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Open Access Open Badges Research article

Intra-seasonal dynamics in metabolic processes of 13C/12C and 18O/16O in components of Scots pine twigs from southern Siberia interpreted with a conceptual framework based on the Carbon Metabolism Oscillatory Model

Victor Voronin1, Alexander A Ivlev2, Vladimir Oskolkov1 and Tatjana Boettger3*

Author Affiliations

1 Department of Bioindication, Siberian Institute of Plant Physiology and Biochemistry, Siberian Branch, Russian Academy of Sciences, 132 Lermontov Street, 664033, Irkutsk, Russia

2 Department of Soil, Agrochemistry & Ecology, State Agrarian University, Timiryazevskaya Street 49, 127550, Moscow, Russia

3 UFZ - Helmholtz Centre for Environmental Research, Department of Catchment Hydrology, Theodor-Lieser-Straße 4, D-06120, Halle (Saale), Germany

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BMC Plant Biology 2012, 12:76  doi:10.1186/1471-2229-12-76

Published: 30 May 2012



Carbon isotope data from conifer trees play an important role in research on the boreal forest carbon reservoir in the global carbon cycle. Carbon isotopes are routinely used to study interactions between the environment and tree growth. Moreover, carbon isotopes became an essential tool for the evaluation of carbon assimilation and transport from needles into reserve pools, as well as the allocation of stored assimilates within a tree. The successful application and interpretation of carbon isotopes rely on the coherence of isotopic fractionation modeling. This study employs a new Carbon Metabolism Oscillatory Model (CMOM) to interpret the experimental data sets on metabolic seasonal dynamics of 13C/12 C and 18O/16O ratios measured in twig components of Scots pine growing in southern Siberia (Russia).


The dynamics of carbon isotopic variables were studied in components of Pinus sylvestris L. in light and in dark chambers during the vegetation period from 14 June to 28 July 2006. At the beginning of this period water-soluble organic matter, mostly labile sugars (including sucrose as the main component) and newly formed bulk needle material, displayed relatively “light” δ13C values (depletion in 13 C). Then, 13 C content increased again with noticeable “depletion” events in the middle of the growth period. A gradual 13 C accumulation took place in the second half of the vegetation period. Similar effects were observed both in the light and in the dark with some temporal shifts. Environmental factors did not influence the δ13C values. A gradual 12C-depletion effect was noticed in needles of the previous year. The δ13C values of sucrose and proteins from needle biomass altered independently from each other in the light chamber. A distinct negative correlation between δ13C and δ18O values was revealed for all studied variables.


The abrupt 13C depletion recorded by all tested trees for the period from June to July provides clear evidence of the transition from the dominant role of reserve carbohydrate pool (RCP) during the first half of the growth season to the preferable current year carbohydrate pool (CCP) consumption by new needles during its second half. The investigation of the isotopic signatures of Pinus sylvestris L. emphasizes the pivotal role of the intra-seasonal dynamics in carbon metabolism through the transport of assimilates from autotrophic (needles) to heterotrophic (twigs) organs of the studied trees. This provides an explanation for changes of carbon isotopic values observed within the growth season. The CMOM-based results support the hypothesis of the integration of three carbohydrate pools by photosynthesizing cells. The fluctuations of the carbon isotope ratios in different carbohydrate pools underlie various physiological processes in the tree metabolism. The possible mechanisms and pathways of formation of these carbohydrate pools are further discussed. Hence, CMOM provides a reasonable explanation for the absence of the impact of environmental conditions on the needle isotopic variables, the 12C-depletion effects and the use of RCP in needles. The model explains the negative connections between δ13C and δ18O values in all studied variables.