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 measured the effects of drift on genetic differentiation, and on false positive and false negatives detection using geographic clines. 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 in young hybrid zones, drift increases overall genomic divergence, distorts cline shapes and increases both false positive and false negative rates. In old hybrid zones, genomic divergence and cline distortion are higher. Our results suggest that geographic clines are most useful for outlier analysis in young hybrid zones with large populations of hybrid individuals.
