Disentangling adaptation from drift in bottlenecked and reintroduced
populations of Alpine ibex
Abstract
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 SNP data. Within the simulated dataset, the false
positive rates were high for all selection detection methods 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.