When populations of a rare species are small, isolated and declining under climate change, some populations may become locally maladapted. Detecting this maladaptation may allow effective rapid conservation interventions, even if based on incomplete knowledge. Population maladaptation may be estimated by finding genome–environment associations (GEA) between allele frequencies and environmental variables across a local species range, and identifying populations whose allele frequencies do not fit with these trends. We can then design assisted gene flow strategies for maladapted populations, to adjust their allele frequencies, entailing lower levels of intervention than with undirected conservation action. Here, we investigate this strategy in Scottish populations of the montane plant dwarf birch (Betula nana). In genome‐wide restriction site‐associated single nucleotide polymorphism (SNP) data, we found 267 significant associations between SNP loci and environmental variables. We ranked populations by maladaptation estimated using allele frequency deviation from the general trends at these loci; this gave a different prioritization for conservation action than the Shapely Index, which seeks to preserve rare neutral variation. Populations estimated to be maladapted in their allele frequencies at loci associated with annual mean temperature were found to have reduced catkin production. Using an environmental niche modelling (ENM) approach, we found annual mean temperature (35%), and mean diurnal range (15%), to be important predictors of the dwarf birch distribution. Intriguingly, there was a significant correlation between the number of loci associated with each environmental variable in the GEA and the importance of that variable in the ENM. Together, these results suggest that the same environmental variables determine both adaptive genetic variation and species range in Scottish dwarf birch. We suggest an assisted gene flow strategy that aims to maximize the local adaptation of dwarf birch populations under climate change by matching allele frequencies to current and future environments.

中文翻译：

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对矮桦局部适应的基因组评估，以告知辅助基因流。

当稀有物种的种群规模较小、孤立且在气候变化的影响下减少时，一些种群可能会适应当地环境。即使基于不完整的知识，检测到这种适应不良也可能允许有效的快速保护干预措施。可以通过寻找当地物种范围内等位基因频率和环境变量之间的基因组-环境关联（GEA）来估计种群适应不良，并识别等位基因频率与这些趋势不相符的种群。然后，我们可以为适应不良的种群设计辅助基因流策略，以调整其等位基因频率，从而比无指导的保护行动需要更低水平的干预。在这里，我们在苏格兰山地植物矮桦（Betula nana）种群中研究了这一策略。在全基因组限制性位点相关单核苷酸多态性 (SNP) 数据中，我们发现 SNP 位点与环境变量之间存在 267 个显着关联。我们根据这些位点的等位基因频率偏差估计的适应不良对人群进行排名；这为保护行动提供了与 Shapely 指数不同的优先顺序，Shapely 指数旨在保护罕见的中性变化。据估计，与年平均温度相关的基因座的等位基因频率适应不良的种群被发现减少了柔荑花序的产量。使用环境生态位建模 (ENM) 方法，我们发现年平均温度 (35%) 和平均日较差 (15%) 是矮桦树分布的重要预测因子。有趣的是，GEA 中与每个环境变量相关的基因座数量与 ENM 中该变量的重要性之间存在显着相关性。总之，这些结果表明相同的环境变量决定了苏格兰矮桦的适应性遗传变异和物种范围。我们提出了一种辅助基因流策略，旨在通过将等位基因频率与当前和未来的环境相匹配，最大限度地提高矮桦种群在气候变化下的局部适应能力。