The temperature response of photosynthesis is one of the key factors determining predicted responses to warming in global vegetation models (GVMs). The response may vary geographically, owing to genetic adaptation to climate, and temporally, as a result of acclimation to changes in ambient temperature. Our goal was to develop a robust quantitative global model representing acclimation and adaptation of photosynthetic temperature responses. We quantified and modelled key mechanisms responsible for photosynthetic temperature acclimation and adaptation using a global dataset of photosynthetic CO 2 response curves, including data from 141 C 3 species from tropical rainforest to Arctic tundra. We separated temperature acclimation and adaptation processes by considering seasonal and common-garden datasets, respectively. The observed global variation in the temperature optimum of photosynthesis was primarily explained by biochemical limitations to photosynthesis, 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 showed that it predicted the observed global variation in optimal temperatures with high accuracy. This novel algorithm should enable improved prediction of the function of global ecosystems in a warming climate.
Bibliographical noteFunding Information:
This research was supported by a Western Sydney University PhD scholarship to DPK. AR was supported by the Next Generation Ecosystem Experiments (NGEE Arctic) project, which is supported by the Office of Biological and Environmental Research in the United States Department of Energy (DOE), Office of Science, and through the United States Department of Energy contract no. DE-SC0012704 to Brookhaven National Laboratory. KYC was supported by an Australian Research Council DECRA (DE160101484). DAW acknowledges an NSERC Discovery grant and funding from the Hawkesbury Institute Research Exchange Program. JU, LT and GW were supported by the Swedish strategic research area BECC (Biodiversity and Ecosystem Services in a Changing Climate; www.becc.lu.se). JQC was supported by the NGEE-Tropics, United States DOE. MDK was supported by Australian Research Council Centre of Excellence for Climate Extremes (CE170100023). MS was supported by an Earl S Tupper postdoctoral fellowship. AMJ and JMW were supported by the Biological and Environmental Research Program in the Office of Science, United States DOE under contract DEAC05-00OR22725. MAC was supported by United States DOE grant DE-SC-0011806 and USDA Forest Service 13-JV-11120101-03. Several of the Eucalyptus datasets included in this study were supported by the Australian Commonwealth Department of the Environment or Department of Agriculture, and the Australian Research Council (including DP140103415). We are grateful to Jens Kattge, Yan Shih-Lin, Alida C. Mau and Remko Duursma for useful discussions.
© 2018 The Authors. New Phytologist © 2018 New Phytologist Trust
- AC curves
- climate of origin
- global vegetation models (GVMs)
- growth temperature
- maximum carboxylation capacity
- maximum electron transport rate