Photosynthesis Research

, Volume 119, Issue 1–2, pp 3–14 | Cite as

Gaps in knowledge and data driving uncertainty in models of photosynthesis

Review

Abstract

Regional and global models of the terrestrial biosphere depend critically on models of photosynthesis when predicting impacts of global change. This paper focuses on identifying the primary data needs of these models, what scales drive uncertainty, and how to improve measurements. Overall, there is a need for an open, cross-discipline database on leaf-level photosynthesis in general, and response curves in particular. The parameters in photosynthetic models are not constant through time, space, or canopy position but there is a need for a better understanding of whether relationships with drivers, such as leaf nitrogen, are themselves scale dependent. Across time scales, as ecosystem models become more sophisticated in their representations of succession they needs to be able to approximate sunfleck responses to capture understory growth and survival. At both high and low latitudes, photosynthetic data are inadequate in general and there is a particular need to better understand thermal acclimation. Simple models of acclimation suggest that shifts in optimal temperature are important. However, there is little advantage to synoptic-scale responses and circadian rhythms may be more beneficial than acclimation over shorter timescales. At high latitudes, there is a need for a better understanding of low-temperature photosynthetic limits, while at low latitudes the need is for a better understanding of phosphorus limitations on photosynthesis. In terms of sampling, measuring multivariate photosynthetic response surfaces are potentially more efficient and more accurate than traditional univariate response curves. Finally, there is a need for greater community involvement in model validation and model-data synthesis.

Keywords

Acclimation Database Ecosystem model Statistical design 

References

  1. Albani M, Medvigy DM, Hurtt GC, Moorcroft PR (2006) The contributions of land-use change, CO2 fertilization, and climate variability to the Eastern US carbon sink. Glob Change Biol 12(12):2370–2390. doi:10.1111/j.1365-2486.2006.01254.x CrossRefGoogle Scholar
  2. Amthor J (2000) The McCree–de Wit–Penning de Vries–Thornley Respiration Paradigms: 30 years later. Ann Bot 86(1):1–20. doi:10.1006/anbo.2000.1175 CrossRefGoogle Scholar
  3. Asner GP, Martin RE (2009) Airborne spectranomics: mapping canopy chemical and taxonomic diversity in tropical forests. Front Ecol Environ 7(5):269–276. doi:10.1890/070152 CrossRefGoogle Scholar
  4. Bernacchi CJ, Rosenthal DM, Pimentel C, Long SP, Farquhar GD (2009) Modeling the temperature dependence of C3 photosynthesis. In: Laisk A, Nedbal L, Govindjee (eds) Photosynthesis in silico: understanding complexity from molecules to ecosystems, vol 29. Springe, Netherlands, pp 231–246CrossRefGoogle Scholar
  5. Berry JA, Beerling DJ, Franks PJ (2010) Stomata: key players in the earth system, past and present. Curr Opin Plant Biol 13(3):233–240. doi:10.1016/j.pbi.2010.04.013 PubMedGoogle Scholar
  6. Bonan B, Lawrence PJ, Oleson KW, Levis S, Jung M, Reichstein M, Lawrence DM, Swenson SC (2011) Improving canopy processes in the Community Land Model version 4 (CLM4) using global flux fields empirically inferred from FLUXNET data. J Geophy Res 116(G2):1–22. doi:10.1029/2010JG001593 Google Scholar
  7. Bonan GB, Oleson KW, Fisher RA, Lasslop G, Reichstein M (2012) Reconciling leaf physiological traits and canopy flux data use of the TRY and FLUXNET databases in the Community Land Model version 4. J Geophys Res 117(25C):1–19. doi:10.1029/2011JG001913 Google Scholar
  8. Booth BBB, Jones CD, Collins M, Totterdell IJ, Cox PM, Sitch S, Huntingford C et al (2012). High sensitivity of future global warming to land carbon cycle processes. Environ Lett, 024002. doi:10.1088/1748-9326/7/2/024002
  9. Clark JS (2005) Why environmental scientists are becoming bayesians. Ecol Lett 8(1):2–14. doi:10.1111/j.1461-0248.2004.00702.x CrossRefGoogle Scholar
  10. Clark JS, Dietze MC, Chakraborty S, Agarwal PK, Wolosin MS, Ibanez I, LaDeau S (2007) Resolving the biodiversity paradox. Ecol Lett 10(8):647–659. doi:10.1111/j.1461-0248.2007.01041.x discussion 659–62PubMedCrossRefGoogle Scholar
  11. Davidson CD (2012) The modeled effects of fire on carbon balance and vegetation abundance in Alaskan Tundra. University of Illinois, Urbana-Champaign, p 163Google Scholar
  12. De Kauwe MG et al (2013) Forest water use and water use efficiency at elevated CO2: a model-data intercomparison at two contrasting temperate forest FACE sites. Glob Change Biol (in press). doi:10.1111/gcb.12164
  13. Dietze MC, Clark JS (2008) Changing the gap dynamics paradigm: vegetative regeneration control on forest response to disturbance. Ecol Monogr 78(3):331–347. doi:10.1890/07-0271.1 CrossRefGoogle Scholar
  14. Dietze M et al (2011) Characterizing the performance of ecosystem models across time scales: A spectral analysis of the North American carbon program site-level synthesis. J Geophys Res 116:1–15. doi:10.1029/2011JG001661 Google Scholar
  15. Dietze MC, LeBauer D, Kooper R (2013) On improving the communication between models and data. Plant Cell Environ (in press). doi:10.1111/pce.12043
  16. Dios VR et al (2012) Endogenous circadian regulation of carbon dioxide exchange in terrestrial ecosystems. Glob Change Biol 18(6):1956–1970. doi:10.1111/j.1365-2486.2012.02664.x CrossRefGoogle Scholar
  17. Dubois J-JB, Fiscus EL, Booker FL, Flowers MD, Reid CD (2007) Optimizing the statistical estimation of the parameters of the Farquhar-von Caemmerer-Berry model of photosynthesis. New phytol 176(2):402–414. doi:10.1111/j.1469-8137.2007.02182.x PubMedCrossRefGoogle Scholar
  18. Ellis RJ (1979) The most abundant protein in the world. Trends Biochem Sci 4(11):241–244. doi:10.1016/0968-0004(79)90212-3 CrossRefGoogle Scholar
  19. Farquhar G, Caemmerer S, Berry JA (1980) A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149(1):78–90PubMedCrossRefGoogle Scholar
  20. Farquhar GD, von Caemmerer S, Berry JA (2001) Models of photosynthesis. Plant physiol 125(1):42–45PubMedCentralPubMedCrossRefGoogle Scholar
  21. Foster DR, Aber JD (2006) Forests in time: the environmental consequences of 1000 years of change in New England. Yale University Press, New HavenGoogle Scholar
  22. Foster D, Swanson F, Aber J, Burke I, Brokaw N, Tilman D, Knapp A (2003) The importance of land-use legacies to ecology and conservation. Bioscience 53(1):77. doi:10.1641/0006-3568 CrossRefGoogle Scholar
  23. Friend AD (2010) Terrestrial plant production and climate change. J Exp Bot 61(5):1293–1309. doi:10.1093/jxb/erq019 PubMedCrossRefGoogle Scholar
  24. Goll DS, Brovkin V, Parida BR, Reick CH, Kattge J, Reich PB, Van Bodegom PM et al (2012) Nutrient limitation reduces land carbon uptake in simulations with a model of combined carbon, nitrogen and phosphorus cycling. Biogeosciences 9(9):3547–3569. doi:10.5194/bg-9-3547-2012 CrossRefGoogle Scholar
  25. Gross LJ, Kirschbaum MUF, Pearcy RW (1991) A dynamic model of photosynthesis in varying light taking account of stomatal conductance, C3-cycle intermediates, photorespiration and Rubisco activation. Plant Cell Environ 14:881–893. doi:10.1111/j.1365-3040.1991.tb00957.x CrossRefGoogle Scholar
  26. Gu L, Pallardy SG, Tu K, Law BE, Wullschleger SD (2010) Reliable estimation of biochemical parameters from C3 leaf photosynthesis-intercellular carbon dioxide response curves. Plant Cell Environ 33(11):1852–1874. doi:10.1111/j.1365-3040.2010.02192.x PubMedCrossRefGoogle Scholar
  27. Harmer SL (2009) The circadian system in higher plants. Annu Rev Plant Biol 60:357–377. doi:10.1146/annurev.arplant.043008.092054 PubMedCrossRefGoogle Scholar
  28. Harpole W et al (2011) Nutrient co-limitation of primary producer communities. Ecol Lett 14:852–862. doi:10.1111/j.1461-0248.2011.01651.x PubMedCrossRefGoogle Scholar
  29. Hennessey TL, Field CB (1991) Circadian rhythms in photosynthesis. Plant Physiol 96:831–836PubMedCentralPubMedCrossRefGoogle Scholar
  30. Hey T, Tansley S, Tolle K (eds) (2009) The fourth paradigm: data-intensive scientific discovery. Microsoft Research, RedmondGoogle Scholar
  31. Kattge J, Knorr W (2007) Temperature acclimation in a biochemical model of photosynthesis: a reanalysis of data from 36 species. Plant Cell Environ 30(9):1176–1190. doi:10.1111/j.1365-3040.2007.01690.x PubMedCrossRefGoogle Scholar
  32. Kattge J, Knorr W, Raddatz T, Wirth C (2009) Quantifying photosynthetic capacity and its relationship to leaf nitrogen content for global-scale terrestrial biosphere models. Glob Change Biol 15(4):976–991. doi:10.1111/j.1365-2486.2008.01744.x CrossRefGoogle Scholar
  33. Kattge J et al (2011) TRY–a global database of plant traits. Glob Change Biol. doi:10.1111/j.1365-2486.2011.02451.x
  34. Leakey ADB, Press MC, Scholes JD, Watling JR (2002) Relative enhancement of photosynthesis and growth at elevated CO2 is greater under sunflecks than uniform irradiance in a tropical rain forest tree seedling. Plant Cell Environ 25(12):1701–1714. doi:10.1046/j.1365-3040.2002.00944.x CrossRefGoogle Scholar
  35. Leakey AD, Bishop KA, Ainsworth EA (2012) A multi-biome gap in understanding of crop and ecosystem responses to elevated CO(2). Curr Opin Plant Biol 15(3):228–236. doi:10.1016/j.pbi.2012.01.009 PubMedCrossRefGoogle Scholar
  36. LeBauer DS, Wang D, Richter KT, Davidson CC, and Dietze MC (2012), Facilitating feedbacks between field measurements and ecosystem models. Ecol Monogr (in press). doi:10.1890/12-0137.1
  37. Leuning R (1995) A critical appraisal of a combined stomatal-photosynthesis model for C3 plants. Plant Cell Environ 18(4):339–355. doi:10.1111/j.1365-3040.1995.tb00370.x CrossRefGoogle Scholar
  38. Lin Y-S, Medlyn BE, Ellsworth DS (2012) Temperature responses of leaf net photosynthesis: the role of component processes. Tree Physiol 32(2):219–231. doi:10.1093/treephys/tpr141 Google Scholar
  39. Long SP, Bernacchi CJ (2003) Gas exchange measurements, what can they tell us about the underlying limitations to photosynthesis? Procedures and sources of error. J Exp Bot 54(392):2393–2401. doi:10.1093/jxb/erg262 PubMedCrossRefGoogle Scholar
  40. Medlyn BE, Dreyer E, Ellsworth D, Forstreuter M, Harley PC, Kirschbaum MUF, Roux XLE (2002) Temperature response of parameters of a biochemically based model of photosynthesis. II. A review of experimental data. Plant Cell Environ 25:1167–1179CrossRefGoogle Scholar
  41. Medlyn BE, Duursma RA, Eamus D, Ellsworth DS, Prentice IC, Barton CVM, Crous KY, De Angelis P, Freeman M, Wingate L (2011) Reconciling the optimal and empirical approaches to modelling stomatal conductance. Glob Change Biol 17(6):2134–2144. doi:10.1111/j.1365-2486.2010.02375.x CrossRefGoogle Scholar
  42. Medvigy DM, Wofsy SC, Munger JW, Hollinger DY, Moorcroft PR (2009) Mechanistic scaling of ecosystem function and dynamics in space and time: ecosystem demography model version 2. J Geophys Res 114(G1):1–21. doi:10.1029/2008JG000812 Google Scholar
  43. Mohan JE, Clark JS, Schlesinger WH (2007) Long-term CO2 enrichment of a forest ecosystem: implications for forest regeneration and succession. Ecol Appl 17(4):1198–1212PubMedCrossRefGoogle Scholar
  44. Moorcroft PR, Hurtt GC, Pacala SW (2001) A method for scaling vegetation dynamics: the ecosystem demography model (ED). Ecol Monogr 71(4):557–586. doi:10.1890/0012-9615(2001)071[0557:AMFSVD]2.0.CO;2Google Scholar
  45. Naumburg E, Ellsworth DS (2002) Short-term light and leaf photosynthetic dynamics affect estimates of daily understory photosynthesis in four tree species. Tree Physiol 22:393–401PubMedCrossRefGoogle Scholar
  46. Neale D, Aitken S, Dietze MC, Kliebenstein D, Mathews S, Oren R, Wegrzyn J, and Whetten R (2010) Tree biology cyber infrastructure, (online). http://www.iplantcollaborative.org/sites/default/files/Tree_BiologyCI_seed_proposal_FINAL.pdf. Accessed 10 Oct 2012
  47. Pacala SW, Canham CD, Saponara J, Silander JA Jr, Kobe RK, Ribbens E (1996) Forest models defined by field measurements: estimation, error analysis and dynamics. Ecol Monogr 66(1):1–43CrossRefGoogle Scholar
  48. Patrick LD, Ogle K, Tissue DT (2009) A hierarchical Bayesian approach for estimation of photosynthetic parameters of C(3) plants. Plant Cell Environ 32(12):1695–1709. doi:10.1111/j.1365-3040.2009.02029.x PubMedCrossRefGoogle Scholar
  49. Reich PB (1987) Quantifying plant response to ozone: a unifying theory. Tree Physiol 3(1):63–91PubMedCrossRefGoogle Scholar
  50. Ricciuto DM, Thornton PE, Schaefer K, Cook RB, Davis KJ (2009) How uncertainty in gap-filled meteorological input forcing at eddy covariance sites impacts modeled carbon and energy flux. Eos Trans Am Geophys Union 90(52):B54AGoogle Scholar
  51. Riley WJ, Still CJ, Torn MS, Berry JA (2002) A mechanistic model of H218O and C18OO fluxes between ecosystems and the atmosphere: model description and sensitivity analyses. Glob Biogeochem Cycles 16(4):1–14. doi:10.1029/2002GB001878 Google Scholar
  52. Schaefer KM et al (2012) A model-data comparison of gross primary productivity: results from the North American carbon program site synthesis. J Geophys Res 117:1–15. doi:10.1029/2012JG001960 Google Scholar
  53. Schwalm CR et al (2010), A model-data intercomparison of CO2 exchange across North America: results from the North American carbon program site synthesis. J Geophy Res 115. doi:10.1029/2009JG001229
  54. Sitch S, Cox PM, Collins WJ, Huntingford C (2007) Indirect radiative forcing of climate change through ozone effects on the land-carbon sink. Nature 448(7155):791–794. doi:10.1038/nature06059 PubMedCrossRefGoogle Scholar
  55. Smith NG, Dukes JS (2013) Plant respiration and photosynthesis in global-scale models: incorporating acclimation to temperature and CO2. Glob Change Biol 19:45–63. doi:10.1111/j.1365-2486.2012.02797.x
  56. Starr G, Oberbauer SF (2003) Photosynthesis of arctic evergreens under snow: implications for tundra ecosystem carbon balance. Ecology 84(6):1415–1420. doi:10.1890/02-3154 CrossRefGoogle Scholar
  57. Townsend AR, Asner GP, Cleveland CC (2008) The biogeochemical heterogeneity of tropical forests. Trends Ecol Evol 23(8):424–431. doi:10.1016/j.tree.2008.04.009 PubMedCrossRefGoogle Scholar
  58. Wang YP, Law RM, Pak B (2010) A global model of carbon, nitrogen and phosphorus cycles for the terrestrial biosphere. Biogeosciences 7:2261–2282. doi:10.5194/bg-7-2261-2010 CrossRefGoogle Scholar
  59. Wang D, Maughan MW, Sun J, Feng X, Miguez F, Lee D, Dietze MC (2012) Impact of nitrogen allocation on growth and photosynthesis of Miscanthus (Miscanthus × giganteus). GCB Bioenergy 4(6):688–697. doi:10.1111/j.1757-1707.2012.01167.x CrossRefGoogle Scholar
  60. Way DA, Pearcy RW (2012) Sunflecks in trees and forests: from photosynthetic physiology to global change biology. Tree Physiol 32(9):1066–1081. doi:10.1093/treephys/tps064 PubMedCrossRefGoogle Scholar
  61. Williams W, Gorton H (1998) Circadian rhythms have insignificant effects on plant gas exchange under field conditions. Physiol Plant 103:247–256CrossRefGoogle Scholar
  62. Wright IJ et al (2004) The worldwide leaf economics spectrum. Nature 428(6985):821–827. doi:10.1038/nature02403 PubMedCrossRefGoogle Scholar
  63. Ziehn T, Kattge J, Knorr W, Scholze M (2011) Improving the predictability of global CO2 assimilation rates under climate change. Geophys Res Lett 38:L10404. doi:10.1029/2011GL047182 Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Department of Earth and EnvironmentBoston UniversityBostonUSA

Personalised recommendations