Data-Driven Modeling of Stomatal, Mesophyll, and Biochemical Regulation of Scots Pine Photosynthesis
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-Driven Modeling of Stomatal, Mesophyll, and Biochemical Regulation of Scots Pine Photosynthesis A. V. Sokolova, b, * and V. K. Bolondinskiic a
Vernadsky Institute of Geochemistry and Analytical Chemistry, Russian Academy of Sciences, Moscow, 119991 Russia b Kharkevitch Institute For Information Transmission Problems (Kharkevich Institute), Russian Academy of Sciences, Moscow, 127051 Russia c Forest Research Institute, Karelian Research Center, Russian Academy of Sciences, Petrozavodsk, 185910 Russia, *e-mail: [email protected] Received April 10, 2019; revised April 29, 2020; accepted May 11, 2020
Abstract—Distinguishing between components (stomatal conductivity, mesophyll conductivity, carbon dioxide fixation in chloroplasts, and respiration) of the complex biochemical mechanism of plant photosynthesis is a challenging problem. To solve it, we used both experimental data obtained in natural conditions (time series of recorded indicators) and a specially designed experiment: determination of carbon dioxide compensation points. A specialized processing method (balanced identification) and the corresponding information technology made it possible to consider a number of models, unbiasedly evaluate the significance of the hypotheses, and determine the errors of models describing the dataset. The evolutionary method of modifying models (from simple to complex, with modeling error as the selection criterion) made it possible to choose a model whose complexity corresponds to the used experimental data. The model thus constructed is applied to calculate the assimilation of CO2 by forest ecosystems and WUE (Water Use Efficiency). Keywords: photosynthesis, Scots pine, biochemical regulation, balanced identification, WUE, CO2 and H2O flows DOI: 10.1134/S0016702920100146
INTRODUCTION The global temperature increase over the past decades is often interpreted as a consequence of the fast increase in the concentration of greenhouse gases (first and foremost, CO2) in the atmosphere (Grace 2000; IPCC, 2013). The increase rate of CO2 concentration in the atmosphere in the late 2000s reached 2.20 ± 0.01 ppm/year (i.e., 1.7% a year) and continued growing (https://www.esrl.noaa.gov/gmd/ccgg/ trends/global.html#global). The maximum CO2 concentration measured on Mauna-Kea volcano, Hawaii (19°29′ N, 155°36′ W, elevation 3400 m) was 412.30 ppm as of January 2020. Interpretations of the possible reasons for this global increase in the CO2 concentrations are so far ambiguous. This process may be controlled by natural variations but not only CO2 emissions from anthropogenic sources (Peters et al., 2012). Vegetation actively exchanges CO2 with the atmosphere and thus operates as an important regulator of the climatic system. On continents, where local biospheric absorbants play an important role, CO2 concentrations in the atmosphere (particularly at daytime) are much lower. Forestlands of northern and central taiga actively uptake CO2, and hence, photo-
synthesis modeling is of crucial importance for evaluating the CO2 budget in
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