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Mycorrhiza-mediated competition between plants and decomposers drives soil carbon storage

Abstract

Soil contains more carbon than the atmosphere and vegetation combined1. Understanding the mechanisms controlling the accumulation and stability of soil carbon is critical to predicting the Earth’s future climate2,3. Recent studies suggest that decomposition of soil organic matter is often limited by nitrogen availability to microbes4,5,6 and that plants, via their fungal symbionts, compete directly with free-living decomposers for nitrogen6,7. Ectomycorrhizal and ericoid mycorrhizal (EEM) fungi produce nitrogen-degrading enzymes, allowing them greater access to organic nitrogen sources than arbuscular mycorrhizal (AM) fungi8,9,10. This leads to the theoretical prediction that soil carbon storage is greater in ecosystems dominated by EEM fungi than in those dominated by AM fungi11. Using global data sets, we show that soil in ecosystems dominated by EEM-associated plants contains 70% more carbon per unit nitrogen than soil in ecosystems dominated by AM-associated plants. The effect of mycorrhizal type on soil carbon is independent of, and of far larger consequence than, the effects of net primary production, temperature, precipitation and soil clay content. Hence the effect of mycorrhizal type on soil carbon content holds at the global scale. This finding links the functional traits of mycorrhizal fungi to carbon storage at ecosystem-to-global scales, suggesting that plant–decomposer competition for nutrients exerts a fundamental control over the terrestrial carbon cycle.

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Figure 1: The relationship between soil carbon and nitrogen content to a depth of one metre in AM and EEM ecosystems.
Figure 2: The relationships between soil carbon content to a depth of one metre and MAT, MAP, clay content and NPP.

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Acknowledgements

We thank L. Nave and the International Soil Carbon Network for access to their database. C. Hawkes provided feedback during data collection and initial analyses of C storage. C. Iversen, J. Powers and M. Vadeboncouer provided unpublished data that contributed to this analysis. D. Jacquier provided the Australian soil database and E. Carlston helped to extract data from the Australian soil database. C. Shaw provided the Siltanen soil carbon database and the Forest Ecosystem Carbon Database of Canadian soils. T. Baisden provided scans of the California Soil-Vegetation Survey. E. Brzostek, N. Fowler, P. Groffman, E. Hobbie, B. Schlesinger and B. Waring provided feedback on earlier versions of this manuscript. The Center for Tropical Forest Science (CTFS) and Smithsonian Institution Geo-observatories (SIGEO) provided funding for the collection and analysis of soil profile data at large forest dynamics plots, and we thank the many collaborators, field assistants and laboratory technicians who assisted in the collection and analysis of soil profile data. This work benefited from extensive data contributions to the International Soil Carbon Network from both the USDA Natural Resources Conservation Service, National Cooperative Soil Survey, and the US Geological Survey. C.A. was supported by a fellowship from the University of Texas at Austin and by the National Science Foundation Graduate Research Fellowship Program (grant DGE-1110007). A.C.F. was supported by NSF grant number DEB 07-43564 and DOE grants 10-DOE-1053 and DE-SC0006916. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

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Contributions

C.A. and B.L.T. collected the data. C.A. performed all statistical analyses. C.A. and A.C.F. conceptualized the work and wrote the manuscript. All authors contributed to revisions.

Corresponding author

Correspondence to Colin Averill.

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The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Soil C versus N in the first 50 cm of mineral soil.

Purple symbols are EEM observations and black symbols are AM observations. Plotted lines represent univariate regression lines of the respective subsets of the data. We note that plotted lines are univariate regressions of data subsets and are included for visualization purposes only. Removal of the surface organic horizon did not qualitatively change the interpretation of the data. Both the full model and the best AICc-selected model had a significant interactive effect between mycorrhizal type and soil N on soil C storage, with EEM systems storing 1.6 times more C per unit N than AM systems (P < 0.0001).

Extended Data Figure 2 Soil C versus N excluding boreal observations.

Purple symbols are EEM observations and black symbols are AM observations. Plotted lines represent univariate regression lines of the respective subsets of the data. We note that plotted lines are univariate regressions of data subsets and are included for visualization purposes only. Both the full model and the best AICc-selected model showed a significant interactive effect of mycorrhizal type and soil N on soil C storage, with EEM systems storing 1.6 times more C per unit N than AM systems (P = 0.0014).

Extended Data Figure 3 Soil C versus N limiting data set to observations with less than 3.5 kg N m−2.

Purple symbols are EEM observations and black symbols are AM observations. Plotted lines represent univariate regression lines of the respective subsets of the data. We note that plotted lines are univariate regressions of data subsets and are included for visualization purposes only. Both the full model and the best AICc-selected model found a significant interactive effect of mycorrhizal type and soil N on soil C storage, with EEM systems storing 1.4 times more C per unit N than AM systems (P = 0.0304).

Extended Data Figure 4 Soil C versus N excluding grassland observations.

Purple symbols are EEM observations and black symbols are AM observations. Plotted lines represent univariate regression lines of the respective subsets of the data. We note that plotted lines are univariate regressions of data subsets and are included for visualization purposes only. Both the full model and the best AICc-selected model found a significant interactive effect of mycorrhizal type and soil N on soil C storage, with EEM systems storing 1.5 times more C per unit N than AM systems (P = 0.0023).

Extended Data Figure 5 Soil C versus N restricting the analysis to temperate and tropical forest observations only.

Purple symbols are EEM and black symbols are AM observations. Plotted lines represent univariate regression lines of the respective subsets of the data. We note that plotted lines are univariate regressions of data subsets and are included for visualization purposes only. Both the full model and the best AICc-selected model incorporated the interactive effect of mycorrhizal type and soil N on soil C storage, with EEM systems storing 1.3 times more C per unit N than AM systems, although the effect was marginally not significant (P = 0.0690). We re-emphasize that the full model incorporates biome type, and weights observations by the inverse of their C values, to prevent undue influence of large observations on the estimated effect size.

Extended Data Table 1 Mineral soil (0–50 cm) analysis regression output from the best AICc-selected model
Extended Data Table 2 Removing boreal forests analysis from the best AICc-selected model
Extended Data Table 3 Restricting range of N content analysis from the best AICc-selected model
Extended Data Table 4 Removing grasslands analysis from the best AICc-selected model
Extended Data Table 5 Temperate and tropical biomes only, from the best AICc-selected model

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Averill, C., Turner, B. & Finzi, A. Mycorrhiza-mediated competition between plants and decomposers drives soil carbon storage. Nature 505, 543–545 (2014). https://doi.org/10.1038/nature12901

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