The leaf-scale mass-based photosynthetic optimization model better predicts photosynthetic acclimation than the area-based

Abstract

Background and Aims Leaf-scale photosynthetic optimization models can quantitatively predict photosynthetic acclimation and have become important means of improving vegetation and land surface models. Previous models have generally been based on the optimality assumption of maximizing the net photosynthetic assimilation per unit leaf area (i.e., the area-based optimality), while overlooking other optimality assumption such as maximizing the net photosynthetic assimilation per unit leaf dry mass (i.e., the mass-based optimality). Methods This paper compares the predicted results of photosynthetic acclimation to different environmental conditions between the area-based optimality and the mass-based optimality models. The predictions are then verified using the observational data from the literatures. Key Results The mass-based optimality model better predicted photosynthetic acclimation to growth light intensity, air temperature and CO2 concentration, and captured more variability in photosynthetic traits than the area-based optimality models. Conclusions The findings suggest that the mass-based optimality approach may be a promising strategy for improving the predictive power and accuracy of optimization models, which have been widely used in various studies related to plant carbon issues.