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Abstract Pigment dynamics in temperate evergreen forests remain poorly characterized, despite their year-round photosynthetic activity and importance for carbon cycling. Developing rapid, nondestructive methods to estimate pigment composition enables high-throughput assessment of plant acclimation states. In this study, we investigate the seasonality of eight chlorophyll and carotenoid pigments and hyperspectral reflectance data collected at both the needle (400–2400 nm) and canopy (420–850 nm) scales in Pinus palustris (longleaf pine) at the Ordway Swisher Biological Station in north-central Florida, USA. Needle spectra were obtained at three distinct times throughout the year, while tower-based spectra were collected continuously over a nine-month period. Seasonal trends in photoprotective pigments (e.g., lutein and xanthophylls) and photosynthetic pigments (e.g., chlorophylls) aligned closely with seasonal changes in photosynthetically active radiation and gross primary productivity. To track inter-tree and seasonal variability in pigment pools with hyperspectral reflectance data, we used correlation analyses and ridge regression models. Ridge regression models using the full hyperspectral range outperformed predictions using standard linear regression with specific wavelengths in a normalized difference index fashion. Ridge regression successfully predicted all pigment pools (R2 > 0.5) with comparable accuracy at both the needle and canopy scales. The models performed best for lutein, neoxanthin, antheraxanthin, and chlorophyll a and b - which had greater inter-tree and seasonal variation - and achieved moderate accuracy for violaxanthin, alpha-carotene, and beta-carotene. These results provide a foundation for scaling biochemical traits from ground-based sensors to airborne and satellite platforms, particularly in ecosystems with subtle changes in pigment dynamics. 

Abstract Plants differ widely in how soil drying affects stomatal conductance (g_s) and leaf water potential (ψ_leaf), and in the underlying physiological controls. Efforts to breed crops for drought resilience would benefit from a better understanding of these mechanisms and their diversity. We grew 12 diverse genotypes of common bean (Phaseolus vulgarisL.) and four of tepary bean (P. acutifolius;a highly drought resilient species) in the field under irrigation and post‐flowering drought, and quantified responses ofg_sandψ_leaf, and their controls (soil water potential [ψ_soil], evaporative demand [Δw] and plant hydraulic conductance [K]). We hypothesised that (i) common beans would be more “isohydric” (i.e., exhibit strong stomatal closure in drought, minimisingψ_leafdecline) than tepary beans, and that genotypes with largerψ_leafdecline (more “anisohydric”) would exhibit (ii) smaller increases in Δw, due to less suppression of evaporative cooling by stomatal closure and hence less canopy warming, but (iii) largerKdeclines due toψ_leafdecline. Contrary to our hypotheses, we found that half of the common bean genotypes were similarly anisohydric to most tepary beans; canopy temperature was cooler in isohydric genotypes leading to smaller increases in Δwin drought; and that stomatal closure andKdecline were similar in isohydric and anisohydric genotypes.g_sandψ_leafwere virtually insensitive to drought in one tepary genotype (G40068). Our results highlight the potential importance of non‐stomatal mechanisms for leaf cooling, and the variability in drought resilience traits among closely related crop legumes. 

Abstract. Accurate assessment of leaf functional traits is crucial for a diverse range of applications from crop phenotyping to parameterizing global climate models. Leaf reflectance spectroscopy offers a promising avenue to advance ecological and agricultural research by complementing traditional, time-consuming gas exchange measurements. However, the development of robust hyperspectral models for predicting leaf photosynthetic capacity and associated traits from reflectance data has been hindered by limited data availability across species and environments. Here we introduce the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems. The GSTI repository currently encompasses over 7500 observations from 397 species and 41 sites gathered from 36 published and unpublished studies, thereby offering a key resource for developing and validating hyperspectral models of leaf photosynthetic capacity. The GSTI database is developed on GitHub (https://github.com/plantphys/gsti, last access: 4 January 2026) and published to ESS-DIVE https://doi.org/10.15485/2530733, Lamour et al., 2025). It includes gas exchange data, derived photosynthetic parameters, and key leaf traits often associated with traditional gas exchange measurements such as leaf mass per area and leaf elemental composition. By providing a standardized repository for data sharing and analysis, we present a critical step towards creating hyperspectral models for predicting photosynthetic traits and associated leaf traits for terrestrial plants.