Estimating CO₂ response in a mixed broadleaf forest using the dynamic assimilation technique

Estimating the CO₂ response of forest trees is of great significance in plant photosynthesis research. CO₂ response measurement is traditionally employed under steady state conditions. With the development of open-path gas exchange systems, the Dynamic Assimilation Technique (DAT), allows measurement under non-steady state conditions. This greatly improves the efficiency and data density of CO₂ response measurement. This study aims to assess the accuracy and effectiveness of different models in fitting DAT data, addressing the current gap in verification of model performance.

This research was conducted for three common broadleaf tree species (Ulmus macrocarpa, Fraxinus mandshurica, and Tilia amurensis) in North Eastern China. Among the three species, Fraxinus mandshurica is the most adapted to high CO₂ concentration conditions. Four models were compared, the rectangular hyperbola (RH) model, the Michaelis-Menten (MM) model, the modified rectangular hyperbola (MRH) model and a non-rectangular hyperbola (NRH) model.

Considering the model parsimony and parameter accuracy, the NRH model emerged as the best choice (R² = 0.9966, RMSE = 0.1862, AIC=-199.86). This study provides a reference for the further application of DAT in the field of photosynthesis.