Identifying local adaptation in bottlenecked species is essential for conservation management. Selection detection methods have an important role in species management plans, assessments of adaptive capacity, and looking for responses to climate change. Yet, the allele frequency changes exploited in selection detection methods are similar to those caused by the strong neutral genetic drift expected during a bottleneck. Consequently, it is often unclear what accuracy selection detection methods have across bottlenecked populations. In this study, simulations were used to explore if signals of selection could be confidently distinguished from genetic drift across 23 bottlenecked and reintroduced populations of Alpine ibex (Capra ibex). The meticulously recorded demographic history of the Alpine ibex was used to generate comprehensive simulated SNP data. The simulated SNPs were then used to benchmark the confidence we could place in outliers identified in empirical Alpine ibex RADseq derived SNP data. Within the simulated data set, the false positive rates were high for all selection detection methods (F_ST outlier scans and Genetic-Environment Association analyses) but fell substantially when two or more methods were combined. True positive rates were consistently low and became negligible with increased stringency. Despite finding many outlier loci in the empirical Alpine ibex SNPs, none could be distinguished from genetic drift-driven false positives. Unfortunately, the low true positive rate also prevents the exclusion of recent local adaptation within the Alpine ibex. The baselines and stringent approach outlined here should be applied to other bottlenecked species to ensure the risk of false positive, or negative, signals of selection are accounted for in conservation management plans.

中文翻译：

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从瓶颈和重新引入的高山野山羊种群的漂移中解开适应

确定瓶颈物种的当地适应性对于保护管理至关重要。选择检测方法在物种管理计划、适应能力评估和寻找对气候变化的响应方面具有重要作用。然而，选择检测方法中利用的等位基因频率变化与瓶颈期间预期的强中性遗传漂移引起的变化相似。因此，通常不清楚在瓶颈人群中使用哪些准确度选择检测方法。在这项研究中，模拟被用来探索是否可以自信地将选择信号与 23 个瓶颈和重新引入的高山野山羊（Capra ibex）种群的遗传漂变区分开来。）。精心记录的高山野山羊的人口统计历史用于生成全面的模拟 SNP 数据。然后使用模拟的 SNP 来衡量我们可以在经验性 Alpine ibex RADseq 派生的 SNP 数据中确定的异常值的置信度。在模拟数据集中，所有选择检测方法（F _ST异常值扫描和遗传环境关联分析），但当两种或多种方法结合使用时会大幅下降。真阳性率一直很低，并且随着严格性的增加变得可以忽略不计。尽管在经验性高山野山羊 SNP 中发现了许多异常位点，但没有一个能与遗传漂移驱动的假阳性区分开来。不幸的是，低真阳性率也阻止了高山野山羊最近的局部适应被排除在外。此处概述的基线和严格方法应适用于其他瓶颈物种，以确保在保护管理计划中考虑到误报或负选择信号的风险。