Coordination of Leaf Structural and Chemical Traits in Predicting Photosynthetic Capacity of Woody Plants in Subtropical Evergreen Broadleaf Forests

Functional plant traits are essential indicators of biodiversity conservation and ecosystem management. Understanding the relationships among leaf traits allows for the estimation of difficult-to-measure traits from those that are easier to quantify and widely distributed. This approach enhances the ability to analyze trait variation, identify scaling relationships, and address constraints in estimating plant-atmosphere carbon exchange. However, research on the correlation between photosynthetic capacity and leaf traits remains limited. Therefore, this study aims to explore the differences and correlations between leaf traits and photosynthetic capacity parameters across different growth forms. In this study, the leaf traits in 21 dominant woody species from a subtropical evergreen broad-leaved forest in southwestern China were assessed. Key photosynthetic parameters, including the maximum net photosynthetic rate, apparent quantum yield, light compensation point, and light saturation point, were investigated. Additionally, leaf traits such as leaf tissue density, leaf dry matter content, specific leaf area (SLA), leaf area, chlorophyll content, carbon content per unit leaf area, nitrogen content per unit leaf area (Narea), and carbon-to-nitrogen ratio were analyzed. Overall, no significant variations in maximum photosynthetic rate or photosynthetic quantum efficiency were observed between tree and shrub species in this forest. Shrub species exhibited greater adaptability and compensatory capacity to light conditions during photosynthesis. Using multiple linear regression models, SLA was identified as the key structural trait and Narea as the primary chemical trait for predicting the photosynthetic capacity of woody plants in this region. Nevertheless, for different growth forms, selecting optimal parameters for classification modeling in abiotic predictive models of photosynthetic capacity is recommended to improve prediction accuracy for subtropical evergreen broad-leaved forest plants.