Detection of Outlier Loci
Prior to assessing population structure and adaptation, we identified
outlier loci (i.e., loci putatively under the influence of selection)
and divided our datasets into (1) putatively adaptive and (2) putatively
neutral markers for each species. To detect outlier loci, we used four
population differentiation (PD) methods and two environmental
association (EA) methods that implement different methodologies (Table
2, Appendix D).
The use of both PD and EA methods allows for the conservative detection
of outlier loci by comparing complementary methods with different
assumptions and biases. PD methods use measures of differentiation
(FST or in the case of ordination approaches,
multivariate distances) to identify outlier loci, where extreme levels
of differentiation (i.e., those outside of neutral expectations) are
likely candidates for selection (Liggins et al. 2019). EA methods use
correlations between environmental variables and allele frequencies to
identify loci putatively under selection (Rellstab et al. 2015). While
both methods are prone to false positives, combining approaches is an
effective strategy for identifying true outlier loci (de Villemereuil et
al. 2014; François et al. 2016; Liggins et al. 2019). Here, we retained
only loci identified as outliers by at least three methods and divided
each of our datasets into neutral and outlier loci.