Predicting drought vulnerability with leaf reflectance spectra in Amazonian trees

Hydraulic traits mediate trade-offs between growth and mortality in plants yet characterizing these traits at the community level remains challenging, particularly in the Amazon, where they vary widely across species and environments. While previous studies have used reflectance-based estimates, hydraulic traits, which arise from wood and/or whole-plant anatomy and physiology, have not been comprehensively explored.For the first time, we comprehensively investigated the use of leaf reflectance to predict hydraulic traits alongside leaf functional traits in tropical evergreen and deciduous trees. For 196 Amazonian trees, we measured water potential, leaf mass per area (LMA), leaf reflectance, hydraulic conductivity curves (e.g., P50), and wood density (WD). We examined the relationships between leaf reflectance and traits using partial least square regression (PLSR).Our findings indicate that leaf reflectance accurately predicts variation in LMA (R² = 0.8), and reasonably estimates xylem water potential (R² = 0.51) and WD (R² = 0.52). However, P50 predictions were much less reliable (R² = 0.27), with water absorption bands greatly influencing the PLSR model. Leaf phenological strategy had little impact on the results.These findings suggest that reflectance-based remote sensing could monitor water status and forest carbon dynamics through water potential and wood density, respectively. However, our case study applying the PLSR approach to hyperspectral canopy spectra to predict wood density revealed challenges to upscaling. Despite these limitations, remote sensing of forest hydraulic traits at scale could enhance our understanding of drought vulnerability and carbon dynamics in Amazonian forests, with significant implications for conservation.