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 estimating V 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 plant V cmax based on mean growing season environmental conditions. However, the ability of optimality theory to explain seasonal variation in V cmax has not been fully investigated. Here, we adapt an optimality-based model to simulate daily V cmax,25C (V cmax at a standardized temperature of 25°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 seasonal V 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 plant V cmax,25C across the 10 sites, with a medium root mean square error of 12.3 μmol m −2 s −1 , which suggests that seasonal changes in V 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.

Eco‐evolutionary optimality as a means to improve vegetation and land‐surface models

“…The model described in Box 2 predicts a number of related physiological characteristics correctly, including the global pattern of V cmax in relation to light, temperature and vapour pressure deficit (VPD) (Smith et al ., 2019), seasonal variations of V cmax across diverse ecosystems (Jiang et al ., 2020), elevational trends in photosynthetic traits and primary production (Peng et al ., 2020), and the response of V cmax to atmospheric CO 2 (Smith & Keenan, 2020). Specifically, the model predicts a decline in V cmax with increasing ambient CO 2 (Wang et al ., 2017), and a steeper increase with decreasing ambient CO 2 .…”

Spectroscopy outperforms leaf trait relationships for predicting photosynthetic capacity across different forest types

“…More recently, researchers have also explored the use of optimality approaches (e.g. Prentice et al ., 2014) that rely on the photosynthetic least‐cost theory (Wright et al ., 2003) to infer V c,max25 given ambient climate conditions (Wang et al ., 2017; Smith et al ., 2019; Jiang et al ., 2020). These various approaches vary in their accuracy and remain limited to the estimation of V c,max25 at the ecosystem scale rather than at the individual organism level.…”

Optimal model complexity for terrestrial carbon cycle prediction

“…Canopy phenology is sensitive to environmental conditions (e.g., Jolly et al, 2005;Forkel et al, 2015) and plant carbon economic constraints (e.g., Flack-Prain et al, 2021) driving interannual variation in leaf area dynamics. The growing season index (GSI) is a piecewise model linking canopy phenology to linear functions of day length, temperature, and vapor pressure deficit scaled 0-1 (GSI; Jolly et al, 2005).…”

“…Canopy phenology is sensitive to environmental conditions (e.g., Jolly et al, 2005;Forkel et al, 2015) and plant carbon economic constraints (e.g., Flack-Prain et al, 2021) driving interannual variation in leaf area dynamics. The growing season index (GSI) is a piecewise model linking canopy phenology to linear functions of day length, temperature, and vapor pressure deficit scaled 0-1 (GSI; Jolly et al, 2005). The GSI model was implemented in Smallman et al (2017) and augmented to include a requirement for new leaf area to lead to an increase in GPP greater than a critical threshold retrieved as part of CARDAMOM.…”