We investigated the distribution of genetic variation within and between seven subpopulations in a riparian population of Silene tatarica in northern Finland by using amplified fragment length polymorphism (AFLP) markers. A Bayesian approach-based clustering program indicated that the marker data contained not only one panmictic population, but consisted of seven clusters, and that each original sample site seems to consist of a distinct subpopulation. A coalescent-based simulation approach shows recurrent gene flow between subpopulations. Relative high FST values indicated a clear subpopulation differentiation. However, amova analysis and UPGMA-dendrogram did not suggest any hierarchical regional structuring among the subpopulations. There was no correlation between geographical and genetic distances among the subpopulations, nor any correlation between the subpopulation census size and amount of genetic variation. Estimates of gene flow suggested a low level of gene flow between the subpopulations, and the assignment tests proposed a few long-distance bidirectional dispersal events between the subpopulations. No apparent difference was found in within-subpopulation genetic diversity among upper, middle and lower regions along the river. Relative high amounts of linkage disequilibrium at subpopulation level indicated recent population bottlenecks or admixture, and at metapopulation levels a high subpopulation turnover rate. The overall pattern of genetic variation within and between subpopulations also suggested a 'classical' metapopulation structure of the species suggested by the ecological surveys.

The grey wolf (Canis lupus) is an iconic large carnivore that has increasingly been recognized as an apex predator with intrinsic value and a keystone species. However, wolves have also long represented a primary source of human-carnivore conflict, which has led to long-term persecution of wolves, resulting in a significant decrease in their numbers, genetic diversity and gene flow between populations. For more effective protection and management of wolf populations in Europe, robust scientific evidence is crucial. This review serves as an analytical summary of the main findings from wolf population genetic studies in Europe, covering major studies from the 'pre-genomic era' and the first insights of the 'genomics era'. We analyse, summarize and discuss findings derived from analyses of three compartments of the mammalian genome with different inheritance modes: maternal (mitochondrial DNA), paternal (Y chromosome) and biparental [autosomal microsatellites and single nucleotide polymorphisms (SNPs)]. To describe large-scale trends and patterns of genetic variation in European wolf populations, we conducted a meta-analysis based on the results of previous microsatellite studies and also included new data, covering all 19 European countries for which wolf genetic information is available: Norway, Sweden, Finland, Estonia, Latvia, Lithuania, Poland, Czech Republic, Slovakia, Germany, Belarus, Russia, Italy, Croatia, Bulgaria, Bosnia and Herzegovina, Greece, Spain and Portugal. We compared different indices of genetic diversity in wolf populations and found a significant spatial trend in heterozygosity across Europe from south-west (lowest genetic diversity) to north-east (highest). The range of spatial autocorrelation calculated on the basis of three characteristics of genetic diversity was 650-850 km, suggesting that the genetic diversity of a given wolf population can be influenced by populations up to 850 km away. As an important outcome of this synthesis, we discuss the most pressing issues threatening wolf populations in Europe, highlight important gaps in current knowledge, suggest solutions to overcome these limitations, and provide recommendations for science-based wolf conservation and management at regional and Europe-wide scales.

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The Finnish wolf population (Canis lupus) was sampled during three different periods (1996-1998, 1999-2001 and 2002-2004), and 118 individuals were genotyped with 10 microsatellite markers. Large genetic variation was found in the population despite a recent demographic bottleneck. No spatial population subdivision was found even though a significant negative relationship between genetic relatedness and geographic distance suggested isolation by distance. Very few individuals did not belong to the local wolf population as determined by assignment analyses, suggesting a low level of immigration in the population. We used the temporal approach and several statistical methods to estimate the variance effective size of the population. All methods gave similar estimates of effective population size, approximately 40 wolves. These estimates were slightly larger than the estimated census size of breeding individuals. A Bayesian model based on Markov chain Monte Carlo simulations indicated strong evidence for a long-term population decline. These results suggest that the contemporary wolf population size is roughly 8% of its historical size, and that the population decline dates back to late 19th century or early 20th century. Despite an increase of over 50% in the census size of the population during the whole study period, there was only weak evidence that the effective population size during the last period was higher than during the first. This may be caused by increased inbreeding, diminished dispersal within the population, and decreased immigration to the population during the last study period.

We examined the genetic diversity and structure of wolf populations in northwestern Russia. Populations in Republic of Karelia and Arkhangelsk Oblast were sampled during 1995-2000, and 43 individuals were genotyped with 10 microsatellite markers. Moreover, 118 previously genotyped wolves from the neighbouring Finnish population were used as a reference population. A relatively large amount of genetic variation was found in the Russian populations, and the Karelian wolf population tended to be slightly more polymorphic than the Arkhangelsk population. We found significant inbreeding (F = 0.094) in the Karelian, but not in the Arkhangelsk population. The effective size estimates of the Karelian wolf population based on the approximate Bayesian computation and linkage disequilibrium methods were 39.9 and 46.7 individuals, respectively. AMOVA-analysis and exact test of population differentiation suggested clear differentiation between the Karelian, Arkhangelsk and Finnish wolf populations. Indirect estimates of gene flow based on the level of population differentiation (/ ST = 0.152) and frequency of private alleles (0.029) both suggested a low level of gene flow between the populations (Nm = 1.4 and Nm = 3.7, respectively). Assignment analysis of Karelian and Finnish populations suggested an even lower number of recent migrants (less than 0.03) between populations, with a larger amount of migration from Finland to Karelia than vice versa. Our findings emphasise the role of physical obstacles and territorial behaviour in creating barriers to gene flow between populations in relatively limited geographical areas, even in large-bodied mammalian species with long-distance dispersal capabilities and an apparently continuous population structure.