Improving models of photosynthetic thermal acclimation: Which parameters are most important and how many should be modified?

Abstract

Photosynthetic temperature acclimation could strongly affect coupled vegetation-atmosphere feedbacks in the global carbon cycle, especially as the climate warms. Thermal acclimation of photosynthesis can be modelled as changes in the parameters describing the direct effect of temperature on photosynthetic capacity (i.e., activation energy, E_a ; deactivation energy, H_d ; entropy parameter, ΔS) or the basal value of photosynthetic capacity (i.e., photosynthetic capacity measured at 25°C). However, the impact of acclimating these parameters (individually or in combination) on vegetative carbon gain is relatively unexplored. Here we compare the ability of 66 photosynthetic temperature acclimation scenarios to improve the ability of a spatially explicit canopy carbon flux model, MAESTRA, to predict eddy covariance data from a loblolly pine forest. We show that: (1) incorporating seasonal temperature acclimation of basal photosynthetic capacity improves the model's ability to capture seasonal changes in carbon fluxes and outperforms acclimation of other single factors (i.e., E_a or ΔS alone); (2) multifactor scenarios of photosynthetic temperature acclimation provide minimal (if any) improvement in model performance over single factor acclimation scenarios; (3) acclimation of E_a should be restricted to the temperature ranges of the data from which the equations are derived; and (4) model performance is strongly affected by the H_d parameter. We suggest that a renewed effort be made into understanding whether basal photosynthetic capacity, E_a , H_d and ΔS co-acclimate across broad temperature ranges to determine whether and how multifactor thermal acclimation of photosynthesis occurs.

Keywords: J max; V cmax; Eddy covariance; climate change; modified Arrhenius equation; scaling; seasonality; temperature.