Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Letter
  • Published:

Towards a universal model for carbon dioxide uptake by plants

Nature Plants volume 3, pages 734–741 (2017)Cite this article

Subjects

Abstract

Gross primary production (GPP)—the uptake of carbon dioxide (CO2) by leaves, and its conversion to sugars by photosynthesis—is the basis for life on land. Earth System Models (ESMs) incorporating the interactions of land ecosystems and climate are used to predict the future of the terrestrial sink for anthropogenic CO2 1. ESMs require accurate representation of GPP. However, current ESMs disagree on how GPP responds to environmental variations1,2, suggesting a need for a more robust theoretical framework for modelling3,4. Here, we focus on a key quantity for GPP, the ratio of leaf internal to external CO2 (χ). χ is tightly regulated and depends on environmental conditions, but is represented empirically and incompletely in today’s models. We show that a simple evolutionary optimality hypothesis5,6 predicts specific quantitative dependencies of χ on temperature, vapour pressure deficit and elevation; and that these same dependencies emerge from an independent analysis of empirical χ values, derived from a worldwide dataset of >3,500 leaf stable carbon isotope measurements. A single global equation embodying these relationships then unifies the empirical light-use efficiency model7 with the standard model of C3 photosynthesis8, and successfully predicts GPP measured at eddy-covariance flux sites. This success is notable given the equation’s simplicity and broad applicability across biomes and plant functional types. It provides a theoretical underpinning for the analysis of plant functional coordination across species and emergent properties of ecosystems, and a potential basis for the reformulation of the controls of GPP in next-generation ESMs.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Partial residual plots from the regression of logit-tranformed values of χ derived from the global leaf stable carbon isotope dataset against environmental predictors.
Fig. 2: Site-mean values of χ.
Fig. 3: Monthly GPP at flux sites.

References

  1. Ciais, P. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) Ch. 6, 465–570 (Cambridge Univ. Press, Cambridge, New York, 2014).

  2. Friedlingstein, P. et al. Uncertainties in CMIP5 climate projections due to carbon cycle feedbacks. J. Climate 27, 511–526 (2014).

    Article  Google Scholar 

  3. Prentice, I. C., Liang, X., Medlyn, B. E. & Wang, Y. P. Reliable, robust and realistic: the three R’s of next-generation land-surface modelling. Atmos. Chem. Phys. 15, 5987–6005 (2015).

    Article  CAS  Google Scholar 

  4. Wang, H., Prentice, I. C. & Davis, T. W. Biophsyical constraints on gross primary production by the terrestrial biosphere. Biogeosciences 11, 5987–6001 (2014).

    Article  Google Scholar 

  5. Prentice, I. C., Dong, N., Gleason, S. M., Maire, V. & Wright, I. J. Balancing the costs of carbon gain and water transport: testing a new theoretical framework for plant functional ecology. Ecol. Lett. 17, 82–91 (2014).

    Article  PubMed  Google Scholar 

  6. Wright, I. J., Reich, P. B. & Westoby, M. Least-cost input mixtures of water and nitrogen for photosynthesis. Am. Nat. 161, 98–111 (2003).

    PubMed  Google Scholar 

  7. Monteith, J. L. Solar radiation and productivity in tropical ecosystems. J. Appl. Ecol. 9, 747–766 (1972).

    Article  Google Scholar 

  8. 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).

    Article  CAS  PubMed  Google Scholar 

  9. Medlyn, B. E. Physiological basis of the light use efficiency model. Tree Physiol. 18, 167–176 (1998).

    Article  PubMed  Google Scholar 

  10. Ali, A. et al. A global scale mechanistic model of the photosynthetic capacity (LUNA V1.0). Geosci. Model Dev. 9, 587–606 (2016).

    Article  Google Scholar 

  11. Cai, W. et al. Large differences in terrestrial vegetation production derived from satellite-based light use efficiency models. Rem. Sens. 6, 8945–8965 (2014).

    Article  Google Scholar 

  12. De Kauwe, M. G. et al. Forest water use and water use efficiency at elevated CO2: a model-data intercomparison at two contrasting temperate forest FACE sites. Glob. Change Biol. 19, 1759–1779 (2013).

    Article  Google Scholar 

  13. Medlyn, B. E. et al. Reconciling the optimal and empirical approaches to modelling stomatal conductance. Glob. Change Biol. 17, 2134–2144 (2011).

    Article  Google Scholar 

  14. Diefendorf, A. F., Mueller, K. E., Wing, S. L., Koch, P. L. & Freeman, K. H. Global patterns in leaf 13C discrimination and implications for studies of past and future climate. Proc. Natl Acad. Sci. USA 107, 5738–5743 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Cowan, I. & Farquhar, G. Stomatal function in relation to leaf metabolism and environment. Sym. Soc. Exp. Biol. 31, 471–505 (1977).

  16. Givnish, T. J. On the Economy of Plant Form and Function Vol. 6 (Cambridge Univ. Press, Cambridge, 1986).

  17. Cornwell, W. K. et al. A Global Dataset of Leaf ∆ 13 C Values (Zenodo, accessed 27 April 2017); http://dx.doi.org/10.5281/zenodo.569501

  18. Farquhar, G. D., Ehleringer, J. R. & Hubick, K. T. Carbon isotope discrimination and photosynthesis. Ann. Rev. Plant Biol. 40, 503–537 (1989).

    Article  CAS  Google Scholar 

  19. Körner, C., Farquhar, G. & Wong, S. Carbon isotope discrimination by plants follows latitudinal and altitudinal trends. Oecologia 88, 30–40 (1991).

    Article  PubMed  Google Scholar 

  20. Lin, Y.-S. et al. Optimal stomatal behaviour around the world. Nat. Clim. Change 5, 459–464 (2015).

    Article  CAS  Google Scholar 

  21. Maire, V. et al. The coordination of leaf photosynthesis links C and N fluxes in C3 plant species. PLoS ONE 7, e38345 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Haxeltine, A. & Prentice, I. C. A general model for the light-use efficiency of primary production. Funct. Ecol. 10, 551–561 (1996).

    Article  Google Scholar 

  23. Kattge, J. & Knorr, W. Temperature acclimation in a biochemical model of photosynthesis: a reanalysis of data from 36 species. Plant Cell Environ. 30, 1176–1190 (2007).

    Article  CAS  PubMed  Google Scholar 

  24. Collatz, G., Berry, J., Farquhar, G. & Pierce, J. The relationship between the Rubisco reaction mechanism and models of photosynthesis. Plant Cell Environ. 13, 219–225 (1990).

    Article  CAS  Google Scholar 

  25. Beer, C. et al. Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science 329, 834–838 (2010).

    Article  CAS  PubMed  Google Scholar 

  26. Yuan, W. et al. Global comparison of light use efficiency models for simulating terrestrial vegetation gross primary production based on the LaThuile database. Agr. Forest Meteorol. 192, 108–120 (2014).

    Article  Google Scholar 

  27. Ainsworth, E. A. & Long, S. P. What have we learned from 15 years of free-air CO2 enrichment (FACE)? A meta-analytic review of the responses of photosynthesis, canopy properties and plant production to rising CO2. New Phytol. 165, 351–372 (2005).

    Article  PubMed  Google Scholar 

  28. Frank, D. C. et al. Water-use efficiency and transpiration across European forests during the Anthropocene. Nat. Clim. Change 5, 579–583 (2015).

    Article  CAS  Google Scholar 

  29. Maire, V. et al. Global effects of soil and climate on leaf photosynthetic traits and rates. Global Ecol. Biogeogr. 24, 706–717 (2015).

    Article  Google Scholar 

  30. Chen, J.-L., Reynolds, J. F., Harley, P. C. & Tenhunen, J. D. Coordination theory of leaf nitrogen distribution in a canopy. Oecologia 93, 63–69 (1993).

    Article  PubMed  Google Scholar 

  31. Vogel, H. Temperaturabhängigkeitsgesetz der Viskosität von Flüssigkeiten. Physik Z 22, 645–646 (1921).

    CAS  Google Scholar 

  32. Jacob, D. Introduction to Atmospheric Chemistry (Princeton Univ. Press, Princeton, 1999).

  33. Bernacchi, C. J., Singsaas, E. L., Pimentel, C., Portis, A. R. Jr & Long, S. P. Improved temperature response functions for models of Rubisco-limited photosynthesis. Plant Cell Environ. 24, 253–259 (2001).

    Article  CAS  Google Scholar 

  34. New, M., Lister, D., Hulme, M. & Makin, I. A high-resolution data set of surface climate over global land areas. Clim. Res. 21, 1–25 (2002).

    Article  Google Scholar 

  35. Keenan, T. F., Sabate, S. & Gracia, C. Soil water stress and coupled photosynthesis–conductance models: bridging the gap between conflicting reports on the relative roles of stomatal, mesophyll conductance and biochemical limitations to photosynthesis. Agr. Forest Meteorol. 150, 443–453 (2010).

    Article  Google Scholar 

  36. Sun, Y. et al. Impact of mesophyll diffusion on estimated global land CO2 fertilization. Proc. Natl Acad. Sci. USA 111, 15774–15779 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Flexas, J., Ribas-Carbó, M., Diaz-Espejo, A., Galmés, J. & Medrano, H. Mesophyll conductance to CO2: current knowledge and future prospects. Plant Cell Environ. 31, 602–621 (2008).

    Article  CAS  PubMed  Google Scholar 

  38. Gu, J., Yin, X., Stomph, T.-J., Wang, H. & Struik, P. C. Physiological basis of genetic variation in leaf photosynthesis among rice (Oryza sativa L.) introgression lines under drought and well-watered conditions. J. Exp. Bot. 63, 5137–5153 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Douthe, C., Dreyer, E., Epron, D. & Warren, C. Mesophyll conductance to CO2, assessed from online TDL-AS records of 13CO2 discrimination, displays small but significant short-term responses to CO2 and irradiance in Eucalyptus seedlings. J. Exp. Bot. 62, 5335–5346 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Barbour, M., Warren, C., Farquhar, G., Forrester, G. & Brown, H. Variability in mesophyll conductance between barley genotypes, and effects on transpiration efficiency and carbon isotope discrimination. Plant Cell Environ. 33, 1176–1185 (2010).

  41. Ubierna, N. & Farquhar, G. D. Advances in measurements and models of photosynthetic carbon isotope discrimination in C3 plants. Plant Cell Environ. 37, 1494–1498 (2014).

    Article  CAS  PubMed  Google Scholar 

  42. Warren, C. R. Stand aside stomata, another actor deserves centre stage: the forgotten role of the internal conductance to CO2 transfer. J. Exp. Bot. 59, 1475–1487 (2008).

    Article  CAS  PubMed  Google Scholar 

  43. Smith, E. L. The influence of light and carbon dioxide on photosynthesis. J. Gen. Physiol. 20, 807–830 (1937).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Harley, P. C., Thomas, R. B., Reynolds, J. F. & Strain, B. R. Modelling photosynthesis of cotton grown in elevated CO2. Plant Cell Environ. 15, 271–282 (1992).

    Article  CAS  Google Scholar 

  45. Lloyd, J. & Farquhar, G. D. 13C discrimination during CO2 assimilation by the terrestrial biosphere. Oecologia 99, 201–215 (1994).

    Article  PubMed  Google Scholar 

  46. New, M., Hulme, M. & Jones, P. Representing twentieth- century space-time climate variability. Part I: development of a 1961–90 mean monthly terrestrial climatology. J. Climate 12, 829–856 (1999).

    Article  Google Scholar 

  47. Weedon, G. P. et al. The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA-Interim reanalysis data. Water Resour. Res. 50, 7505–7514 (2014).

    Article  Google Scholar 

  48. Xiao, X., Zhang, Q., Hollinger, D., Aber, J. & Moore, B. I. Modeling gross primary production of an evergreen needleleaf forest using MODIS and climate data. Ecol. Appl. 15, 954–969 (2005).

    Article  Google Scholar 

  49. Kaplan, J. O. Geophysical Applications of Vegetation Modeling Thesis, Max Planck Institute for Biogeochemistry (2001).

Download references

Acknowledgements

We thank Y.-S. Lin, V. Maire, B. Medlyn, B. Stocker and IIASA colleagues for discussions, and R. Keeling for comments on successive drafts. The paper is a contribution to the AXA Chair Programme on Biosphere and Climate Impacts and Imperial College’s initiative on Grand Challenges in Ecosystems and the Environment. Research is supported by a National Basic Research Programme of China (2013CB956602) grant to C.P. and H.W., the National Natural Science Foundation of China (Grant no. 31600388) to H.W., an Australian Research Council Discovery grant (‘Next-generation vegetation model based on functional traits’) to I.C.P. and I.J.W., an Australian National Data Service (ANDS) grant (‘Ecosystem production in space and time’) to I.C.P. and Terrestrial Ecosystem Research Council (TERN) grants (‘Ecosystem Modelling and Scaling Infrastructure’) to I.C.P. and B.J.E. TERN and ANDS are supported by the Australian Government National Collaborative Infrastructure Strategy. T.F.K. acknowledges financial support from the Laboratory Directed Research and Development fund under the auspices of DOE, BER Office of Science at Lawrence Berkeley National Laboratory and a Macquarie University Research Fellowship. In addition to the authors of this paper, data were provided by M. Barbour, L. Cernusak, T. Dawson, D. Ellsworth, G. Farquhar, H. Griffiths, C. Keitel, A. Knohl, P. Reich, D. Williams, R. Bhaskar, H. Cornelissen, A. Richards, S. Schmidt, F. Valladares, C. Körner, E.-D. Schulze, N. Buchmann and L. Santiago. We used ‘free and fair use’ eddy-covariance data acquired by the FLUXNET community and, in particular, by the following networks: AmeriFlux (US Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program), AsiaFlux, CarboEuropeIP, Fluxnet-Canada (supported by CFCAS, NSERC, BIOCAP, Environment Canada and NRCan), OzFlux and TCOS-Siberia. We acknowledge the financial support to the eddy-covariance data harmonization provided by CarboEuropeIP, FAO- GTOS-TCO, iLEAPS, Max Planck Institute for Biogeochemistry, National Science Foundation, University of Tuscia, Université Laval and Environment Canada and US Department of Energy, and the database development and technical support from Berkeley Water Center, Lawrence Berkeley National Laboratory, Microsoft Research eScience, Oak Ridge National Laboratory, University of California–Berkeley and University of Virginia.

Author information

Authors and Affiliations

  1. State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, College of Forestry, Northwest A & F University, Yangling, 712100, Shaanxi, China

    Han Wang, I. Colin Prentice & Changhui Peng

  2. Department of Biological Sciences, Macquarie University, North Ryde, NSW 2109, Australia

    Han Wang, I. Colin Prentice, Trevor F. Keenan, Ian J. Wright & Bradley J. Evans

  3. Ecosystems Services and Management Program, International Institute for Applied Systems Analysis, A-2361, Laxenburg, Austria

    Han Wang

  4. AXA Chair of Biosphere and Climate Impacts, Department of Life Sciences, Imperial College London, Silwood Park Campus, Buckhurst Road, Ascot, SL5 7PY, UK

    I. Colin Prentice & Tyler W. Davis

  5. Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA

    Trevor F. Keenan

  6. United States Department of Agriculture-Agricultural Research Service, Robert W. Holley Center for Agriculture and Health, Ithaca, NY, 14853, USA

    Tyler W. Davis

  7. Ecology and Evolution Research Centre, School of Biological, Earth and Environmental Sciences, The University of New South Wales, Randwick, NSW 2052, Australia

    William K. Cornwell

  8. Faculty of Agriculture and Environment, Department of Environmental Sciences, The University of Sydney, Sydney, NSW, 2006, Australia

    Bradley J. Evans

  9. Department of Biological Sciences, Institute of Environmental Sciences, University of Quebec at Montreal, C.P. 8888, Succ. Centre-Ville, Montréal, Québec, H3C 3P8, Canada

    Changhui Peng

Authors
  1. Han Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. I. Colin Prentice
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Trevor F. Keenan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tyler W. Davis
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ian J. Wright
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. William K. Cornwell
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Bradley J. Evans
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Changhui Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

H.W. and I.C.P. derived the predictions. H.W. carried out all the analyses and constructed the Figures and Tables. I.C.P. and T.F.K. contributed to the analysis and writing. T.W.D., B.J.E. and I.C.P. developed and tested the flux partitioning method. T.W.D. developed the global flux database and all the GPP computations. I.J.W. proposed least-cost hypothesis and contributed to the analysis. W.K.C. originated and compiled the Δ13C dataset. H.W. and I.C.P. wrote the first draft, and all authors contributed to the final draft.

Corresponding authors

Correspondence to Han Wang or Changhui Peng.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Supplementary Information

Detailed description of the theoretical model.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, H., Prentice, I.C., Keenan, T.F. et al. Towards a universal model for carbon dioxide uptake by plants. Nature Plants 3, 734–741 (2017). https://doi.org/10.1038/s41477-017-0006-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41477-017-0006-8

This article is cited by

Search

Advanced search

Quick links

Nature Briefing Microbiology

Sign up for the Nature Briefing: Microbiology newsletter — what matters in microbiology research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: Microbiology