A narrow window for geographic cline analysis using genomic data: effects of age, drift, and migration on error rates

The use of genomic and phenotypic data to scan for outliers is a mainstay
for studies of hybridization and speciation. Geographic cline analysis of
natural hybrid zones is widely used to identify putative signatures of
selection by detecting deviations from baseline patterns of introgression.
As with other outlier-based approaches, demographic histories can make
neutral regions appear to be under selection and vice versa. In this
study, we use a forward-time individual-based simulation approach to
evaluate the robustness of geographic cline analysis under different
evolutionary scenarios. We modeled multiple stepping-stone hybrid zones
with distinct age, deme sizes, and migration rates, and evolving under
different types of selection.  We found that drift
distorts cline shapes and increases false positive rates for signatures of
selection. This effect increases with hybrid zone age, particularly if
migration between demes is low.  Drift can also distort
the signature of deleterious effects of hybridization, with genetic
incompatibilities and particularly underdominance prone to spurious typing
as adaptive introgression. Our results suggest that geographic clines are
most useful for outlier analysis in young hybrid zones with large
populations of hybrid individuals. Current approaches may overestimate
adaptive introgression and underestimate selection against maladaptive
genotypes.