Summary 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 CO2 response curves, including data from 141 C3 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.

Earth system models (ESMs) use photosynthetic capacity, indexed by the maximum Rubisco carboxylation rate (V cmax), to simulate carbon assimilation and typically rely on empirical estimates, including an assumed dependence on leaf nitrogen determined from soil fertility. In contrast, new theory, based on biochemical coordination and co‐optimization of carboxylation and water costs for photosynthesis, suggests that optimal V cmax can be predicted from climate alone, irrespective of soil fertility. Here, we develop this theory and find it captures 64% of observed variability in a global, field‐measured V cmax dataset for C3 plants. Soil fertility indices explained substantially less variation (32%). These results indicate that environmentally regulated biophysical constraints and light availability are the first‐order drivers of global photosynthetic capacity. Through acclimation and adaptation, plants efficiently utilize resources at the leaf level, thus maximizing potential resource use for growth and reproduction. Our theory offers a robust strategy for dynamically predicting photosynthetic capacity in ESMs.

Key words: A-C i curve, leaf respiration during the day (R day ), maximum carboxylation rate (V cmax ), net photosynthetic rate at saturating irradiance and at ambient atmospheric CO 2 concentration (A sat ). SummarySimulations of photosynthesis by terrestrial biosphere models typically need a specification of the maximum carboxylation rate (V cmax ). Estimating this parameter using A-C i curves (net photosynthesis, A, vs intercellular CO 2 concentration, C i ) is laborious, which limits availability of V cmax data. However, many multispecies field datasets include net photosynthetic rate at saturating irradiance and at ambient atmospheric CO 2 concentration (A sat ) measurements, from which V cmax can be extracted using a 'one-point method'.We used a global dataset of A-C i curves (564 species from 46 field sites, covering a range of plant functional types) to test the validity of an alternative approach to estimate V cmax from A sat via this 'one-point method'.If leaf respiration during the day (R day ) is known exactly, V cmax can be estimated with an r 2 value of 0.98 and a root-mean-squared error (RMSE) of 8.19 lmol m À2 s À1 . However, R day typically must be estimated. Estimating R day as 1.5% of V cmax, we found that V cmax could be estimated with an r 2 of 0.95 and an RMSE of 17.1 lmol m À2 s À1 . The one-point method provides a robust means to expand current databases of fieldmeasured V cmax , giving new potential to improve vegetation models and quantify the environmental drivers of V cmax variation.