Amazon forest response to CO2 fertilization dependent on plant phosphorus acquisition

Abstract

Global terrestrial models currently predict that the Amazon rainforest will continue to act as a carbon sink in the future, primarily owing to the rising atmospheric carbon dioxide (CO2) concentration. Soil phosphorus impoverishment in parts of the Amazon basin largely controls its functioning, but the role of phosphorus availability has not been considered in global model ensembles—for example, during the Fifth Climate Model Intercomparison Project. Here we simulate the planned free-air CO2 enrichment experiment AmazonFACE with an ensemble of 14 terrestrial ecosystem models. We show that phosphorus availability reduces the projected CO2-induced biomass carbon growth by about 50% to 79 ± 63 g C m−2 yr−1 over 15 years compared to estimates from carbon and carbon–nitrogen models. Our results suggest that the resilience of the region to climate change may be much less than previously assumed. Variation in the biomass carbon response among the phosphorus-enabled models is considerable, ranging from 5 to 140 g C m−2 yr−1, owing to the contrasting plant phosphorus use and acquisition strategies considered among the models. The Amazon forest response thus depends on the interactions and relative contributions of the phosphorus acquisition and use strategies across individuals, and to what extent these processes can be upregulated under elevated CO2.

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Fig. 1: The predicted effect of eCO2 on biomass C, productivity and biomass compartments for C, CN and CNP models.
Fig. 2: Strength of P feedbacks in controlling the biomass C response to eCO2 for the six CNP models.
Fig. 3: Key responses of biomass C gain, stoichiometry, allocation and P dynamics to eCO2 for the CNP models.

Data availability

Model output data used for the analyses and figures are archived in a GitHub repository (https://github.com/Kaaze7/AmzFACE-model-ensemble-2019).

Code availability

Code used for the analyses and figures are archived in a GitHub repository (https://github.com/Kaaze7/AmzFACE-model-ensemble-2019).

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Acknowledgements

The AmazonFACE research program provided logistical support to conduct this study (https://amazonface.inpa.gov.br/). This study was funded by the Inter-American Development Bank through a technical cooperation agreement with the Brazilian Ministry of Science, Technology, Innovation and Communications (Grant BR-T1284), by Brazil’s Coordination for the Improvement of Higher Education Personnel (CAPES) Grant 23038.007722/2014-77, by the Amazonas Research Foundation (FAPEAM) Grant 2649/2014 and the São Paulo Research Foundation (FAPESP) Grant 2015/02537-7. We thank the many scientists, field and laboratory technicians, students and other personnel involved in the development of the models, in collecting and analysing the field data and in the planning and execution of the AmazonFACE program. We thank the German Research Foundation (DFG) for financing one of the workshops that made this study possible (grant no. RA 2060/4-1). T.F.D., S.G., A.G. and C.A.Q. thank the USAID for funding via the PEER program (grant agreement AID-OAA-A-11-00012). A.P.W. and R.J.N. were supported by the FACE Model–Data Synthesis project, X.Y. and Q.Z. were supported by the Energy Exascale Earth System (E3SM) program and J.A.H. was supported by the Next Generation Ecosystem Experiments—Tropics project; all were funded by the US Department of Energy, Office of Science, Office of Biological and Environmental Research under contract numbers DE-AC02-05CH11231 and DE-AC05-00OR22725. M.G.dK. acknowledges support from the Australian Research Council Centre of Excellence for Climate Extremes (CE170100023) and the New South Wales Research Attraction and Acceleration Program. Y.P.W., B.P. and V.H. acknowledge support from the National Earth System Science Program of the Australian Government. D.S.G. is funded by the ‘IMBALANCE-P’ project of the European Research Council (ERC-2013-SyG-610028). L.M.M. acknowledges funding from the UK’s Natural Environment Research Council (NERC) grant nos NE/LE007223/1 and NE/N017951/1. F.L. acknowledges funding from EU FP7 LUC4C program (GA603542). K.F. is funded by the DFG (grant no. RA 2060/5-1).

Author information

D.M.L., A.R. and K.F. conceived the study. L.F., S.G., A.G., F.H., R.J.N., C.A.Q., K.J.S. and O.J.V.-B. collected field data. K.F., D.S.G., M.G.dK., M.J., V.H., J.A.H., F.L., L.M.M., B.P., C.vR., Y.-P.W., X.Y., S.Z. and Q.Z. performed model simulations. K.F. wrote the manuscript with contributions from all the co-authors.

Correspondence to Katrin Fleischer.

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Supplementary Figs. 1–9, and Supplementary Tables 1–4

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