Information on spatial and temporal patterns of genetic diversity is a prerequisite to understanding the demography of populations, and is fundamental to successful management and conservation of species. In the sea, it has been observed that oceanographic and other physical forces can constitute barriers to gene flow that may result in similar population genetic structures in different species. Such similarities among species would greatly simplify management of genetic biodiversity. Here, we tested for shared genetic patterns in a complex marine area, the Baltic Sea. We assessed spatial patterns of intraspecific genetic diversity and differentiation in seven ecologically important species of the Baltic ecosystem—Atlantic herring (Clupea harengus), northern pike (Esox lucius), European whitefish (Coregonus lavaretus), three-spined stickleback (Gasterosteus aculeatus), nine-spined stickleback (Pungitius pungitius), blue mussel (Mytilus spp.), and bladderwrack (Fucus vesiculosus). We used nuclear genetic data of putatively neutral microsatellite and SNP loci from samples collected from seven regions throughout the Baltic Sea, and reference samples from North Atlantic areas. Overall, patterns of genetic diversity and differentiation among sampling regions were unique for each species, although all six species with Atlantic samples indicated strong resistence to Atlantic-Baltic gene-flow. Major genetic barriers were not shared among species within the Baltic Sea; most species show genetic heterogeneity, but significant isolation by distance was only detected in pike and whitefish. These species-specific patterns of genetic structure preclude generalizations and emphasize the need to undertake genetic surveys for species separately, and to design management plans taking into consideration the specific structures of each species.

The Baltic Sea has a rare type of brackish water environment which harbours unique genetic lineages of many species. The area is highly influenced by anthropogenic activities and is affected by eutrophication, climate change, habitat modifications, fishing and stocking. Effective genetic management of species in the Baltic Sea is highly warranted in order to maximize their potential for survival, but shortcomings in this respect have been documented. Lack of knowledge is one reason managers give for why they do not regard genetic diversity in management. Here, the current knowledge of population genetic patterns of species in the Baltic Sea is reviewed and summarized with special focus on how the information can be used in management. The extent to which marine protected areas (MPAs) protect genetic diversity is also investigated in a case study of four key species. Sixty‐one species have been studied genetically in the Baltic Sea, but comprehensive genetic information exists for only seven of them. Genetic monitoring shows genetic stability in some species but fluctuations and genetic changes in others. About half of the scientific studies published during the last 6 years provide conservation advice, indicating a high interest in the scientific community for relating results to practical management. Populations in MPAs do not differ genetically from populations outside MPAs, indicating that MPAs in the Baltic Sea do not protect genetic diversity specifically, but that populations in MPAs are a representative subset of populations in the Baltic Sea. Recommendations are provided for cases where genetic information is available but not used in management, particularly for non‐commercial species with important ecosystem function. Improved channels for effective communication between academia and practical management on Baltic Sea genetic biodiversity are needed. A web page that can be used for knowledge transfer is highlighted here.

Genetic diversity is needed for species’ adaptation to changing selective pressures and is particularly important in regions with rapid environmental change such as the Baltic Sea. Conservation measures should consider maintaining large gene pools to maximize species’ adaptive potential for long-term survival. In this study, we explored concerns regarding genetic variation in international and national policies that governs biodiversity and evaluated if and how such policy is put into practice in management plans governing Baltic Sea Marine Protected Areas (MPAs) in Sweden, Finland, Estonia, and Germany. We performed qualitative and quantitative textual analysis of 240 documents and found that agreed international and national policies on genetic biodiversity are not reflected in management plans for Baltic Sea MPAs. Management plans in all countries are largely void of goals and strategies for genetic biodiversity, which can partly be explained by a general lack of conservation genetics in policies directed toward aquatic environments.

Understanding spatiotemporal population genetic patterns is important for conservation management of ecologically and socioeconomically important species. This is particularly so in species-poor environments such as the brackish Baltic Sea. We examined over 600 northern pike (Esox lucius), a coastal predator and treasured sport fish, collected over major parts of the Baltic Sea coastline. We found low genetic divergence among populations, indicating a contrasting genetic structure of brackish water coastal spawners compared with previous reports on anadromous Baltic pike migrating up freshwater streams for spawning. A pattern of genetic isolation by distance either over shortest waterway or primarily along the mainland coast with islands as stepping stones suggested that gene flow is primarily taking place among neighboring populations, possibly with some migration over open water. Temporal data showed a stable genetic structure over a decade. Within a single sampling year, however, spatial divergence was larger during spawning than feeding season, indicating increased mixing of populations during the feeding season. Management should assure connectivity among brackish spawning grounds and large population sizes at identified core areas.

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 (F _ST = 0.1; R _ST = 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 F _ST and R _ST 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.