Terrestrial photosynthesis is the largest flux of carbon dioxide between the atmosphere and the Earth’s surface. Photosynthesis is a dynamic process that shows strong acclimation to environmental conditions, making predictions difficult under non-stable environments. Optimality theory provides an avenue for predicting photosynthetic acclimation and a recently developed model has shown that photosynthetic rates are optimized to environmental conditions at large spatial scales. Here, we present multiple studies using the theory as a null model to help better understand acclimation processes over space and time. Specifically, we address four classical questions in plant ecophysiology: (1) What is the mechanism driving photosynthetic downregulation under elevated CO2? (2) What causes seasonal shifts in photosynthesis? (3) Is photosynthetic capacity driven by soil nutrient availability or photosynthetic demand? (4) Under what conditions is it better to have C3 or C4 metabolism? To answer each question, we compare responses predicted from optimality to responses measured in observational and manipulation studies.
Results/Conclusions
With regard to question 1, we find that downregulation of photosynthesis can be primarily explained by an optimal downregulation of Rubisco carboxylation capacity, without consideration of nutrient availability constraints. This result runs counter to predictions from the progressive nutrient limitation hypothesis. However, we find that, under elevated CO2, plants still seem to overinvest in carboxylation, resulting in rates of photosynthesis that are consistently light limited, an effect unexplained by theory. We find that seasonal shifts in photosynthesis can be well explained by changes in light availability and temperature alone, indicating that plants possess the capacity to acclimate quickly to seasonal environmental change. Using a nitrogen and light manipulation study, we show that photosynthetic capacity is determined by photosynthetic demand, as predicted by optimality. Nutrient availability, on the other hand, influences non-photosynthetic process such as growth and storage. This resolves the mechanisms underlying biomass stimulation by nitrogen. Finally, we develop and test a new optimality model for C4 photosynthesis and show C4 photosynthesis shows the strongest advantage over C3 photosynthesis at high temperatures, rather than high light or low CO2. These results demonstrate the power of optimality theory for elucidating the mechanisms underlying plant physiological process responses to variable environmental conditions.