Abstract
The temperature response of photosynthesis is one of the key processes determining predicted responses to warming in global vegetation models (GVMs). It varies geographically, due to genetic adaptation, and temporally, due to acclimation to growth temperature. Our goal was to develop a robust quantitative model representing acclimation and adaptation of photosynthetic temperature responses globally.
We quantified and modelled key mechanisms responsible for photosynthetic temperature acclimation and adaptation using a global dataset of photosynthetic CO2 response curves including data from 141 tree species from tropics to Arctic tundra. We separated temperature acclimation and adaptation processes by considering seasonal and common-garden datasets.
The observed global variation in the temperature optimum of photosynthesis was primarily explained by changes in biochemistry, rather than stomatal conductance or respiration. We found acclimation to growth temperature to be a stronger driver of this variation, than adaptation to temperature at climate of origin.
We developed a summary model to represent photosynthetic temperature responses and adaptation and showed it predicts the observed global variation in optimal temperatures with high accuracy. These novel algorithms should enable improved prediction of the function of global forest ecosystems in a warming climate.
