A physiological model for predicting dynamics of tree stem‐wood non‐structural carbohydrates

Abstract

Non‐structural carbohydrates (NSC) are considered important in the metabolism of wood plants, and they are highly dynamic. Accurate and detailed modelling of NSC dynamics may increase the understanding of physiological processes. We constructed a physiological model that simulates NSC dynamics from photosynthesis, transpiration and growth carbon demand. With this model, we tested the hypotheses that production, allocation and storage explain tree‐ and forest‐level carbohydrates balance, and especially that cambial activities explain the major portion of carbon consumption. We applied this model at one plot in Harvard Forest, Massachusetts, USA. The predicted results of stem‐wood NSC dynamics of 20 deciduous broad‐leaved trees corresponded well with measurements. NSC (i.e. product from photosynthesis) allocations depended on thermal time and the development stage of each organ. Water movements driven by water potential gradients in whole‐trees also influenced tree growth, which was a necessary process for predicting the daily NSC dynamics. Synthesis. This model combined wood growth at whole‐tree scale and carbohydrates balance at cellular scale with short‐term vegetation hydraulics. The model provides a foundation to further understand the complex interactions between environmental factors and tree carbon allocation.

Effects of Multiple Planting Densities on Lignin Metabolism and Lodging Resistance of the Strip Intercropped Soybean Stem

“…Carbohydrates produced by photosynthesis of leaves were significantly positively correlated with the lodging resistance index of the stem, which was important substances involved in metabolism, and their distribution in plants was affected by both individual development and environmental factors [70][71][72]. The previous study has found that the Nandou12 (shade-resistance variety) with high contents of cellulose, soluble sugar, sucrose, and starch could increase the lodging resistance index of stems of intercropped soybean seedling [21].…”

Using climate‐driven leaf phenology and growth to improve predictions of gross primary productivity in North American forests

“…The latest version, 2.0, coupled a NSC pool for predicting NSC production (i.e., photosynthesis), allocation (i.e., biomass growth and respiration), and storage (i.e., set as an NSC slow pool; Fang et al., 2020). Adding the NSC active and slow pools allowed each component of growth (i.e., structural carbon: leaf, wood, and fine root) to be decoupled in time from instantaneous GPP and NPP.…”

“…pool and the slow pool), the climate, and the growth potential (Fang et al, 2020;Schiestl-Aalto et al, 2015):…”

“…In FORCCHN2, the daily leaf growth (L G : kg C/m 2 ) is regulated by three factors-the size of the NSC pool (i.e., the sum of the active pool and the slow pool), the climate, and the growth potential (Fang et al, 2020;Schiestl-Aalto et al, 2015):…”

“…Here we have revised a prognostic forest‐dynamic‐carbon model, FORCCHN V2.0 (Fang et al., 2020), to estimate the forest GPP across all of North America through the climate‐driven phenology and leaf growth simulations. We explicitly simulated short‐ and long‐term conditions and photosynthesis relationship, with a goal of improving both intra‐ and interannual predicted performance.…”