The Leaf-Scale Mass-Based Photosynthetic Optimization Model Better Predicts Photosynthetic Acclimation Than the Area-Based

Leaf-scale photosynthetic optimization models can quantitatively predict photosynthetic acclimation and have become an 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 assumptions such as maximizing the net photosynthetic assimilation per unit leaf dry mass (i.e. the mass-based optimality). 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. 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. 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. To address the debate on whether photosynthetic optimization models should assume that leaves optimize their net carbon assimilation per unit leaf area or per unit leaf dry mass, we developed models based on the area-based optimality and the mass-based optimality. We then compared the model predictions with the data from the literature. We present the first evidence that the mass-based optimality model outperforms the area-based optimality models in predicting photosynthetic acclimation to growth light intensity, air temperature and CO2 concentration. The mass-based optimality model captures more variability in leaf photosynthetic traits than the area-based optimality models. The results suggest that the mass-based optimality model may be a promising method for improving predictions of photosynthetic traits in different environments and under future climate change.