Using approximate Bayesian computation to infer photosynthesis model parameters

ZENG Ji-Ye1 TAN Zheng-Hong2 SAIGUSA Nobuko1

(1.National Institute for Environmental Studies, Tsukuba 305-8506, Japan)
(2.Department of Environmental Science, Hainan University, Haikou, China 570228)

【Abstract】We developed a method, namely Adaptive Population Monte Carlo Approximate Bayesian Computation (APMC), to estimate the parameters of Farquhar photosynthesis model. Treating the canopy as a big leaf, we applied this method to derive the parameters at canopy scale. Validations against observational data showed that the parameters estimated based on the APMC optimization were un-biased for predicting the photosynthetic rate. We conclude that APMC has greater advantages in estimating the model parameters than those of the conventional nonlinear regression models.

【Keywords】 Monte Carlo; big-leaf model; Farquhar photosynthesis model; net ecosystem exchange;

【DOI】

【Funds】 National Natural Science Foundation of China (31200347, 31660142)

Download this article

(Translated by CHEN YF)

    References

    Amthor JS (1994). Scaling CO2-photosynthesis relationships from the leaf to the canopy. Photosynthesis Research, 39, 321–350.

    Andrieu C, Doucet A, Holenstein R (2010). Particle Markov chain Monte Carlo methods. Journal of the Royal Statistical Society, Series B, 72, 269–342.

    Baldocchi D (1994). An analytical solution for coupled leaf photosynthesis and stomatal conductance models. Tree Physiology, 14, 1069–1079.

    Ball JT, Woodrow JT, Berry JA (1987). A model predicting stomatal conductance and its contribution to the control of photosynthesis under different environmental conditions. In: Biggins J ed. Progress in Photosynthesis Research, Vol. 4. Proceedings of the 7th International Congress on Photosynthesis. Matins Nijhoff, Dordrecht, the Netherlands. 221–224.

    Beaumont MA (2010). Approximate Bayesian computation in evolution and ecology. Annual Review of Ecology, Evolution, and Systematics, 41, 379–406.

    Csilléry K, Blum MGB, Gaggiotti OE, Francois O (2010). Approximate Bayesian Computation (ABC) in practice. Trends in Ecology and Evolution, 25, 410–418.

    Cubasch U, Wuebbles D, Chen D, Facchini MC, Frame D, Mahowald N, Winther JG (2013). Introduction. In: Stocker TF, Qin D, Plattner GK, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM eds. Climate Change 2013: The Physical Science Basis. Cambridge University Press, Cambridge, UK.

    Dai Y, Dickinson RE, Wang YP (2004). A Two-Big-Leaf Model for canopy temperature, photosynthesis, and stomatal conductance. Journal of Climate, 17, 2281–2299.

    Damour G, Simonneau T, Cochard H, Urban L (2010). An overview of models of stomatal conductance at the leaf level. Plant, Cell & Environment, 33, 1419–1438.

    de Pury DGG, Farquhar GD (1997). Simple scaling of photosynthesis from leaves to canopies without the errors of big-leaf models. Plant, Cell & Environment, 20, 537–557.

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

    Gao M, Zhang H (2012). Sequential Monte Carlo methods for parameter estimation in nonlinear state–space models. Computers and Geosciences, 44, 70–77.

    Groenendijk M, Dolman AJ, Ammann C, Arneth A, Cescatti A, Dragoni D, Gash JHC, Gianelle D, Gioli B, Kiely G, Knohl A, Law BE, Lund M, Marcolla B, van der Molen MK, Montagnani L, Moors E, Richardson AD, Roupsard O, Verbeeck H, Wohlfahrt G (2011). Seasonal variation of photosynthetic model parameters and leaf area index from global Fluxnet eddy covariance data. Journal of Geophysical Research, 116, G04027. doi:10.1029/2011JG001742.

    Gu L (2010). Reliable estimation of biochemical parameters from C3 leaf photosynthesis–intercellular carbon dioxide response curves. Plant, Cell & Environment, 33, 1852–1874.

    Hartig F, Dislich C, Wiegand T, Huth A (2014). Technical note: Approximate Bayesian parameterization of a process-based tropical forest model. Biogeosciences, 11, 1261–1272.

    Hirano T, Hirata R, Fujinuma Y, Saigusa N, Yamamoto S, Harazono Y, Takada M, Inukai K, Inoue G (2003). CO2 and water vapor exchange of a larch forest in northern Japan. Tellus, 55B, 244–257.

    Jarvis PG (1995). Scaling processes and problems. Plant, Cell & Environment, 18, 1079–1089.

    June T, Evans JR, Farquhar GD (2004). A simple new equation for the reversible temperature dependence of photosynthetic electron transport: A study on soybean leaf. Functional Plant Biology, 31, 275–283.

    Kosugi Y, Takanashi S, Ueyama M, Ohkubo S, Tanaka H, Matsumoto K, Yoshifuji N, Ataka M, Sakabe A (2013). Determination of the gas exchange phenology in an evergreen coniferous forest from 7 years of eddy covariance flux data using an extended big-leaf analysis. Ecological Research, 28, 373–385.

    Lenormand M, Jabot F, Deffuant G (2013). Adaptive approximate Bayesian computation for complex models. Computational Statistics, 28, 2777–2796.

    Mac Eachern SN, Clyde M, Liu J (1999). Sequential importance sampling for nonparametric Bayes models: The next generation. Canadian Journal of Statistics, 27, 251–267.

    Marin JM, Pudlo P, Robert CP, Ryder RJ (2012). Approximate Bayesian computational methods. Statistics and Computing, 22, 1167–1180.

    Medlyn BE, Dreyer E, Ellsworth D, Forstreuter M, Harley PC, Kirschbaum MUF, Le Roux X, Montpied P, Strassemeyer J, Walcroft A, Wang K, Loustau D (2002a). Temperature response of parameters of a biochemically based model of photosynthesis. II. A review of experimental data. Plant, Cell & Environment, 25, 1167–1179.

    Medlyn BE, Loustau D, Delzon S (2002b). Temperature response of parameters of a biochemically based model of photosynthesis. I. Seasonal changes in mature maritime pine (Pinus pinaster Ait.). Plant, Cell & Environment, 25, 1155–1165.

    Saigusa N, Yamamoto S, Hirata R, Ohtani Y, Ide R, Asanuma J, Gamo M, Hirano T, Kondo H, Kosugi Y, Li S-G, Nakai Y, Takagi K, Tani M, Wang H (2008). Temporal and spatial variations in the seasonal patterns of CO2 flux in boreal, temperate, and tropical forests in East Asia. Agricultural and Forest Meteorology, 148, 700–713.

    Sellers PJ, Randall DA, Collatz GJ, Berry JA, Field CB, Dazlich DA, Zhang C, Collelo GD, Bounoua L (1996). A revised land surface parameterization (SiB2) for atmospheric GCMs. Part 1: Model formulation. Journal of Climate, 9, 676–705.

    Sprintsin M, Chen JM, Desai A, Gough CM (2012). Evaluation of leaf-to-canopy upscaling methodologies against carbon flux data in North America. Journal of Geophysical Research, 117, G01023. doi:10.1029/2010JG001407.

    Toni T, Welch D, Strelkowa N, Ipsen A, Stumpf MPH (2009). Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems. Journal of the Royal Society Interface, 6, 187–202.

    von Caemmerer S, Farquhar G, Berry J (2009). Biochemical model of C3 photosynthesis. In: Laisk A, Nedbal L, Govindjee eds. Photosynthesis in Silico: Understanding Complexity from Molecules to Ecosystems. Springer, Dordrecht, the Netherlands. 209–230.

    Vrugt JA, Sadegh M (2013). Toward diagnostic model calibration and evaluation: Approximate Bayesian computation. Water Resources Research, 49, 4335–4345.

    Wang H, Saigusa N, Yamamoto S, Kondo H, Hirano T, Toriyama A, Fujinuma F (2004). Net ecosystem CO2 exchange over a larch forest in Hokkaido, Japan. Atmospheric Environment, 38, 7021–7032.

    Wang YP, Leuning R (1998). A two-leaf model for canopy conductance, photosynthesis and partitioning of available energy I: Model description and comparison with a multilayered model. Agricultural and Forest Meteorology, 91, 89–111.

    Xu L, Baldocchi DD (2003). Seasonal trends in photosynthetic parameters and stomatal conductance of blue oak (Quercus douglasii) under prolonged summer drought and high temperature. Tree Physiology, 23, 865–877.

    Yin X, Struik PC (2009). C3 and C4 photosynthesis models: An overview from the perspective of crop modelling. Wageningen Journal of Life Sciences, 57, 27–38.

This Article

ISSN:1005-264X

CN:11-3397/Q

Vol 41, No. 03, Pages 378-385

March 2017

Downloads:0

Share
Article Outline

Abstract

  • 1 Methods
  • 2 Results
  • 3 Discussion and conclusion
  • References