Data from: Phenotypic drought stress prediction of European beech (Fagus sylvatica) by genomic prediction and remote sensing

Current climate change species response models usually do not include evolution. We integrated remote sensing with population genomics to improve phenotypic response prediction to drought stress in the key forest tree species European beech (Fagus sylvatica L.). We used whole-genome sequencing of pooled DNA from natural stands along an ecological gradient from humid-cold to warm-dry climate. We phenotyped stands for leaf area index (LAI) and moisture stress index (MSI) for the period 2016–2022. We predicted this data with matching meteorological data and a newly developed genomic population prediction score in a Generalised Linear Model. Model selection showed that the addition of genomic prediction decisively increased the explanatory power. We then predicted the response of beech to future climate change under evolutionary adaptation scenarios. A moderate climate change scenario would allow persistence of adapted beech forests, but not worst-case scenarios. Our approach can thus guide mitigation measures, such as allowing natural selection or proactive evolutionary management.

现有的气候变化物种响应模型通常未纳入演化维度。本研究将遥感技术与种群基因组学相结合，旨在优化关键林木物种欧洲山毛榉（Fagus sylvatica L.，European beech）对干旱胁迫的表型响应预测能力。本研究采集了沿湿润冷凉至温暖干旱气候生态梯度分布的天然林分的混合DNA并开展全基因组测序，同时于2016至2022年间对各林分的叶面积指数（Leaf Area Index, LAI）与水分胁迫指数（Moisture Stress Index, MSI）开展表型测定。本研究结合匹配的气象数据与新开发的基因组种群预测评分，通过广义线性模型（Generalised Linear Model, GLM）对上述数据进行拟合预测。模型选择结果表明，引入基因组预测变量可显著提升模型的解释效力。随后，本研究针对不同演化适应情景，预测了欧洲山毛榉对未来气候变化的响应模式：适度气候变化情景下，经适应性演化的欧洲山毛榉林可实现存续，但在最坏情景下则无法达成该目标。因此，本研究方法可为气候变化缓解措施制定提供参考，例如允许自然选择或开展前瞻性演化管理。