Coupel-Ledru Aude, Pallas Benoît, Delalande Magalie, Boudon Frédéric, Carrié Emma, Martinez Sébastien, Regnard Jean-Luc, Costes Evelyne
UMR AGAP, Univ Montpellier, CIRAD, INRA, Montpellier SupAgro, 34398 Montpellier Cedex 5, France.
3Present Address: University of Bristol, School of Biological Sciences, Life Science Building, 24 Tyndall Avenue, Bristol, BS8 1TQ UK.
Hortic Res. 2019 May 1;6:52. doi: 10.1038/s41438-019-0137-3. eCollection 2019.
Despite previous reports on the genotypic variation of architectural and functional traits in fruit trees, phenotyping large populations in the field remains challenging. In this study, we used high-throughput phenotyping methods on an apple tree core-collection (1000 individuals) grown under contrasted watering regimes. First, architectural phenotyping was achieved using T-LiDAR scans for estimating convex and alpha hull volumes and the silhouette to total leaf area ratio (). Second, a semi-empirical index ( ) was computed from chlorophyll fluorescence measurements, as a proxy for leaf photosynthesis. Last, thermal infrared and multispectral airborne imaging was used for computing canopy temperature variations, water deficit, and vegetation indices. All traits estimated by these methods were compared to low-throughput in planta measurements. Vegetation indices and alpha hull volumes were significantly correlated with tree leaf area and trunk cross sectional area, while values showed strong correlations with photosynthesis measurements collected on an independent leaf dataset. By contrast, correlations between stomatal conductance and canopy temperature estimated from airborne images were lower, emphasizing discrepancies across measurement scales. High heritability values were obtained for almost all the traits except leaf photosynthesis, likely due to large intra-tree variation. Genotypic means were used in a clustering procedure that defined six classes of architectural and functional combinations. Differences between groups showed several combinations between architectural and functional traits, suggesting independent genetic controls. This study demonstrates the feasibility and relevance of combining multi-scale high-throughput methods and paves the way to explore the genetic bases of architectural and functional variations in woody crops in field conditions.
尽管此前已有关于果树结构和功能性状基因型变异的报道，但在田间对大量群体进行表型分析仍具有挑战性。在本研究中，我们对在不同水分管理条件下生长的苹果核心种质群体（1000个个体）采用了高通量表型分析方法。首先，利用T-LiDAR扫描进行结构表型分析，以估算凸包体积、α包体积以及轮廓与总叶面积比（）。其次，根据叶绿素荧光测量值计算出一个半经验指数（），作为叶片光合作用的替代指标。最后，利用热红外和多光谱航空成像来计算冠层温度变化、水分亏缺和植被指数。将这些方法估算的所有性状与低通量的植株内测量结果进行比较。植被指数和α包体积与树叶面积和树干横截面积显著相关，而值与在独立叶片数据集上收集的光合作用测量值呈现出强相关性。相比之下，根据航空图像估算的气孔导度与冠层温度之间的相关性较低，这凸显了不同测量尺度之间的差异。除叶片光合作用外，几乎所有性状均获得了较高的遗传力值，这可能是由于树内存在较大变异。基因型均值被用于聚类分析，定义了六类结构和功能组合。组间差异显示出结构和功能性状之间的多种组合，表明存在独立的遗传控制。本研究证明了结合多尺度高通量方法的可行性和相关性，并为在田间条件下探索木本作物结构和功能变异的遗传基础铺平了道路。
