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Carbon and nitrogen cycling in Yedoma permafrost controlled by microbial functional limitations

Nature Geoscience volume 13, pages 794–798 (2020)Cite this article

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Abstract

Warming-induced microbial decomposition of organic matter in permafrost soils constitutes a climate-change feedback of uncertain magnitude. While physicochemical constraints on soil functioning are relatively well understood, the constraints attributable to microbial community composition remain unclear. Here we show that biogeochemical processes in permafrost can be impaired by missing functions in the microbial community—functional limitations—probably due to environmental filtering of the microbial community over millennia-long freezing. We inoculated Yedoma permafrost with a functionally diverse exogenous microbial community to test this mechanism by introducing potentially missing microbial functions. This initiated nitrification activity and increased CO2 production by 38% over 161 days. The changes in soil functioning were strongly associated with an altered microbial community composition, rather than with changes in soil chemistry or microbial biomass. The present permafrost microbial community composition thus constrains carbon and nitrogen biogeochemical processes, but microbial colonization, likely to occur upon permafrost thaw in situ, can alleviate such functional limitations. Accounting for functional limitations and their alleviation could strongly increase our estimate of the vulnerability of permafrost soil organic matter to decomposition and the resulting global climate feedback.

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Fig. 1: Changes in permafrost bacterial communities with inoculation by ST.
Fig. 2: Changes in permafrost carbon and nitrogen fluxes and pools with ST.
Fig. 3: Abundances of archaeal and bacterial amoA genes in permafrost inoculated by ST over 161 days.

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Data availability

Sequence data supporting the findings of this study have been deposited at ENA under the project number PRJEB29467. Processed data files supporting the findings are found at figshare (https://doi.org/10.6084/m9.figshare.7713308). Source data are provided with this paper.

Code availability

Scripts used to produce the figures and tables presented here are found at figshare (https://doi.org/10.6084/m9.figshare.7713308). The bioinformatics and analysis pipeline used to reproduce our findings is found at https://bitbucket.org/smonteux/functional_limitations/.

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Acknowledgements

This study was funded by grants from the Wallenberg Academy Fellowship (KAW 2012.0152), Swedish Research Council (Dnr 621–2011–5444), Formas (Dnr 214–2011–788) and Kempestiftelserna (JCK-1822) all awarded to E.D., by a grant from Formas (Dnr 2017–01182) awarded to E.J.K., by the European Union ClimMani COST Action (ES1308 COST ClimMani) Short Term Scientific Mission, by the Arctic Research Centre at Umeå University (Arcum) strategic funding grants awarded to S.M. and by the Department of Forest Mycology and Plant Pathology, SLU. We thank S. Lebeer and the ENdEMIC team for hosting part of the molecular work, T. H. Douglas from the US Army Cold Regions Research and Engineering Laboratory’s Permafrost Tunnel (Alaska) for assistance and permission to sample, and the staff of Abisko Scientific Research Station for hospitality and logistic support.

Author information

Author notes

  1. These authors contributed equally: Sylvain Monteux, Frida Keuper.

Authors and Affiliations

  1. Climate Impacts Research Centre—Department of Ecology and Environmental Sciences, Umeå University, Abisko, Sweden

    Sylvain Monteux, Frida Keuper, Konstantin Gavazov, Eveline J. Krab, Josefine Walz & Ellen Dorrepaal

  2. Department of Soil and Environment, Swedish University of Agricultural Sciences (SLU), Uppsala, Sweden

    Sylvain Monteux & Eveline J. Krab

  3. BioEcoAgro Joint Research Unit, INRAE, Barenton-Bugny, France

    Frida Keuper

  4. UMR Écosystème Prairial—INRAE, VetAgro Sup, Clermont-Ferrand, France

    Sébastien Fontaine & Sandrine Revaillot

  5. Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Lausanne, Switzerland

    Konstantin Gavazov

  6. Department of Forest Mycology and Plant Pathology, Swedish University of Agricultural Sciences (SLU), Uppsala, Sweden

    Sara Hallin & Jaanis Juhanson

  7. Plants and Ecosystems—Department of Biology, Universiteit Antwerpen, Wilrijk, Belgium

    Erik Verbruggen & James T. Weedon

  8. Systems Ecology—Department of Ecological Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    James T. Weedon

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  1. Sylvain Monteux
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Contributions

S.M., F.K., S.F., J.T.W. and E.D. designed the study. F.K., S.F., S.M. and S.R. performed the experiment. S.M., E.V. and J.T.W. collected and analysed the DNA data. F.K. and K.G. collected and analysed the PLFA data. S.M., J.J. and S.H. collected and analysed the quantitative real-time PCR data. F.K., S.F., S.M. and S.R. collected and analysed all other data. S.M., F.K., E.D., S.F., J.W. and E.J.K. designed and performed the experiment reproducing these findings as presented in Extended Data Fig. 8. S.M. and F.K. wrote the manuscript with contributions from all authors.

Corresponding authors

Correspondence to Sylvain Monteux or Frida Keuper.

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Extended data

Extended Data Fig. 1 Summary of initial chemistry of the permafrost soil used for incubation and of the donor soil used for the soil transfer treatment (top, means ± s.e.m., n = 3) and estimated average C and N content of incubated jars for each treatment (bottom).

†: data from ref. 27 (10.1038/nature06275). *: Welch’s test P < 0.05, pH values could not be tested due to virtually inexistent variance (replicate measurements were identical).

Source data

Extended Data Fig. 2 Changes in Yedoma permafrost fungal communities with soil transfer (ST).

a, Differential abundance of OTUs between ST and control samples over three sampling times (days 1, 15, 161, n = 9 for control soils, n = 8 for ST soils); each bar is a significantly changing OTU, arranged by decreasing fold-change within a class; positive fold-change indicates higher relative abundance in ST samples; crosses indicate most abundant OTUs (>0.5% rarefied observations). b, Phylum-/class-level summary of average relative abundances for control and ST samples. c, Alpha diversity (Abundance-based Coverage Estimator) of fungal communities in control and ST samples; means ± s.e.m. d, e, Differential abundance of OTUs between ST and control samples after 1 (d) and 161 (e) days of incubation. Due to one ST sample failing sequencing, the test could not be carried out for day 15. OTU percentage denotes the proportion of OTUs with significantly different abundance among those present at the respective date; reads percentage represents the proportion of rarefied reads these OTUs represent at the respective date. f, Fungal alpha-diversity response to rarefaction depth in control and ST samples over the 161 days incubation, based on 10 rarefactions at 12 evenly-spaced depths between 10 and 12000 reads per sample. g, Relative abundance along the 161 days incubation, of the OTUs in control and ST samples which were overall affected by ST (Changing, see panel a), not affected by ST (Unaffected) or present only in either control or ST samples (Specific). The proportion of reads belonging to Changing OTUs differ from those in panels (d-e) because they refer to the test carried over the entire incubation period (as in panel a) rather than within each date. (b–c, g): n = 3 except for ST, day 15 where n = 2. Vertical lines in (b–c) separate the preincubation donor soil (left) from the incubated samples (right); no fungal sequences could be obtained in preincubation permafrost.

Extended Data Fig. 3 Soil transfer (ST) and time effects on permafrost microbial communities, soil chemistry, microbial biomass and functional genes (ANOVA).

*: log-transformation; **: square-root transformation; ‡ Kruskal χ2 or Dunn Z; †: all Control soils- and §: day 1 samples- excluded from analysis because of values below detection limit.

Source data

Extended Data Fig. 4

Absolute differences in cumulative CO2 production (CO2-C) and in total dissolved carbon (TDC) between control and soil transfer samples over 161 days of incubation. Means ± s.e.m. (n = 3).

Extended Data Fig. 5 Microbial proxies of microbial biomass in Yedoma permafrost with and without soil transfer (ST).

a, Microbial biomass C; b, total PLFA; c, Bacterial PLFA; d, Fungal PLFA; e, Protozoan PLFA; f, Fungal: bacterial PLFA ratio; g, 16 S rRNA gene copy number. Light bars and symbols are control samples, dark bars and symbols are ST. (g): Vertical line separates preincubation permafrost (control) and donor soil (left; ‘pre-‘) from incubated samples (right), ANOVA and pairwise differences are based on incubated samples only. Asterisks over bars denote significant differences with ST at a given day (when the ST x time interaction is significant), letters indicate significant differences between days (main effect); n.s. = non-significant (P > 0.05), * P ≤ 0.05; ** P ≤ 0.01; *** P ≤ 0.001. Means ± s.e.m. (n = 3).

Extended Data Fig. 6 Cumulative soil organic matter (SOM)-derived aerobic CO2 production after 161 days incubation of permafrost with or without Soil transfer (bars with black outline correspond to values in Fig. 2a), and with addition of nutrients (NPK), 13C-cellulose and their combination.

CO2 production of the donor soil is shown in the inset as measured when incubated with nutrients (solid red outline), and estimated when incubated without nutrients using data from ref. 52 (Fontaine et al., 2011 (10.1016/j.soilbio.2010.09.017, dashed black outline). The green ‘+’ symbol in the ‘Permafrost + Soil transfer’ black outline bar represents the expected effect of Soil transfer assuming no biotic interactions (that is 97.5% CO2 production of control soil + 2.5% CO2 production of donor soil without NPK). ANOVA statistics and effect sizes shown are derived from permafrost only, means ± s.e.m. (n = 3).

Extended Data Fig. 7 RandomForests variable selection (VSURF) on bacterial phyla relative abundance, bacterial and archaeal amoA and 16 S genes abundance, alpha-diversity, soil chemistry and microbial biomass-C to select the variables explaining best the difference in detrended CO2 production rates.

Importance is non-normalized % Increase in Mean Squared Error of a tree when the variable is randomly permuted in out-of-bag (OOB) samples (that is a higher value indicates a higher importance), variables are ranked by decreasing importance. OOBinterpretation is the out-of-bag error of the nested forests (that is grown using this variable as well as all variables with greater importance), the VSURF algorithm selects variables leading to the lowest OOBinterpretation score. Variables in grey were considered uninformative at the thresholding phase, variables in bold were selected at the interpretation phase and are termed ‘Community + function’ in Table 1. Variables in bold and italics are included in the multiple linear regression models presented in Table 1.

Source data

Extended Data Fig. 8 Cumulative CO2 production after 389 days of (a) Yedoma permafrost inoculated with soil suspensions from three Arctic active layer soils and the donor soil used in the main experiment and (b) three other permafrost soils inoculated with donor soil suspension.

a, ‘Control’ and ‘ST inoculum’ reproduce the ‘Permafrost’ and ‘ST’ described in the main text, except for using a soil suspension instead of solid soil transfer as inoculum, and sterile ddH2O in controls. AL1, AL2 and AL3 are likewise soil suspension inocula originating from three distinct active layer soils. Different letters denote significant (Holm-adjusted P < 0.05) differences between inocula. b, ‘Control’ for each permafrost type is inoculated with sterile ddH2O, ‘ST inoculum’ is as in (a). Asterisks denote significantly higher values than control, within a soil type (Welch’s one-tailed two sample t-test, **: P < 0.01; ***: P < 0.001). a, b: Means ± s.e.m., n = 4. Coloured and grey error bars in the lower part denote the quantity of total dissolved carbon (mg C. g soil DW−1) added with the soil suspensions upon inoculation. OMC%: Organic matter content (determined by loss on ignition at 475 °C) in %. A description of the active layer soils used for preparing inoculum suspensions (a) and of the permafrost soils inoculated with ST inoculum (b) is found in Supplementary Methods.

Extended Data Fig. 9 Measured and interpolated CO2 production rates in Yedoma permafrost without (grey) and with soil transfer (black) over the course of 161 days.

Circles denote measurements of flasks destructively harvested at day 161, triangles represent rates derived by linear interpolation from these data, used to calculate cumulative CO2 production. Crosses represent the rates measured at the same dates on the set of flasks destructively harvested at day 71, for comparison. Numbers in red indicate the days of measurements linked to destructive harvests. Means ± SE (n = 3), error bars are shown unless smaller than the plotting symbols, asterisks indicate significant differences between control and ST soils at a given date, for the measured rates (that is circles and crosses; **P < 0.01; ***P < 0.001), note the log10 y-axis.

Extended Data Fig. 10 Changes in Yedoma permafrost bacterial communities with soil transfer (ST).

a, Bacterial alpha-diversity response to rarefaction depth in control and ST samples over the 161-days incubation, based on 10 rarefactions at 12 evenly-spaced depths between 10 and 12000 reads per sample. b–f, Differential abundance of bacterial OTUs between ST and control samples after (b) 1; (c) 15; (d) 30; (e) 75 and (f) 161 days of incubation. Each bar is a significantly-changing OTU, arranged by decreasing fold-change within a phylum or class; positive fold-change indicates higher relative abundance in ST samples; crosses indicate the most abundant OTUs (>0.5% rarefied observations); arrows indicate nitrifiers; OTU percentage denotes the proportion of OTUs with significantly changing abundance among those present at the respective date; reads percentage denotes the proportion of (rarefied) reads these OTUs represent at the respective date. g, Relative abundance along the 161-day incubation, of the OTUs in control and ST samples which were overall affected by ST (Changing, see Fig. 1a), not affected by ST (Unaffected), or present in only either control or ST samples (Specific); the proportion of reads belonging to Changing OTUs differs from those in panels (b-f) because they refer to the test carried over the entire incubation period (as in Fig. 1a) rather than within each date.

Supplementary information

Supplementary Information

Supplementary Discussion, Methods and Table 1.

Source data

Source Data Fig. 1

a, DESEQ negative-binomial Wald test statistics, relative abundance and putative nitrifier status of OTUs affected by ST. b, Relative abundance of bacterial phyla. c, Bacterial alpha-diversity abundance-based coverage estimator.

Source Data Fig. 2

a, CO2 production rates and cumulative CO2 production. b, TDC content. c, Ammonium content. d, Nitrate + nitrite content.

Source Data Fig. 3

a, Archaeal amoA gene copy number. b, Bacterial amoA gene copy number.

Source Data Extended Data Fig. 1

Excel file layout of Extended Data Fig. 1.

Source Data Extended Data Fig. 3

Excel file layout of Extended Data Fig. 3.

Source Data Extended Data Fig. 7

Excel file layout of Extended Data Fig. 7.

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Monteux, S., Keuper, F., Fontaine, S. et al. Carbon and nitrogen cycling in Yedoma permafrost controlled by microbial functional limitations. Nat. Geosci. 13, 794–798 (2020). https://doi.org/10.1038/s41561-020-00662-4

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