Acclimation and adaptation components of the temperature dependence of plant photosynthesis at the global scale

Kumarathunge DP1, Medlyn BE1, Drake JE2 and Tjoelker MG1

  1. Hawkesbury Institute for the Environment, Hawkesbury Campus, Western Sydney University, Locked Bag 1797, Penrith NSW 2751, Australia.
  2. Forest and Natural Resource Management, SUNY-ESF, 310B Bray Hall, 1 Forestry Drive, Syracuse, NY 13210, USA.

Temperature dependence of leaf photosynthesis (An-T response) is a key determinant in modelling plant growth. Hence, the way that any Earth System Model (ESM) handles the An-T response is critical. It is known that there are differences in the optimum temperature for net photosynthesis (Topt) across species. However, it is unknown how much each of the underlying component processes (biochemical, stomatal and respiratory) contribute to these differences in optimum. Additionally, it is unknown whether differences across species are largely genetic (adaptation) or plastic (acclimation). In this study, we hypothesise that Topt is more strongly related to climate of origin than growth environment, and that all three component processes contribute to differences in Topt. 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.