Abstract
As an essential species across European forests, Scots pine (Pinus sylvestris L.) plays a vital ecological and economic role, yet its physiological variability underlying its adaptive potential remains underexplored. Understanding this intraspecific variability is crucial for uncovering the genetic basis of adaptation. Traditional genetic evaluations require large sample sizes and are time-consuming, whereas hyperspectral sensing/imaging enables rapid, nondestructive assessment of physiological traits across many individuals, facilitating more efficient exploration of adaptive variation. We assessed needle functional traits (NFTs) linked to foliar structure, water content, and pigment composition in clonal seed orchards over two seasons, integrating hyperspectral measurements at needle and canopy levels with genotyping using a new 50 K single-nucleotide polymorphism (SNP) array. Linear mixed models revealed substantial genetic variation, with the carotenoid-to-total-chlorophyll ratio showing the highest heritability (0.29) among pigment traits, and structural/water-related traits reaching heritability values up to 0.38. Significant genetic correlations were observed between stress-related traits (pigment content, equivalent water thickness) and reflectance, suggesting that spectral traits could serve as proxies for indirect selection of adaptive traits or in breeding programs. Low genotype-by-environment interaction and stable clonal performance across years further underscore the reliability of these traits for identifying resilient genotypes. Overall, our findings highlight hyperspectral phenotyping and NFTs as promising tools for accelerating climate-adaptive breeding in Scots pine.