Alpine ibex simulation files

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 dataset, the false positive
rates were high for all selection detection methods (Fst 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.