The genetic structure of the Lithuanian wolf population

Cent. Eur. J. Biol. 8(5) 2013 440-447 DOI: 10.2478/s11535-013-0154-9 Central European Journal of Biology The genetic structure of the Lithuanian wolf population Research Article Laima Baltrūnaitė 1, *, Linas Balčiauskas 1, Mikael Åkesson 2 1 Nature Research Centre, LT-08412 Vilnius, Lithuania 2 Grimsö Wildlife Research Station, Department of Ecology, Swedish University of Agricultural Sciences, SE-730 91 Riddarhyttan, Sweden Received 24 August 2012; Accepted 26 January 2013 Abstract: Lithuanian wolves form part of the larger Baltic population, the distribution of which is continuous across the region. In this paper, we evaluate the genetic diversity of the Lithuanian wolf population using mitochondrial DNA analysis and 29 autosomal microsatellite loci. Analysis of the mtdna control region (647 bp) revealed 5 haplotypes distributed among 29 individuals and high haplotype diversity (0.658). Two haplotypes were distributed across the country, whilst the others were restricted to eastern Lithuania. Analysis of microsatellites revealed high heterozygosity (H E =0.709) and no evidence for a recent bottleneck. Using detection of first generation migrants, four individuals appeared to assign better with populations genetically differentiated from those resident in Lithuania. These immigrants were males carrying rare mitochondrial haplotypes and were encountered in the eastern part of the country, this indicates that Lithuania is subject to immigration from differentiated populations. Additionally, we did not detect any signs of recent hybridisation with dogs. Keywords: Canis lupus Genetic structure Lithuania Microsatellites Mitochondrial DNA Versita Sp. z o.o. 1. Introduction The Gray wolf Canis lupus is widespread throughout Europe, but its distribution is patchy, with semi-isolated populations varying in sizes from less than fifty to several thousand. The conservation status of the species also varies from being strictly protected in some countries to a legitimate game species in others [1,2]. Several studies (reviewed in Randi [3]) have found that wolves, even though capable of dispersing over large distances and across difficult terrain, show genetic differentiation at smaller geographical scales [e.g. 4,5]. This highlights the importance of applying knowledge about genetic diversity and differentiation when preserving species distributed across large geographic areas. Wolf management is largely centred on resolving conflicts between wolves and humans [3], in several cases this is attempted by controlling the distribution and number of wolves. Such management actions might, however, result in smaller and more distant populations, consequently leading to increased loss of genetic variation, the accumulation of deleterious alleles and higher levels of inbreeding [6]. The conservation of wolves and other large predators may therefore rely on our capabilities to ensure connectivity between these semi-isolated populations. Genetic research on European wolves may therefore provide vital information when defining proper units of conservation and assessing their genetic status with regard to, for example, effective population size, inbreeding and hybridisation level with dogs [7]. The Baltic wolf population has a continuous distribution range extending through Estonia, Latvia, Lithuania, Russia (Kaliningrad and western parts of continental Russia), eastern Poland, northern Ukraine and Belarus, potentially reaching 3600 individuals [2]. Knowledge relating to the genetic structure amongst wolves in this region is, however, sparse and restricted to Latvia, Poland/Belarus (Białowieża National Park), 440 * E-mail: laima@ekoi.lt

L. Baltrūnaitė et al. and western Russia. Prior studies have shown that the region contains more or less differentiated populations with high genetic diversity and few indications of recent bottleneck effects, even though many populations have undergone rather dramatic fluctuations in population density [8-10]. Moreover, there are indications that wolves occasionally hybridise with dogs in the region, but there is a lack of strong support for the occurrence of introgressive hybridisation into the population [11,12]. In this paper, we used partial sequences from the mitochondrial DNA and microsatellite markers to investigate the genetic diversity of the Lithuanian wolf population, aiming to: 1) investigate the geographical distribution of mitochondrial haplotypes across the country and to evaluate the haplotype diversity; 2) assess the level of heterozygosity and if the population shows genetic signs of recent bottleneck events related to known fluctuations in wolf abundance during the last century; 4) detect possible immigrants from the other wolf populations (Finnish/Russian); and 5) examine the presence of hybrids among Lithuanian wolves (recent hybridization with dogs was detected in neighboring Latvia [11,12]). 2. Experimental Procedures We obtained 29 samples of tissue and/or hairs from killed wolves in the years 2009 and 2010. DNA was extracted using standard methods [13]. For haplotype determination, we sequenced a fragment of a control region (647 bp) using primers from Parra et al. [14]. PCR was performed using standard techniques. Sequencing was performed using BigDye terminator Cycle sequencing Ready Reaction Kit and separation on an ABI3730XL or ABI3700 (Macrogen Inc., Korea). Sequences were edited and aligned using BIOEDIT 5.0.9 [15], and deposited in the GenBank, accession numbers: JX508631-JX508635. Haplotype diversity and nucleotide diversity were estimated using DNASP 5.10 [16]. Sequence divergence was calculated using the Jukes-Cantor method implemented in MEGA 5.0 [17]. For phylogenetic analysis, we used sequences of wolves and various breeds of dogs from Europe available in the GenBank (http://www.ncbi.nlm.nih. gov/genebank) and published in Parra et al. [14], Vila et al. [18], Björnerfeldt et al. [19], Aggarwal et al. [20], Pilot et al. [21], and Bekaert et al. [22]. Bayesian tree was constructed using the MrBayes program [23]. We used the Transition model including invariable sites and variation among sites (TIM2+I+G), as it was selected as the best-fit model of nucleotide substitution by jmodeltest [24]. Two simultaneous runs were conducted with a sample frequency of every 100 th tree over 3.5 million generations. Before constructing a majority consensus tree, 25% of the initial trees in each run were discarded as burn in periods. The phylogeny was visualized using Tree View 1.6.6 (available from http://evolution.genetics. washington.edu/phylip/software.html). We genotyped 29 autosomal microsatellite loci: 20, 109, 123, 173, 204, 225, 250, 2001, 2010, 2054, 2088, 2137, 2140, 2159, 2168, 2201, AHT002, AHT004, AHT101, AHT106, AHT126, AHT124, AHT133, AHT138, AHT119, AHT121, AHT136, AHT103, AHT125 [25-29]. Genotyping was performed using ABI3730XL (Uppsala Genome Center) and fragment analysis was done using GeneMapper 3.0 (Applied Biosystems). We used Microchecker 2.2.3 [30] to identify possible null alleles, large allele dropouts, systematic scoring of stutter peaks and typographic errors. Analysis suggested the presence of null alleles in 2159 and AHT119 (binomial test: P<0.001). These loci were discarded from all further analysis. Allele frequencies, as well as expected and observed heterozygosity were estimated by ARLEQUIN 3.5 [31]. Inbreeding estimator (F IS ) was calculated using FSTAT 2.9.3 [32], and deviations from Hardy-Weinberg equilibrium and linkage disequilibrium (between all pairs of loci) were tested using GENEPOP 3.3 [33]. To assess bottlenecks, we applied the two-phase mutation model (TPM) as implemented in BOTTLENECK 1.2.02 [34]. We used a one-tailed Wilcoxon test to check for heterozygosity excess as the most appropriate for microsatellite data, with a variance among multiple steps of 12, and 95% as the proportion of SMM, and 10,000 iterations [35]. For comparisons, data sets on wolves from Finland (n=64; sampled from wolves killed between the years 1998-2002) and Russia (Archangelsk (n=13) and Carelia (n=23); sampled from wolves killed between the years 1995-2000) were included in the microsatellite analyses. Population differentiation (global and pairwise) was estimated by Jost s D est value [36] as the most appropriate for accurate estimation of differentiation using DEMEtics [37] and by global and pairwise F ST value [38] for comparison with other results by FSTAT 2.9.3 [32]. Analysis of detection of possible first generation migrants was made by GENECLASS 2 [39]. We used L=L home /L max to measure the likelihood [40]. A Bayesian method was used to compute the most likely population [41]. Probabilities to belong to any population were calculated by applying a Monte-Carlo resampling of 10,000 individuals and using a simulation algorithm from Paetkau et al. [40]. Admixture analysis was performed using STRUCTURE 2.3.3. [42] and NEWHYBRIDS 1.1 441

The genetic structure of the Lithuanian wolf population [43]. Simulation of hybrids (first and second hybrid generations and backcross with wolves and dogs) was performed by HYBRIDLAB [44] using dogs (n=27) data set from 17 different breeds and cross-breeds in Sweden. As a basis for analysis, we used the methodological approach performed by Godinho et al. [45]. 3. Results 3.1 mtdna analysis The sequencing of a fragment of the control region (647 bp) revealed five different haplotypes amongst Lithuanian wolves: W1-LT, W2-LT, W3-LT, W5-LT, and W10-LT. Haplotype diversity was 0.658±0.003 (SD) and nucleotide diversity was 0.010±0.001 (SD). Haplotype W1-LT was found in 10 wolves, W2-LT in 14 wolves, W3-LT in three wolves, W5-LT in one wolf and W10-LT in one wolf. Haplotypes W1-LT and W2-LT were distributed across Lithuania (Figure 1). Haplotype W3-LT was found only in the south-eastern part of the country, whilst W5-LT and W10-LT were registered in single individuals sampled in the northern and eastern parts of Lithuania, respectively. Only one haplotype, namely W5-LT, was identical to other haplotypes in the GenBank (FJ978011, DQ480503). The rest of the haplotypes showed slight differences from known haplotypes of wolves (sequence divergence was 0.002%). Haplotype W2-LT showed low divergence both from wolf and dog haplotypes (in both cases the lowest level of divergence was 0.002%). For the phylogenetic analysis, we used both wolf and dog haplotypes because of the low divergence between Lithuanian haplotype W2-LT and several haplotypes of dogs. The phylogenetic tree (based on 633 bp) revealed two distinct clades separating wolves and dogs with only a very few exceptions (Figure 2). All wolf haplotypes found in Lithuania were assigned to the wolf clade and belonged to three different subclades: haplotypes W1-LT and W5-LT belonged to subclades consisting of wolves only, whereas haplotypes W2-LT, W3-LT, and W10-LT were grouped to the subclade containing a few haplotypes of dogs (that formed small distinct group). All Lithuanian wolf haplotypes were phylogeographically similar (or identical in case of W5-LT) to haplotypes distributed in Eastern Europe, Southern Europe (Bulgaria, Croatia) and Scandinavia. The divergence amongst wolf sequences in subclades with Lithuanian wolf haplotypes typically varied from 0.002% to 0.007% in comparison to an overall average divergence of 0.017%. 3.2 Microsatellite analysis All 27 loci were polymorphic in Lithuanian wolves, with the average number of alleles equal to 6.85±3.55 (SD), ranging between 2 and 19 alleles. Unbiased expected heterozygosity was 0.709±0.20 (SD) (Table 1). Over all loci, there was a lower number of observed heterozygotes than expected from Hardy-Weinberg equilibrium (F IS =0.022, P<0.01, Table 1). No consistent heterozygote deficiency among the loci could be observed, indicated by the 14 loci (52%) that showed negative F IS. However, two loci (2168 and 2201) showed significant departure from Hardy-Weinberg equilibrium. Significant linkage disequilibrium was found between 52 pairs of loci. After a sequential Bonferroni test, three pairs of loci (2054x2137, AHT002xAHT103, 2168xAHT125) showed significant linkage disequilibrium (P<0.05). Figure 1. Distribution of wolf haplotypes in Lithuania. 442

L. Baltrūnaitė et al. One of these pairs (2168xAHT125) is situated on the same chromosome. There was no evidence that the Lithuanian wolf population has been subject to any recent bottleneck (Wilcoxon test: P=0.495). We evaluated global and pairwise differentiation with available data from Finland and Russia (Carelia and Archangelsk), based on 25 microsatellite loci (AHT106 and AHT126 were not included due to a lack of reference data). Global differentiation showed moderate values amongst Lithuanian, Finnish and both Russian populations (D est =0.157, F ST =0.046). As expected from the geographical distance, pairwise population differentiation was higher between the Lithuanian and Finnish (D est =0.210, F ST =0.062) populations and lower with the Carelian (D est =0.180, F ST =0.048) and Archangelsk populations (D est =0.179, F ST =0.054). Assignment analysis was conducted to identify first generation migrants in GENECLASS 2 using Lithuanian, Finnish, Carelian, and Archangelsk wolf populations. It was revealed that wolf CL12 significantly assigned better with the reference material from the Finnish population with a likelihood ratio 14.6 and low probability to belong to Lithuanian population (P<0.001). Wolves CL4, CL16, and CL28 were identified as possible first generation migrants from a population more similar to the Carelian wolf population (likelihood ratio 2.1, 3.8, and 0.4, respectively, and a probability that they originated from the Lithuanian population of 0.020, 0.009, and 0.044, respectively). All of these wolves were males and carried rare mitochondrial haplotypes: haplotype W5-LT Marker N A H E H O F IS 20 7 0.693 0.667 0.039 109 5 0.608 0.655-0.079 123 6 0.746 0.690 0.077 173 5 0.748 0.621 0.172 204 4 0.731 0.750-0.027 225 3 0.575 0.500 0.132 250 5 0.740 0.759-0.026 2001 5 0.656 0.621 0.055 2010 5 0.800 0.655 0.183 2054 9 0.832 0.679 0.188 2088 6 0.754 0.759-0.006 2137 11 0.862 0.897-0.041 2140 9 0.792 0.897-0.135 2168 14 0.915 0.765 0.168* 2201 19 0.932 0.793 0.152* AHT002 8 0.862 0.864-0.003 AHT004 5 0.618 0.536 0.135 AHT101 8 0.693 0.724-0.046 AHT106 6 0.803 0.862-0.075 AHT126 5 0.630 0.800-0.277 AHT124 2 0.100 0.103-0.037 AHT133 6 0.716 0.828-0.160 AHT138 7 0.820 0.828-0.010 AHT121 8 0.847 0.700 0.182 AHT136 2 0.063 0.063 0.000 AHT103 7 0.779 0.759 0.026 AHT125 8 0.826 0.828-0.002 Mean 6.852 0.709 0.689 0.022 Figure 2. Bayesian phylogenetic tree of wolves and dogs (in italic) based on sequences of an mtdna control region (633 bp). Canis indicus (AY289984) is used as an outgroup. Nodes with bootstrap support >70% are highlighted in bold. Stars indicate the total match between wolf and several dog haplotypes (here, only one haplotype of dogs is presented). Table 1. Allele number (N A ), unbiased expected (H E ) and observed (H O ) heterozygosity and inbreeding coefficient (F IS ) of Lithuanian wolf population. *P<0.001 (after sequential Bonferroni test) 443

The genetic structure of the Lithuanian wolf population was determined in wolf CL12 and haplotype W3-LT was typical of wolves CL4, CL16, and CL28 (both haplotypes are identical or similar to known haplotypes distributed in Russia and Finland, e.g. FJ978011, DQ480503, FJ978012, and FJ978009, see in Figure 2). For hybrid detection, we pooled the data on Lithuanian wolves and dogs and assumed this new data set as one population. Using Structure, wolves and dogs were assigned to two distinct clusters with average proportion of membership q=0.984 (±0.029) and q=0.993 (±0.007), respectively (Figure 3). Factorial correspondence analysis also confirmed separation between wolves and dogs. Further, we simulated hybrids between Lithuanian wolves and dogs using HybridLab. An admixture analysis was performed by Structure using the admixture model. Comparisons were made between wolves, dogs, first and second generations of hybrids (F1, F2), and backcrosses of wolves and dogs with F1 (wolves x F1, dogs x F1) (Figure 3). Individual assignment was made using the threshold q>0.85. All wolves were assigned to a distinct cluster (Cluster I), while all dogs but one (q=0.837) assigned to the second cluster (Cluster II). First and second generation hybrids were correctly assigned as wolf-dog intermediates in 98% and 96% of the cases respectively. A large proportion of the backcrossed individuals were significantly assigned to one of the two clusters (40% of wolf-backcrosses were assigned to Cluster I and 44% of dog-backcrosses with Cluster II). Using Newhybrids, we also investigated the probability of all Lithuanian wolves belonging to any of the first or second generation classes. The posterior probabilities to belong to the genotype class of purebred wolves all exceeded 0.9, which indicates a lack of any recent hybrids amongst Lithuanian wolves. Nevertheless, the high percentage of significant assignment of simulated backcrosses to wolf or dog clusters did not allow us analyse admixture at the backcross level. 4. Discussion The analysis of an mtdna control region (647 bp) revealed 5 haplotypes in Lithuanian wolves and high haplotype diversity (0.66). Pilot et al. [46] divided the wolves of Eastern Europe into ten subpopulations (based on 257 bp of the control region) assigning Lithuania, Latvia, Belarus, eastern Poland, northern Ukraine, and western Russia to subpopulations with relatively low haplotype diversity (0.52-0.55). Our results (haplotype diversity had the same value also for short fragment, 257 bp) are more similar to some regions of western Russia and northern Belarus (0.67-0.73) [10,46]. Amongst Lithuanian wolves, two haplotypes (W1-LT and W2-LT) were distributed across the country, whilst the other three (W3-LT, W5-LT, W10-LT) were rarely found and restricted to northern and south-eastern areas. The haplotype W5-LT was the only one that is identical to previously known haplotypes distributed in Latvia, Russia, Finland and Sweden [21]. The other four haplotypes had slight differences from known haplotypes and showed the lowest divergence from haplotypes typical of wolves from Scandinavia and Eastern and Southern Europe [19,21,47-49]. However, the haplotype W2-LT also showed low divergence from haplotypes of dogs. The phylogenetic analysis showed a clear distinction between haplotypes of wolves and dogs with few individuals grouped in the opposite clusters. All Lithuanian wolf haplotypes were grouped in the wolf-dominated cluster. Several haplotypes of dogs were assigned to the wolf cluster, but they all formed a small distinct group. This might be related to the history Figure 3. Structure analysis of Lithuanian wolves and dogs assuming two distinct genetic clusters with K=2 (A). Factorial correspondence analysis was computed for Lithuanian wolves and dogs individuals using Genetix. FA I, FA II are the first and second principal factors of variability, respectively (B). Structure analysis of Lithuanian wolves, dogs, and simulated by HybridLab hybrids genotypes: F1 and F2 are the first and second generation hybrids; F1 x wolf, F1 x dog are backcrosses (C). 444

L. Baltrūnaitė et al. of multiple origins of dogs from wolves or the dogs breeding history [18,50]. The microsatellite analysis revealed a slightly lower number of heterozygotes than expected from Hardy-Weinberg equilibrium and no evidence for any recent bottleneck in the Lithuanian population. High heterozygosity is typical of populations of the Eastern Europe: Latvia 0.73, Poland 0.73, north-western Russia 0.64-0.71, western Russia 0.78, and our study 0.71 [8-10,51]. Previous studies showed no bottleneck effect in populations of Latvia and north-western Russia (Carelia, Archangelsk), but it was detected in western Russia (Volgodskaja, Tverskaja, Smolenskaja, and Kaluzshkaja Regions) [8-10,51]. The abundance of the Lithuanian wolf population has widely fluctuated over the last century, ranging from 100-150 individuals in 1934 1938 and 1960 1970 to more than 1700 wolves after the Second World War. In the last decade, the minimum estimated count of wolves in Lithuania was 200-300 individuals [52]. It is probable that the continuous range of the Baltic wolf population, with its connectivity with other wolf populations and consequently a certain degree of migration, has maintained a sufficient amount of genetic diversity in the Lithuanian wolf population despite the fluctuations in abundance. Indeed, assignment analysis identified one wolf as a significant migrant from the population similar to the Finnish wolf population and three wolves as possible first generation migrants from a population similar to the Carelian population. This indicates that Lithuania is subject to immigration from differentiated populations. During migration, wolves from north-eastern Europe first reach the eastern part of Lithuania. All of these wolves were males which hunted in north-eastern and south-eastern Lithuania, and they carried rare haplotypes (W3-LT and W5-LT). Malebiased migration of wolves was also observed between the Alps and north Apennines [53]. The presence of immigrants might explain a linkage disequilibrium found in a few pairs of loci and slight deviation from Hardy- Weinberg equilibrium. Omitting detected immigrants, the Lithuanian wolf population was in Hardy-Weinberg equilibrium (P>0.07). The analysis of possible hybridisation in Lithuania did not show any clear indication of any wolf to be admixed. However, our results do not allow us to detect hybrids beyond first and second generations. Providing the samples for genetic studies, hunters did not note any atypical morphology amongst the hunted wolves that would indicate hybridization. Godinho et al. [45] demonstrated that hybridization in wolf populations is restricted to peripheral and recently expanded wolf populations, neither of which are the case for the Lithuanian population. However, documented cases of hybridisation in neighboring Latvia highlight the importance of monitoring of putative hybrids [11,12]. The obtained results indicate the need for further research concerning two main topics: 1) the study of the genetic structure of the entire Baltic wolf population and its probable substructure; and 2) the evaluation of migrant origin, dispersal level and direction amongst neighboring wolf populations. Acknowledgements We are grateful to Lithuanian hunters for providing wolf samples and to Jouni Aspi for providing us with the samples from the Finland and Russia. Moreover, we thank three anonymous reviewers for providing valuable comments and Jos Stratford for review of the English language. The study was supported by The Ministry of Environment of the Republic of Lithuania. References [1] Salvatori V., Linnell J., Report on the conservation status and threats for wolves (Canis lupus) in Europe, Council of Europe T-PVS/Inf, 2005, 16, 1-24 [2] Jedrzejewski W., Jedrzejewska B., Andersone- Lilley Z., Balciauskas L., Mannil P., Ozolins J., et al., Synthesizing wolf ecology and management in Eastern Europe: similarities and contrasts with North America, In: Musiani M., Boitani L., Paquet P.C. (Eds.), The world of wolves: new perspectives on ecology, behaviour and management, University of Calgary Press, Calgary, 2010 [3] Randi E., Genetics and conservation of wolves Canis lupus in Europe, Mamm. Rev., 2011, 41, 99-111 [4] Carmichael L., Nagy J.A., Larter N.C., Strobeck C., Prey specialization may influence patterns of gene flow in wolves ot the Canadian Northwest, Mol. Ecol., 2001, 10, 2787-2798 [5] Musiani M., Leonard J.A., Cluff H.D., Gates C.C., Mariani S., Paquet P.C., et al., Differentiation of tundra/taiga and boreal coniferous forest wolves: genetic, coat colour and association with migratory caribou, Mol. Ecol., 2007, 16, 4149-4170 [6] Frankham R., Ballon J.D., Briscoe D.A., Introduction to conservation genetics, 2nd ed., Cambridge 445

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