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

Area of Science:

• Ecology
• Plant Physiology
• Remote Sensing

Background:

• Leaf traits are crucial for predicting ecosystem function in terrestrial biosphere models (TBMs).
• Leaf maximum carboxylation capacity (V_c,max) is a key trait for photosynthesis modeling, often inferred from other traits.
• The robustness of trait-V_c,max relationships across diverse forest types is not well understood.

Purpose of the Study:

• To investigate the consistency of relationships between V_c,max and various leaf traits across different forest types.
• To evaluate the effectiveness of reflectance spectroscopy in estimating V_c,max and other leaf traits.
• To challenge traditional trait-based approaches in TBMs and propose alternatives.

Main Methods:

• Collected data on leaf traits (morphological, biochemical, physiological) and reflectance spectra from three contrasting Chinese forest types.
• Analyzed trait-V_c,max relationships within and across forest types.
• Developed models using reflectance spectroscopy to estimate V_c,max and other leaf traits.

Main Results:

• Found weak and forest type-specific relationships between V_c,max and traditional leaf traits (R² ≤ 0.15).
• Demonstrated that reflectance spectroscopy established a single, robust model for V_c,max across forest biomes (R² = 0.77).
• Showcased spectroscopy's accuracy in estimating multiple leaf traits (R² = 0.75–0.94).

Conclusions:

• Empirical trait-V_c,max relationships used in TBMs may be unreliable for estimating terrestrial plant photosynthesis.
• Reflectance spectroscopy provides an efficient and robust method for characterizing V_c,max and multitrait variability.
• Spectroscopy offers critical insights for improving ecosystem modeling and functional trait ecology.