zhou J. Shi,
    • Yujia Qin,
    • Ye Deng,
    • Lei Cheng,
    • Liyou Wu,
    • Zhili He,
    • Joy D. Van Nostrand &
    • Jizhong Zhou
  3. Department of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma 73019, USA

    • Kai Xue,
    • Mengting M. Yuan,
    • Zhou J. Shi,
    • Yujia Qin,
    • Ye Deng,
    • Lei Cheng,
    • Liyou Wu,
    • Zhili He,
    • Joy D. Van Nostrand,
    • Yiqi Luo &
    • Jizhong Zhou
  4. Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 10085, China

    • Ye Deng
  5. College of Life Sciences, Zhejiang University, Hangzhou 310058, China

    • Lei Cheng
  6. Department of Biology, University of Florida, Gainesville, Florida 32611, USA

    • Rosvel Bracho &
    • Edward. A. G. Schuur
  7. Woods Hole Research Center, Falmouth, Massachusetts 02540, USA

    • Susan Natali
  8. Center for Ecosystem Sciences and Society, Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona 86001, USA

    • Edward. A. G. Schuur
  9. School of Civil and Environmental Engineering, and School of Biology, Georgia Institute of Technology, Atlanta, Georgia 30332, USA

    • Chengwei Luo &
    • Konstantinos T. Konstantinidis
  10. Center for Microbial Ecology, Michigan State University, East Lansing, Michigan 48824, USA

    • Qiong Wang,
    • James R. Cole &
    • James M. Tiedje
  11. Earth Science Division, Lawrence Berkeley National Laboratory, Berkeley, California 94270, USA

    • Jizhong Zhou


All authors contributed intellectual input and assistance to this study and manuscript preparation. J.Z., E.A.G.S., Y.L., J.M.T. and K.T.K. developed the original concepts. K.X., M.M.Y., Z.J.S., L.W., Z.H., Y.Q., Y.D., J.D.V.N., Q.W. and C.L. contributed reagents and data analysis. R.B. handled all soils processing and subsampling for microbial analysis. S.N. provided key field data. M.M.Y. and L.C. did sequencing and GeoChip hybridization. K.X., M.M.Y. and J.Z. performed data analysis and integration. K.X., M.M.Y. and J.Z. wrote the paper with help from E.A.G.S., K.T.K., Y.L., J.R.C. and J.M.T.

Competing financial interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to:

Author details

Supplementary information

PDF files

  1. Supplementary Information (2 MB)

    Supplementary Information

Additional data