Spatial heterogeneity in long term forestry genetics trials can obscure genetic variation and influence conclusions made based on the data. Nearest neighbour adjustment methods have been employed to account for spatial patterns, however such methods are limited in their applicability to longitudinal provenance trial data because of the need to account for repeated measures as well as the interest in preserving among-site variation. In this study, a novel approach combining nearest neighbour adjustment techniques with longitudinal data analysis concepts was developed to adjust lodgepole pine ( var. Douglas) provenance trial data for spatial heterogeneity. Individual height-age logistic growth curves were fit to each tree to age 35, and the horizontal asymptote parameters of the height-age curves were adjusted for spatial patterns using iterative nearest neighbour adjustments. The methods developed in this study successfully removed positive spatial correlation and reduced the interaction between block and provenance, thus reducing changes in population ranking among blocks within sites. The adjustments applied caused large shifts in population ranks at many sites, indicating that seed selection decisions based on data not adjusted for spatial heterogeneity could lead to selecting non-optimal populations for a given site. This research provides a methodological framework for adjusting longitudinal genetics trial data for underlying site conditions and spatial patterns. Additionally, a spatially adjusted version of a large and extensively utilized provenance trial dataset was created for use in future analyses.

中文翻译：

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迭代最近邻年龄-身高曲线调整：解决空间异质性对纵向林业种源试验数据的影响

长期林业遗传学试验中的空间异质性可能会掩盖遗传变异并影响基于数据得出的结论。最近邻调整方法已被用来解释空间模式，然而，由于需要考虑重复测量以及保留站点间差异的兴趣，此类方法对纵向来源试验数据的适用性受到限制。在本研究中，开发了一种将最近邻调整技术与纵向数据分析概念相结合的新方法，用于调整黑松（道格拉斯变种）种源试验数据的空间异质性。将每棵树的个体高度-年龄逻辑生长曲线拟合到35岁，并且使用迭代最近邻调整来调整高度-年龄曲线的水平渐近线参数以适应空间模式。本研究开发的方法成功地消除了正空间相关性，并减少了区块与来源之间的相互作用，从而减少了地点内区块之间人口排名的变化。所应用的调整导致许多地点的种群排名发生巨大变化，这表明基于未针对空间异质性进行调整的数据的种子选择决策可能会导致为给定地点选择非最佳种群。这项研究提供了一个方法框架，用于调整纵向遗传学试验数据以适应潜在的场地条件和空间模式。此外，还创建了一个大型且广泛使用的起源试验数据集的空间调整版本，以供将来的分析使用。