Optimal Seed Deployment Under Climate Change Using Spatial Models: Application to Loblolly Pine in the Southeastern US

Provenance tests are a common tool in forestry designed to identify superior genotypes for planting at specific locations. The trials are replicated experiments established with seed from parent trees collected from different regions and grown at several locations. In this work, a Bayesian spatial approach is developed for modeling the expected relative performance of seed sources using climate variables as predictors associated with the origin of seed source and the planting site. The proposed modeling technique accounts for the spatial dependence in the data and introduces a separable Matérn covariance structure that provides a flexible means to estimate effects associated with the origin and planting site locations. The statistical model was used to develop a quantitative tool for seed deployment aimed to identify the location of superior performing seed sources that could be suitable for a specific planting site under a given climate scenario. Cross-validation results indicate that the proposed spatial models provide superior predictive ability compared to multiple linear regression methods in unobserved locations. The general trend of performance predictions based on future climate scenarios suggests an optimal assisted migration of loblolly pine seed sources from southern and warmer regions to northern and colder areas in the southern USA. Supplementary materials for this article are available online.

种源试验（Provenance tests）是林业领域常用的研究工具，旨在筛选适配特定立地的优良基因型。此类试验为重复实验，采用从不同区域采集的母树种子建立，并在多个立地开展种植。
本研究开发了一种贝叶斯空间建模方法，以与种源起源及种植立地相关的气候变量作为预测因子，对种源的预期相对表现进行建模。所提出的建模方法可考量数据中的空间相关性，并引入可分离的Matérn协方差结构，为估算与种源起源及种植立地相关的效应提供了灵活手段。
该统计模型被用于开发一套种源调配量化工具，旨在识别在给定气候情景下，适配特定种植立地的优良表现种源的分布位置。交叉验证结果表明，相较于多元线性回归方法，所提出的空间模型在未观测立地上具备更优异的预测性能。
基于未来气候情景的表现预测总体趋势显示，美国南部地区火炬松（loblolly pine）种源的最优辅助迁移路径为从温暖的南部区域迁移至更偏北且更寒冷的区域。本文补充材料可在线获取。