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

Leaf trait relationships are widely used to predict ecosystem function in terrestrial biosphere models (TBMs), in which leaf maximum carboxylation capacity (V-c,V-max), an important trait for modelling photosynthesis, can be inferred from other easier-to-measure traits. However, whether trait-V-c,V-max relationships are robust across different forest types remains unclear. Here we used measurements of leaf traits, including one morphological trait (leaf mass per area), three biochemical traits (leaf water content, area-based leaf nitrogen content, and leaf chlorophyll content), one physiological trait (V-c,V-max), as well as leaf reflectance spectra, and explored their relationships within and across three contrasting forest types in China. We found weak and forest type-specific relationships between V-c,V-max and the four morphological and biochemical traits (R-2 <= 0.15), indicated by significantly changing slopes and intercepts across forest types. By contrast, reflectance spectroscopy effectively collapsed the differences in the trait-V-c,V-max relationships across three forest biomes into a single robust model for V-c,V-max (R-2 = 0.77), and also accurately estimated the four traits (R-2 = 0.75-0.94). These findings challenge the traditional use of the empirical trait-V-c,V-max relationships in TBMs for estimating terrestrial plant photosynthesis, but also highlight spectroscopy as an efficient alternative for characterising V-c,V-max and multitrait variability, with critical insights into ecosystem modelling and functional trait ecology.

Automated summary

The relationships between leaf maximum carboxylation capacity (Vc,Vmax) and other morphological and biochemical traits were found to be weak and specific to forest types, challenging the traditional use of these relationships in terrestrial biosphere models. Reflectance spectroscopy, however, effectively collapsed differences in these relationships across forest types into a single robust model for Vc,Vmax and accurately estimated multiple traits, providing critical insights for ecosystem modeling and functional trait ecology.