Needle‐ and Canopy‐Level Genetic Variation in Scots Pine ( Pinus sylvestris L.) Revealed by Hyperspectral Phenotyping Across Sites and Seasons

Abstract

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.

Plant phenotyping relevance

針葉および林冠レベルのハイパースペクトル測定を用いて植物の機能形質を評価し、育種への再利用可能性を検討しており、フェノタイピング手法の適用が中心的です。

Code and data availability

The paper's Data Availability Statement points to a public Figshare deposit (DOI 10.6084/m9.figshare.27134907.v2) containing the data supporting the study's hyperspectral phenotyping and genetic analyses. This URL is in the allowed list and the identifier occurs verbatim in the quote. No separate author analysis code,