The moose (Alces alces) is the most intensely managed game species in Fennoscandia; approximately one-third of the population, ca. 160,000 animals, is harvested annually. Despite the species' biological and socioeconomic importance, there are knowledge gaps with respect to its intraspecific diversity and genetic structure. Recent studies of moose in neighboring countries report 2 genetic groups in Finland, 3 in Norway with one of them suggested to be of ancient origin, and no indications of bottlenecks. To delineate the spatial genetic landscape of the Swedish moose, we used allozyme variability from over 20,000 georeferenced moose collected all over Sweden in combination with 12 microsatellites (n > 1,200) and mitochondrial DNA (mtDNA) sequences (n = 44). We combined individual-based and traditional statistical approaches with coalescence-based simulations. The results indicate a complex history with bottlenecks and recent expansions that is consistent with historical records. Swedish moose are separated into 2 major genetic groups, a northern and a southern one, where the southern group is further divided into 3 subgroups. The 2 main subpopulations are moderately differentiated (FST = 0.1; RST = 0.07) and separated by sharp genetic discontinuities occurring over a relatively narrow transition zone in central Sweden that coincides with a similar, previously reported transition zone in Norway. This differentiation is not reflected in mtDNA variation, where no significant divergence was observed. Together with the FST and RST similarities, this suggests that the 2 major subpopulations in Sweden reflect divergence shaped after the postglacial recolonization of Scandinavia. Neighborhood size assessments indicate that gene flow is relatively restricted with an estimated average dispersal distance of 3.5–11.1 km, and spatial autocorrelograms suggest that genetic similarity decreases almost linearly over space resulting in continuous genetic clines within major subgroups. Management areas largely coincide with genetic clusters, simplifying the integration of genetic information into management.
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Vol. 97 • No. 1