Population genetic structure of European wildcats inhabiting the area between the Dinaric Alps and the Scardo-Pindic mountains

Population genetic structure of European wildcats inhabiting the area between the Dinaric Alps and the Scardo-Pindic mountains | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Population genetic structure of European wildcats inhabiting the area between the Dinaric Alps and the Scardo-Pindic mountains Felicita Urzi, Nikica Šprem, Hubert Potočnik, Magda Sindičić, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-443648/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Habitat fragmentation and loss have contributed significantly to the demographic decline of European wildcat populations and hybridization with domestic cats poses a threat to the loss of genetic purity of the species. In this study we used microsatellite markers to analyse genetic variation and structure of the wildcat populations from the area between the Dinaric Alps and the Scardo-Pindic mountains in Slovenia, Croatia, Serbia and North Macedonia. We also investigated hybridisation between populations of wildcats and domestic cats in the area. One hundred and thirteen samples from free-leaving European wildcats and thirty-two samples from domestic cats were analysed. Allelic richness across populations ranged from 3.61 to 3.98. The observed Ho values ranged between 0.57 and 0.71. The global F ST value for the four populations was 0.080 (95% CI 0.056–0.109) and differed significantly from zero (P < 0.001). The highest F ST value was observed between the populations North Macedonia and Slovenia and the lowest between Slovenia and Croatia. We also found a signal for the existence of isolation by distance between populations. Our results showed that wildcats are divided in two genetic clusters largely consistent with a geographic division into a genetically diverse northern group (Slovenia, Croatia) and genetically eroded south-eastern group (Serbia, N. Macedonia). Hybridisation rate between wildcats and domestic cats varied between 13% and 52% across the regions. Population Genetics genetic variation microsatellite markers hybridisation wildcat Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction According to a revised taxonomy the European wildcat is classified into two subspecies Felis silvestris silvestris Schreber , 1777 and Felis silvestris caucasica Satunin , 1905, distributed in European forest habitats including islands of Britain, Sicily and Crete 1 . The species is legally protected by both Bern Convention 2 and European Union’s Habitats Directive, which consider it "strictly protected" 3 . International Union for Conservation of Nature (IUCN), categorized wildcat as “Least Concern”, since it has been evaluated together with Felis lybica species distributed over vast regions of Asia and Africa, and without considering the demographic decline and fragmentation of European wildcat populations 4 . Hundreds of years of combined negative factors, including habitat loss, have resulted in the extinction of the European wildcat from most of its former range in many parts of Europe 5 . In addition, transport networks, urban areas as well as agricultural landscapes divide natural habitats into small isolated patches and create barriers that restrict gene flow and ultimately leads to a hidden genetic structure within the European wildcat populations 4,6−8 . Many recent studies 9 – 12 showed that wildcat populations are geographically structured and conservation strategies should improve gene flow by restoring ecological corridors within biogeographical units 13 , 14 . Human – induced mortality and disease transmission 7,15−17 are also important threats to wildcats in Europe 5 , 18 , but the loss of genetic purity due to hybridisation with domestic cats ( Felis catus ) (i.e. the introgression of some alleles present in domestic cats into the genotype of the wildcats) 4 , 19 , 20 is a threat that has attracted the most attention from the scientific community and the public. Hybridisation between wildcats and domestic cats can lead to i) disruption of local genetic adaptations, ii) loss of genetic integrity of the European wildcats and even extinction of the subspecies 21 . Introgression of artificially selected traits of domestic cats into species of conservation concern may affects their fitness and leads to outbreeding depression in wild populations 22 . The extent of gene flow from domestic cats to wildcats varies in intensity across Europe and may exhibit significant local differences, most likely based on historical or ecological traits 23 – 26 . For example, a high level of hybridisation has been observed in Scottish 23 , and Hungarian 25 wildcats, while a low hybridisation rate was found in sampled wildcats from Italy, Bulgaria, Portugal, and Germany 9 , 27 , 28 . Identifying areas with different levels of the domestic cat gene introgression in European wildcat populations could help recognizing factors that have facilitated introgression rates in the past and/or that currently hinder or accelerate hybridisation. Since the level of hybridisation appears to be low in some regions and high in others, it is likely that other factors, such as differences in habitat structure and behaviour, have played a role in reducing hybridisation 9 . According to the genetic analysis, the European wildcats are subdivided into five main phylogeographic clusters, each corresponding to five biogeographic groups, distributed in the Iberian Peninsula, Italian Peninsula and the region of Sicily, Central Europe, Central Germany and Northern Balkans (Dinaric Alps) 11 , 27 . These geographically distinct groups represent the living remains of the Pleistocene refugial population 29 . More detailed analyses within each of the phylogeographic clusters could clarify the current patterns of structuring within population, since a possible influence of the "refugia within the refugia" existed throughout the Pleistocene 30 . On the other hand, recent habitat loss and fragmentation have led to population bottlenecks and a reduction in genetic diversity 25 . But despite the awareness of the importance of gaining more insight into the underlying patterns of genetic variability and genetic integrity of local populations, data on population structure are lacking in most European regions, except for Italy 10 , France 12 , Germany 9 and Spain 28 . The work of Mattucci et al., 11 , which included 39 samples from the area of Slovenian Dinaric Alps, and did not include any samples of wildcat populations of Balkan Peninsula, underlines the importance of clarifying evolutionary history of the wildcats in this area. In this study we used microsatellite markers to analyse genetic variation and structure of the wildcat populations from north-western Dinaric Alps to the Scardo-Pindic mountain system. Regardless they probably originate from the same Pleistocene refugium, we investigated whether geographical isolation is reflected in the genetic architecture of wildcat populations and how population structure has been affected by recent human management. Finally, we investigated hybridisation between populations of wildcats and domestic cats. Results Genetic variation between wildcat populations A total of 20 loci (18 used for analysis) on 145 individuals (113 wildcats and 32 domestic cats) from four countries were examined (treated as "populations", Table 2 ). Two loci (FCA090 and FCA094) were hard to read and were therefore excluded from all analyses. All 18 microsatellites were polymorphic, showing from seven (FCA058, FCA149) to 18 (FCA096) alleles per locus. The independent replication of 10% of the samples provided no evidence for false alleles. The allele sizes differed in the expected multiples of the microsatellite repeats. Eight out of 72 comparisons of loci by sample location deviated significantly from the expectations of Hardy-Weinberg ( P < 0.05). In the 18 microsatellite loci (with less than 5% of null alleles in all populations) the average null alleles frequencies per locus ranged from zero (FCA096) to 0.091 (FCA088) with an average of 0.033. No identical genotypes were observed, the low values for probability of identity (PID) suggest that individuals in the study were not highly related: PID = 7.8 x10 − 19 , PIDsibs = 1.2x x10 − 7 in wildcats; PID = 3.7 x10 − 21 , PIDsibs = 3.2 x10 − 8 in domestic cats. Table 1 Location, sample size, number of detected hybrids and brief history of European wildcat populations inhabiting the area between the Dinaric Alps and the Scardo-Pindic Mts. Country ab N Hybrids Historical Management National status (census) Brief description of populations Slovenia SI 22 3 Hunted until the 1992. There is no management plan and no coordinated monitoring. Protected (1000) Large population occupying optimal habitats in Dinaric Mts. Size reduced in the 1970, recovered afterwards. Distributed in Alps, Dinaric Mts. and small Pannonian areas in the northeast of the country. Croatia HR 55 9 Hunted until 2013. Before 2013 hunting was only allowed in areas north of Sava river. There is no management plan and no coordinated monitoring. Protected, (2196) (no data available for population size) Lack of data regarding the history of population. Distributed in all suitable habitats from Dinaric Mts. to Pannonian region. Serbia SR 29 5 Still game species. In the province Vojvodina hunted until the 90s. There is no management plan and no national monitoring. Protected in north province Vojvodina (no data available for population size) In the north of Vojvodina province, the distribution is associated with wooded river banks (rivers Danube, Tisa, Begej and Tamiš). In the southern area of Vojvodina (the entire Srem region and southeast of the Banat) and south of rivers Sava and Danube wildcat occurs in forest habitats. North Macedonia MK 7 1 Hunted until 2009.There is no management plan and no coordinated monitoring. Protected from 2009 (no data available for population size) No available data. Table 2 Genetic diversity among European wildcat populations in the area between the Dinaric Alps and the Scardo-Pindic Mts. based on 18 microsatellite loci Country ab N Hybrids He ± SD Ho ± SD F IS HWE A ± SD AR ± SD PID PIDsibs Slovenia SI 22 3 0.685 ± 0.199 0.697 ± 0.237 -0.019 0.664 5.39 ± 1.76 3.61 ± 0.91 3.2 x 10 − 16 5.4 x 10 − 07 Croatia HR 55 9 0.724 ± 0.196 0.715 ± 0.203 0.013 0.044 7.55 ± 2.20 3.98 ± 0.91 7.8 x 10 − 19 1.2 x 10 − 07 Serbia SR 29 15 0.694 ± 0.221 0.658 ± 0.229 0.054 0.181 5.78 ± 2.01 3.84 ± 1.12 6.7 x 10 − 17 4.6 x 10 − 07 N. Macedonia MK 7 1 0.692 ± 0.203 0.570 ± 0.265 0.191 0.004 4.28 ± 1.32 3.74 ± 1.05 6.3 x 10 − 15 1.4 x 10 − 06 Croatia (domestic) DcHR 32 0.783 ± 0.207 0.697 ± 0.273 0.110 < 0.001 8.66 ± 1.88 4.43 ± 0.69 3.7 x10 − 21 3.2 x 10 − 08 Note . Standard deviations are for average values; P < 0.05 for HWE and F IS (before Bonferroni correction) is indicated in bold He expected heterozygosity, Ho observed heterozygosity, F IS inbreeding coefficient, HWE Hardy–Weinberg equilibrium, A number of alleles, AR allelic richness (calculated by the rarefaction method for the lowest sample size n = 10), PID: probability-of-identity The populations of MK and HR showed significant deviations from HWE based on exact tests in GENEPOP ( P < 0.05), additionally the population from MK showed deviation also based on F IS (significantly positive values). It can be expected that the deviation from HWE in MK population is a consequence of the small sample size included in the analysis. The HR population showed no significant deviation from HWE after the Bonferroni correction (Table 2 ). The number of alleles per locus in wildcats ranged from 2 to 18 with a mean of 6.77. Allelic richness across populations ranged from 3.61 to 3.98, with the highest values in HR and SR populations. A similar pattern was observed for Ho with values between 0.57 and 0.71 and He with values between 0.68 and 0.72, with MK population showing the lowest Ho. The global F ST value for the four populations was 0.080 (95% CI 0.056–0.109) and differed significantly from zero ( P < 0.001). The pairwise F ST values between populations ranged from 0.004 to 0.148 with the mean of the pairwise F ST = 0.070 ± 0.060 (± SD ) and also differed significantly from zero ( P < 0.05) (Table 3 ). The highest F ST value was observed between the populations MK and SI and the lowest between SI and HR. Table 3 Pairwise values of F ST among European wildcat populations and domestic cat Population Slovenia Croatia Serbia North Macedonia Croatia 0.004 Serbia 0.025 0.019 North Macedonia 0.050 0.028 0.018 Domestic 0.147 0.127 0.148 0.136 Note. All F ST values are significant Exclusion Of Hybrids Individuals Detection of simulated hybrids with NewHybrids All controls were correctly identified by the NEWHYBRIDS software, with posterior probabilities of 0.99 for wildcats and 0.95 for domestic cats. The results of the identification of genotypes of the simulated hybrids are presented in the Supplementary Fig. 1, panel a1, b1, c1. Detection and exclusion of hybrids STRUCTURE and NEWHYBRIDS analysis concordantly identified two samples of with genotypes of domestic cats and 25% ( n = 28) of hybrids across all wildcat samples using the exclusion criterion given by a z value of less than 0.85 (NEWHYBRIDS) and a q value of less than 0.80 (STRUCTURE). Numbers of hybrids varied across countries: SI 13% ( n = 3), HR 16% ( n = 9), SR 52% ( n = 15), MK 14% ( n = 1). Nineteen of the 28 hybrids were classified as F2 hybrid (SI, n = 3; HR, n = 4; SR, n = 12), four were classified as back-crosses of F1 to wildcat (HR, n = 2; SR, n = 2), four as back-crosses of F1 to domestic cat (HR, n = 3; SR, n = 1) and two as domestic cats P2 population (SI, n = 1; SR, n = 1) (see Supplementary Fig. 1, panel a2, b2, c2). Genetic And Spatial Clustering The STRUCTURE analysis clearly separated the European wildcat samples from domestic cats (K = 2; Fig. 1 a). The estimated probability value for each K indicate the smallest value at K = 3 (see Supplementary Fig. 2). Increasing K to 3 split the European wildcats into two subclusters, separating populations SI and HR from populations SR and MK; no additional structuring was found at K = 4. In the separate analysis performed only with the European wildcat samples, the highest ΔK values were obtained with K = 2 (Fig. 1 b), suggesting a division between western and south-eastern populations (Fig. 2 ). For the logarithm probability of K it was possible to observe the lowest value of K = 2 that captures the maximum degree of structure detected in the data (see Supplementary Fig. 3). K = 3 and K = 4 showed no further difference in geographic structuring, suggesting that the two wildcat clusters are largely consistent with a geographic division into a northern group (SI, HR) and a south-eastern group (SR, MK) (Fig. 1 b). The FCA plot, which was based on individual genotypes, clearly separated individuals along the second axis and two main groups were identified according to their rough geographical origin. The first factorial axis explained 47.8% of the variance within populations. Along the second axis, SI and HR populations were separated from SR and MK populations (Fig. 3 ). DAPC according to the Bayesian Information Criterion, which also includes domestic cat genotypes, indicated that there are two genetic clusters of wildcats that distinguish individuals according to their geographical origin (in north-south gradient). Domestic cats belong to an independent cluster. The first principal component distinguished between domestic cats and wildcat clusters, and the second principal component showed a distinction between two wildcat clusters (see Supplementary Fig. 4). The ellipses, which describe the spatial extension of the clusters, did not overlap, which indicates a strong genetic structuring. The AMOVA result highly supported group structuring revealed by STRUCTURE, DAPC and FCA, the variance was 1.47 and significant ( P < 0.001) (Table 4 ). Table 4 Hierarchical analysis of molecular variance (AMOVA) based on microsatellite data Source of variation Variance Among populations 0.69 (0.076) Within population 2.01 (0.112) Among group 1.47 (< 0.001) Note. Values in bold are significant (P < 0.001). The populations correspond to four populations defined by country (according to Table 1 ), groups correspond to the two clusters according to the result of FCA and STRUCTURE analysis (K = 2). P values are given in parenthesis. Isolation by distance Microsatellite based genetic distances were correlated with geographical distances among populations ( t-value = 3.012, P = 0.003, R 2 = 0.0013), supporting the hypothesis of isolation by distance (Fig. 4 ). Discussion By utilizing microsatellites, we have determined the genetic variation and population structure in the European wildcats inhabiting area between the Dinaric Alps, the Pannonian Plain and the Scardo - Pindic mountains. We thus fill a knowledge gap in the wider area of the SE Europe, where data on the genetic prospects of this endangered felid are completely lacking. But genetic data are not the only one lacking. Both historical and recent data on wildcat distribution, abundance, mortality and other ecological factors that might affect genetic structure are missing for most countries included in our study. Mortality data is available only for Slovenia, for the 1950–1990 period, when annual culling varied between 70 and 493 individuals with an average of 193 wildcats culled per year 31 . In 1970s, based on hunters’ observations, Slovenian Hunting Association estimated the population size on up to 1000 individuals, mostly distributed in the sub-Mediterranean and Dinaric karst with occasional occurrence in the northern areas of Slovenia 31 , 32 . Other demographic data is not available for any of the countries, so our results really do provide one of the rare insights into ecology of the species in this area. Across our study area we found a slightly higher observed heterozygosity (Ho = 0.57–0.71) than reported in the study by Matucci et al. 11 , where the Ho across Europe ranged between 0.58–0.63. However, this value varies greatly among countries; e.g. central Germany 0.50–0.79 33 , France 0.39–1.00 34 , Portugal 0.42–0.82 28 , Hungary 0.42–0.87 25 . On a national level, we are expecting that genetic diversity in SI and HR populations can be sufficient to maintain adequate variation for adaptive evolution, especially due to observed gene flow between the countries and low hybridisation rate. High diversity was also confirmed with mitochondrial DNA control region of wildcats from Croatia 35 . On the contrary signs of genetic erosion were observed in both SR and MK populations. In Serbia we found relatively high diversity, but greater F IS value compared to the SI and CR populations and very high level of introgression of domestic alleles. SR wildcat population is fragmented and occupies patches of suitable habitats along wooded river banks in the northern part and forest habitat patches in the central and southern parts of the country which may present barriers to gene flow and consequently affect genetic integrity. Thus, additional studies are needed to reveal fine-scale genetic structure in this area. Lower genetic diversity that we found among wildcats sampled in MK should be considered with caution due to the low sample size but could be an indication of recent genetic bottlenecks or geographic isolation due to various human impacts 5,36−38 . The highest F ST values in our study were observed between the northern SI population and the southern MK population, and lowest between the closest SI and HR population, which is in congruent with geographical distances. We found a signal for the existence of isolation by distance (Fig. 4 ) between populations, while AMOVA data indicate the existence of two genetic groups in which the SI and HR populations overlap. The existing admixture among them is reflected by a weak pattern of isolation by distance and congruent results for the population divisions obtained by FCA, DAPC and STRUCTURE. The wildcat is a solitary and territorial species, living in low-density populations 33 , 39 , a small-scale genetic structure within the regions could be a consequence of natural processes and indicates relatively low dispersal potential of the species 40 . It is also possible that habitat fragmentation in a more urbanized part of the Dinaric region contributed to the lower connectivity 41 and subsequent F ST values found within this area. It has also been shown in central Germany that anthropogenic and natural landscape barriers can limit the wildcat's dispersal potential and the consequences are reflected in the genome 33 . Our findings indicate that habitat between SI and HR is continuous and barely limits gene flow for the wildcats, but it is difficult to draw conclusion considering SR and MK population. All populations were not evenly sampled, we did not analyse samples from eastern part of Croatia, so we have a sampling gap between Croatia and Serbia along the Pannonian Basin. Also, due to ad-hoc sampling we have quite low coverage in Serbia and very scarce in North Macedonia, which might affect values of genetic differentiation 42 . Furthermore, our northern and south - eastern populations might display the recolonisation from different refugia. A model of late Pleistocene isolation and genetic diversification of European wildcat populations into three main Mediterranean glacial refuges in the southern Iberian, Italian, and Balkan peninsulas was proposed by Hewitt 43 and Mattucci et al., 11 . Authors also state that this genetic divergence cannot be explained by recent fragmentation, but only by prolonged periods of isolation with historically no or limited gene flow 10 , 11 . Mattucci et al., 10 suggest a scenario of ancient population isolation in Alpine (Italy, Slovenia) and Mediterranean (Istrian peninsula; south west of Slovenia and Croatia) refuges which increase genetic divergence between European wildcats in the Eastern Alps and Apennines. They also showed that the European wildcat populations are subdivided into at least five main biogeographic groups with divergence times from the Late Pleistocene, but sampling in Dinaric Alps, Pannonian Plain and Scardo - Pindic mountains was scarce (only samples from Slovenia and Bosnia and Herzegovina were included in the study) 11 . So Mattucci et al., 11 already noted that Structure results suggest that local populations could be genetically subdivided at smaller geographical scale and but additional genetic markers analysis is needed to point us into this direction. Studies on Eurasian lynx ( Lynx lynx ), the only other wild Felidae present in this area, showed that the mitochondrial lineage restricted to the Balkan lynx population was the first one to diverge from the other Eurasian lynx haplogroups. Balkan lynx haplogroup was the most divergent and its split was dated around 96.6 kya 44 , while the three Mediterranean wildcat lineages probably originated 21,000–125,000 years ago 11 . Past population isolation in Balkan refuges with genetically diverse groups of populations, leading to a more recent ‘refugia within refugia’ concept as also seen in other species (e.g. wild boar) 45 . Lack of information on the demographic history of the researched populations and gaps in our sampling prevent us from drawing firm conclusions, so additional studies based on fine-grained grid sampling are needed to reveal fine-scale genetic structure in this area. Our analyses are in line with previous studies showing that the genetic integrity of the European wildcat is not compromised in most regions of Europe 11 , 25 , 26 , however in some areas introgression can be substantial and could be caused by environmental circumstances that enable long-lasting hybridisation 23 , 25 , 46 . In our study none of the hybrids found were classified as hybrids of the first generation which shows low level of recent hybridisation in all studied populations. In SI and SR mainly second-generation hybrids were found, while in HR a half of hybrids were classified as back-crosses and a half as second-generation hybrids. In the case of SR, due to high hybridisation rate, this result could reflect a long-lasting hybridisation that persists in the area and might be the consequence of high density of stray domestic cats, especially on the periphery of remote village areas. By using genomic approaches Mattucci et al., 27 found that some of the hybrids with possible F2 origins actually showed admixture traces dating back from 9 to 11 generations in the past. Since F2 hybrids are expected to occur in rare cases, a high proportion of F2 hybrids in the SI and SR populations could be the result of method bias and brings misassignments of individuals that can represent the product of repeated crosses among F1 or F2 individuals, rather than true second-generation hybrids. We used STRUCTURE and NEWHYBRIDS for classifying the hybrids and both methods are not accurate enough to classifying the hybrids with repeated crossbreeding between different hybrid generations. Results of Q scores generated by STRUCTURE allow for a relatively simplistic tracking of estimated proportion of ancestry, so that backcrosses cannot be distinguished from more complex hybrids. Even if NEWHYBRIDS classifies the hybrids into generational categories, some misassignments of individuals could occur due to strict hybrid categories used in this approach. In the area from the Dinaric Alps to the Scardo-Pindic Mts. wildcats are divided in two genetic clusters largely consistent with a geographic division into a genetically diverse northern group (SI, HR) and genetically eroded south-eastern group (SR, MK). But wider sampling, especially in MK and neighbouring countries, like Bosnia and Herzegovina, would help to clarify the evolutionary history in this part of Europe. The apparent loss of genetic integrity due to hybridisation with domestic cats found in Serbia urges specific conservation measures to maintain evolutionary potential of this species. Methods Study area The study area, across four countries (Slovenia, Croatia, Serbia and North Macedonia) is located at the intersection of three major European geographical units, namely Dinaric Alps, Pannonian Basin, and the Scardo-Pindic Mts. system, which is a continuation of Dinarides in the southern part of Balkan Peninsula. The two-mountain systems, Dinaric Alps (extending from Slovenia to Albania) and Scardo-Pindic Mts. (from Kosovo to Greece) are characterized by small plateaus and meadows at high altitude up to 2,764 m a.s.l. The forest cover consists mainly of beech ( Fagus sylvatica ), fir ( Abies alba ) and spruce ( Picea abies ) associations 47 . The climate varies, but roughly with a continental climate in the north-east Pannonian region, a severe alpine climate in the mountain regions and a sub-Mediterranean climate in the coastal region along the Adriatic Sea 48 . Topographically, the area is highly heterogeneous, interrupted by ditches, bays and rocks developed on limestone and dolomite rocks. Besides wildcat, several other carnivorous species are present in the region; brown bear ( Ursus arctos ), grey wolf ( Canis lupus ), Eurasian lynx ( Lynx lynx ), golden jackal ( Canis aureus ), red fox ( Vulpes vulpes ), and several species of mustelids ( Mustelidae ). Unlike the mountainous regions, there are no large carnivorous species in the Pannonian region, but there are areas with increasing populations of the golden jackal. Management of wildcats is somewhat different in each country and is governed by national conservation policy (see Table 1 ). Sampling A total of 113 samples from free-leaving putative European wildcats were collected over a period from 2012 to 2020 in Slovenia (SI), Croatia (HR), Serbia (SR) and North Macedonia (MK) (see Supplementary information Fig. 5) from dead (legal hunting, natural mortality and vehicle collisions) or from live-trapped individuals for telemetry studies (Table 1 ). They were morphologically identified as wildcats by collectors. Blood samples from domestic cats were taken in Croatia (32 samples in total) from animals admitted for treatment at University Hospital of Faculty of Veterinary Medicine, University of Zagreb. All samples were stored at -80°C, tissue samples in 95% ethanol, whole blood samples in sodium citrate vacutainer. Based on the country of origin, the wildcat individuals were grouped into four groups (for the purposes of this paper further considered as "populations"). DNA extraction and microsatellite genotyping Genomic DNA was extracted using the peqGOLD Blood & Tissue DNA Mini Kit (VWR International Ltd., Leuven, Belgium) according to manufacturer’s instructions. The concentration and purity of the extracted DNA in the final elution volume was measured with Qubit® dsDNA BR Assay Kit (Invitrogen) on 3.0 Qubit Fluorometer (Life Technologies). Nineteen autosomal dinucleotide and one tetranucleotide (FCA 441) microsatellites (Supplementary Table S1), originally identified in domestic cats 49 and screened in studies in wildcats and domestic cats 10 , 11 , 50 , were amplified in six PCR multiplex reactions with ready-to-use KAPA2G Fast Multiplex Mix (Kapa Biosystems). According to the manufacturer's instructions we used 2 µL template DNA and 0.3 mM final concentration for each primer used in the set. The amplification was performed under the following conditions: initial PCR activation for 3 min at 95°C, followed by 35 cycles of denaturation for 15 s at 95°C, annealing for 30 s at 58°C, extension for 30 s at 72°C and final extension for 10 min at 72°C. The fragment analysis was performed on a SeqStudio sequencer (Thermo Fisher Scientific) using the GeneScan LIZ500 (-250) standard (Applied Biosystems). The results were validated with the software GENEMAPPER v.5.0 (Applied Biosystems). Negative controls were included in all extraction and PCR steps. About 10% of the randomly selected samples were replicated independently to check for false alleles. Analyses of genetic variation Using the FREENA program 51 , we estimated the proportion of the null allele (NA) at each locus in each population with respect to the fact that the presence of null alleles can cause a significant heterozygote deficit and deviation from Hardy-Weinberg equilibrium (HWE). We used the software GENEPOP 4.2 52 to test for deviations from HWE. The exact test to assess the heterozygosity deficiency was performed for each population (country). The baseline significance level was set at 0.05 and a Bonferroni procedure was applied in multiple comparisons to compensate for the risk of a bloated type 1 deficiency. The mean number of alleles (A), observed (H O ) and expected (H E ) heterozygosity 53 , and inbreeding coefficients (F IS ) were calculated for each population with GENETIX 4.05.2 54 separately for the domestic, and wildcat populations. The probability of identity and sibling-identity were calculated with the Excel macro GenAlEx v6.5 55 . Allelic richness for each population (AR) was estimated following a rarefaction method in the program FSTAT 2.9.4 56 . The genetic differentiation between wildcat populations and between domestic cats and wildcats was estimated using pairwise F ST in GENEPOP 4.2 according to Weir & Cockerham 57 , and significant differences from zero F ST estimates were tested with 1,000 permutations. All subsequent analyses were performed after excluding individuals with admixed genotypes (see paragraph “Control population for hybrid simulation”). Exclusion of hybrids individuals Determination of control population for hybrid simulation Based on the initial STRUCTURE analysis, there was a statistically supported split between wildcat and domestic cat clusters (Fig. 1 a). Ten individuals from each of our four population (SI, HR, SR, MK) and ten individuals from HR domestic cat population was selected for the control population used in the simulation of hybridisation (to obtain clear hybrid genotypes). The controls for pure parental wildcats (P1) and domestic cat (P2) were determined above ( z designation) by NEWHYBRIDS v1.1 58 . These z values were used to determine the ten animals from each population that were the “most pure”, with the z value above 0.99. These individuals were used as control animals for each population in subsequent analyses. Simulated hybrids One hundred simulated genotypes (for each population independently) were generated from the control populations P1 and P2 in HYBRIDLAB v1.0 59 for: parental wildcats (P1) and domestic cat populations (P2), F1, F2, back-crosses of F1 to wildcat (P1Bx) and domestic cat (P2Bx) and a second back-cross of P1Bx to wildcat controls (P1Bx2) and P2Bx to domestic cat controls (P2Bx2). The simulated genotypes were analysed using NEWHYBRIDS. A burn- in period of 5 000 was followed by 10 000 sweeps based on the graphical version of NEWHYBRIDS (see Supplementary Fig. 1, panel a1, b1, c1). Ten replicates using Jeffrey’s priors were tested and summarized using CLUMPAK. These simulated data were also analysed using STRUCTURE with K values of one to four with 100 000 burn-in and a data collection of 100 000 chains. The “Admixture Model” was applied. This protocol was replicated 10 times per each K-value. STRUCTURE HARVESTER was used to evaluate which K-value was most likely. The results of the replicated runs were combined with the Greedy algorithm of CLUMPP and the summary outputs were graphically displayed using RStudio and R version 3.6.2 60 . Detection of hybrids All 113 complete genotypes of putative wildcats were analysed with NEWHYBRIDS. A burn-in period of 5 000 was followed by 10 000 sweeps. Ten replicates using Jeffrey’s priors were tested. CLUMPAK was used to summarise the ten replicates for each prior. Individuals with a z designation value lower than 0.85 for either P1 or P2 were classified as a hybrid. Hybrids were further assigned to F1, F2, P1Bx or P2Bx, based on admixture analyses of observed and simulated cat data sets. In addition, we used program STRUCTURE (as described by Mattucci et al., 10 ) to compare the results of hybrids identification by NEWHYBRIDS; the admixed genotypes were identified at a threshold qi < 0.80. All individuals with assigned admixed genotype using NEWHYBRIDS and STRUCTURE methods were removed from the data sets. Genetic and spatial clustering after hybrids exclusion Population genetic clusters were revealed using STRUCTURE 2.3.4 61 on two datasets: i) wildcat populations and domestic cats, and ii) wildcat populations only. In STRUCTURE, ten independent runs were performed for each K-value in the range of one to ten using a model assuming admixture with correlated allele frequencies. Each run included a burn-in period of 100,000 replications followed by 100,000 Markov Chain Monte Carlo (MCMC) iterations. The results of the replicated runs for each K value from two to ten were combined using STRUCTURE HARVESTER v 0.6.94 62 and the optimal K value was selected using the ΔK method developed by Evanno et al., (2005). The results of the replicated runs for the optimal K value were combined using the Greedy algorithm of CLUMPP 1.1.1 64 and the summary results were plotted with DISTRUCT 1.1 65 . The genetic relationships between all genotyped individuals were represented by Factorial Correspondence Analysis (FCA) using GENETIX. The distribution of the individuals in a 2D space was compared by eye with the geographical location of the localities. We also investigated the genetic structure with the R-package, adegenet 2.0.0 66 , using RStudio 67 and R version 3.6.2 60 . We used the Discriminant Analysis of Principal Components (DAPC) 68 , multivariate method in this package to identify the most likely number of clusters (K). The analysis of molecular variance (AMOVA) 69 was performed in ARLEQUIN 3.5. 70 to test the genetic differences between individuals and populations and between the optimal number of clusters identified by STRUCTURE (K = 2). The statistical significance of the variance components was investigated using 999 permutations in the R-package ade4 v1.7-13. 71 . Finally, we tested isolation by distance (IBD) patterns within all populations using the Mantel function in the R-package adegenet. Declarations Ethical statements Samples of European wildcats were collected from dead (legal hunting, natural mortality and vehicle collisions) or from live-trapped individuals for telemetry studies. All methods were carried out in accordance with the Ethical and Welfare Standards presented in the (Official Gazette of the Republic of Croatia 102/2017), Regulation on the Protection of Animals Used for Scientific Purposes (Official Gazette of the Republic of Croatia 55/13), with the approval of the Bioethical Committee for the Protection and Welfare of Animals of the University of Zagreb Faculty of Agriculture (UR.BR. 251-71-29-02/19-21-2). The study is reported in accordance with ARRIVE guidelines 72 . Acknowledgements We would like to thank to all responsible population managers who organized sampling, and to numerous hunters who provided samples of wildcats. We would like to thank Sandra Potušek for her help with the laboratory work, and Laura Iacolina for thoughtful comments on data analyses. Funding: This study was funded by: (i) the Slovenian Research Agency (programme group P1–0386), (ii) the STARBIOS2 European Union’s Horizon 2020 Research and Innovation Program under grant agreement No. 709517 oriented to promote responsible research and innovation in biosciences; (iii) the RESBIOS European Union’s Horizon 2020 Research and Innovation Program (No. 872146); (iv) COST Action G-Bike (CA18134), supported by COST (European Cooperation in Science and Technology); (v) Ministry of Education, Sciences and Technological Development Republic of Serbia (No. 451-03-9/2021-14/200178). Author contributions statement E.B. and N.Š. conceived of the presented idea. F.U. and E.B. wrote the manuscript with support from M.S. F.U. carried out the laboratory work. F.U. and L.D. performed the analysis of the data. H.P., M.S., D.Ć and D.M. provide the samples. All authors discussed the results and contributed to the final manuscript. Additional information I declare that the authors have no competing interests as defined by Nature Research, or other interests that might be perceived to influence the results and/or discussion reported in this paper. Supplementary Information is available for this paper. Correspondence and requests for materials should be addressed to E. B. Reprints and permissions information is available at www.nature.com/reprints . Data availability statements The data that support the findings of this study are available from the corresponding author upon reasonable request. References Kitchener, A. C. et al. A revised taxonomy of the Felidae: The final report of the Cat Classification Task Force of the IUCN Cat Specialist Group . Cat News vol. 11 (2017). ISSN 1027-2992 Genovesi, P. & Shine, C. European strategy on invasive alien species: Convention on the Conservation of European Wildlife and Habitats (Bern Convention). in Council of Europe. (2004). ISBN 92-871-5487-2 Cauncil of Europe. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-443648","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":23760996,"identity":"e97b84c2-7b6b-4deb-ac6d-210ca1b14c7a","order_by":0,"name":"Felicita Urzi","email":"","orcid":"","institution":"University of Primorska, Natural Sciences and Information Technologies","correspondingAuthor":false,"prefix":"","firstName":"Felicita","middleName":"","lastName":"Urzi","suffix":""},{"id":23760997,"identity":"fbd07986-dd5a-4f2e-95f0-50c37835afda","order_by":1,"name":"Nikica Šprem","email":"","orcid":"","institution":"University of Zagreb","correspondingAuthor":false,"prefix":"","firstName":"Nikica","middleName":"","lastName":"Šprem","suffix":""},{"id":23760998,"identity":"301f55ce-3533-4def-8e30-3eaebaefd974","order_by":2,"name":"Hubert Potočnik","email":"","orcid":"","institution":"University of Ljubljana","correspondingAuthor":false,"prefix":"","firstName":"Hubert","middleName":"","lastName":"Potočnik","suffix":""},{"id":23760999,"identity":"ddd05a84-97a6-4fa5-9051-e649d375805f","order_by":3,"name":"Magda Sindičić","email":"","orcid":"","institution":"University of Zagreb","correspondingAuthor":false,"prefix":"","firstName":"Magda","middleName":"","lastName":"Sindičić","suffix":""},{"id":23761000,"identity":"4a13e2a9-f38c-44f3-b0d3-eb706c309805","order_by":4,"name":"Dean Konjević","email":"","orcid":"","institution":"University of Zagreb","correspondingAuthor":false,"prefix":"","firstName":"Dean","middleName":"","lastName":"Konjević","suffix":""},{"id":23761001,"identity":"1883f6e9-e391-4765-8412-8ddc038c4e19","order_by":5,"name":"Duško Ćirović","email":"","orcid":"","institution":"University of Belgrade","correspondingAuthor":false,"prefix":"","firstName":"Duško","middleName":"","lastName":"Ćirović","suffix":""},{"id":23761002,"identity":"e15554f9-bb1c-4ad0-b2dc-7e0aca8be872","order_by":6,"name":"Andrea Rezić","email":"","orcid":"","institution":"University of Zagreb","correspondingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"","lastName":"Rezić","suffix":""},{"id":23761005,"identity":"e79c315b-4ce7-426f-9396-dc50fbcc1ac2","order_by":7,"name":"Luka Duniš","email":"","orcid":"","institution":"University of Primorska, Natural Sciences and Information Technologies","correspondingAuthor":false,"prefix":"","firstName":"Luka","middleName":"","lastName":"Duniš","suffix":""},{"id":23761006,"identity":"4b44b836-0a19-42e9-9670-5bd4a8666d6b","order_by":8,"name":"Dime Melovski","email":"","orcid":"","institution":"Georg-August University Goettingen, Wildlife Sciences","correspondingAuthor":false,"prefix":"","firstName":"Dime","middleName":"","lastName":"Melovski","suffix":""},{"id":23761007,"identity":"25f17e83-8cc7-487b-9c5b-6a905ec0e3ec","order_by":9,"name":"Elena Buzan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYDACdjB5AEQwPgCzmQlpYUZoYTYgWQubBFHu4mdmPvbgB8OdxPntZ49V8+64xyDfTkCLZDNbumEPw7PExp68tNu8Z4oZDA4T0GJwmMdMgofhcGIzQ47Zbd62BAYDQn6xP8z/TfIPUEsb/xuzYpAW+WZCtjDzsEmDbOmRyDFjBmlhIOQwicNsZtIyBs+MZ0i8MZaceyaBh6Bf+Nubn0m+qbgjO78/x/DD2x0JcvL9BwjogTgPSjM2MPAQox4JALWMglEwCkbBKMAAAJz9OnJ7HmQuAAAAAElFTkSuQmCC","orcid":"","institution":"University of Primorska, Natural Sciences and Information Technologies","correspondingAuthor":true,"prefix":"","firstName":"Elena","middleName":"","lastName":"Buzan","suffix":""}],"badges":[],"createdAt":"2021-04-20 15:29:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-443648/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-443648/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":8597898,"identity":"d5c19981-f2c6-4d33-b75c-446c7b366fd2","added_by":"auto","created_at":"2021-04-29 14:22:10","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":53854,"visible":true,"origin":"","legend":"Genetic structure. Genetic structure of European wildcat populations (Slovenia (SI), Croatia (HR), Serbia (SR) and North Macedonia (MK), domestic cats (DcHR)) and domestic cats (1a) and only European wildcats (1b) revealed by STRUCTURE) (see Table 1.) Each individual is represented by a line proportionally partitioned into colour segments corresponding to its membership in particular clusters. K is the number of clusters. Black lines separate the individuals from different populations (according to Table 1). ","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-443648/v1/15d59466a454464441ed3b93.jpg"},{"id":8597988,"identity":"0430ef11-c5ef-4ffa-bc11-530c9103b111","added_by":"auto","created_at":"2021-04-29 14:25:10","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":65952,"visible":true,"origin":"","legend":"Genetic structure of European wildcats based on spatial clustering of individuals according to the best model, dividing four populations into two clusters (K = 2; see also Figure 1b).","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-443648/v1/22271b777dd1fe34c97715b9.jpg"},{"id":8597989,"identity":"012d423a-0b74-40b2-8604-d47f51012e1b","added_by":"auto","created_at":"2021-04-29 14:25:11","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":28463,"visible":true,"origin":"","legend":"A two-dimensional plot of the FCA performed using GENETIX. European wildcats from different populations are indicated by different colours. The first axis explained 47.8% (P = 0.010), and the second explained 31.2% (P = 0.072) of the variance.","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-443648/v1/ecfab55ba5e1bad1c5d7fde4.jpg"},{"id":8597990,"identity":"c855c137-69e3-4dec-a291-83b7ae4d0496","added_by":"auto","created_at":"2021-04-29 14:25:11","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":26400,"visible":true,"origin":"","legend":"Isolation by distance. Pairwise Edwards genetic distances between individuals (Dgen), plotted against the Euclidean geographical distances (Dgeo; km) for the same individuals. Local density of points plotted using a two-dimensional Kernel density.","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-443648/v1/5e1552e8c2d18b545e1ef98c.jpg"},{"id":13689451,"identity":"88bbedf9-cdb2-4025-8450-76dd8b2cccec","added_by":"auto","created_at":"2021-09-17 12:29:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":646307,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-443648/v1/15e68297-10cb-4571-bd16-5b16a58ae4b3.pdf"},{"id":8597987,"identity":"03432346-0ce0-4097-b98c-5e8bc8064850","added_by":"auto","created_at":"2021-04-29 14:25:10","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":885424,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableFigureWildcatPopulationstructure.docx","url":"https://assets-eu.researchsquare.com/files/rs-443648/v1/30c65af34c11481a8c90a6fb.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Population genetic structure of European wildcats inhabiting the area between the Dinaric Alps and the Scardo-Pindic mountains","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAccording to a revised taxonomy the European wildcat is classified into two subspecies \u003cem\u003eFelis silvestris silvestris Schreber\u003c/em\u003e, 1777 and \u003cem\u003eFelis silvestris caucasica Satunin\u003c/em\u003e, 1905, distributed in European forest habitats including islands of Britain, Sicily and Crete \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The species is legally protected by both Bern Convention \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e and European Union\u0026rsquo;s Habitats Directive, which consider it \"strictly protected\" \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. International Union for Conservation of Nature (IUCN), categorized wildcat as \u0026ldquo;Least Concern\u0026rdquo;, since it has been evaluated together with \u003cem\u003eFelis lybica\u003c/em\u003e species distributed over vast regions of Asia and Africa, and without considering the demographic decline and fragmentation of European wildcat populations \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eHundreds of years of combined negative factors, including habitat loss, have resulted in the extinction of the European wildcat from most of its former range in many parts of Europe \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. In addition, transport networks, urban areas as well as agricultural landscapes divide natural habitats into small isolated patches and create barriers that restrict gene flow and ultimately leads to a hidden genetic structure within the European wildcat populations \u003csup\u003e4,6\u0026minus;8\u003c/sup\u003e. Many recent studies \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e showed that wildcat populations are geographically structured and conservation strategies should improve gene flow by restoring ecological corridors within biogeographical units \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Human \u0026ndash; induced mortality and disease transmission \u003csup\u003e7,15\u0026minus;17\u003c/sup\u003e are also important threats to wildcats in Europe \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, but the loss of genetic purity due to hybridisation with domestic cats (\u003cem\u003eFelis catus\u003c/em\u003e) (i.e. the introgression of some alleles present in domestic cats into the genotype of the wildcats) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e is a threat that has attracted the most attention from the scientific community and the public.\u003c/p\u003e\n\u003cp\u003eHybridisation between wildcats and domestic cats can lead to i) disruption of local genetic adaptations, ii) loss of genetic integrity of the European wildcats and even extinction of the subspecies \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Introgression of artificially selected traits of domestic cats into species of conservation concern may affects their fitness and leads to outbreeding depression in wild populations \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The extent of gene flow from domestic cats to wildcats varies in intensity across Europe and may exhibit significant local differences, most likely based on historical or ecological traits \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. For example, a high level of hybridisation has been observed in Scottish \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, and Hungarian \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e wildcats, while a low hybridisation rate was found in sampled wildcats from Italy, Bulgaria, Portugal, and Germany \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Identifying areas with different levels of the domestic cat gene introgression in European wildcat populations could help recognizing factors that have facilitated introgression rates in the past and/or that currently hinder or accelerate hybridisation. Since the level of hybridisation appears to be low in some regions and high in others, it is likely that other factors, such as differences in habitat structure and behaviour, have played a role in reducing hybridisation \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAccording to the genetic analysis, the European wildcats are subdivided into five main phylogeographic clusters, each corresponding to five biogeographic groups, distributed in the Iberian Peninsula, Italian Peninsula and the region of Sicily, Central Europe, Central Germany and Northern Balkans (Dinaric Alps) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. These geographically distinct groups represent the living remains of the Pleistocene refugial population \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. More detailed analyses within each of the phylogeographic clusters could clarify the current patterns of structuring within population, since a possible influence of the \"refugia within the refugia\" existed throughout the Pleistocene \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. On the other hand, recent habitat loss and fragmentation have led to population bottlenecks and a reduction in genetic diversity \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. But despite the awareness of the importance of gaining more insight into the underlying patterns of genetic variability and genetic integrity of local populations, data on population structure are lacking in most European regions, except for Italy \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, France \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, Germany \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e and Spain \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. The work of Mattucci et al., \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, which included 39 samples from the area of Slovenian Dinaric Alps, and did not include any samples of wildcat populations of Balkan Peninsula, underlines the importance of clarifying evolutionary history of the wildcats in this area.\u003c/p\u003e\n\u003cp\u003eIn this study we used microsatellite markers to analyse genetic variation and structure of the wildcat populations from north-western Dinaric Alps to the Scardo-Pindic mountain system. Regardless they probably originate from the same Pleistocene refugium, we investigated whether geographical isolation is reflected in the genetic architecture of wildcat populations and how population structure has been affected by recent human management. Finally, we investigated hybridisation between populations of wildcats and domestic cats.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003cp\u003e\u003cstrong\u003eGenetic variation between wildcat populations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 20 loci (18 used for analysis) on 145 individuals (113 wildcats and 32 domestic cats) from four countries were examined (treated as \"populations\", Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Two loci (FCA090 and FCA094) were hard to read and were therefore excluded from all analyses. All 18 microsatellites were polymorphic, showing from seven (FCA058, FCA149) to 18 (FCA096) alleles per locus. The independent replication of 10% of the samples provided no evidence for false alleles. The allele sizes differed in the expected multiples of the microsatellite repeats. Eight out of 72 comparisons of loci by sample location deviated significantly from the expectations of Hardy-Weinberg (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In the 18 microsatellite loci (with less than 5% of null alleles in all populations) the average null alleles frequencies per locus ranged from zero (FCA096) to 0.091 (FCA088) with an average of 0.033. No identical genotypes were observed, the low values for probability of identity (PID) suggest that individuals in the study were not highly related: PID\u0026thinsp;=\u0026thinsp;7.8 x10\u003csup\u003e\u0026minus;\u0026thinsp;19\u003c/sup\u003e, PIDsibs\u0026thinsp;=\u0026thinsp;1.2x x10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e in wildcats; PID\u0026thinsp;=\u0026thinsp;3.7 x10\u003csup\u003e\u0026minus;\u0026thinsp;21\u003c/sup\u003e, PIDsibs\u0026thinsp;=\u0026thinsp;3.2 x10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e in domestic cats.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eLocation, sample size, number of detected hybrids and brief history of European wildcat populations inhabiting the area between the Dinaric Alps and the Scardo-Pindic Mts.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCountry\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eab\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHybrids\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHistorical\u003c/p\u003e\n\u003cp\u003eManagement\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNational status (census)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eBrief description of populations\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSlovenia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHunted until the 1992. There is no management plan and no coordinated monitoring.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eProtected (1000)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLarge population occupying optimal habitats in Dinaric Mts. Size reduced in the 1970, recovered afterwards. Distributed in Alps, Dinaric Mts. and small Pannonian areas in the northeast of the country.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCroatia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHunted until 2013. Before 2013 hunting was only allowed in areas north of Sava river. There is no management plan and no coordinated monitoring.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eProtected, (2196) (no data available for population size)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLack of data regarding the history of population. Distributed in all suitable habitats from Dinaric Mts. to Pannonian region.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSerbia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStill game species. In the province Vojvodina hunted until the 90s. There is no management plan and no national monitoring.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eProtected in north province Vojvodina\u003c/p\u003e\n\u003cp\u003e(no data available for population size)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIn the north of Vojvodina province, the distribution is associated with wooded river banks (rivers Danube, Tisa, Begej and Tami\u0026scaron;). In the southern area of Vojvodina (the entire Srem region and southeast of the Banat) and south of rivers Sava and Danube wildcat occurs in forest habitats.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNorth Macedonia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMK\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHunted until 2009.There is no management plan and no coordinated monitoring.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eProtected from 2009 (no data available for population size)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo available data.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003e\u003cstrong\u003eGenetic diversity among European wildcat populations in the area between the Dinaric Alps and the Scardo-Pindic Mts. based on 18 microsatellite loci\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCountry\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eab\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHybrids\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHe\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHo\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eF\u003csub\u003eIS\u003c/sub\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHWE\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eA\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAR\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePID\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePIDsibs\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSlovenia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e0.685\u0026thinsp;\u0026plusmn;\u0026thinsp;0.199\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e0.697\u0026thinsp;\u0026plusmn;\u0026thinsp;0.237\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.664\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e5.39\u0026thinsp;\u0026plusmn;\u0026thinsp;1.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e3.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.2 x 10\u003csup\u003e\u0026minus;\u0026thinsp;16\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.4 x 10\u003csup\u003e\u0026minus;\u0026thinsp;07\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCroatia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e0.724\u0026thinsp;\u0026plusmn;\u0026thinsp;0.196\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e0.715\u0026thinsp;\u0026plusmn;\u0026thinsp;0.203\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.044\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e7.55\u0026thinsp;\u0026plusmn;\u0026thinsp;2.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e3.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.8 x 10\u003csup\u003e\u0026minus;\u0026thinsp;19\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.2 x 10\u003csup\u003e\u0026minus;\u0026thinsp;07\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSerbia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e0.694\u0026thinsp;\u0026plusmn;\u0026thinsp;0.221\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e0.658\u0026thinsp;\u0026plusmn;\u0026thinsp;0.229\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.054\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.181\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e5.78\u0026thinsp;\u0026plusmn;\u0026thinsp;2.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e3.84\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.7 x 10\u003csup\u003e\u0026minus;\u0026thinsp;17\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.6 x 10\u003csup\u003e\u0026minus;\u0026thinsp;07\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN. Macedonia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMK\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e0.692\u0026thinsp;\u0026plusmn;\u0026thinsp;0.203\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e0.570\u0026thinsp;\u0026plusmn;\u0026thinsp;0.265\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.191\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e4.28\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e3.74\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.3 x 10\u003csup\u003e\u0026minus;\u0026thinsp;15\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.4 x 10\u003csup\u003e\u0026minus;\u0026thinsp;06\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCroatia (domestic)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDcHR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e0.783\u0026thinsp;\u0026plusmn;\u0026thinsp;0.207\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e0.697\u0026thinsp;\u0026plusmn;\u0026thinsp;0.273\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.110\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e8.66\u0026thinsp;\u0026plusmn;\u0026thinsp;1.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e4.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.7 x10 \u003csup\u003e\u0026minus;\u0026thinsp;21\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.2 x 10\u003csup\u003e\u0026minus;\u0026thinsp;08\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"12\"\u003e\u003cem\u003eNote\u003c/em\u003e. Standard deviations are for average values; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for HWE and F\u003csub\u003eIS\u003c/sub\u003e (before Bonferroni correction) is indicated in bold He expected heterozygosity, Ho observed heterozygosity, F\u003csub\u003eIS\u003c/sub\u003e inbreeding coefficient, HWE Hardy\u0026ndash;Weinberg equilibrium, A number of alleles, AR allelic richness (calculated by the rarefaction method for the lowest sample size n\u0026thinsp;=\u0026thinsp;10), PID: probability-of-identity\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe populations of MK and HR showed significant deviations from HWE based on exact tests in GENEPOP (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), additionally the population from MK showed deviation also based on F\u003csub\u003eIS\u003c/sub\u003e (significantly positive values). It can be expected that the deviation from HWE in MK population is a consequence of the small sample size included in the analysis. The HR population showed no significant deviation from HWE after the Bonferroni correction (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The number of alleles per locus in wildcats ranged from 2 to 18 with a mean of 6.77. Allelic richness across populations ranged from 3.61 to 3.98, with the highest values in HR and SR populations. A similar pattern was observed for Ho with values between 0.57 and 0.71 and He with values between 0.68 and 0.72, with MK population showing the lowest Ho.\u003c/p\u003e\n\u003cp\u003eThe global F\u003csub\u003eST\u003c/sub\u003e value for the four populations was 0.080 (95% CI 0.056\u0026ndash;0.109) and differed significantly from zero (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The pairwise F\u003csub\u003eST\u003c/sub\u003e values between populations ranged from 0.004 to 0.148 with the mean of the pairwise F\u003csub\u003eST\u003c/sub\u003e = 0.070\u0026thinsp;\u0026plusmn;\u0026thinsp;0.060 (\u0026plusmn;\u0026thinsp;\u003cem\u003eSD\u003c/em\u003e) and also differed significantly from zero (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The highest F\u003csub\u003eST\u003c/sub\u003e value was observed between the populations MK and SI and the lowest between SI and HR.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003e\u003cstrong\u003ePairwise values of F\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eST\u003c/strong\u003e\u003c/sub\u003e \u003cstrong\u003eamong European wildcat populations and domestic cat\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePopulation\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSlovenia\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCroatia\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSerbia\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNorth Macedonia\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCroatia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSerbia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNorth Macedonia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.050\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.028\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDomestic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.147\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.127\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.148\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.136\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote.\u003c/em\u003e All F\u003csub\u003eST\u003c/sub\u003e values are significant\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\u003cp\u003e\u003cstrong\u003eExclusion Of Hybrids Individuals\u003c/strong\u003e\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003cp\u003e\u003cstrong\u003eDetection of simulated hybrids with NewHybrids\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll controls were correctly identified by the NEWHYBRIDS software, with posterior probabilities of 0.99 for wildcats and 0.95 for domestic cats. The results of the identification of genotypes of the simulated hybrids are presented in the Supplementary Fig.\u0026nbsp;1, panel a1, b1, c1.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003cp\u003e\u003cstrong\u003eDetection and exclusion of hybrids\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSTRUCTURE and NEWHYBRIDS analysis concordantly identified two samples of with genotypes of domestic cats and 25% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;28) of hybrids across all wildcat samples using the exclusion criterion given by a z value of less than 0.85 (NEWHYBRIDS) and a q value of less than 0.80 (STRUCTURE). Numbers of hybrids varied across countries: SI 13% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3), HR 16% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9), SR 52% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;15), MK 14% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1). Nineteen of the 28 hybrids were classified as F2 hybrid (SI, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3; HR, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4; SR, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;12), four were classified as back-crosses of F1 to wildcat (HR, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2; SR, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2), four as back-crosses of F1 to domestic cat (HR, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3; SR, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1) and two as domestic cats P2 population (SI, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1; SR, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1) (see Supplementary Fig.\u0026nbsp;1, panel a2, b2, c2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenetic And Spatial Clustering\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\u003cp\u003eThe STRUCTURE analysis clearly separated the European wildcat samples from domestic cats (K\u0026thinsp;=\u0026thinsp;2; Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea). The estimated probability value for each K indicate the smallest value at K\u0026thinsp;=\u0026thinsp;3 (see Supplementary Fig.\u0026nbsp;2). Increasing K to 3 split the European wildcats into two subclusters, separating populations SI and HR from populations SR and MK; no additional structuring was found at K\u0026thinsp;=\u0026thinsp;4.\u003c/p\u003e\n\u003cp\u003eIn the separate analysis performed only with the European wildcat samples, the highest \u0026Delta;K values were obtained with K\u0026thinsp;=\u0026thinsp;2 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb), suggesting a division between western and south-eastern populations (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). For the logarithm probability of K it was possible to observe the lowest value of K\u0026thinsp;=\u0026thinsp;2 that captures the maximum degree of structure detected in the data (see Supplementary Fig.\u0026nbsp;3). K\u0026thinsp;=\u0026thinsp;3 and K\u0026thinsp;=\u0026thinsp;4 showed no further difference in geographic structuring, suggesting that the two wildcat clusters are largely consistent with a geographic division into a northern group (SI, HR) and a south-eastern group (SR, MK) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e\n\u003cp\u003eThe FCA plot, which was based on individual genotypes, clearly separated individuals along the second axis and two main groups were identified according to their rough geographical origin. The first factorial axis explained 47.8% of the variance within populations. Along the second axis, SI and HR populations were separated from SR and MK populations (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eDAPC according to the Bayesian Information Criterion, which also includes domestic cat genotypes, indicated that there are two genetic clusters of wildcats that distinguish individuals according to their geographical origin (in north-south gradient). Domestic cats belong to an independent cluster. The first principal component distinguished between domestic cats and wildcat clusters, and the second principal component showed a distinction between two wildcat clusters (see Supplementary Fig.\u0026nbsp;4). The ellipses, which describe the spatial extension of the clusters, did not overlap, which indicates a strong genetic structuring.\u003c/p\u003e\n\u003cp\u003eThe AMOVA result highly supported group structuring revealed by STRUCTURE, DAPC and FCA, the variance was 1.47 and significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003e\u003cstrong\u003eHierarchical analysis of molecular variance (AMOVA) based on microsatellite data\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSource of variation\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariance\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAmong populations\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.69 (0.076)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWithin population\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.01 (0.112)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAmong group\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.47\u003c/strong\u003e (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\"\u003e\u003cem\u003eNote.\u003c/em\u003e Values in bold are significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The populations correspond to four populations defined by country (according to Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), groups correspond to the two clusters according to the result of FCA and STRUCTURE analysis (K\u0026thinsp;=\u0026thinsp;2). P values are given in parenthesis.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003cp\u003e\u003cstrong\u003eIsolation by distance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMicrosatellite based genetic distances were correlated with geographical distances among populations (\u003cem\u003et-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.012, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.0013), supporting the hypothesis of isolation by distance (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eBy utilizing microsatellites, we have determined the genetic variation and population structure in the European wildcats inhabiting area between the Dinaric Alps, the Pannonian Plain and the Scardo - Pindic mountains. We thus fill a knowledge gap in the wider area of the SE Europe, where data on the genetic prospects of this endangered felid are completely lacking. But genetic data are not the only one lacking. Both historical and recent data on wildcat distribution, abundance, mortality and other ecological factors that might affect genetic structure are missing for most countries included in our study. Mortality data is available only for Slovenia, for the 1950\u0026ndash;1990 period, when annual culling varied between 70 and 493 individuals with an average of 193 wildcats culled per year \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. In 1970s, based on hunters\u0026rsquo; observations, Slovenian Hunting Association estimated the population size on up to 1000 individuals, mostly distributed in the sub-Mediterranean and Dinaric karst with occasional occurrence in the northern areas of Slovenia \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Other demographic data is not available for any of the countries, so our results really do provide one of the rare insights into ecology of the species in this area.\u003c/p\u003e\n\u003cp\u003eAcross our study area we found a slightly higher observed heterozygosity (Ho\u0026thinsp;=\u0026thinsp;0.57\u0026ndash;0.71) than reported in the study by Matucci et al. \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, where the Ho across Europe ranged between 0.58\u0026ndash;0.63. However, this value varies greatly among countries; e.g. central Germany 0.50\u0026ndash;0.79 \u003csup\u003e33\u003c/sup\u003e, France 0.39\u0026ndash;1.00 \u003csup\u003e34\u003c/sup\u003e, Portugal 0.42\u0026ndash;0.82 \u003csup\u003e28\u003c/sup\u003e, Hungary 0.42\u0026ndash;0.87 \u003csup\u003e25\u003c/sup\u003e. On a national level, we are expecting that genetic diversity in SI and HR populations can be sufficient to maintain adequate variation for adaptive evolution, especially due to observed gene flow between the countries and low hybridisation rate. High diversity was also confirmed with mitochondrial DNA control region of wildcats from Croatia \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. On the contrary signs of genetic erosion were observed in both SR and MK populations. In Serbia we found relatively high diversity, but greater F\u003csub\u003eIS\u003c/sub\u003e value compared to the SI and CR populations and very high level of introgression of domestic alleles. SR wildcat population is fragmented and occupies patches of suitable habitats along wooded river banks in the northern part and forest habitat patches in the central and southern parts of the country which may present barriers to gene flow and consequently affect genetic integrity. Thus, additional studies are needed to reveal fine-scale genetic structure in this area. Lower genetic diversity that we found among wildcats sampled in MK should be considered with caution due to the low sample size but could be an indication of recent genetic bottlenecks or geographic isolation due to various human impacts \u003csup\u003e5,36\u0026minus;38\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe highest F\u003csub\u003eST\u003c/sub\u003e values in our study were observed between the northern SI population and the southern MK population, and lowest between the closest SI and HR population, which is in congruent with geographical distances. We found a signal for the existence of isolation by distance (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) between populations, while AMOVA data indicate the existence of two genetic groups in which the SI and HR populations overlap. The existing admixture among them is reflected by a weak pattern of isolation by distance and congruent results for the population divisions obtained by FCA, DAPC and STRUCTURE.\u003c/p\u003e\n\u003cp\u003eThe wildcat is a solitary and territorial species, living in low-density populations \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, a small-scale genetic structure within the regions could be a consequence of natural processes and indicates relatively low dispersal potential of the species \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. It is also possible that habitat fragmentation in a more urbanized part of the Dinaric region contributed to the lower connectivity \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e and subsequent F\u003csub\u003eST\u003c/sub\u003e values found within this area. It has also been shown in central Germany that anthropogenic and natural landscape barriers can limit the wildcat's dispersal potential and the consequences are reflected in the genome \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Our findings indicate that habitat between SI and HR is continuous and barely limits gene flow for the wildcats, but it is difficult to draw conclusion considering SR and MK population. All populations were not evenly sampled, we did not analyse samples from eastern part of Croatia, so we have a sampling gap between Croatia and Serbia along the Pannonian Basin. Also, due to ad-hoc sampling we have quite low coverage in Serbia and very scarce in North Macedonia, which might affect values of genetic differentiation \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFurthermore, our northern and south - eastern populations might display the recolonisation from different refugia. A model of late Pleistocene isolation and genetic diversification of European wildcat populations into three main Mediterranean glacial refuges in the southern Iberian, Italian, and Balkan peninsulas was proposed by Hewitt \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e and Mattucci et al., \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Authors also state that this genetic divergence cannot be explained by recent fragmentation, but only by prolonged periods of isolation with historically no or limited gene flow \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Mattucci et al.,\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e suggest a scenario of ancient population isolation in Alpine (Italy, Slovenia) and Mediterranean (Istrian peninsula; south west of Slovenia and Croatia) refuges which increase genetic divergence between European wildcats in the Eastern Alps and Apennines. They also showed that the European wildcat populations are subdivided into at least five main biogeographic groups with divergence times from the Late Pleistocene, but sampling in Dinaric Alps, Pannonian Plain and Scardo - Pindic mountains was scarce (only samples from Slovenia and Bosnia and Herzegovina were included in the study)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. So Mattucci et al., \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e already noted that Structure results suggest that local populations could be genetically subdivided at smaller geographical scale and but additional genetic markers analysis is needed to point us into this direction. Studies on Eurasian lynx (\u003cem\u003eLynx lynx\u003c/em\u003e), the only other wild Felidae present in this area, showed that the mitochondrial lineage restricted to the Balkan lynx population was the first one to diverge from the other Eurasian lynx haplogroups. Balkan lynx haplogroup was the most divergent and its split was dated around 96.6 kya \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, while the three Mediterranean wildcat lineages probably originated 21,000\u0026ndash;125,000 years ago \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Past population isolation in Balkan refuges with genetically diverse groups of populations, leading to a more recent \u0026lsquo;refugia within refugia\u0026rsquo; concept as also seen in other species (e.g. wild boar) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Lack of information on the demographic history of the researched populations and gaps in our sampling prevent us from drawing firm conclusions, so additional studies based on fine-grained grid sampling are needed to reveal fine-scale genetic structure in this area.\u003c/p\u003e\n\u003cp\u003eOur analyses are in line with previous studies showing that the genetic integrity of the European wildcat is not compromised in most regions of Europe \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, however in some areas introgression can be substantial and could be caused by environmental circumstances that enable long-lasting hybridisation \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. In our study none of the hybrids found were classified as hybrids of the first generation which shows low level of recent hybridisation in all studied populations. In SI and SR mainly second-generation hybrids were found, while in HR a half of hybrids were classified as back-crosses and a half as second-generation hybrids. In the case of SR, due to high hybridisation rate, this result could reflect a long-lasting hybridisation that persists in the area and might be the consequence of high density of stray domestic cats, especially on the periphery of remote village areas. By using genomic approaches Mattucci et al., \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e found that some of the hybrids with possible F2 origins actually showed admixture traces dating back from 9 to 11 generations in the past. Since F2 hybrids are expected to occur in rare cases, a high proportion of F2 hybrids in the SI and SR populations could be the result of method bias and brings misassignments of individuals that can represent the product of repeated crosses among F1 or F2 individuals, rather than true second-generation hybrids. We used STRUCTURE and NEWHYBRIDS for classifying the hybrids and both methods are not accurate enough to classifying the hybrids with repeated crossbreeding between different hybrid generations. Results of Q scores generated by STRUCTURE allow for a relatively simplistic tracking of estimated proportion of ancestry, so that backcrosses cannot be distinguished from more complex hybrids. Even if NEWHYBRIDS classifies the hybrids into generational categories, some misassignments of individuals could occur due to strict hybrid categories used in this approach.\u003c/p\u003e\n\u003cp\u003eIn the area from the Dinaric Alps to the Scardo-Pindic Mts. wildcats are divided in two genetic clusters largely consistent with a geographic division into a genetically diverse northern group (SI, HR) and genetically eroded south-eastern group (SR, MK). But wider sampling, especially in MK and neighbouring countries, like Bosnia and Herzegovina, would help to clarify the evolutionary history in this part of Europe. The apparent loss of genetic integrity due to hybridisation with domestic cats found in Serbia urges specific conservation measures to maintain evolutionary potential of this species.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003cp\u003e\u003cstrong\u003eStudy area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study area, across four countries (Slovenia, Croatia, Serbia and North Macedonia) is located at the intersection of three major European geographical units, namely Dinaric Alps, Pannonian Basin, and the Scardo-Pindic Mts. system, which is a continuation of Dinarides in the southern part of Balkan Peninsula.\u003c/p\u003e\n\u003cp\u003eThe two-mountain systems, Dinaric Alps (extending from Slovenia to Albania) and Scardo-Pindic Mts. (from Kosovo to Greece) are characterized by small plateaus and meadows at high altitude up to 2,764 m a.s.l. The forest cover consists mainly of beech (\u003cem\u003eFagus sylvatica\u003c/em\u003e), fir (\u003cem\u003eAbies alba\u003c/em\u003e) and spruce (\u003cem\u003ePicea abies\u003c/em\u003e) associations \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. The climate varies, but roughly with a continental climate in the north-east Pannonian region, a severe alpine climate in the mountain regions and a sub-Mediterranean climate in the coastal region along the Adriatic Sea \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Topographically, the area is highly heterogeneous, interrupted by ditches, bays and rocks developed on limestone and dolomite rocks. Besides wildcat, several other carnivorous species are present in the region; brown bear (\u003cem\u003eUrsus arctos\u003c/em\u003e), grey wolf (\u003cem\u003eCanis lupus\u003c/em\u003e), Eurasian lynx (\u003cem\u003eLynx lynx\u003c/em\u003e), golden jackal (\u003cem\u003eCanis aureus\u003c/em\u003e), red fox (\u003cem\u003eVulpes vulpes\u003c/em\u003e), and several species of mustelids (\u003cem\u003eMustelidae\u003c/em\u003e). Unlike the mountainous regions, there are no large carnivorous species in the Pannonian region, but there are areas with increasing populations of the golden jackal.\u003c/p\u003e\n\u003cp\u003eManagement of wildcats is somewhat different in each country and is governed by national conservation policy (see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003cp\u003e\u003cstrong\u003eSampling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 113 samples from free-leaving putative European wildcats were collected over a period from 2012 to 2020 in Slovenia (SI), Croatia (HR), Serbia (SR) and North Macedonia (MK) (see Supplementary information Fig.\u0026nbsp;5) from dead (legal hunting, natural mortality and vehicle collisions) or from live-trapped individuals for telemetry studies (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). They were morphologically identified as wildcats by collectors. Blood samples from domestic cats were taken in Croatia (32 samples in total) from animals admitted for treatment at University Hospital of Faculty of Veterinary Medicine, University of Zagreb. All samples were stored at -80\u0026deg;C, tissue samples in 95% ethanol, whole blood samples in sodium citrate vacutainer. Based on the country of origin, the wildcat individuals were grouped into four groups (for the purposes of this paper further considered as \"populations\").\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003cp\u003e\u003cstrong\u003eDNA extraction and microsatellite genotyping\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenomic DNA was extracted using the peqGOLD Blood \u0026amp; Tissue DNA Mini Kit (VWR International Ltd., Leuven, Belgium) according to manufacturer\u0026rsquo;s instructions. The concentration and purity of the extracted DNA in the final elution volume was measured with Qubit\u0026reg; dsDNA BR Assay Kit (Invitrogen) on 3.0 Qubit Fluorometer (Life Technologies). Nineteen autosomal dinucleotide and one tetranucleotide (FCA 441) microsatellites (Supplementary Table S1), originally identified in domestic cats \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e and screened in studies in wildcats and domestic cats \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, were amplified in six PCR multiplex reactions with ready-to-use KAPA2G Fast Multiplex Mix (Kapa Biosystems). According to the manufacturer's instructions we used 2 \u0026micro;L template DNA and 0.3 mM final concentration for each primer used in the set. The amplification was performed under the following conditions: initial PCR activation for 3 min at 95\u0026deg;C, followed by 35 cycles of denaturation for 15 s at 95\u0026deg;C, annealing for 30 s at 58\u0026deg;C, extension for 30 s at 72\u0026deg;C and final extension for 10 min at 72\u0026deg;C. The fragment analysis was performed on a SeqStudio sequencer (Thermo Fisher Scientific) using the GeneScan LIZ500 (-250) standard (Applied Biosystems). The results were validated with the software GENEMAPPER v.5.0 (Applied Biosystems). Negative controls were included in all extraction and PCR steps. About 10% of the randomly selected samples were replicated independently to check for false alleles.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003cp\u003e\u003cstrong\u003eAnalyses of genetic variation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing the FREENA program \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, we estimated the proportion of the null allele (NA) at each locus in each population with respect to the fact that the presence of null alleles can cause a significant heterozygote deficit and deviation from Hardy-Weinberg equilibrium (HWE). We used the software GENEPOP 4.2 \u003csup\u003e52\u003c/sup\u003e to test for deviations from HWE. The exact test to assess the heterozygosity deficiency was performed for each population (country). The baseline significance level was set at 0.05 and a Bonferroni procedure was applied in multiple comparisons to compensate for the risk of a bloated type 1 deficiency.\u003c/p\u003e\n\u003cp\u003eThe mean number of alleles (A), observed (H\u003csub\u003eO\u003c/sub\u003e) and expected (H\u003csub\u003eE\u003c/sub\u003e) heterozygosity \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e, and inbreeding coefficients (F\u003csub\u003eIS\u003c/sub\u003e) were calculated for each population with GENETIX 4.05.2 \u003csup\u003e54\u003c/sup\u003e separately for the domestic, and wildcat populations. The probability of identity and sibling-identity were calculated with the Excel macro GenAlEx v6.5 \u003csup\u003e55\u003c/sup\u003e. Allelic richness for each population (AR) was estimated following a rarefaction method in the program FSTAT 2.9.4 \u003csup\u003e56\u003c/sup\u003e. The genetic differentiation between wildcat populations and between domestic cats and wildcats was estimated using pairwise F\u003csub\u003eST\u003c/sub\u003e in GENEPOP 4.2 according to Weir \u0026amp; Cockerham \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e, and significant differences from zero F\u003csub\u003eST\u003c/sub\u003e estimates were tested with 1,000 permutations. All subsequent analyses were performed after excluding individuals with admixed genotypes (see paragraph \u0026ldquo;Control population for hybrid simulation\u0026rdquo;).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003cp\u003e\u003cstrong\u003eExclusion of hybrids individuals\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n\u003cp\u003e\u003cstrong\u003eDetermination of control population for hybrid simulation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the initial STRUCTURE analysis, there was a statistically supported split between wildcat and domestic cat clusters (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea). Ten individuals from each of our four population (SI, HR, SR, MK) and ten individuals from HR domestic cat population was selected for the control population used in the simulation of hybridisation (to obtain clear hybrid genotypes). The controls for pure parental wildcats (P1) and domestic cat (P2) were determined above (\u003cem\u003ez\u003c/em\u003e designation) by NEWHYBRIDS v1.1 \u003csup\u003e58\u003c/sup\u003e. These \u003cspan class=\"ItalicUnderline\"\u003ez\u003c/span\u003e values were used to determine the ten animals from each population that were the \u0026ldquo;most pure\u0026rdquo;, with the \u003cem\u003ez\u003c/em\u003e value above 0.99. These individuals were used as control animals for each population in subsequent analyses.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\n\u003cp\u003e\u003cstrong\u003eSimulated hybrids\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOne hundred simulated genotypes (for each population independently) were generated from the control populations P1 and P2 in HYBRIDLAB v1.0 \u003csup\u003e59\u003c/sup\u003e for: parental wildcats (P1) and domestic cat populations (P2), F1, F2, back-crosses of F1 to wildcat (P1Bx) and domestic cat (P2Bx) and a second back-cross of P1Bx to wildcat controls (P1Bx2) and P2Bx to domestic cat controls (P2Bx2).\u003c/p\u003e\n\u003cp\u003eThe simulated genotypes were analysed using NEWHYBRIDS. A burn- in period of 5 000 was followed by 10 000 sweeps based on the graphical version of NEWHYBRIDS (see Supplementary Fig.\u0026nbsp;1, panel a1, b1, c1). Ten replicates using Jeffrey\u0026rsquo;s priors were tested and summarized using CLUMPAK. These simulated data were also analysed using STRUCTURE with K values of one to four with 100 000 burn-in and a data collection of 100 000 chains. The \u0026ldquo;Admixture Model\u0026rdquo; was applied. This protocol was replicated 10 times per each K-value. STRUCTURE HARVESTER was used to evaluate which K-value was most likely. The results of the replicated runs were combined with the Greedy algorithm of CLUMPP and the summary outputs were graphically displayed using RStudio and R version 3.6.2 \u003csup\u003e60\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\n\u003cp\u003e\u003cstrong\u003eDetection of hybrids\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll 113 complete genotypes of putative wildcats were analysed with NEWHYBRIDS. A burn-in period of 5 000 was followed by 10 000 sweeps. Ten replicates using Jeffrey\u0026rsquo;s priors were tested. CLUMPAK was used to summarise the ten replicates for each prior. Individuals with a \u003cem\u003ez\u003c/em\u003e designation value lower than 0.85 for either P1 or P2 were classified as a hybrid. Hybrids were further assigned to F1, F2, P1Bx or P2Bx, based on admixture analyses of observed and simulated cat data sets.\u003c/p\u003e\n\u003cp\u003eIn addition, we used program STRUCTURE (as described by Mattucci et al., \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e) to compare the results of hybrids identification by NEWHYBRIDS; the admixed genotypes were identified at a threshold qi\u0026thinsp;\u0026lt;\u0026thinsp;0.80. All individuals with assigned admixed genotype using NEWHYBRIDS and STRUCTURE methods were removed from the data sets.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n\u003cp\u003e\u003cstrong\u003eGenetic and spatial clustering after hybrids exclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePopulation genetic clusters were revealed using STRUCTURE 2.3.4 \u003csup\u003e61\u003c/sup\u003e on two datasets: i) wildcat populations and domestic cats, and ii) wildcat populations only. In STRUCTURE, ten independent runs were performed for each K-value in the range of one to ten using a model assuming admixture with correlated allele frequencies. Each run included a burn-in period of 100,000 replications followed by 100,000 Markov Chain Monte Carlo (MCMC) iterations. The results of the replicated runs for each K value from two to ten were combined using STRUCTURE HARVESTER v 0.6.94 \u003csup\u003e62\u003c/sup\u003e and the optimal K value was selected using the \u0026Delta;K method developed by Evanno et al., (2005). The results of the replicated runs for the optimal K value were combined using the Greedy algorithm of CLUMPP 1.1.1 \u003csup\u003e64\u003c/sup\u003e and the summary results were plotted with DISTRUCT 1.1 \u003csup\u003e65\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe genetic relationships between all genotyped individuals were represented by Factorial Correspondence Analysis (FCA) using GENETIX. The distribution of the individuals in a 2D space was compared by eye with the geographical location of the localities. We also investigated the genetic structure with the R-package, adegenet 2.0.0 \u003csup\u003e66\u003c/sup\u003e, using RStudio \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e and R version 3.6.2 \u003csup\u003e60\u003c/sup\u003e. We used the Discriminant Analysis of Principal Components (DAPC) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e, multivariate method in this package to identify the most likely number of clusters (K).\u003c/p\u003e\n\u003cp\u003eThe analysis of molecular variance (AMOVA) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e was performed in ARLEQUIN 3.5. \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e to test the genetic differences between individuals and populations and between the optimal number of clusters identified by STRUCTURE (K\u0026thinsp;=\u0026thinsp;2). The statistical significance of the variance components was investigated using 999 permutations in the R-package ade4 v1.7-13. \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. Finally, we tested isolation by distance (IBD) patterns within all populations using the Mantel function in the R-package adegenet.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical statements \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSamples of European wildcats were collected from dead (legal hunting, natural mortality and vehicle collisions) or from live-trapped individuals for telemetry studies.\u003c/p\u003e\n\u003cp\u003eAll methods were carried out in accordance with the Ethical and Welfare Standards presented in the (Official Gazette of the Republic of Croatia 102/2017), Regulation on the Protection of Animals Used for Scientific Purposes (Official Gazette of the Republic of Croatia 55/13), with the approval of the Bioethical Committee for the Protection and Welfare of Animals of the University of Zagreb Faculty of Agriculture (UR.BR. 251-71-29-02/19-21-2).\u003c/p\u003e\n\u003cp\u003eThe study is reported in accordance with ARRIVE guidelines \u003csup\u003e72\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAcknowledgements \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank to all responsible population managers who organized sampling, and to numerous hunters who provided samples of wildcats. We would like to thank Sandra Potu\u0026scaron;ek for her help with the laboratory work, and Laura Iacolina for thoughtful comments on data analyses.\u003c/p\u003e\n\u003cp\u003eFunding: This study was funded by: (i) the Slovenian Research Agency (programme group P1\u0026ndash;0386), (ii) the STARBIOS2 European Union\u0026rsquo;s Horizon 2020 Research and Innovation Program under grant agreement No. 709517 oriented to promote responsible research and innovation in biosciences; (iii) the RESBIOS European Union\u0026rsquo;s Horizon 2020 Research and Innovation Program (No. 872146); (iv) COST Action G-Bike (CA18134), supported by COST (European Cooperation in Science and Technology); (v) Ministry of Education, Sciences and Technological Development Republic of Serbia (No. 451-03-9/2021-14/200178).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eE.B. and N.\u0026Scaron;. conceived of the presented idea. F.U. and E.B. wrote the manuscript with support from M.S. F.U. carried out the laboratory work. F.U. and L.D. performed the analysis of the data. H.P., M.S., D.Ć and D.M. provide the samples. All authors discussed the results and contributed to the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI declare that the authors have no competing interests as defined by Nature Research, or other interests that might be perceived to influence the results and/or discussion reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e is available for this paper.\u003c/p\u003e\n\u003cp\u003eCorrespondence and requests for materials should be addressed to E. B.\u003c/p\u003e\n\u003cp\u003eReprints and permissions information is available at \u003ca href=\"http://www.nature.com/reprints\"\u003ewww.nature.com/reprints\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKitchener, A. C. \u003cem\u003eet al.\u003c/em\u003e \u003cem\u003eA revised taxonomy of the Felidae: The final report of the Cat Classification Task Force of the IUCN Cat Specialist Group\u003c/em\u003e. \u003cem\u003eCat News\u003c/em\u003e vol. 11 (2017). ISSN 1027-2992\u003c/li\u003e\n\u003cli\u003eGenovesi, P. \u0026amp; Shine, C. 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The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research. \u003cem\u003eJ Cereb Blood Flow Metab\u003c/em\u003e. \u003cstrong\u003e40\u003c/strong\u003e, 1769\u0026ndash;1777 (2020). https://doi.org/10.1371/journal.pbio.3000410\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"genetic variation, microsatellite markers, hybridisation, wildcat","lastPublishedDoi":"10.21203/rs.3.rs-443648/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-443648/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHabitat fragmentation and loss have contributed significantly to the demographic decline of European wildcat populations and hybridization with domestic cats poses a threat to the loss of genetic purity of the species.\u003c/p\u003e \u003cp\u003eIn this study we used microsatellite markers to analyse genetic variation and structure of the wildcat populations from the area between the Dinaric Alps and the Scardo-Pindic mountains in Slovenia, Croatia, Serbia and North Macedonia. We also investigated hybridisation between populations of wildcats and domestic cats in the area. One hundred and thirteen samples from free-leaving European wildcats and thirty-two samples from domestic cats were analysed. Allelic richness across populations ranged from 3.61 to 3.98. The observed Ho values ranged between 0.57 and 0.71. The global F\u003csub\u003eST\u003c/sub\u003e value for the four populations was 0.080 (95% CI 0.056\u0026ndash;0.109) and differed significantly from zero (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The highest F\u003csub\u003eST\u003c/sub\u003e value was observed between the populations North Macedonia and Slovenia and the lowest between Slovenia and Croatia. We also found a signal for the existence of isolation by distance between populations. Our results showed that wildcats are divided in two genetic clusters largely consistent with a geographic division into a genetically diverse northern group (Slovenia, Croatia) and genetically eroded south-eastern group (Serbia, N. Macedonia). Hybridisation rate between wildcats and domestic cats varied between 13% and 52% across the regions.\u003c/p\u003e","manuscriptTitle":"Population genetic structure of European wildcats inhabiting the area between the Dinaric Alps and the Scardo-Pindic mountains","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-04-29 14:22:08","doi":"10.21203/rs.3.rs-443648/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2021-07-18T14:39:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-07-16T04:54:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"314ed5df-0796-41ad-b123-7791b4255626","date":"2021-06-30T05:39:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-05-18T07:27:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"b2255ab5-2fa8-4950-abc7-6c22093cd8b8","date":"2021-05-10T06:43:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"343b135e-8926-4986-9224-25dbf0a05ac6","date":"2021-05-03T09:05:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-04-29T08:43:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-04-29T08:21:33+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2021-04-28T15:18:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2021-04-28T12:08:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2021-04-20T15:24:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"adf640ea-1598-45af-984f-ae9de3db5e18","owner":[],"postedDate":"April 29th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":3987668,"name":"Population Genetics"}],"tags":[],"updatedAt":"2021-08-12T03:14:04+00:00","versionOfRecord":[],"versionCreatedAt":"2021-04-29 14:22:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-443648","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-443648","identity":"rs-443648","version":["v1"]},"buildId":"J0_U0BvcaRcwD8yVFaRlm","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}