Genetic admixture of distinct gene pools is the consequence of complex spatiotemporal processes that could have involved massive migration and local mating during the history of a species. However, current methods for estimating individual admixture proportions lack the incorporation of such a piece of information. Here, we extend Bayesian clustering algorithms by including global trend surfaces and spatial autocorrelation in the prior distribution on individual admixture coefficients. We test our algorithm by using spatially explicit and realistic coalescent simulations of colonization followed by secondary contact. By coupling our multiscale spatial analyses with a Bayesian evaluation of model complexity and fit, we show that the algorithm provides a correct description of smooth clinal variation, while still detecting zones of sharp variation when they are present in the data. We also apply our approach to understand the population structure of the killifish, Fundulus heteroclitus, for which the algorithm uncovers a presumed contact zone in the Atlantic coast of North America.
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