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Microbial growth rate is a stronger predictor of soil organic carbon than carbon use efficiency

Nature Ecology & Evolution volume 10, pages 372–381 (2026)Cite this article

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Abstract

The extent to which microbial processes control soil organic carbon (SOC) dynamics remains uncertain. Carbon use efficiency (CUE), that is, the fraction of assimilated carbon allocated to growth, has been used as a key parameter but its relationship with SOC reflects carbon partitioning rather than the absolute magnitude of microbial fluxes. The microbial growth rate could provide a more mechanistic link to SOC accumulation because it quantifies biomass production and reflects necromass formation. Here we combine a global ¹⁸O–H2O dataset (n = 268 paired observations) with outputs from four land surface models to test whether growth rate predicts SOC more strongly than CUE. In the incubation experiments, growth rates are more closely associated with SOC than CUE, although soil properties and climate explain equal or greater variance. Models reproduce the stronger role of growth rate over CUE but tend to underestimate the abiotic controls. The models also emphasize CUE as the main predictor of the SOC-to-net primary production ratio, in contrast to observations, which indicates the soil’s capacity to retain plant carbon inputs. Together, these findings identify the microbial growth rate as a diagnostic that can help bridge models with empirical data and guide a more balanced representation of microbial and mineral controls in SOC projections.

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Fig. 1: Conceptual model of microbial regulation of soil carbon cycling.
Fig. 2: Relationships between SOC and microbial or ecosystem properties based on observational data.
Fig. 3: Variable importance of microbial and environmental factors for SOC and SOC-to-NPP.
Fig. 4: Spatial distributions, variability and correlations of microbial properties across four land surface models.

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Data availability

The global observational dataset is available via figshare at https://doi.org/10.6084/m9.figshare.30070084.v1 (ref. 58). Simulation outputs from the four land surface models (ORCHIDEE-CENTURY, ORCHIDEE-MIMICS, CABLE-CASA and JULES-RothC) are also available via figshare at https://figshare.com/s/95f8435036d7f6825a53 (ref. 69). All custom R scripts used for data processing, statistical analyses and figure generation are openly available via Zenodo at https://doi.org/10.5281/zenodo.17800780 (ref. 70).

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Acknowledgements

We thank Y. Xi and C. Zhou (Laboratoire des Sciences du Climat et de l’Environnement) for their valuable feedback during manuscript preparation and revision. This study was supported by the CALIPSO project funded by Schmidt Sciences. G.M. and D.S.G. acknowledge support from the EJP Soil ICONICA project. E.S. and S.M. were supported by the European Research Council under the European Union’s Horizon 2020 Research and Innovation Programme (grant no. 101001608). H.Z. was supported by the National Natural Science Foundation of China (grant no. 41030052). J.H. was supported by the Natural Science Foundation of Sichuan Province (grant no. 2025ZNSFSC1033) and the Postdoctoral Fellowship Program of the China Postdoctoral Science Foundation (grant no. GZB20250584). Support for Y.-P.W. was provided in part by the Terrestrial Ecosystem Research Network, an Australian Government National Collaborative Research Infrastructure Strategy-enabled project.

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Author notes

  1. These authors contributed equally: Xianjin He, Gaëlle Marmasse.

Authors and Affiliations

  1. Laboratoire des Sciences du Climat et de l’Environnement, IPSL-LSCE, CEA/CNRS/UVSQ/Université Paris Saclay, Orme des Merisiers, Gif sur Yvette, France

    Xianjin He, Gaëlle Marmasse, Philippe Ciais, Elsa Abs & Daniel S. Goll

  2. College of Forestry, Sichuan Agricultural University, Chengdu, China

    Junxi Hu

  3. Department of Physical Geography and Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden

    Rebecca M. Varney, Stefano Manzoni & Erik Schwarz

  4. Faculty of Environment, Science and Economy, University of Exeter, Exeter, UK

    Rebecca M. Varney

  5. CSIRO Environment, Private Bag 10, Melbourne, Victoria, Australia

    Ying-Ping Wang

  6. Guangdong Provincial Key Laboratory of Applied Botany, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, China

    Ying-Ping Wang

  7. Institute of Biology, Freie Universität Berlin, Berlin, Germany

    Yongxing Cui

  8. Key Laboratory of Geographical Processes and Ecological Security of Changbai Mountains, Ministry of Education, School of Geographical Sciences, Northeast Normal University, Changchun, China

    Edith Bai

  9. Changchun Normal University, Changchun, China

    Edith Bai

  10. School of Forest Resources, University of Maine, Orono, MA, USA

    Rose Z. Abramoff

  11. Carbon-Water Research Station in Karst Regions of Northern Guangdong, School of Geography and Planning, Sun Yat-Sen University, Guangzhou, China

    Haicheng Zhang

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  1. Xianjin He
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Contributions

X.H., G.M. and D.S.G. conceived and designed the study. J.H. provided the observational dataset. S.M., P.C., Y.-P.W., R.Z.A. and E.A. contributed to the initial development of the study concept and discussions. Y.C. and E.B. supplied the data that supported the preliminary exploratory analyses. P.C. and D.S.G. secured the project funding. All authors contributed to the writing, discussion and revision of the paper.

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Correspondence to Junxi Hu or Daniel S. Goll.

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He, X., Marmasse, G., Hu, J. et al. Microbial growth rate is a stronger predictor of soil organic carbon than carbon use efficiency. Nat Ecol Evol 10, 372–381 (2026). https://doi.org/10.1038/s41559-025-02961-8

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