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Exploring the instability of the relationship between maximum potential electron transport rate and maximum carboxylation rate in cool-temperate deciduous forests
Agricultural and Forest Meteorology  (IF5.734),  Pub Date : 2021-08-24, DOI: 10.1016/j.agrformet.2021.108614
Guangman Song, Quan Wang, Jia Jin

A clear understanding of the relationship between two photosynthetic capacity parameters, maximum carboxylation rate (Vcmax) and maximum electron transport rate (Jmax), is expected to reveal plant photosynthetic physiology strategies and is critical in determining the carbon and water cycle in the terrestrial biosphere. Increasing evidence in recent suggests the relationship is unstable in spatiotemporal contexts, casting a doubt on deriving Jmax solely from a single linear function of Vcmax as in most gas exchange models do. In this study, we have examined the instability of the relationship in terms of seasonal and interannual courses (from 2006 to 2014) as well as at different altitudes (550, 900, and 1500 m a.s.l.) and in different leaf types (sunlit and shaded) of the only dominant species of Fagus crenata Blume in typical cool temperate forests in Japan based on a long-term field dataset. Our results clearly indicated that there is not only remarkable seasonal variability in the two parameters themselves but also large variations in their ratios. The slope parameter of the best fitted single linear function for estimating Jmax from Vcmax varied also in different seasons and different years and different leaf types, as well as at different sites. Further exploration of the slope parameter with internal leaf traits and external climatic drivers revealed that the leaf chlorophyll content had a significant correlation with the slope parameter (R2 = 0.72, RMSE = 0.08, RPD = 2.00) but the impacts of climatic drivers were rather versatile. Our findings should hence advance understanding of the dynamics of photosynthetic capacity and the instability of the Jmax–Vcmax relationship, which should be considered explicitly in the future in order to substantially improve model predictions in terrestrial carbon cycle models.