Articles | Volume 21, issue 14
https://doi.org/10.5194/bg-21-3441-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-21-3441-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
When and why microbial-explicit soil organic carbon models can be unstable
Department of Physical Geography and Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden
Samia Ghersheen
Department of Soil and Environment, Swedish University of Agricultural Sciences, Uppsala, Sweden
Salim Belyazid
Department of Physical Geography and Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden
Stefano Manzoni
Department of Physical Geography and Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden
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Biogeosciences, 22, 2691–2705, https://doi.org/10.5194/bg-22-2691-2025, https://doi.org/10.5194/bg-22-2691-2025, 2025
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While laboratory studies have identified many drivers and their effects on the carbon emission pulse after rewetting of dry soils, a validation with field data is still missing. Here, we show that the carbon emission pulse in the laboratory and in the field increases with soil organic carbon and temperature, but their trends with pre-rewetting dryness and moisture increment at rewetting differ. We conclude that the laboratory findings can be partially validated.
Daniel Escobar, Stefano Manzoni, Jeimar Tapasco, Patrik Vestin, and Salim Belyazid
Biogeosciences, 22, 2023–2047, https://doi.org/10.5194/bg-22-2023-2025, https://doi.org/10.5194/bg-22-2023-2025, 2025
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We studied carbon dynamics in afforested, drained peatlands using the ForSAFE-Peat model over two forest rotations. Our simulations showed that, while trees store carbon, significant soil carbon losses occur, particularly early on, indicating that forest growth may not fully offset these losses once carbon time dynamics are considered. This emphasises the need to consider both soil and harvested wood products when evaluating the climate impact of such systems.
Martin Thurner, Kailiang Yu, Stefano Manzoni, Anatoly Prokushkin, Melanie A. Thurner, Zhiqiang Wang, and Thomas Hickler
Biogeosciences, 22, 1475–1493, https://doi.org/10.5194/bg-22-1475-2025, https://doi.org/10.5194/bg-22-1475-2025, 2025
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Nitrogen concentrations in tree tissues (leaves, branches, stems, and roots) are related to photosynthesis, growth, and respiration and thus to vegetation carbon uptake. Our novel database allows us to identify the controls of tree tissue nitrogen concentrations in boreal and temperate forests, such as tree age/size, species, and climate. Changes therein will affect tissue nitrogen concentrations and thus also vegetation carbon uptake.
Daniela Guasconi, Sara A. O. Cousins, Stefano Manzoni, Nina Roth, and Gustaf Hugelius
SOIL, 11, 233–246, https://doi.org/10.5194/soil-11-233-2025, https://doi.org/10.5194/soil-11-233-2025, 2025
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This study assesses the effects of experimental drought and soil amendment on soil and vegetation carbon pools at different soil depths. Drought consistently reduced soil moisture and aboveground biomass, while compost increased total soil carbon content and aboveground biomass, and effects were more pronounced in the topsoil. Root biomass was not significantly affected by the treatments. The contrasting response of roots and shoots improves our understanding of ecosystem carbon dynamics.
Boris Ťupek, Aleksi Lehtonen, Stefano Manzoni, Elisa Bruni, Petr Baldrian, Etienne Richy, Bartosz Adamczyk, Bertrand Guenet, and Raisa Mäkipää
EGUsphere, https://doi.org/10.5194/egusphere-2024-3813, https://doi.org/10.5194/egusphere-2024-3813, 2024
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We explored soil microbial respiration (Rh) kinetics of low-dose and long-term N fertilization in N-limited boreal forest in connection to CH₄, and N₂O fluxes, soil, and tree C sinks. The insights show that N fertilization effects C retention in boreal forest soils through modifying Rh sensitivities to soil temperature and moisture. The key findings reveal that N-enriched soils exhibited reduced sensitivity of Rh to moisture, which on annual level contributes to enhanced soil C sequestration.
Stefano Manzoni and M. Francesca Cotrufo
Biogeosciences, 21, 4077–4098, https://doi.org/10.5194/bg-21-4077-2024, https://doi.org/10.5194/bg-21-4077-2024, 2024
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Organic carbon and nitrogen are stabilized in soils via microbial assimilation and stabilization of necromass (in vivo pathway) or via adsorption of the products of extracellular decomposition (ex vivo pathway). Here we use a diagnostic model to quantify which stabilization pathway is prevalent using data on residue-derived carbon and nitrogen incorporation in mineral-associated organic matter. We find that the in vivo pathway is dominant in fine-textured soils with low organic matter content.
Boris Ťupek, Aleksi Lehtonen, Alla Yurova, Rose Abramoff, Bertrand Guenet, Elisa Bruni, Samuli Launiainen, Mikko Peltoniemi, Shoji Hashimoto, Xianglin Tian, Juha Heikkinen, Kari Minkkinen, and Raisa Mäkipää
Geosci. Model Dev., 17, 5349–5367, https://doi.org/10.5194/gmd-17-5349-2024, https://doi.org/10.5194/gmd-17-5349-2024, 2024
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Updating the Yasso07 soil C model's dependency on decomposition with a hump-shaped Ricker moisture function improved modelled soil organic C (SOC) stocks in a catena of mineral and organic soils in boreal forest. The Ricker function, set to peak at a rate of 1 and calibrated against SOC and CO2 data using a Bayesian approach, showed a maximum in well-drained soils. Using SOC and CO2 data together with the moisture only from the topsoil humus was crucial for accurate model estimates.
Veronika Kronnäs, Klas Lucander, Giuliana Zanchi, Nadja Stadlinger, Salim Belyazid, and Cecilia Akselsson
Biogeosciences, 20, 1879–1899, https://doi.org/10.5194/bg-20-1879-2023, https://doi.org/10.5194/bg-20-1879-2023, 2023
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In a future climate, extreme droughts might become more common. Climate change and droughts can have negative effects on soil weathering and plant health.
In this study, climate change effects on weathering were studied on sites in Sweden using the model ForSAFE, a climate change scenario and an extreme drought scenario. The modelling shows that weathering is higher during summer and increases with global warming but that weathering during drought summers can become as low as winter weathering.
Stefano Manzoni, Simone Fatichi, Xue Feng, Gabriel G. Katul, Danielle Way, and Giulia Vico
Biogeosciences, 19, 4387–4414, https://doi.org/10.5194/bg-19-4387-2022, https://doi.org/10.5194/bg-19-4387-2022, 2022
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Increasing atmospheric carbon dioxide (CO2) causes leaves to close their stomata (through which water evaporates) but also promotes leaf growth. Even if individual leaves save water, how much will be consumed by a whole plant with possibly more leaves? Using different mathematical models, we show that plant stands that are not very dense and can grow more leaves will benefit from higher CO2 by photosynthesizing more while adjusting their stomata to consume similar amounts of water.
Cited articles
Abramoff, R., Xu, X., Hartman, M., O'Brien, S., Feng, W., Davidson, E., Finzi, A., Moorhead, D., Schimel, J., Torn, M., and Mayes, M. A.: The Millennial model: in search of measurable pools and transformations for modeling soil carbon in the new century, Biogeochemistry, 137, 51–71, https://doi.org/10.1007/s10533-017-0409-7, 2018. a
Abs, E., Chase, A. B., and Allison, S. D.: How do soil microbes shape ecosystem biogeochemistry in the context of global change?, Environ. Microbiol., 25, 780–785, https://doi.org/10.1111/1462-2920.16331, 2023. a, b, c, d
Abs, E., Chase, A. B., Manzoni, S., Ciais, P., and Allison, S. D.: Microbial evolution–An under-appreciated driver of soil carbon cycling, Glob. Change Biol., 30, e17268, https://doi.org/10.1111/gcb.17268, 2024. a, b, c
Bassiouni, M., Manzoni, S., and Vico, G.: Optimal plant water use strategies explain soil moisture variability, Adv. Water Resour., 173, 104405, https://doi.org/10.1016/j.advwatres.2023.104405, 2023. a
Bradford, M. A., Wieder, W. R., Bonan, G. B., Fierer, N., Raymond, P. A., and Crowther, T. W.: Managing uncertainty in soil carbon feedbacks to climate change, Nat. Clim. Change, 6, 751–758, https://doi.org/10.1038/nclimate3071, 2016. a, b, c
Calabrese, S., Mohanty, B. P., and Malik, A. A.: Soil microorganisms regulate extracellular enzyme production to maximize their growth rate, Biogeochemistry, 158, 303–312, https://doi.org/10.1007/s10533-022-00899-8, 2022. a, b
Crameri, F.: Geodynamic diagnostics, scientific visualisation and StagLab 3.0, Geosci. Model Dev., 11, 2541–2562, https://doi.org/10.5194/gmd-11-2541-2018, 2018a. a
Crameri, F.: Scientific colour maps (4.0), Zenodo [code], https://doi.org/10.5281/zenodo.1243862, 2018b. a
Chakrawal, A., Herrmann, A. M., Koestel, J., Jarsjö, J., Nunan, N., Kätterer, T., and Manzoni, S.: Dynamic upscaling of decomposition kinetics for carbon cycling models, Geosci. Model Dev., 13, 1399–1429, https://doi.org/10.5194/gmd-13-1399-2020, 2020. a
Chakrawal, A., Calabrese, S., Herrmann, A. M., and Manzoni, S.: Interacting Bioenergetic and Stoichiometric Controls on Microbial Growth, Front. Microbiol., 13, 859063, https://doi.org/10.3389/fmicb.2022.859063, 2022. a
Cotrufo, M. F. and Lavallee, J. M.: Soil organic matter formation, persistence, and functioning: A synthesis of current understanding to inform its conservation and regeneration, in: Advances in Agronomy, 172, 1–66, Academic Press, ISBN 978-0-323-98953-4, https://doi.org/10.1016/bs.agron.2021.11.002, 2022. a
He, X., Abramoff, R. Z., Abs, E., Georgiou, K., Zhang, H., and Goll, D. S.: Model uncertainty obscures major driver of soil carbon, Nature, 627, E1–E3, https://doi.org/10.1038/s41586-023-06999-1, 2024. a
Horn, R. A. and Johnson, C. R.: Topics in Matrix Analysis, Cambrigde University Press, ISBN: 0-521-46713-6, 1994. a
Kuzyakov, Y.: Priming effects: Interactions between living and dead organic matter, Soil Biol. Biochem., 42, 1363–1371, https://doi.org/10.1016/j.soilbio.2010.04.003, 2010. a
Kuzyakov, Y., Friedel, J., and Stahr, K.: Review of mechanisms and quantification of priming effects, Soil Biol. Biochem., 32, 1485–1498, https://doi.org/10.1016/S0038-0717(00)00084-5, 2000. a
Lennon, J. T., Abramoff, R. Z., Allison, S. D., Burckhardt, R. M., DeAngelis, K. M., Dunne, J. P., Frey, S. D., Friedlingstein, P., Hawkes, C. V., Hungate, B. A., Khurana, S., Kivlin, S. N., Levine, N. M., Manzoni, S., Martiny, A. C., Martiny, J. B. H., Nguyen, N. K., Rawat, M., Talmy, D., Todd-Brown, K., Vogt, M., Wieder, W. R., and Zakem, E. J.: Priorities, opportunities, and challenges for integrating microorganisms into Earth system models for climate change prediction, mBio, 15, e00455-24, https://doi.org/10.1128/mbio.00455-24, 2024. a
Malik, A. A., Puissant, J., Goodall, T., Allison, S. D., and Griffiths, R. I.: Soil microbial communities with greater investment in resource acquisition have lower growth yield, Soil Biol. Biochem., 132, 36–39, https://doi.org/10.1016/j.soilbio.2019.01.025, 2019. a
Malik, A. A., Martiny, J. B. H., Brodie, E. L., Martiny, A. C., Treseder, K. K., and Allison, S. D.: Defining trait-based microbial strategies with consequences for soil carbon cycling under climate change, ISME J., 14, 1–9, https://doi.org/10.1038/s41396-019-0510-0, 2020. a
Manzoni, S. and Porporato, A.: Soil carbon and nitrogen mineralization: Theory and models across scales, Soil Biol. Biochem., 41, 1355–1379, https://doi.org/10.1016/j.soilbio.2009.02.031, 2009. a
Manzoni, S., Schaeffer, S., Katul, G., Porporato, A., and Schimel, J.: A theoretical analysis of microbial eco-physiological and diffusion limitations to carbon cycling in drying soils, Soil Biol. Biochem., 73, 69–83, https://doi.org/10.1016/j.soilbio.2014.02.008, 2014. a
Manzoni, S., Chakrawal, A., and Ledder, G.: Decomposition rate as an emergent property of optimal microbial foraging, Frontiers in Ecology and Evolution, 11, 1094269, https://doi.org/10.3389/fevo.2023.1094269, 2023. a
Richardson, G. P.: Problems with causal-loop diagrams, Syst. Dynam. Rev., 2, 158–170, https://doi.org/10.1002/sdr.4260020207, 1986. a, b
Schimel, J. P. and Weintraub, M. N.: The implications of exoenzyme activity on microbial carbon and nitrogen limitation in soil: a theoretical model, Soil Biol. Biochem., 35, 549–563, https://doi.org/10.1016/S0038-0717(03)00015-4, 2003. a, b, c, d
Schwarz, E.: Numerical stability analysis of an archetypal microbial-explicit soil organic carbon model, Zenodo [code], https://doi.org/10.5281/zenodo.12749207, 2024. a
Tang, J. and Riley, W. J.: Competitor and substrate sizes and diffusion together define enzymatic depolymerization and microbial substrate uptake rates, Soil Biol. Biochem., 139, 107624, https://doi.org/10.1016/j.soilbio.2019.107624, 2019. a
Tang, J. Y. and Riley, W. J.: A total quasi-steady-state formulation of substrate uptake kinetics in complex networks and an example application to microbial litter decomposition, Biogeosciences, 10, 8329–8351, https://doi.org/10.5194/bg-10-8329-2013, 2013. a, b
Tao, F., Huang, Y., Hungate, B. A., Manzoni, S., Frey, S. D., Schmidt, M. W. I., Reichstein, M., Carvalhais, N., Ciais, P., Jiang, L., Lehmann, J., Wang, Y.-P., Houlton, B. Z., Ahrens, B., Mishra, U., Hugelius, G., Hocking, T. D., Lu, X., Shi, Z., Viatkin, K., Vargas, R., Yigini, Y., Omuto, C., Malik, A. A., Peralta, G., Cuevas-Corona, R., Di Paolo, L. E., Luotto, I., Liao, C., Liang, Y.-S., Saynes, V. S., Huang, X., and Luo, Y.: Microbial carbon use efficiency promotes global soil carbon storage, Nature, 618, 981–985, https://doi.org/10.1038/s41586-023-06042-3, 2023. a, b, c, d, e
Tao, F., Houlton, B. Z., Frey, S. D., Lehmann, J., Manzoni, S., Huang, Y., Jiang, L., Mishra, U., Hungate, B. A., Schmidt, M. W. I., Reichstein, M., Carvalhais, N., Ciais, P., Wang, Y.-P., Ahrens, B., Hugelius, G., Hocking, T. D., Lu, X., Shi, Z., Viatkin, K., Vargas, R., Yigini, Y., Omuto, C., Malik, A. A., Peralta, G., Cuevas-Corona, R., Di Paolo, L. E., Luotto, I., Liao, C., Liang, Y.-S., Saynes, V. S., Huang, X., and Luo, Y.: Reply to: Model uncertainty obscures major driver of soil carbon, Nature, 627, E4–E6, https://doi.org/10.1038/s41586-023-07000-9, 2024a. a
Tao, F., Houlton, B. Z., Huang, Y., Wang, Y.-P., Manzoni, S., Ahrens, B., Mishra, U., Jiang, L., Huang, X., and Luo, Y.: Convergence in simulating global soil organic carbon by structurally different models after data assimilation, Global Change Biol., 30, e17297, https://doi.org/10.1111/gcb.17297, 2024b. a
Todd-Brown, K. E. O., Randerson, J. T., Post, W. M., Hoffman, F. M., Tarnocai, C., Schuur, E. A. G., and Allison, S. D.: Causes of variation in soil carbon simulations from CMIP5 Earth system models and comparison with observations, Biogeosciences, 10, 1717–1736, https://doi.org/10.5194/bg-10-1717-2013, 2013. a, b, c, d
Varney, R. M., Chadburn, S. E., Burke, E. J., and Cox, P. M.: Evaluation of soil carbon simulation in CMIP6 Earth system models, Biogeosciences, 19, 4671–4704, https://doi.org/10.5194/bg-19-4671-2022, 2022. a, b, c, d
Wang, G., Post, W. M., and Mayes, M. A.: Development of microbial-enzyme-mediated decomposition model parameters through steady-state and dynamic analyses, Ecol. Appl., 23, 255–272, https://doi.org/10.1890/12-0681.1, 2013. a, b, c
Wang, G., Jagadamma, S., Mayes, M. A., Schadt, C. W., Megan Steinweg, J., Gu, L., and Post, W. M.: Microbial dormancy improves development and experimental validation of ecosystem model, ISME J., 9, 226–237, https://doi.org/10.1038/ismej.2014.120, 2015. a
Wang, Y. P., Chen, B. C., Wieder, W. R., Leite, M., Medlyn, B. E., Rasmussen, M., Smith, M. J., Agusto, F. B., Hoffman, F., and Luo, Y. Q.: Oscillatory behavior of two nonlinear microbial models of soil carbon decomposition, Biogeosciences, 11, 1817–1831, https://doi.org/10.5194/bg-11-1817-2014, 2014. a, b, c, d, e, f, g, h
Wang, Y. P., Jiang, J., Chen-Charpentier, B., Agusto, F. B., Hastings, A., Hoffman, F., Rasmussen, M., Smith, M. J., Todd-Brown, K., Wang, Y., Xu, X., and Luo, Y. Q.: Responses of two nonlinear microbial models to warming and increased carbon input, Biogeosciences, 13, 887–902, https://doi.org/10.5194/bg-13-887-2016, 2016. a, b, c, d, e, f
Wieder, W. R., Bonan, G. B., and Allison, S. D.: Global soil carbon projections are improved by modelling microbial processes, Nat. Clim. Change, 3, 909–912, https://doi.org/10.1038/nclimate1951, 2013. a
Wieder, W. R., Grandy, A. S., Kallenbach, C. M., and Bonan, G. B.: Integrating microbial physiology and physio-chemical principles in soils with the MIcrobial-MIneral Carbon Stabilization (MIMICS) model, Biogeosciences, 11, 3899–3917, https://doi.org/10.5194/bg-11-3899-2014, 2014. a
Wieder, W. R., Allison, S. D., Davidson, E. A., Georgiou, K., Hararuk, O., He, Y., Hopkins, F., Luo, Y., Smith, M. J., Sulman, B., Todd‐Brown, K., Wang, Y., Xia, J., and Xu, X.: Explicitly representing soil microbial processes in Earth system models, Global Biogeochem. Cy., 29, 1782–1800, https://doi.org/10.1002/2015GB005188, 2015. a, b, c, d
Wieder, W. R., Hartman, M. D., Sulman, B. N., Wang, Y., Koven, C. D., and Bonan, G. B.: Carbon cycle confidence and uncertainty: Exploring variation among soil biogeochemical models, Glob. Change Biol., 24, 1563–1579, https://doi.org/10.1111/gcb.13979, 2018. a, b
Wilson, C. H. and Gerber, S.: Theoretical insights from upscaling Michaelis–Menten microbial dynamics in biogeochemical models: a dimensionless approach, Biogeosciences, 18, 5669–5679, https://doi.org/10.5194/bg-18-5669-2021, 2021. a
Wutzler, T. and Reichstein, M.: Priming and substrate quality interactions in soil organic matter models, Biogeosciences, 10, 2089–2103, https://doi.org/10.5194/bg-10-2089-2013, 2013. a, b
Short summary
The occurrence of unstable equilibrium points (EPs) could impede the applicability of microbial-explicit soil organic carbon models. For archetypal model versions we identify when instability can occur and describe mathematical conditions to avoid such unstable EPs. We discuss implications for further model development, highlighting the important role of considering basic ecological principles to ensure biologically meaningful models.
The occurrence of unstable equilibrium points (EPs) could impede the applicability of...
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