树干皮层光合作用--生理生态功能和测定方法

大部分植物的树干(枝条)等部位含有能进行光合作用的绿色组织,树皮叶绿素含量最高可达750 mg/m2。这些绿色组织能够再固定树干内部的CO2(来源于自身组织呼吸或者木质部液流运输),使树干向大气排放的CO2量减少60%—90%皮层光合作用是树干生理活动的重要组成部分,其与树干呼吸和液流速率之间均有密切关系,对植物的碳平衡有重要作用。概述了皮层光合作用的生理生态功能;介绍了皮层光合作用测定和计算方法;讨论了皮层光合作用研究存在的问题;通过加入皮层光合作用的测量修正质量平衡法,以减少树干呼吸测定的不确定性。建议综合运用稳定碳同位素示踪、CO2和O2微传感器、树干液流技术等,准确地区分树干内部CO2的来源及比例,分析各个组分与影响因素的关系。同时,在微观上揭示皮层光合作用的基因组调控功能,在宏观上探讨尺度扩展、模型模拟,并与涡度协方差技术和遥感技术相融合以提高区域尺度估算的精度。

Stem corticular photosynthesis: ecophysiological functions and their measurement

It is currently widely accepted that, aside from green leaves, other plant organs are able to assimilate carbon via the reductive carboxylic acid cycle. Branches, stems, and even roots often have chloroplast-containing cells. The bark of some trees contains up to 750 mg/m² of chlorophyll. Photosynthetic activity in trees, bushes, and shrubs has been recorded in the living bark of young twigs, branches and main stems, in addition to the living cells of wood, and sometimes even in the pith. Chlorophyll-containing bark and wood tissue are principally subordinated to non-photosynthetic functions, but typically perform effective internal CO₂ recycling using CO₂ released from respiration. Chloroplast-containing tissues may re-fix 60%-90% of internal CO₂ that has respired from woody tissues or has been transported from xylem sap. Many different terms are used to describe "nonfoliar" CO₂ fixation in twigs, branches, and stems; including, bark photosynthesis, corticular photosynthesis, chlorenchymal CO₂-reduction, stem-internal CO₂-fixation, chlorenchymal CO₂-refixation, and stem photosynthesis. It is hypothesized that corticular photosynthesis is driven by stem-internal CO₂ derived from mitochondrial respiration and maybe also gaseous xylem efflux. Corticular photosynthesis is an essential physiological process in the trunk that positively contributes to total plant carbon, due to its close relationship with stem respiration and sap flow. First, our review summarized the main physiological and ecological functions of corticular photosynthesis. As corticular photosynthesis works in the same way as leaf photosynthesis, photosynthetic carbon reduction is driven by a combination of effective chloroplast structure, essential enzymatic functions, water, light, and carbon dioxide. We showed that these main factors are present in sufficient quantities within the chlorenchymal bark tissues of trees. Corticular photosynthesis, sap flux velocity, and the CO₂ concentration of xylem sapwood all influence stem CO₂ efflux. Observations of the relationship between sap flux and CO₂ efflux may help explain why CO₂ efflux changes with stand age or tree size, in addition to differences between similar trees growing in different environments. Second, we described the principal methods used to measure and calculate corticular photosynthesis. It is now evident that standard measurements of CO₂ efflux to the atmosphere, such as a flux chamber covering a segment of tree stem to estimate the rate of woody tissue respiration, do not adequately account for internal fluxes in CO₂. The new mass balance approach of measuring corticular photosynthesis may provide a more accurate way of estimating the rate of woody tissue respiration. A more complete assessment of internal CO₂ fluxes in stems will improve our understanding about the carbon balance of trees. Third, we discuss the problems and challenges associated with the study of corticular photosynthesis. The unpredictability of stem respiration measurements could be reduced by incorporating corticular photosynthesis measurements into the mass balance correction. We propose that the combined approach of using stable carbon isotope tracing, CO₂ and O₂ micro-sensors, and sap-flow techniques should be used in future so that the fraction of each source of internal CO₂ in the stem-and the respective determinant-may be accurately determined. In addition, we propose that the genomic regulatory mechanisms that influence corticular photosynthesis should be investigated to understand this important process at the gene level. Finally, we suggest that it is important to integrate scaling and model-fitting with eddy covariance and remote sensing techniques to improve estimation accuracy at a regional scale.