Study sites and sampling
Tissue samples of wild boar were collected over a five-year period (2009–2013) from 35 sampling sites across Lithuania (Fig.1). A total of 96 S. scrofa individuals legally harvested by licensed hunters in different parts of Lithuania were investigated. A decision was taken to focus on the single population of Lithuania and a sample size that would be sufficient to characterise population-level genetic diversity when using microsatellites.
The main habitats favoured by boars vary from semi-arid environments to marshes, forests and alpine grasslands . In Lithuania, wild boars mostly prefer habitats of deciduous with spruce and mixed spruce-deciduous forests . Wild boar samples were collected from different regions of Lithuania representing different landscapes. The samples were arranged by grouping individuals into four regional subpopulations (I, II, III, and IV) while also considering the country’s fragmentation by its major roads (E67, E85) with high volumes of traffic (Fig. 1). The first (I) and second (II) sampling areas covered mixed forests and grasslands, deciduous broad-leaved woods were dominant in the third (III) sampling area, and pine Pinus sylvestris forests were prevalent in the fourth (IV) sampling area.
Fresh muscle, spleen and blood were sampled from wild boars and either stored in plastic tubes (5–30 ml) filled with 96% alcohol or kept frozen at a temperature of − 20 °C. All the samples were legally collected and deposited at the State Food and Veterinary Service of the Republic of Lithuania (SFVS). The study did not involve the collection of samples from live animals. An ethics statement was not required. The hunters collected samples in accordance with national regulations on wild boar management.
Amplification and genotyping
In this research, samples were extracted using the “DNeasy Blood and Tissue Kit” (Qiagen, Catalogue No. 69506) following the manufacturer’s instructions. The concentration and purity of the isolated DNA were determined using Nanodrop 2000 Spectrophotometer (Thermo Scientific, DE, USA). Samples were used immediately for amplification or stored at − 20 °C for later use.
A set of 15 microsatellite markers were selected from the list of microsatellite markers recommended by the International Society of Animal Genetics (ISAG) – Food and Agriculture Organization (FAO) . The markers were grouped into two multiplex (SW24, S0386, S0355, SW353, SW936, SW72, S0070, S0107 and S0026, S0155, S0005, SW2410, SW830, SW632, SWR1941) reactions based on their size and annealing temperature.
PCR reactions were carried out in a total volume of 25 μL, containing 1 μL of DNA template, fluorescent forward primer (2 μM) and non-fluorescent reverse primer (2 μM), and 2x QuantiTect Multiplex PCR NoROX Master Mix (Ref. 204,743, QIAGEN GmbH). PCR reactions were carried out in the following steps: 10 min an initial denaturation at 95 °C, 30 or 35 cycles at 95 °C for 30 s depending on the primer set used, annealing at an optimal temperature ranging from 57 to 58 °C, extension at 72 °C for 1 min, then a final extension at 72 °C for 30 min. The ABI 3100 (Applied Biosystems, USA) DNA Analyzer was used to genotype alleles with a GeneScanTM-500 ROX size standard (Applied Biosystems). Gene Mapper 3.7 (Applied Biosystems) software was used to estimate the size of the alleles.
In order to estimate the population genetic structure of wild boars in Lithuania, the number of alleles per locus (NA), observed heterozygosity (Ho) and expected heterozygosity (HE) under Hardy-Weinberg assumptions were obtained in GenAlEx v6.1 . Deviations from the Hardy-Weinberg equilibrium (HWE) were tested with a Markov chain algorithm with 10,000 dememorisation steps, 100 batches and 1000 iterations using Genepop v.4.0 . The P values for HWE were corrected for multiple comparisons by applying a sequential Bonferroni correction, with an initial probability of p = 0.05 . To assess the genetic relationships between subpopulations, pairwise Nei’s genetic distances  were calculated between each pair of the sample sites using the same software. GenAlEx was further used to carry out principal coordinates analysis (PCoA) enabling the visualisation of genetic variation distribution across individuals, analysis of molecular variance (AMOVA) and F-statistics (Fst, Fis, and Fit). FST values were estimated according to Weir and Cockerham’s  version of Wright’s F-statistic using the FSTAT program package , followed by sequential Bonferroni correction for multiple tests .
The Mantel test  was used with 999 permutations in the GenAlEx software to test for evidence of isolation by distance (IBD).
The determination of the most probable number of clusters for Lithuania’s wild boar population (K value) was assessed by the STRUCTURE program version 2.3.4 . The probabilistic method was conducted with 200,000 replications in burn-in and 800,000 replications in the Markov Chain Monte Carlo (MCMC). Twenty clustering simulations (runs) were performed for each possible value of K (K = 1 to K = 10). The outputs of STRUCTURE were submitted to Structure Harvester online program version 0.6.94 (http://taylor0.biology.ucla.edu/structureHarvester/) to estimate the optimal value of K using the Delta (K) method  and allowing for different estimates of K in accordance with Janes et al. . Based on the resulting values of K, a clustering analysis of the studied sampling sites was performed and graphical output generated using CLUMPAK’s main pipeline (http://clumpak.tau.ac.il ).