Genotype-environment interaction is pervasive in forest genetics. Delineation of spatial breeding zones (BZs) is fundamental for accommodating genotype-environment interaction. Here we developed a BZ classification pipeline for the forest tree Eucalyptus globulus in 2 Australian regions based on phenotypic, genomic, and pedigree data, as well on a detailed environmental characterization (“envirotyping”) and spatial mapping of BZs. First, the factor analytic method was used to model additive genetic variance and site–site genetic correlations (r_B) in stem volume across 48 trials of 126,467 full-sib progeny from 2 separate breeding programs. Thirty-three trials were envirotyped using 145 environmental variables (EVs), involving soil and landscape (71), climate (73), and management (1) EVs. Next, sparse partial least squares-discriminant analysis was used to identify EVs that were required to predict classification of sites into 5 non-exclusive BZ classes based on r_B. Finally, these BZs were spatially mapped across the West Australian and “Green Triangle” commercial estates by enviromic prediction using EVs for 80 locations and 15 sets of observed climate data to represent temporal variation. The factor analytic model explained 85.9% of estimated additive variance. Our environmental classification system produced within-zone mean r_B between 0.76 and 0.84, which improves upon the existing values of 0.62 for Western Australia and 0.67 for Green Triangle as regional BZs. The delineation of 5 BZ classes provides a powerful framework for increasing genetic gain by matching genotypes to current and predicted future environments.

基因型与环境的相互作用在森林遗传学中普遍存在。空间育种区（BZ）的划分对于适应基因型与环境的相互作用至关重要。在这里，我们基于表型、基因组和谱系数据以及详细的环境特征（“环境分型”）和 BZ 的空间映射，为澳大利亚 2 个地区的森林树蓝桉开发了 BZ 分类流程。首先，在 2 个独立育种计划的 126,467 个全同胞后代的 48 项试验中，使用因子分析方法对茎体积的加性遗传方差和位点遗传相关性 ( r _{B ) 进行建模。}使用 145 个环境变量 (EV) 对 33 项试验进行了环境类型分析，涉及土壤和景观 (71)、气候 (73) 和管理 (1) EV。接下来，使用稀疏偏最小二乘判别分析来识别根据r _B预测站点分类为 5 个非排他性 BZ 类所需的 EV 。最后，通过环境预测，使用电动汽车对 80 个地点和 15 组观测到的气候数据来表示时间变化，对这些 BZ 进行了西澳大利亚和“绿色三角”商业地产的空间映射。因子分析模型解释了 85.9% 的估计加性方差。我们的环境分类系统产生的区域内平均值r _B介于 0.76 和 0.84 之间，这比西澳大利亚州现有值 0.62 和绿三角区域 BZ 值 0.67 有所改进。5 个 BZ 类别的划分提供了一个强大的框架，通过将基因型与当前和预测的未来环境相匹配来增加遗传增益。