Management of forest genetics is shifting from a paradigm focused on increasing timber volume to a prioritization of climate adaptation. Functional traits related to foliar structure, photosynthetic and photoprotective pigments, and stress underlie climate adaptation and have spectral signatures that can be quantified with remote sensing. Common-garden trials present an opportunity to assess the genetic basis of multispectral reflectance dynamics across genotypes. We analyzed multitemporal drone remote sensing of 1350 individual trees from 88 populations from diverse geographic and climatic provenances in a provenance trial of interior spruce (Picea engelmannii, P. glauca, and their hybrids) to assess patterns of genetic differentiation, local adaptation to climate, and hybridization from multispectral reflectance. We quantified early-summer, mid-summer, late-summer, and late-winter multispectral vegetation indices for each population and derived variables describing changes in these indices during winter-to-summer photosynthetic green-up and early-to-late-summer decline. Spectral traits revealed moderate population differentiation (Vpop = 14.4 % — 39.9 %) and significant (P < .005) patterns of local adaptation to provenance warmest-month temperature and elevation. Derived green-up and decline indices revealed additional relationships for coldest-month temperature, date of first frost, precipitation-as-snow, and climatic moisture deficit. Principal components described leaf area greenness, the magnitude of green-up, and seasonal decline in the red edge. Hierarchical clustering of these principal components identified eight geographically and climatically distinct clusters which captured major patterns in hybridization. Seasonal dynamics of vegetation indices, assessed with multitemporal drone remote sensing, can identify important patterns in hybridization and adaptation to climate which are not evident from spectral reflectance assessed at one time of year. These dynamic spectral traits have the potential to quantify the functional basis of local adaptation in common-garden trials and facilitate the selection of resilient genotypes for future climates.

中文翻译：

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使用多光谱无人机图像进行表型分析的季节性植被动态：室内云杉 （Picea engelmannii × glauca） 公共花园试验中的遗传分化、气候适应和杂交


森林遗传学管理正在从专注于增加木材量的范式转变为优先考虑气候适应。与叶面结构、光合和光保护色素以及胁迫相关的功能性状是气候适应的基础，并且具有可以通过遥感量化的光谱特征。共同花园试验为评估跨基因型的多光谱反射动力学的遗传基础提供了机会。在室内云杉（Picea engelmannii、P. glauca 及其杂交种）的原产地试验中，我们分析了来自不同地理和气候来源的 88 个种群的 1350 棵单树的多时相无人机遥感，以评估遗传分化模式、对气候的局部适应和多光谱反射的杂交。我们量化了每个种群的初夏、仲夏、夏末和冬末多光谱植被指数，并得出了描述冬至夏光合绿化和夏初至夏末衰退期间这些指数变化的变量。光谱性状揭示了中等种群分化 （Vpop = 14.4 % — 39.9 %） 和显著 （P < .005） 本地对种源、最热月份温度和海拔的适应模式。得出的绿化和下降指数揭示了最冷月份温度、第一次霜冻日期、降水为雪和气候水分不足的额外关系。主成分描述了叶面积的绿度、绿化幅度和红边的季节性下降。这些主成分的分层聚类确定了八个地理和气候上不同的聚类，它们捕获了杂交中的主要模式。 使用多时相无人机遥感评估植被指数的季节性动态可以识别杂交和适应气候的重要模式，这些模式在一年中的某个时间评估的光谱反射率中并不明显。这些动态光谱特征有可能量化公共花园试验中局部适应的功能基础，并有助于为未来气候选择弹性基因型。