An optimality-based model explains seasonal variation in C3 plant photosynthetic capacity

Abstract

The maximum rate of carboxylation (V-cmax) is an essential leaf trait determining the photosynthetic capacity of plants. Existing approaches for estimatingV(cmax)at large scale mainly rely on empirical relationships with proxies such as leaf nitrogen/chlorophyll content or hyperspectral reflectance, or on complicated inverse models from gross primary production or solar-induced fluorescence. A novel mechanistic approach based on the assumption that plants optimize resource investment coordinating with environment and growth has been shown to accurately predict C3 plantV(cmax)based on mean growing season environmental conditions. However, the ability of optimality theory to explain seasonal variation inV(cmax)has not been fully investigated. Here, we adapt an optimality-based model to simulate dailyV(cmax,25C)(V(cmax)at a standardized temperature of 25 degrees C) by incorporating the effects of antecedent environment, which affects current plant functioning, and dynamic light absorption, which coordinates with plant functioning. We then use seasonalV(cmax,25C)field measurements from 10 sites across diverse ecosystems to evaluate model performance. Overall, the model explains about 83% of the seasonal variation in C3 plantV(cmax,25C)across the 10 sites, with a medium root mean square error of 12.3 mu mol m(-2) s(-1), which suggests that seasonal changes inV(cmax,25C)are consistent with optimal plant function. We show that failing to account for acclimation to antecedent environment or coordination with dynamic light absorption dramatically decreases estimation accuracy. Our results show that optimality-based approach can accurately reproduce seasonal variation in canopy photosynthetic potential, and suggest that incorporating such theory into next-generation trait-based terrestrial biosphere models would improve predictions of global photosynthesis.