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Carbon loss from northern circumpolar permafrost soils amplified by rhizosphere priming

Nature Geoscience volume 13, pages 560–565 (2020)Cite this article

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Abstract

As global temperatures continue to rise, a key uncertainty of climate projections is the microbial decomposition of vast organic carbon stocks in thawing permafrost soils. Decomposition rates can accelerate up to fourfold in the presence of plant roots, and this mechanism—termed the rhizosphere priming effect—may be especially relevant to thawing permafrost soils as rising temperatures also stimulate plant productivity in the Arctic. However, priming is currently not explicitly included in any model projections of future carbon losses from the permafrost area. Here, we combine high-resolution spatial and depth-resolved datasets of key plant and permafrost properties with empirical relationships of priming effects from living plants on microbial respiration. We show that rhizosphere priming amplifies overall soil respiration in permafrost-affected ecosystems by ~12%, which translates to a priming-induced absolute loss of ~40 Pg soil carbon from the northern permafrost area by 2100. Our findings highlight the need to include fine-scale ecological interactions in order to accurately predict large-scale greenhouse gas emissions, and suggest even tighter restrictions on the estimated 200 Pg anthropogenic carbon emission budget to keep global warming below 1.5 °C.

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Fig. 1: The RPE.
Fig. 2: Depth distribution of soil and root properties, and the RPE.
Fig. 3: Spatial distribution of the RPE across the northern circumpolar permafrost region in 2010 and 2100 (RCPs 4.5 and 8.5).

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Data availability

References to published data used in this study can be found in Supplementary Table 1 (PrimeSCale model), Supplementary Table 2 (meta-analysis of priming studies) and Supplementary Table 3 (meta-analysis of root depth profiles for tundra and boreal), as well as in the main text. Datasets generated for this study are available in the Bolin Centre Database (https://bolin.su.se/data/keuper-wild-2020) and include: (1) intermediate output data of the PrimeSCale model (.xls); and (2) output (Geotiff) and metadata.

Code availability

The custom code for the PrimeSCale 1.0 model, including model scripts, complementary function scripts and input data for the model, is available from the Bolin Centre code repository: https://git.bolin.su.se/bolin/keuper-wild-2020.

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Acknowledgements

We thank P. Thornton, F. Dijkstra, Y. Carrillo and R. E. Hewitt for providing additional information on published data. Figure 1a–c is courtesy of R. Miedema (IN Produktie, Amsterdam). This study was supported by funding from: the Swedish Research Council (VR) (grant number 621-2011-5444), Formas (grant number 214-2011-788) and the Knut and Alice Wallenberg Foundation (grant number KAW 2012.0152) (all awarded to E.D.); Academy of Finland-funded projects SCART (grant number 267463) and WASCO (grant number 305471), Emil Aaltonen Foundation-funded project ‘eat-less-water’, the European Research Council under the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement number 819202), and Maa- ja vesitekniikan tuki ry (all awarded to M.K.); the JPI Climate Project COUP-Austria (BMWFW-6.020/0008) (awarded to A.R.); two projects funded by the Swedish Research Council, the EU JPI Climate COUP project (E0689701) and the Project INCA (E0641701)–Marie Sklodowska-Curie Actions cofund (600398) (awarded to G.H.); the Deutsche Forschungsgemeinschaft (BE 6485/1-1) (to C.B.); and the US DOE BER RGMA programme through the RUBISCO SFA and ECRP projects (to C.K.).

Author information

Author notes

  1. The authors contributed equally: Frida Keuper, Birgit Wild.

Authors and Affiliations

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

    Frida Keuper

  2. Climate Impacts Research Centre, Department of Ecology and Environmental Science, Umeå University, Abisko, Sweden

    Frida Keuper, Gesche Blume-Werry, Konstantin Gavazov, Eveline J. Krab, Sylvain Monteux & Ellen Dorrepaal

  3. Department of Environmental Science, Stockholm University, Stockholm, Sweden

    Birgit Wild & Christian Beer

  4. Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden

    Birgit Wild, Christian Beer, Gustaf Hugelius & Peter Kuhry

  5. Department of Earth Sciences, University of Gothenburg, Gothenburg, Sweden

    Birgit Wild

  6. Water and Development Research Group, Aalto University, Espoo, Finland

    Matti Kummu & Mika Jalava

  7. Institute of Soil Science, Department of Earth Sciences, Universität Hamburg, Hamburg, Germany

    Christian Beer

  8. Center for Earth System Research and Sustainability, Universität Hamburg, Hamburg, Germany

    Christian Beer

  9. Experimental Plant Ecology, Institute of Botany and Landscape Ecology, Greifswald University, Greifswald, Germany

    Gesche Blume-Werry

  10. VetAgro Sup, UMR Ecosystème Prairial, INRAE, Clermont Ferrand, France

    Sébastien Fontaine

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

    Konstantin Gavazov

  12. Institute of Soil Science, Leibniz Universität Hannover, Hannover, Germany

    Norman Gentsch & Georg Guggenberger

  13. V. N. Sukachev Institute of Forest SB-RAS, Krasnoyarsk, Russia

    Georg Guggenberger

  14. Department of Physical Geography, Stockholm University, Stockholm, Sweden

    Gustaf Hugelius & Peter Kuhry

  15. Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Charles Koven

  16. Department of Soil and Environment, Swedish University of Agricultural Sciences, Uppsala, Sweden

    Eveline J. Krab

  17. Centre for Microbiology and Environmental Systems Science, University of Vienna, Vienna, Austria

    Andreas Richter

  18. International Institute for Applied Systems Analysis, Laxenburg, Austria

    Andreas Richter

  19. Department of Environmental Sciences and Engineering, Government College University Faisalabad, Faisalabad, Pakistan

    Tanvir Shahzad

  20. Systems Ecology, Department of Ecological Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands

    James T. Weedon

Authors
  1. Frida Keuper
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  2. Birgit Wild
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Contributions

F.K. and E.D. conceived of the idea. F.K., B.W. and E.D. led the conceptual model development in collaboration with M.K., C.B., G.B.-W., S.F., K.G., G.G., G.H., E.J.K., P.K., S.M., A.R. and J.T.W. The model was implemented by M.K. and M.J. C.B., N.G., G.H., C.K. and P.K. provided additional data. M.K., G.H., C.K., J.T.W. and E.D. performed additional statistical analyses. F.K. and B.W. wrote the manuscript with contributions from all authors.

Corresponding authors

Correspondence to Frida Keuper or Birgit Wild.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Primary Handling Editors: Tamara Goldin; Xujia Jiang.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 The PrimeSCale model estimates rhizosphere priming effect (RPE)-induced SOC losses and consists of three modules: Soil, Plant, and Soil Respiration.

SOIL: Soil grid cubes potentially susceptible to the RPE were identified by first quantifying SOC stocks at 0–3 m depth21,54,55, then excluding continuously frozen (depth > ALT)24 or non vegetated grid cubes (GPP = 0)23,24, as the RPE depends on the presence of living plant roots. An additional sensitivity analysis was performed using only the fraction of the SOC stock with C/N below 20. PLANT: RPE ratios (the ratio of SOC respiration from plant-affected soil divided by SOC respiration from not plant-affected soil) were estimated for each grid cube. The relationship between the RPE ratio and root respiration as a proxy for plant belowground C allocation, was based on a meta-analysis of studies that quantify RPE in experiments with intact plants. Root respiration was estimated using current and future GPP data for each grid cell in our study area and spread over 5 cm depth increments using root depth distribution functions. These functions were based on a meta-analysis of rooting depth patterns in permafrost-affected boreal forests as well as different tundra vegetation types25,39. SOIL RESPIRATION: Basal SOC respiration (that is, SOC respiration without RPE) was calculated using a relationship between the fraction of SOC that is respired (Rh) and GPP, derived from the Community Land Model (CLM)24, in combination with high resolution SOC21,54,55 and GPP spatial databases23,24. The GPP serves here as a proxy for climatic conditions that favour both GPP and basal SOC respiration. SOC respiration from plant-affected soil (that is, SOC respiration with RPE) was calculated by combining the RPE ratios with the basal SOC respiration values (Rh) for each individual grid cube. For more details on the Soil, Plant and Soil Respiration Modules see Methods.

Extended Data Fig. 2 Relative root depth distribution in five tundra and boreal forest vegetation types for the northern circumpolar permafrost area for 2010 and 2100 (RCP 8.5).

Relative root distribution was calculated over 60 soil layers (5 cm increments) for erect-shrub, graminoid, prostrate-shrub and wetland tundra as well as boreal forest on permafrost, reflecting depth distributions depending on vegetation type specific root depth distribution, and observed and projected area specific active layer thickness. Shaded areas depict uncertainty ranges (10th–90th percentile), and the spatial distribution of vegetation types for years 2010 and 2100 is presented in Extended Data Fig. 9.

Extended Data Fig. 3 Relative root depth distribution in five tundra and boreal forest vegetation types normalized to active layer thickness (ALT) for present and 2100 (RCP 8.5).

Relative root distribution was calculated over 60 soil layers (5 cm increments) with standardized ALT in erect-shrub, graminoid, prostrate-shrub and wetland tundra as well as and boreal forest on permafrost, shaded areas depict 10th -90th percentile.

Extended Data Fig. 4 Relative root depth distribution in tundra and boreal forest with varying active layer thickness (ALT).

Relative root distribution was calculated over 60 soil layers (5 cm increments) with varying ALT in erect-shrub, graminoid, prostrate-shrub and wetland tundra as well as boreal forest on permafrost.

Extended Data Fig. 5 Depth distribution of absolute root respiration, averaged across the northern circumpolar permafrost area for 2010 and 2100 (RCP 8.5).

Shaded areas depict 10th–90th percentile uncertainty range.

Extended Data Fig. 6 Spatial distribution of coefficients of variation for the RPE-ratio and the absolute RPE in the northern circumpolar permafrost region.

a-i, Spatial distribution of the coefficients of variation for the RPE-ratio (a–c, for 2010 and 2100, RCP 4.5 and 8.5); Spatial distribution of the coefficients of variation of the absolute rhizosphere priming effect under two scenarios, either assuming that all plant-affected SOC is susceptible to the RPE (d–f, no C/N threshold scenario) or that microbial carbon limitation is required (g–i, C/N threshold scenario) for 2010 and 2100, RCP 4.5 and 8.5.

Extended Data Fig. 7 Spatial patterns of model input data.

Gross primary production (GPP) for the years 2010 and 2100 (RCP4.5 and 8.5 scenario) (a–c); GPP without CO2-fertilization, used in soil respiration estimates (d–f); Active layer thickness (ALT) for the years 2010 and 2100 (RCP4.5 and RCP8.5 scenarios) to a depth of 3 m. (g–i); SOC stocks in Gelisols to a depth of 3 m, for organic, cryoturbated, and mineral horizons (j–l); and (m–o) soil type percentage of each Gelisol suborder (Histels, Orthels and Turbels). Note that maps show percentage of full soil coverage and will not sum to 100% in locations where permafrost-free soils occur.

Extended Data Fig. 8 Source studies used to constrain the RPE ratio function.

RPE ratio plotted against root respiration. RPE ratio was calculated as ratio of SOC respiration from planted over unplanted soil, and root respiration was estimated from plant-associated respiration. Data were derived from a meta-analysis of studies quantifying RPE in experiments with intact plants (12 studies comprising 65 individual treatment combinations, see Supplementary Table 2 for details and source references). The solid line shows the saturating function corresponding to the posterior medians; the shaded area indicates the 95% posterior credible region for the parameters of this function. The dotted line indicates an RPE ratio of 1, that is no RPE.

Extended Data Fig. 9

Current (year 2010) and future (year 2100) spatial distribution of tundra and boreal vegetation types. Data are for the northern permafrost area, based on the CAVM25 and its future projections39.

Extended Data Fig. 10

Active layer dependent relative depth distribution curves for soil respiration (Rh). Data were extracted from the CLM model, specific for each active layer depth.

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Supplementary Tables 1–9 and methods.

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Keuper, F., Wild, B., Kummu, M. et al. Carbon loss from northern circumpolar permafrost soils amplified by rhizosphere priming. Nat. Geosci. 13, 560–565 (2020). https://doi.org/10.1038/s41561-020-0607-0

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