RESUMO
Drought is one of the main factors contributing to tree mortality worldwide and drought events are set to become more frequent and intense in the face of a changing climate. Quantifying water stress of forests is crucial in predicting and understanding their vulnerability to drought-induced mortality. Here, we explore the use of high-resolution spectroscopy in predicting water stress indicators of two native Australian tree species, Callitris rhomboidea and Eucalyptus viminalis. Specific spectral features and indices derived from leaf-level spectroscopy were assessed as potential proxies to predict leaf water potential (Ψleaf), equivalent water thickness (EWT) and fuel moisture content (FMC) in a dedicated laboratory experiment. New spectral indices were identified that enabled very high confidence linear prediction of Ψleaf for both species (R2 > 0.85) with predictive capacity increasing when accounting for a breakpoint in the relationships using segmented regression (E. viminalis, R2 > 0.89; C. rhomboidea, R2 > 0.87). EWT and FMC were also linearly predicted to a high accuracy (E. viminalis, R2 > 0.90; C. rhomboidea, R2 > 0.80). This study highlights the potential of spectroscopy as a tool for predicting measures of plant water noninvasively, enabling broader applications for monitoring and managing plant water stress.
RESUMO
Crustose coralline algae (CCA) are a highly diverse group of habitat-forming, calcifying red macroalgae (Rhodophyta) with unique adaptations to diverse irradiance regimes. A distinctive CCA phenotype adaptation, which allows them to maximize photosynthetic performance in low light, is their content of a specific group of light-harvesting pigments called phycobilins. In this study, we assessed the potential of noninvasive hyperspectral imaging (HSI) in the visible spectrum (400-800 nm) to describe the phenotypic variability in phycobilin content of an Antarctic coralline, Tethysphytum antarcticum (Hapalidiales), from two distinct locations. We validated our measurements with pigment extractions and spectrophotometry analysis, in addition to DNA barcoding using the psbA marker. Targeted spectral indices were developed and correlated with phycobilin content using linear mixed models (R2 = 0.64-0.7). Once applied to the HSI, the models revealed the distinct phycoerythrin spatial distribution in the two site-specific CCA phenotypes, with thin and thick crusts, respectively. This study advances the capabilities of hyperspectral imaging as a tool to quantitatively study CCA pigmentation in relation to their phenotypic plasticity, which can be applied in laboratory studies and potentially in situ surveys using underwater hyperspectral imaging systems.