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Microsatellite genotypes, cluster membership and metadata of Central European wolves (Canis lupus)

Citation

Szewczyk, Maciej; Mysłajek, Robert (2019), Microsatellite genotypes, cluster membership and metadata of Central European wolves (Canis lupus), Dryad, Dataset, https://doi.org/10.5061/dryad.xwdbrv195

Abstract

Local extinction and recolonization events can shape genetic structure of subdivided animal populations. The gray wolf (Canis lupus) was extirpated from most of Europe, but recently recolonized big part of its historical range. An exceptionally dynamic expansion of wolf population is observed in the western part of the Great European Plain. Nonetheless, genetic consequences of this process have not yet been fully understood. We aimed to assess genetic diversity of this recently established wolf population in Western Poland (WPL), determine its origin and provide novel data regarding the population genetic structure of the grey wolf in Central Europe. We utilized both spatially explicit and non-explicit Bayesian clustering approaches, as well as a model-independent, multivariate method DAPC, to infer genetic structure in large dataset of wolf microsatellite genotypes. To put the patterns observed in studied population into a broader biogeographic context we also analyzed a mtDNA control region fragment widely used in previous studies.

In comparison to a source population, we found slightly reduced allelic richness and heterozygosity in the newly recolonized areas west of the Vistula river. We discovered relatively strong west-east structuring in lowland wolves, probably reflecting founder-flush and allele surfing during range expansion, resulting in clear distinction of WPL, eastern lowland and Carpathian genetic groups. Interestingly, wolves from recently recolonized mountainous areas (Sudetes Mts, SW Poland) clustered together with lowland, but not Carpathian wolf populations. We also identified an area in Central Poland that seems to be a melting pot of western, lowland eastern and Carpathian wolves. We conclude that the process of dynamic recolonization of Central European lowlands lead to the formation of a new, genetically distinct wolf population. Together with the settlement and establishment of packs in mountains by lowland wolves and vice versa, it suggests that demographic dynamics and possibly anthropogenic barriers rather than ecological factors (e.g. natal habitat-biased dispersal patterns) shape the current wolf genetic structure in Central Europe.

Methods

Non-invasive samples N=2110 (mainly scats – N=1792 but also urine – N=139, hair – N=139, blood from estrus – N=29 and swabs from wolf kills – N=11) were collected from 2011 to 2018 by authors and trained volunteers all year round, during long-distance wolf tracking on forest roads, tourist trails and around known wolf dens and rendezvous sites. We also gathered tissue and hair samples from wolves killed in traffic accidents (N=97), illegally shot or snared (N=29) or found dead due to diseases and other natural causes (N=16). Additionally, we analyzed blood and hair samples of animals injured in traffic accidents or by poachers (N=9). Lastly, we analyzed tissue samples from wolves hunted legally in Lithuania (N=63) and Slovakia (N=23). No animals were specifically killed or captured for this study.

DNA isolation from non-invasive samples was performed in a separate cleanroom to avoid contamination. DNA from scats was isolated either with QIAamp DNA Stool Mini Kit (Qiagen) or Exgene™ Stool DNA Mini kit (GeneAll Biotechnology), while for hairs, swabs and FTA cards we used Exgene™ Genomic DNA Micro kit (GeneAll Biotechnology) or QIAamp DNA Investigator Kit (Qiagen). DNA from tissues and precipitated urine samples was isolated with Exgene™ Tissue SV kit (GeneAll Biotechnology).

Loci were amplified in three 10 μl multiplex reactions, each containing 5 μl Multiplex PCR Master Mix (Qiagen), 0.25 μg/μl BSA, primers at concentration 0.2 μM each and 4.2 ul DNA extract. PCR was started with initial denaturation (95 °C, 15 min) followed by 4 cycles of 94 °C (30 s), 60 °C (90 s) and 72 °C (60 s); another 5 cycles of 94 °C (30 s), 58 °C (90 s) and 72 °C (60 s), 5 cycles of 94 °C (30 s), 56 °C (90 s) and 72 °C (60 s); another 5 cycles of 94 °C (30 s), 54 °C (90 s) and 72 °C (60 s), 25 cycles of 94 °C (30 s), 50 °C (90 s) and 72 °C (60 s), and a final elongation 30 min at 72 °C. PCR products were separated by electrophoresis using an ABI3730/xl Genetic Analyzer with the internal size standard GS600 LIZ™ (Applied Biosystems) and allele sizes were binned manually using PeakScanner 1.0 software. To avoid allele dropout and genotyping errors we utilized a multitube amplification approach. However, as the reliability of a genotype could be predicted based on sample quality, the number of multitube replicates varied depending on amplification quality. Initially each non-invasive DNA sample was amplified twice. At this stage low quality samples (>35% missing data) were discarded and good quality genotypes (no missing data, ≤1 mismatch between replicates) were accepted. For the remaining samples two additional PCR repetitions were performed and consensus genotypes were reconstructed from tetraplicates.

Usage Notes

Missing data coded as 0.

Funding

National Science Centre, Poland, Award: DEC-2014/12/S/NZ8/00624