Abstract：
Methods using genomic information to forecast potential population maladaptation to climate change or new environments are becoming increasingly common, yet the lack of model validation poses serious hurdles toward their incorporation into management and policy. Here, we compare the validation of maladaptation estimates derived from two methods-Gradient Forests (GFoffset) and the risk of non-adaptedness (RONA)-using exome capture pool-seq data from 35 to 39 populations across three conifer taxa: two Douglas-fir varieties and jack pine. We evaluate sensitivity of these algorithms to the source of input loci (markers selected from genotype-environment associations [GEA] or those selected at random). We validate these methods against 2- and 52-year growth and mortality measured in independent transplant experiments. Overall, we find that both methods often better predict transplant performance than climatic or geographic distances. We also find that GFoffset and RONA models are surprisingly not improved using GEA candidates. Even with promising validation results, variation in model projections to future climates makes it difficult to identify the most maladapted populations using either method. Our work advances understanding of the sensitivity and applicability of these approaches, and we discuss recommendations for their future use.
摘要：
使用基因组信息来预测潜在的人口对气候变化或新环境的适应不良的方法正变得越来越普遍。然而，缺乏模型验证对将其纳入管理和政策构成了严重障碍。这里,我们使用来自三个针叶树类群的35至39个种群的外显子组捕获池seq数据，比较了从梯度森林（GFoffset）和非适应风险（RONA）这两种方法得出的适应不良估计的验证：两个道格拉斯冷杉品种和杰克松。我们评估了这些算法对输入基因座来源（从基因型-环境关联[GEA]或随机选择的标记）的敏感性。我们针对独立移植实验中测量的2年和52年生长和死亡率验证了这些方法。总的来说,我们发现，这两种方法通常比气候或地理距离更好地预测移植性能。我们还发现，使用GEA候选物，GFoffset和RONA模型出人意料地没有得到改善。即使有有希望的验证结果，模型预测对未来气候的变化使得使用这两种方法都很难确定最不适应的种群。我们的工作推进了对这些方法的敏感性和适用性的理解，我们讨论了它们未来使用的建议。
