Terrestrial ecosystems remove about 30 per cent of the carbon dioxide (CO2) emitted by human activities each year1, yet the persistence of this carbon sink depends partly on how plant biomass and soil organic carbon (SOC) stocks respond to future increases in atmospheric CO2 (refs. 2,3). Although plant biomass often increases in elevated CO2 (eCO2) experiments4,5,6, SOC has been observed to increase, remain unchanged or even decline7. The mechanisms that drive this variation across experiments remain poorly understood, creating uncertainty in climate projections8,9. Here we synthesized data from 108 eCO2 experiments and found that the effect of eCO2 on SOC stocks is best explained by a negative relationship with plant biomass: when plant biomass is strongly stimulated by eCO2, SOC storage declines; conversely, when biomass is weakly stimulated, SOC storage increases. This trade-off appears to be related to plant nutrient acquisition, in which plants increase their biomass by mining the soil for nutrients, which decreases SOC storage. We found that, overall, SOC stocks increase with eCO2 in grasslands (8 ± 2 per cent) but not in forests (0 ± 2 per cent), even though plant biomass in grasslands increase less (9 ± 3 per cent) than in forests (23 ± 2 per cent). Ecosystem models do not reproduce this trade-off, which implies that projections of SOC may need to be revised.
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All the empirical data that support the main findings of this study have been deposited in Figshare (https://figshare.com/projects/Effects_of_elevated_CO2_on_soil_and_ecosystem_carbon_storage/74721) and GitHub (https://github.com/cesarterrer/SoilC_CO2). FACE-MDS data can be accessed at https://www.osti.gov/dataexplorer/biblio/dataset/1480327. CMIP5 data can be accessed at https://esgf-index1.ceda.ac.uk/search/cmip5-ceda/. TRENDY data can be requested at http://dgvm.ceh.ac.uk/index.html.
The R code used in the analysis presented in this paper is available in GitHub and can be accessed at https://github.com/cesarterrer/SoilC_CO2.
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We thank C. Körner, R. Norby, M. Schneider, K. Treseder, M. Hoosbeek and others for sharing data and advice. We thank the TRENDY, CMIP5 and FACE-MDS teams for the provision of the model simulations. C.T. was supported by a Lawrence Fellow award through Lawrence Livermore National Laboratory (LLNL). This work was performed under the auspices of the US Department of Energy by LLNL under contract DE-AC52-07NA27344 and was supported by the LLNL-Laboratory Directed Research and Development (LDRD) programme under project number 20-ERD-055. J.B.F. contributed to this research from the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. Government sponsorship acknowledged. Funding provided in part by the NASA Interdisciplinary Science (IDS) programme, and by the US Department of Energy, Office of Science, Office of Biological and Environmental Research, Terrestrial Ecosystem Science Program under Award Numbers DE-SC0008317, DE-SC0016188 and the LLNL Soil Science Focus Area (SFA) SCW1632. B.A.H. and K.J.v.G. were supported by the US Department of Energy through the Terrestrial Ecosystem Science Program DE-SC0010632. The FACE Model-Data Synthesis was supported by the US Department of Energy, Office of Science, Biological and Environmental Research programme. Oak Ridge National Laboratory is operated by UT-Battelle LLC under contract DE-AC05-00OR22725 with the US Department of Energy. The BioCON experiment was funded by the Long-Term Ecological Research (LTER) grants DEB-0620652, DEB-1234162 and DEB-1831944, Long-Term Research in Environmental Biology (LTREB) grants DEB-1242531 and DEB-1753859, Biological Integration Institutes grant NSF-DBI-2021898, Ecosystem Sciences grant DEB-1120064, and Biocomplexity grant DEB-0322057, and by the US Department of Energy Programs for Ecosystem Research grant DE-FG02-96ER62291.
The authors declare no competing interests.
Peer review information Nature thanks Jonathan Sanderman and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Extended Data Fig. 1 Effects of eCO2 on aboveground biomass production versus effects of eCO2 on litter production and SOC storage.
a, Effect of eCO2 on litter production as the effect of eCO2 on aboveground biomass production increases. b, Effect of CO2 on SOC storage as the effect of CO2 on aboveground biomass production increases. Results for non-fertilized field eCO2 experiments (n = 10, and n = 35, respectively). Grey shading around regression lines represents the 95% confidence intervals. Dots represent individual experiments, with dot size in b proportional to the weights in the meta-regression.
Extended Data Fig. 2 Variable importance of 19 predictors of the effects of CO2 on SOC and biomass stocks.
a, b, Varying importance of the effect of CO2 on SOC stocks in relative (a) and absolute terms (b) across the full dataset (n = 108). c, Varying importance of the effect of CO2 on SOC stocks (%) across the subset of eCO2 experiments in ‘intact’ soils (n = 73). d, Varying importance of the effect of CO2 on plant aboveground biomass (n = 138). The varying importance in a, b and d is quantified based on a meta-forest model. The varying importance in c is quantified based on the sum of AICc weights, which allows for the quantification of the importance of interactions between predictors. As an initial step, moderators that consistently displayed negative variable importance (that is, that showed a reduction in predictive performance) were automatically dropped. LAImax, LAImean, MAP and MAT are defined in Extended Data Table 1.
Extended Data Fig. 3 Effects of eCO2 on SOC stocks and plant biomass in nitrogen-fertilized eCO2 studies.
n = 35. a, b, Effects are expressed as a regression (a) and overall effects in meta-analysis (b). Dot sizes in a represent the individual studies and are drawn proportional to the weights in the model. The regression with the subset of non-fertilized studies is also shown in a for comparison. Dots in b represent the effect sizes and 95% confidence intervals from the meta-analysis.
Extended Data Fig. 4 Analysis of variables potentially explaining the observed effects of eCO2 on SOC.
Effects of eCO2 on root biomass (n = 45), fine-root production (n = 11), litter C:N (n = 16) and background SOC stocks (n = 38), between ecosystem types (grassland versus forest) and nutrient-acquisition strategies (AM versus ECM). Boxplots show the median, the first to third quartile, the 1.5× interquartile ranges, and outliers.
Extended Data Fig. 5 Partial dependence plots of the six most important predictors of the effect of eCO2 on SOC stocks across 108 experiments.
The figure shows the predicted CO2 effect in relative (a) and absolute terms (b) across each predictor and the most important interaction between predictors (right panels) in a random-forest meta-analysis. Error bands represent 95% confidence intervals. Partial regression plots give a graphical depiction of the marginal effect of a variable on the response and the shape and direction of the relationship. Little variation in the predicted effect of eCO2 across the values of a predictor generally reflects the low predictive power of the predictor. However, important predictors may show little variation in the predicted effect of eCO2 when involved in interactions, so the right panels show the most important interaction in the model. More details about the different predictors may be found in Extended Data Table 1. From a total of 19 predictors, only the six most important predictors and the most important interaction are shown here.
Extended Data Fig. 6 Representativeness of the scaling-up predictors of the effect of eCO2 on SOC stocks.
Histograms showing the distribution of both the predictors in the training dataset of CO2 experiments and the data used to scale up the global distribution of the effect. Predictions exclude regions between –15 to 15 and from 60° to 90° latitude owing to the lack of experiments.
Extended Data Fig. 7 Relationship between the effects of CO2 on aboveground biomass and SOC across individual models from three model ensembles.
a, FACE Model Data Synthesis Phase 2. Individual model results are represented by coloured symbols and lines. Each symbol represents one site; lines represent model-specific linear regressions. To ease interpretation of the results and the comparison with Fig. 4, axis limits are set. Dashed lines and error bands (grey shading) represent the linear regression line and standard error across all experiment-by-model results. b, TRENDY v7 models. c, CMIP5 models.
Extended Data Fig. 8 Difference between expected CO2 effects on SOC stocks based on TRENDY models and scaled up on the basis of experiments.
Expected values result from the relationship between βsoil and βplant coded in models. Positive values (red colour) indicate an overestimation by models; negative values (blue colour) indicate an underestimation by models.
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Terrer, C., Phillips, R.P., Hungate, B.A. et al. A trade-off between plant and soil carbon storage under elevated CO2. Nature 591, 599–603 (2021). https://doi.org/10.1038/s41586-021-03306-8