Disentangling adaptation from drift in bottlenecked and reintroduced populations of Alpine ibex

Identifying local adaptation in bottlenecked species is essential for effective conservation management. Selection detection methods are often applied to bottlenecked species and have an important role in species management plans, assessments of the species adaptive capacity, and looking for responses to major threats like climate change. Yet, the allele frequency changes driven by selection and 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 may offer within bottlenecked populations. In this study, we used simulations to explore if signals of selection could be confidently distinguished from genetic drift across 23 bottlenecked and reintroduced populations of Alpine ibex (Capra ibex). We used the meticulously recorded demographic history of the Alpine ibex to generate a comprehensive simulated SNP data. The simulated SNPs were then used to benchmark the confidence we could place in putative outliers identified through selection scans on empirical Alpine ibex SNP data. Within the simulated dataset, the false positive rates were high for all selection detection methods but fell substantially when two or more selection detection methods were combined. However, the true positive rates were consistently low and became essentially negligible after this increased stringency. Despite the detection of many putative outlier loci in the empirical Alpine ibex RADseq data, none met the threshold needed to distinguish them from genetic drift-driven false positives. Unfortunately, the low true positive rate also creates a paradox, by preventing the exclusion of recent local adaptation within the Alpine ibex.