Atmospheric carbon dioxide enrichment (eCO2) can enhance plant carbon uptake and growth1,2,3,4,5, thereby providing an important negative feedback to climate change by slowing the rate of increase of the atmospheric CO2 concentration6. Although evidence gathered from young aggrading forests has generally indicated a strong CO2 fertilization effect on biomass growth3,4,5, it is unclear whether mature forests respond to eCO2 in a similar way. In mature trees and forest stands7,8,9,10, photosynthetic uptake has been found to increase under eCO2 without any apparent accompanying growth response, leaving the fate of additional carbon fixed under eCO2 unclear4,5,7,8,9,10,11. Here using data from the first ecosystem-scale Free-Air CO2 Enrichment (FACE) experiment in a mature forest, we constructed a comprehensive ecosystem carbon budget to track the fate of carbon as the forest responded to four years of eCO2 exposure. We show that, although the eCO2 treatment of +150 parts per million (+38 per cent) above ambient levels induced a 12 per cent (+247 grams of carbon per square metre per year) increase in carbon uptake through gross primary production, this additional carbon uptake did not lead to increased carbon sequestration at the ecosystem level. Instead, the majority of the extra carbon was emitted back into the atmosphere via several respiratory fluxes, with increased soil respiration alone accounting for half of the total uptake surplus. Our results call into question the predominant thinking that the capacity of forests to act as carbon sinks will be generally enhanced under eCO2, and challenge the efficacy of climate mitigation strategies that rely on ubiquitous CO2 fertilization as a driver of increased carbon sinks in global forests.
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Data included in this paper are available via Figshare (https://doi.org/10.6084/m9.figshare.11634315.v1).
Codes for processing the data are available via GitHub (https://github.com/mingkaijiang/EucFACE_carbon_budget_paper.git).
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EucFACE was built as an initiative of the Australian Government as part of the Nation-building Economic Stimulus Package, and is supported by the Australian Commonwealth in collaboration with Western Sydney University. We acknowledge technical support by V. Kumar, C. McNamara and S. Wohl, and the team who assisted with data collection. This work was partially supported by the following grants from the Australian Research Council (ARC): DP130102501 (to J.R.P. and I.C.A.), DP170104634 (to B.K.S. and P.B.R.), DP170102766 (to E.P. and M.G.T.), DP110105102 and DP160102452 (to D.S.E.). M.G.D.K. acknowledges funding from the ARC Centre of Excellence for Climate Extremes (CE170100023), the ARC Discovery Grant (DP190101823) and support from the NSW Research Attraction and Acceleration Program. R.L.S received funding from Research Foundation Flanders and the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement number 665501. R.O.-H. is financially supported by a Ramón y Cajal Fellowship from MICIU (RYC-2017-22032). E.H.J.N. and B.M.d.S. received funding from the VILLUM Center for Plant Plasticity (VKR023054), the VILLUM Young Investor Program fellowship (VKR013167), and a Danish Independent Research Council Sapere Aude Research Talent Post-Doctoral Stipend (6111-00379B). Ü.N. and A.K. were supported by the European Commission through the European Regional Fund (Center of Excellence EcolChange). S.Z. was supported by the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant agreement number 647204, QUINCY).
The authors declare no competing interests.
Peer review information Nature thanks Kirsti Ashworth, Benjamin Bond-Lamberty, Yiqi Luo and Andrew Richardson for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
a, View of the forest and facility from above (photograph taken by D.S.E.). b, View of the understorey vegetation and infrastructure inside a plot (photograph taken by M.J.). c, View from below of the canopy structure and the crane (photograph taken by M.J.).
Extended Data Fig. 2 Mean annual temperature (MAT) and mean annual precipitation (MAP) for major forest biomes and a selected list of tree-based elevated CO2 experiments.
Gridded temperature and precipitation data were obtained from the Climate Research Unit monthly dataset at 0.5-degree resolution75. Global biome boundaries and definitions were taken from ref. 76 and were spatially aggregated onto the Climate Research Unit resolution, following ref. 77. Only forest biomes were included here, including: tropical and subtropical moist broadleaf forests; tropical and subtropical dry broadleaf forests; tropical and subtropical coniferous forest; temperate broadleaf and mixed forests; temperate coniferous forests; boreal forests/taiga; and Mediterranean forests, woodlands, and scrub. The list of elevated CO2 experiments includes seven FACE experiments and a Whole-Tree Chamber (WTC) experiment, namely: EucFACE, DukeFACE, ORNLFACE, AspenFACE, PopFACE, WebFACE, BiForFACE and FlakalidenWTC. The site-specific climate, tree age and net primary production (NPP) under ambient CO2 treatment were collected from refs. 3,9,10,11,78,79. The top inset compares global forest NPP against standing age using data collected from ref. 80. We included data with forest age <500 years, and the NPP reported in ref. 80 includes both overstorey and understorey. The bottom inset compares soil total nitrogen and labile phosphorus across the eCO2 experiments. Soil total nitrogen was extracted from ref. 81 using the spatial coordinates of each experiment, while soil labile phosphorus was spatially extracted from ref. 82. The two dotted lines indicate N:P ratios of 20:1 and 100:1, respectively. The count in the colour scale refers to the number of grids that fall within each climate space.
Extended Data Fig. 3 Estimates of GPP (a, b) and soil respiration (Rsoil) (c, d) based on different methods for both ambient (a, c) and elevated (b, d) CO2 treatment at EucFACE.
For estimates of GPP, we compared the model simulated total GPP of overstorey and understorey (GPPo and GPPu, respectively), with the sum of data-driven estimates of net primary production (NPP) and autotrophic respiration (Ra), which include NPP of overstorey leaf (NPPol), stem (NPPstem), fine root (NPPfroot), intermediate root (NPPiroot), coarse root (NPPcroot), twigs, barks and seeds (NPPother), understorey aboveground (NPPua), leaf consumption by insects (NPPins), and respiratory fluxes of overstorey leaf (Rol), stem (Rstem), root (Rroot), understorey aboveground (Rua), growth (Rgrow), and volatile carbon emission (VC). For estimates of Rsoil, we compared direct estimates of Rsoil scaled up from soil chamber measurements, with the sum of litterfall and independent estimates of root respiration (Litter + Rroot), assuming no net change in soil carbon stock over time. Here litterfall was inferred from the NPP of overstorey leaf (NPPol), fine root (NPPfroot), intermediate root (NPPiroot), twigs, barks and seeds (NPPother), understorey aboveground (NPPua), and frass production (Frass). These evaluations provide independent mass balance checks of the estimated ecosystem carbon budget. Each colour represents a flux variable. Each point and vertical line represent treatment mean and standard deviation, respectively, based on plot-level estimates of the aggregated flux (n = 3). Values were normalized by a linear mixed model with pre-treatment LAI as a covariate to account for pre-existing differences.
Extended Data Fig. 4 The fate of additional carbon fixed under elevated CO2 (eCO2) in a mature forest ecosystem (non-normalized analysis case).
a, GPP represents the total eCO2 induced increase in GPPo and GPPu, respectively, NPP + Ra represents the sum of net primary production and autotrophic respiration eCO2 response, and R + ΔCpool represents the sum of ecosystem respiration and carbon storage eCO2 response. b, The relative contributions of individual NPP fluxes to the aggregated NPP response to eCO2, including overstorey leaf (NPPol), stem (NPPstem), fine root (NPPfroot) and understorey aboveground (NPPua). c, The relative contributions of individual respiratory fluxes to the aggregated R response to eCO2, including overstorey leaf (Rol), stem (Rstem), root (Rroot), understorey aboveground (Rua), and heterotroph (Rhetero). d, The relative contributions of individual change in carbon storage to the aggregated ΔCpool response to eCO2, including stem (ΔCstem), fine root (ΔCfroot), leaflitter (ΔClit), understorey aboveground (ΔCua), and soil (ΔCsoil). Variables with an average CO2 effect of <5 g C m−2 yr−1 were excluded from the figure for better visual clarification. Each colour represents a flux variable, each point indicates the net sum of all variables for a column, and the grey error bar represents the plot-level standard deviation (n = 3) of the estimated column sum.
a, The CO2 response of gross ecosystem carbon uptake, including GPPo and GPPu, and soil methane uptake (CH4). b, The eCO2 response of annual incremental change in carbon pool (ΔCpool), including overstorey leaf (ΔCol), stem (ΔCstem), coarse root (ΔCcroot), fine root (ΔCfroot), intermediate root (ΔCiroot), understorey aboveground (ΔCua), leaf litter (ΔClit), soil (ΔCsoil), microbe (ΔCmicr), aboveground insect (ΔCins), and mycorrhizae (ΔCmyco). c, The eCO2 response of NPP, including overstorey leaf (NPPol), stem (NPPstem), coarse root (NPPcroot), fine root (NPPfroot), intermediate root (NPPiroot), understorey aboveground (NPPua), twigs, barks and seeds (NPPother), and leaf insect consumption (NPPins). d, The eCO2 response of ecosystem R and other outgoing fluxes, including respiration fluxes of overstorey leaf (Rol), stem (Rstem), root (Rroot), understorey aboveground (Rua), growth (Rgrow), insect (Rins), heterotroph (Rhetero), and soil (Rsoil), and volatile carbon emission (VC) and DOC leaching. Dots and grey bars represent means and standard deviations of the CO2 treatment difference, predicted by a linear mixed model with plot-specific pre-treatment LAI as a covariate. Red dots indicate negative means and blue dots indicate positive means. Dashed lines indicate change of scale along the x-axis.
Extended Data Fig. 6 Estimates of incremental change in carbon pool averaged over the experimental period under ambient (aCO2) and elevated CO2 (eCO2) treatment effect at EucFACE (ΔCpool).
The ΔCpool variables are overstorey leaf (ΔCol), stem (ΔCstem), coarse root (ΔCcroot), fine root (ΔCfroot), intermediate root (ΔCiroot), understorey aboveground (ΔCua), leaf litter (ΔClit), soil (ΔCsoil), microbe (ΔCmicr), aboveground insect (ΔCins), and mycorrhizae (ΔCmyco). Each coloured bar and black line (error bar) represents the mean and standard deviation for each treatment, with blue representing aCO2 and red representing eCO2 treatment. Dashed lines indicate change of scale along the x axis.
Extended Data Fig. 7 Fitted carbon cycle parameters to trace the fate of the additional carbon under elevated CO2 at EucFACE.
Parameters were estimated by MCMC fitting algorithm, assuming a simplified carbon cycle framework based on data collected from EucFACE. Details of the MCMC approach can be found in the Methods. Plot-level GPP, autotrophic respiration (Ra), heterotrophic respiration (Rhetero), carbon pools of leaf (C′leaf), wood (C′wood), root (C′root), mycorrhizae (C′myco), microbe (C′micr), and soil (C′soil), and the corresponding change in pools were used to constrain the model fitting. NPP was derived as the difference of GPP and Ra. Carbon use efficiency (CUE′) was calculated as NPP/GPP; it differs from the value given in the main text owing to the contribution of NPP allocated to mycorrhizae (NPPmyco). We fitted two carbon pools (C′aglit and C′bglit), four allocation coefficients (aleaf, awood, aroot and amyco), six turnover rates (τleaf, τroot, τmyco, τbglit, τmicr and τsoil), and three fractional coefficients (f ′aglit, f ′bglit and f ′micr) using the MCMC algorithm. The fractional coefficients indicate the fraction of carbon leaving one pool that enters the subsequent pool, with the remainder respired as Rhetero.
Extended Data Fig. 8 Data–model intercomparison of some key carbon cycle parameters, under ambient (aCO2) and elevated CO2 (eCO2).
Parameters include: a, allocation coefficients to leaf, wood, root and other; b, turnover rates of leaf, root, aboveground litter (Aglit), belowground litter (Bglit); and c, turnover rate of soil. Models include: Community Atmosphere Biosphere Land Exchange (CABL), Community Land Model 4 (CLM4), Community Land Model with a phosphorus component (CLMP), Generic Decomposition And Yield (GDAY), Lund-Potsdam-Jena General Ecosystem Simulator (LPJX), Orchidee-C-N (OCNX) and Sheffield Dynamic Global Vegetation Model (SDVM). The model output was generated as part of the model ensemble predictions made in advance of the experiment reported in ref. 17 for EucFACE. Database uncertainties were estimated using the MCMC data assimilation algorithm, with error bars indicating one standard deviation. Allocation to ‘Other’ in the data refers to the allocation to mycorrhizal production, whereas in some models it refers to the allocation to reproductive carbon pool.
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Jiang, M., Medlyn, B.E., Drake, J.E. et al. The fate of carbon in a mature forest under carbon dioxide enrichment. Nature 580, 227–231 (2020). https://doi.org/10.1038/s41586-020-2128-9
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