Amazon forest response to CO2 fertilization dependent on plant phosphorus acquisition


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 (

Code availability

Code used for the analyses and figures are archived in a GitHub repository (


  1. 1.

    Brienen, R. J. W. et al. Long-term decline of the Amazon carbon sink. Nature 519, 344–348 (2015).

  2. 2.

    Phillips, O. L. & Brienen, R. J. W. Carbon uptake by mature Amazon forests has mitigated Amazon nations’ carbon emissions. Carbon Balance Manag. 12, 1 (2017).

  3. 3.

    Cernusak, L. A. et al. Tropical forest responses to increasing atmospheric CO2: current knowledge and opportunities for future research. Funct. Plant Biol. 40, 531–551 (2013).

  4. 4.

    Ciais, P. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) 465–570 (Cambridge Univ. Press, 2013).

  5. 5.

    Cox, P. M. et al. Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability. Nature 494, 341–344 (2013).

  6. 6.

    Huntingford, C. et al. Simulated resilience of tropical rainforests to CO2-induced climate change. Nat. Geosci. 6, 268–273 (2013).

  7. 7.

    Talhelm, A. F. et al. Elevated carbon dioxide and ozone alter productivity and ecosystem carbon content in northern temperate forests. Glob. Chang. Biol. 20, 2492–2504 (2014).

  8. 8.

    Norby, R. J., Warren, J. M., Iversen, C. M., Medlyn, B. E. & McMurtrie, R. E. CO2 enhancement of forest productivity constrained by limited nitrogen availability. Proc. Natl Acad. Sci. USA 107, 19368–19373 (2010).

  9. 9.

    Zaehle, S. et al. Evaluation of 11 terrestrial carbon–nitrogen cycle models against observations from two temperate Free-Air CO2 Enrichment studies. New Phytol. 202, 803–822 (2014).

  10. 10.

    Hofhansl, F. et al. Amazon forest ecosystem responses to elevated atmospheric CO2 and alterations in nutrient availability: filling the gaps with model-experiment integration. Front. Earth Sci. 4, 19 (2016).

  11. 11.

    Norby, R. J. et al. Model-data synthesis for the next generation of forest Free-Air CO2 Enrichment (FACE) experiments. New Phytol. 209, 17–28 (2016).

  12. 12.

    Lloyd, J., Bird, M. I., Veenendaal, E. M. & Kruijt, B. in Global Biogeochemical Cycles in the Climate System 95 (eds Schulze, E.-D. et al.) 95–114 (Academic, 2001).

  13. 13.

    Vitousek, P. M. Litterfall, nutrient cycling, and nutrient limitation in tropical forests. Ecology 65, 285–298 (1984).

  14. 14.

    Quesada, C. A. et al. Basin-wide variations in Amazon forest structure and function are mediated by both soils and climate. Biogeosciences 9, 2203–2246 (2012).

  15. 15.

    Lambers, H., Raven, J. A., Shaver, G. R. & Smith, S. E. Plant nutrient-acquisition strategies change with soil age. Trends Ecol. Evol. 23, 95–103 (2008).

  16. 16.

    Reed, S. C., Yang, X. & Thornton, P. E. Incorporating phosphorus cycling into global modeling efforts: a worthwhile, tractable endeavor. New Phytol. 208, 324–329 (2015).

  17. 17.

    Jiang, M., Caldararu, S., Zaehle, S., Ellsworth, D. S. & Medlyn, B. E. Towards a more physiological representation of vegetation phosphorus processes in land surface models. New Phytol. 222, 1223–1229 (2019).

  18. 18.

    Turner, B. L., Brenes-Arguedas, T. & Condit, R. Pervasive phosphorus limitation of tree species but not communities in tropical forests. Nature 555, 367–370 (2018).

  19. 19.

    Goll, D. S. et al. A representation of the phosphorus cycle for ORCHIDEE (revision 4520). Geosci. Model Dev. 10, 3745–3770 (2017).

  20. 20.

    Wang, Y.-P., Law, R. M. & Pak, B. A global model of carbon, nitrogen and phosphorus cycles for the terrestrial biosphere. Biogeosciences 7, 2261–2282 (2010).

  21. 21.

    Haverd, V. et al. A new version of the CABLE land surface model (Subversion revision r4601) incorporating land use and land cover change, woody vegetation demography, and a novel optimisation-based approach to plant coordination of photosynthesis. Geosci. Model Dev. 11, 2995–3026 (2018).

  22. 22.

    Comins, H. N. & McMurtrie, R. E. Long-term response of nutrient-limited forests to CO2 enrichment; equilibrium behavior of plant–soil models. Ecol. Appl. 3, 666–681 (1993).

  23. 23.

    Zhu, Q., Riley, W. J., Tang, J. & Koven, C. D. Multiple soil nutrient competition between plants, microbes, and mineral surfaces: model development, parameterization, and example applications in several tropical forests. Biogeosciences 13, 341–363 (2016).

  24. 24.

    Yang, X., Thornton, P. E., Ricciuto, D. M. & Post, W. M. The role of phosphorus dynamics in tropical forests—a modeling study using CLM-CNP. Biogeosciences 11, 1667–1681 (2014).

  25. 25.

    Malhi, Y. et al. Comprehensive assessment of carbon productivity, allocation and storage in three Amazonian forests. Glob. Chang. Biol. 15, 1255–1274 (2009).

  26. 26.

    Araújo, A. C. et al. Comparative measurements of carbon dioxide fluxes from two nearby towers in a central Amazonian rainforest: the Manaus LBA site. J. Geophys. Res. 107, 8090 (2002).

  27. 27.

    Quesada, C. A. et al. Soils of Amazonia with particular reference to the RAINFOR sites. Biogeosciences 8, 1415–1440 (2011).

  28. 28.

    Friend, A. D. et al. Carbon residence time dominates uncertainty in terrestrial vegetation responses to future climate and atmospheric CO2. Proc. Natl Acad. Sci. USA 111, 3280–3285 (2014).

  29. 29.

    Walker, A. P. et al. Predicting long-term carbon sequestration in response to CO2 enrichment: how and why do current ecosystem models differ? Glob. Biogeochem. Cycles 29, 476–495 (2015).

  30. 30.

    Vitousek, P. M. Nutrient Cycling and Limitation: Hawai’i as a Model System (Princeton Univ. Press, 2004).

  31. 31.

    Nardoto, G. B. et al. Basin-wide variations in Amazon forest nitrogen-cycling characteristics as inferred from plant and soil 15N:14N measurements. Plant Ecol. Divers. 7, 173–187 (2014).

  32. 32.

    Fyllas, N. M. et al. Basin-wide variations in foliar properties of Amazonian forest: phylogeny, soils and climate. Biogeosciences 6, 2677–2708 (2009).

  33. 33.

    Wieder, W. R., Cleveland, C. C., Smith, W. K. & Todd-Brown, K. Future productivity and carbon storage limited by terrestrial nutrient availability. Nat. Geosci. 8, 441–444 (2015).

  34. 34.

    Medlyn, B. E. et al. Using models to guide field experiments: a priori predictions for the CO2 response of a nutrient- and water-limited native eucalypt woodland. Glob. Chang. Biol. 22, 2834–2851 (2016).

  35. 35.

    Ellsworth, D. S. et al. Elevated CO2 does not increase eucalypt forest productivity on a low-phosphorus soil. Nat. Clim. Chang. 7, 279–282 (2017).

  36. 36.

    Wright, S. J. et al. Plant responses to fertilization experiments in lowland, species-rich, tropical forests. Ecology 99, 1129–1138 (2018).

  37. 37.

    Warren, J. M. et al. Root structural and functional dynamics in terrestrial biosphere models—evaluation and recommendations. New Phytol. 205, 59–78 (2015).

  38. 38.

    Hoosbeek, M. R. Elevated CO2 increased phosphorous loss from decomposing litter and soil organic matter at two FACE experiments with trees. Biogeochemistry 127, 89–97 (2016).

  39. 39.

    Yang, X., Thornton, P. E., Ricciuto, D. M. & Hoffman, F. M. Phosphorus feedbacks constraining tropical ecosystem responses to changes in atmospheric CO2 and climate. Geophys. Res. Lett. 43, 7205–7214 (2016).

  40. 40.

    Vicca, S. et al. Fertile forests produce biomass more efficiently. Ecol. Lett. 15, 520–526 (2012).

  41. 41.

    Wang, Y. & Lambers, H. Root-released organic anions in response to low phosphorus availability: recent progress, challenges and future perspectives. Plant Soil (2019).

  42. 42.

    Gatti, L. V. et al. Drought sensitivity of Amazonian carbon balance revealed by atmospheric measurements. Nature 506, 76–80 (2014).

  43. 43.

    Powell, T. L. et al. Confronting model predictions of carbon fluxes with measurements of Amazon forests subjected to experimental drought. New Phytol. 200, 350–365 (2013).

  44. 44.

    He, M. & Dijkstra, F. A. Drought effect on plant nitrogen and phosphorus: a meta-analysis. New Phytol. 204, 924–931 (2014).

  45. 45.

    Quesada, C. A. et al. Variations in chemical and physical properties of Amazon forest soils in relation to their genesis. Biogeosciences 7, 1515–1541 (2010).

  46. 46.

    Chambers, J. Q. et al. Respiration from a tropical forest ecosystem: partitioning of sources and low carbon use efficiency. Ecol. Appl. 14, 72–88 (2004).

  47. 47.

    Aragão, L. E. O. C. et al. Above- and below-ground net primary productivity across ten Amazonian forests on contrasting soils. Biogeosciences 6, 2759–2778 (2009).

  48. 48.

    Holm, J. A., Chambers, J. Q., Collins, W. D. & Higuchi, N. Forest response to increased disturbance in the central Amazon and comparison to western Amazonian forests. Biogeosciences 11, 5773–5794 (2014).

  49. 49.

    Hadlich, H. L. et al. Recognizing Amazonian tree species in the field using bark tissues spectra. Ecol. Manag. 427, 296–304 (2018).

  50. 50.

    Kucharik, C. J. et al. Testing the performance of a dynamic global ecosystem model: water balance, carbon balance, and vegetation structure. Glob. Biogeochem. Cycles 14, 795–825 (2000).

  51. 51.

    Fisher, R. A. et al. Taking off the training wheels: the properties of a dynamic vegetation model without climate envelopes, CLM4.5(ED). Geosci. Model Dev. 8, 3593–3619 (2015).

  52. 52.

    Medvigy, D., Wofsy, S. C., Munger, J. W., Hollinger, D. Y. & Moorcroft, P. R. Mechanistic scaling of ecosystem function and dynamics in space and time: Ecosystem Demography model version 2. J. Geophys. Res. Biogeosci. 114, G01002 (2009).

  53. 53.

    Smith, B. et al. Implications of incorporating N cycling and N limitations on primary production in an individual-based dynamic vegetation model. Biogeosciences 11, 2027–2054 (2014).

  54. 54.

    Zaehle, S. & Friend, A. D. Carbon and nitrogen cycle dynamics in the O–CN land surface model: 1. Model description, site-scale evaluation, and sensitivity to parameter estimates. Glob. Biogeochem. Cycles 24, GB1005 (2010).

  55. 55.

    Best, M. J. et al. The joint UK land environment simulator (JULES), model description—Part 1: Energy and water fluxes. Geosci. Model Dev. 4, 677–699 (2011).

  56. 56.

    Farquhar, G. D., von Caemmerer, S. & Berry, J. A. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149, 78–90 (1980).

  57. 57.

    Collatz, G. J., Ball, J. T., Grivet, C. & Berry, J. A. Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration—a model that includes a laminar boundary-layer. Agric. Meteorol. 54, 107–136 (1991).

  58. 58.

    Kull, O. & Kruijt, B. Leaf photosynthetic light response: a mechanistic model for scaling photosynthesis to leaves and canopies. Funct. Ecol. 12, 767–777 (1998).

  59. 59.

    Etheridge, D. M. et al. Natural and anthropogenic changes in atmospheric CO2 over the last 1000 years from air in Antarctic ice and firn. J. Geophys. Res. Atmos. 101, 4115–4128 (1996).

  60. 60.

    MacFarling Meure, C. et al. Law Dome CO2, CH4 and N2O ice core records extended to 2000 years BP. Geophys. Res. Lett. 33, L14810 (2006).

  61. 61.

    Lamarque, J. F. et al. Historical (1850–2000) gridded anthropogenic and biomass burning emissions of reactive gases and aerosols: methodology and application. Atmos. Chem. Phys. 10, 7017–7039 (2010).

  62. 62.

    Lamarque, J. F. et al. Global and regional evolution of short-lived radiatively-active gases and aerosols in the Representative Concentration Pathways. Clim. Change 109, 191–212 (2011).

  63. 63.

    Wang, R. et al. Global forest carbon uptake due to nitrogen and phosphorus deposition from 1850 to 2100. Glob. Chang. Biol. 23, 4854–4872 (2017).

  64. 64.

    Tomasella, J. & Hodnett, M. Pedotransfer functions for tropical soils. Dev. Soil Sci. 30, 415–429 (2004).

  65. 65.

    Marthews, T. R. et al. High-resolution hydraulic parameter maps for surface soils in tropical South America. Geosci. Model Dev. 7, 711–723 (2014).

  66. 66.

    De Kauwe, M. G. et al. Where does the carbon go? A model–data intercomparison of vegetation carbon allocation and turnover processes at two temperate forest Free-Air CO2 Enrichment sites. New Phytol. 203, 883–899 (2014).

  67. 67.

    Walker, A. P. et al. Comprehensive ecosystem model–data synthesis using multiple data sets at two temperate forest Free‐Air CO2 Enrichment experiments: model performance at ambient CO2 concentration. J. Geophys. Res. Biogeosci. 119, 937–964 (2014).

  68. 68.

    Medlyn, B. E. et al. Using ecosystem experiments to improve vegetation models. Nat. Clim. Chang. 5, 528–534 (2015).

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The AmazonFACE research program provided logistical support to conduct this study ( 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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