Canis lupus) was extirpated from most of Europe, but recently recolonized big part of its historical range. An exceptionally dynamic expansion of wolf population is observed in the western part of the Great European Plain. Nonetheless, genetic consequences of this process have not yet been fully understood. We aimed to assess genetic diversity of this recently established wolf population in Western Poland (WPL), determine its origin and provide novel data regarding the population genetic structure of the grey wolf in Central Europe. We utilized both spatially explicit and non-explicit Bayesian clustering approaches, as well as a model-independent, multivariate method DAPC, to infer genetic structure in large dataset  of wolf microsatellite genotypes. To put the patterns observed in studied population into a broader biogeographic context we also analyzed a mtDNA control region fragment widely used in previous studies. In comparison to a source population, we found slightly reduced allelic richness and heterozygosity in the newly recolonized areas west of the Vistula river. We discovered relatively strong west-east structuring in lowland wolves, probably reflecting founder-flush and allele surfing during range expansion, resulting in clear distinction of WPL, eastern lowland and Carpathian genetic groups. Interestingly, wolves from recently recolonized mountainous areas  clustered together with lowland, but not Carpathian wolf populations. We also identified an area in Central Poland that seems to be a melting pot of western, lowland eastern and Carpathian wolves. We conclude that the process of dynamic recolonization of Central European lowlands lead to the formation of a new, genetically distinct wolf population. Together with the settlement and establishment of packs in mountains by lowland wolves and vice versa, it suggests that demographic dynamics and possibly anthropogenic barriers rather than ecological factors  shape the current wolf genetic structure in Central Europe.Local extinction and recolonization events can shape genetic structure of subdivided animal populations. The gray wolf ( Founder effects during expansions are one of the main drivers of genetic structuring, leading to establishment of new, genetically distinct populations8. On the other hand, rapid population expansion may promote gene flow and admixture10, sometimes resulting in a nearly homogenous genetic structure11.Range expansions in a heterogeneous environment can diversely shape population genetic diversity and structure of a species12. In Poland, wolves were never completely extirpated despite an intentional extermination program conducted for several decades after the Second World War. However, the population was severely reduced to less than a hundred individuals that survived at the eastern edges of the country13.Wolves were historically the most widespread large terrestrial carnivores in the northern hemisphere. However, centuries of persecution led to the collapse of many wolf populations, resulting in global reduction in its historical range to 68%, including most of western and central Europe14. The population inhabiting Western Poland (WPL), together with wolves recolonizing Germany, western Czech Republic, Denmark and the Netherlands, is considered to be a distinct management unit called the Central European wolf population15. However, results of genetic studies of Polish wolf populations16 that were performed during the first stage of WPL recolonization and included samples collected from 2002 to 2009 in all nine wolf packs living at the time west of the Vistula river, suggested that wolves inhabiting Western Poland and Eastern Germany constitute one population with those from the north-eastern part of Poland, which are classified as belonging to the Baltic population .After implementation of strict protection across the whole Poland in 1998 wolves started to repopulate the vast forest tracts west of the Vistula river. The first phase of recolonization was characterized by jump dispersal leading to establishment of a few packs in distant locations, and a relatively slow pace of population recovery. However, in the second phase the dispersal pattern shifted to a mixture of diffusion and jump dispersal, resulting in the creation of packs in close vicinity to existing groups and much more dynamic population growth17. Earlier study16 indicated concordant mitochondrial and nuclear DNA structuring between Polish lowland and Carpathian wolves that might be attributed either to landscape fragmentation and dispersal barriers or to ecological differences between studied populations resulting in natal habitat-biased dispersal. However, there are documented cases of long distance wolf dispersals in human-dominated European landscapes, with large number of dispersal barriers20. Moreover, several cases of the settlement of lowland wolves in mountainous regions were reported20, putting both hypotheses into question. Furthermore, there are some examples of cryptic structuring of wolf populations that cannot be simply explained by geographic and anthropogenic barriers to dispersal or by ecological factors24, thus highlighting that mechanisms shaping wolf genetic structure are still not fully understood. The currently recolonized areas in WPL are stratified in terms of anthropogenic pressure and landscape characteristics, as they are composed mostly of highly forested lowlands, but also some uplands, the Sudetes mountain range and areas dominated by arable lands25. Hence, the recolonization process is a unique opportunity to study the relationships between habitat selection and genetic origins of wolves.Currently, wolves have already colonized most suitable habitats in WPL14 and such demographic events could result in allele frequencies strikingly different from source populations due to founder effect and allele surfing27. We also attempted to update delimitation of management units for wolves in Central Europe, taking under consideration results of all recent analysis of the wolf genetic structure from this region.Taking advantage of a large number of non-invasive samples collected over eight years in WPL, central, north-eastern, eastern Poland, Lithuania, Belarus and the Carpathians Fig. , we examID) for all genotypes was very low and equaled 4.9 × 10–12 (range 1.2 × 10−9–1.3 × 10−13 depending on a region) for unrelated individuals and 2.4 × 10−5 (range 1.3 × 10−4–9.0 × 10−6) for full siblings.Multilocus consensus genotypes of sufficient quality (at least 9 genotyped loci) were obtained for 1514 samples, in which we identified in total 881 wolf individuals . Identified putative wolf-dog hybrids were not included in this dataset . Generally, allelic richness was significantly lower in the recently recolonized regions , but mean observed heterozygosity did not differ significantly compared to eastern lowland regions (p = 0.08). Inbreeding coefficient (FIS) values were low and ranged from −0.052 to 0.054.When we estimated basic microsatellite variability statistics among sampling regions . The highest recorded were the pairwise FST values between the Carpathians and the recently recolonized regions in western and northern Poland. Also the SEPL region was relatively highly differentiated from other lowland regions, whereas a relatively low level of differentiation was found between Lithuania and CarpMTS and SEPL regions . Pairwise RST values were generally higher than corresponding FST values, as could be expected in case of highly polymorphic microsatellite markers28. However, the observed general pattern was very similar, with very low (often insignificant) differentiation between western regions, moderate to high differentiation between western and eastern lowlands and very high pairwise RST values between CarpMTS and western regions, especially WPL I.Pairwise Fons Fig. . All pai29 , with almost all analyzed individuals bearing haplotypes w1 and w2, both belonging to haplogroup 1We recorded the highest mtDNA diversity in the relatively small CentrPL region that lacks big, continuous forest tracts. We detected there two packs bearing Carpathian w6 haplotype. It was also the only region west of the Vistula river where the w3 haplotype was present. In three packs from this region in which we identified breeding pairs, breeding females had different mtDNA haplotypes than males.21. It was the only case of the w7 in our dataset. We have not detected there any individual with w2 haplotype which was reported to be the most frequent haplotype in central and northern parts of Eastern Poland16.In the SEPL region the rare elsewhere w3 haplotype was dominant. The only exception were two individuals from the northernmost part of the region (not connected directly with the main SEPL forest tracts) carrying the w1 and w6 haplotypes and one individual with the w7 haplotype typical for eastern Belarussensu Chapron et al.15) the optimum number of genetic clusters is three  from those from the western lowlands . By contrast, the smallest subsets from regions CentrPL and SEPL were the least affected . We also recorded that some adult wolves killed in traffic accidents in Western Poland were not related to local packs or died outside of the known wolf range. Thus, we assumed that they were floaters and hence cannot be assigned to local populations. A detailed scrutiny of genetic and field data led to the identification of 27 such individuals . They were retained in non-spatial analyses (as a separate group “putative dispersers”), but excluded from spatial analyses. Furthermore, to improve sampling coverage of eastern populations, we added to analysis samples from western Belarus and from both Belarusian and Ukrainian parts of the Chernobyl Exclusion Zone (herein region BY-UA).STRUCTURE results obtained for higher than K = 3 values suggested the presence of an additional substructure inside the western lowland cluster (e.g. a distinct cluster corresponding to one big forest tract in WPL I region at K = 5). However, such a pattern was not observed in the DAPC analysis. As STRUCTURE was designed to place individuals into Hardy–Weinberg/linkage equilibrium populationsResults of STRUCTURE analysis of this reduced dataset were generally congruent with those obtained for the whole dataset, as K = 3 again obtained the highest support from the Evanno method  our main aim was to explore the genetic structure in the recently recolonized lowlands, and 2) substructuring of the Carpathian population was studied before20. Then the highest change in ΔK was observed at K = 2, reflecting the partitioning of lowland wolves to eastern and western populations . However, a substantial ΔK change was observed also at K = 4 . In the first GENELAND runs we observed an appearance of “ghost populations”. Thus, we afterwards excluded 4 individuals from the Chernobyl zone – a region >300 km distant to our main study area. In subsequent analysis utilizing an uncorrelated allele frequency model, in 6 out of 10 runs GENELAND identified 3 clusters, while in the remaining runs – 4 clusters. On the other hand, two runs with the highest posterior probability were those identifying K = 4. Notably, comparison of individual assignments revealed that results of all of the runs were generally congruent, with the only exception being that south-eastern wolves that formed a distinct cluster at K = 4, at lower K were clustered together with individuals from the north-east Fig. . AssignmST values, where the northern cluster showed a relatively low distance to both western and north-eastern clusters , Carpathian wolves were clearly separated from all lowland wolves by DA1, while DA2 reflected the west-east division in lowlands Fig. . Regionsrs Table . Howeverrs Table .Figure 536. Firstly, we analyzed the rate and direction of gene flow between the areas recolonized by wolves during the last two decades  and those permanently occupied by wolves in the eastern lowlands  and in the Carpathians, roughly reflecting STRUCTURE clustering at K = 3. Concordantly with the STRUCTURE results, we found a moderate level of bidirectional gene flow between eastern populations and recolonized regions, low gene flow from lowlands to the Carpathians and from the Carpathians to eastern lowlands, and almost no gene flow from the Carpathians to western lowlands . However, we observed important regional differences. In the north-western Poland (regions NWPL and WPL II) diversity was relatively high compared to south-western regions . These results correlate well with mtDNA diversity patterns - in northern regions haplotypes w1 and w2 were present at similar frequency, and a third haplotype, w6, was also detected, while the south-west was strongly dominated by only one (w1) haplotype. This can be attributed to different rate of immigration from the east, as more individuals assigned to the eastern populations in clustering analyses were detected in the north-west.Microsatellite diversity measures such as allelic richness were generally lower in recently colonized regions west of the Vistula river compared to Baltic and Carpathian wolf populations  the observed heterozygosities were higher than expected and FIS values were slightly negative. A similar pattern was found when the diversity of clusters identified in STRUCTURE was analyzed: the western clusters at both partitioning levels (K = 3 and K = 5) had the lowest AR, but HO exceeded HE and FIS was negative , while our procedures of isolation and amplification of DNA from non-invasive samples allowed to reduce the average dropout rate to below 4%. Thus, the observed heterozygosity and inbred coefficient values recorded in this study better represent the actual diversity of studied populations. Expected heterozygosity values, which are less biased by large dropout rates, in the study of Czarnomska and coworkers are similar to those reported here.Observed heterozygosity values were much higher than those reported by Czarnomska and coworkersThe clustering solutions of different analyses are generally compatible, showing relatively clear separation of Carpathian, lowland eastern and western wolf populations. The only discrepancies concern the substructure inside the eastern cluster: the northern cluster identified in STRUCTURE was not recognized in spatial GENELAND analysis, while DAPC results suggested that it may be an intermediary cluster between the western and north-eastern clusters. However, designation of a separate south-eastern genetic group was well supported, as assignments of STRUCTURE at K = 5 and GENELAND at K = 4 were concordant, and both the SEPL region and the south-eastern cluster were separated from other groups in DAPC .16, which suggested a north-eastern origin of the few first wolf family groups already established west of the Vistula river. However, allele frequencies were strongly altered, with some alleles  with relatively low frequency in the east but being dominant in the west, indicating founder-flush event43 and allele surfing26. Noteworthy, the western regions were much more differentiated from the Carpathian wolf population than the eastern lowland regions.Surprisingly, in STRUCTURE analyses at K = 2 the eastern lowland wolves clustered with Carpathians, indicating very strong differentiation of the western cluster. However, it did not have any private alleles, as all alleles occurring in this cluster were present in the gene pool of the eastern cluster (at the K = 3 level of partitioning). This is in agreement with the results of an earlier study46 or whether it is connected with functional adaptations to new habitats (e.g. higher anthropopressure tolerance). In several recent studies, also a combination of demographic and adaptive processes in expanding populations was described47, related for example to the “Olympic Village Effect”48. Despite this ambiguity, there is no doubt that the wolf population newly established west of the Vistula river genetically differs from wolves inhabiting Lithuania, Belarus and easternmost parts of Poland and therefore cannot be considered as a part of the Baltic population. Our results are supported also by a recent study suggesting that the wolf population inhabiting eastern Germany (adjacent to western Poland) is genetically distinct from the Baltic wolves49. Thus, the current status of a discrete conservation unit  for the western Polish and German wolves seems to be fully justified.Further genomic, ecological and behavioral research is necessary to determine if the observed genetic distinctiveness of western Polish wolves is just an effect of stochastic processes during range expansion and demographic growth, related for example to higher spreading potential of alleles “surfing” at the expansion front, as described in several empirical studies51, with only one relatively large forest tract indicated as a good wolf habitat by the suitability model25. Although wolves have been recorded there at least since the first decade of 21st century52, their genetic origin have not been studied before. We found that this region is characterized by high wolf genetic diversity at both mitochondrial and nuclear DNA levels. It was the only region studied by us where all four main mtDNA haplotypes were present. Concordantly, we found there wolves representing each of the five clusters identified in STRUCTURE analyses, as well as several admixed individuals. According to GENELAND analyses, it was the region where the delineated borders of all populations (both at K = 3 and K = 4) met. BayesAss analysis indicated high gene flow rate from both western and eastern regions to the CentrPL region, and lower, but non-negligable migration from the Carpathians. Thus, our results suggest that central Poland is a contact zone of Carpathian, lowland south-eastern , north-eastern  and western wolf genetic groups. It also seems to be an admixture hotspot, because we identified there wolf packs in which breeding individuals were assigned to different clusters. Further development of the wolf population in this area is particularly interesting and worth further genetic and ecological research.Central Poland is characterized by high landscape fragmentation53. All of those individuals had a haplogroup 1 mtDNA haplotypes  typical for Central/Eastern European lowlands29. Concordantly, they were assigned to lowland populations in all Bayesian clustering analyses and DAPC. This finding is particularly significant as one of the most likely explanations of the differentiation between Polish lowland and Carpathian wolves was the natal habitat biased dispersal hypothesis21. Areas in the Sudetes Mts occupied by wolves are separated from the western edge of the Carpathian Mts by less than 200 km and their landscape characteristics much more resemble Carpathians than lowlands in western Poland55. Even though we are aware that our results must be treated with caution as the recolonization of the Sudetes Mts is very recent and the dataset includes small number of wolves, they are supported by another study which found that the Czech part of this mountain range was settled by wolves assigned to the lowland population20. Thus, the case of Sudetes highlights the great ecological plasticity of the expanding Central European wolf population. This also highlights the possible importance of anthropogenic factors, as the eastern edge of the Sudetes Mts is separated from the Carpathians by a relatively narrow, but densely populated and urbanized Moravian Gate55.Our dataset included six wolves from all three resident family groups which occurred in the Sudetes Mts (south-western Poland), recolonized by this species after over 200 years of absence56: bigger Polish forests are saturated with wolf packs, which leaves little space for Carpathian dispersers, forcing their long-distance movement abroad to regions that are less saturated, with vacant territories due to heavy harvest of wolves in Ukraine, Belarus and Lithuania.On the other hand, we also detected likely dispersals of Carpathian wolves to lowlands. One pack in the CentrPL region was concordantly classified as of Carpathian origin in mtDNA, STRUCTURE, GENELAND and DAPC analyses. A pack of wolves bearing the Carpathian w6 haplotype was detected also in the NWPL region, but microsatellite data revealed their north-eastern origin, suggesting a past gene flow between the Carpathian and Baltic population. This was supported by the Bayesian clustering analysis of the Lithuanian wolf population, where several individuals bore traces of admixture with the Carpathian population. Admixed individuals were detected also in Belarus, Ukraine and in small forest patches in the region NEPL II, but not in the main forest tracts in lowland Poland, including the SEPL region adjacent to the Carpathian Mountains. It can be explained as a high density blocking effect57. Identification of MUs is primarily justified by the amount of genetic divergence at which populations become demographically independent58. However, to avoid management failures, identification of MUs should consider not only biological or geographical features, but also social and political factors59.One of the most important steps in conservation planning for populations of wild-living species is the recognition of their intraspecific diversity followed by delineation of adequate management units (MUs)61, for which specific key actions has already been proposed62. Originally delineation of those MUs  was based mainly on data on wolf distribution, geographic features (e.g. existence of barriers), habitat quality, dispersal abilities and different management conditions. However, their authors called for further research on population genetics to allow revision of the population structuring60.There are several MUs identified for European wolves63, who described them as “populations with significant divergence of allele frequencies at nuclear or mitochondrial loci, regardless of the phylogenetic distinctiveness of the alleles”. In this study we confirmed separation of Carpathian, Baltic and Central European wolf populations, and supported the proposal of Linnell and coworkers60 to treat them as discrete MUs. Moreover, we suggest to consider an additional MU (South-Eastern European wolf population) located in the south-eastern Poland, which we found to be genetically different from all other subpopulations  confirm that the detected genetic structure of wolf population in Central Europe is shaped more by the recent demographic history than by ecological factors.Although our study focused mainly on the Polish wolf population, we also analyzed samples collected in Lithuania, Belarus, Slovakia and eastern Czech Republic. Thus the total study area stretches between 48°5′–56°4′N and 14°1′–30°5′E. The landscape of this part of Europe was profoundly shaped by Pleistocene glaciations, and consists mainly of lowlands, up to 200 m above sea level, with some frontal and moraine hills, which in northern parts are accompanied by lakes. Southern part of the study area encompasses two mountain ranges – Sudetes Mts.  and Carpathian Mts. .Pinus sylvestris and Norway spruce Picea abies, but in southern and western parts there are larger patches of mixed and deciduous woods with prevalence of beech (Fagus sylvatica), birches (Betula sp.), hornbeam (Carpinus betulus), oaks (Quercus sp.), alders (Alnus sp.) and poplars (Populus sp.).The area has a transitional continental–Atlantic climate, with mean temperatures from −1.1 to 0.6 °C in January, and from 18.1 to 19.5 °C in July. Mean precipitation ranges from 500 to 800 mm. The majority of the area is an agricultural land, however the forest cover is from 30% in Poland to almost 40% in Belarus. Forests are dominated by managed stands of Scots pine Lynx lynx) and brown bear (Ursus arctos) which is restricted to the Carpathian Mts. and lowlands in Baltic states15. Moreover, the region is characterized by abundant populations of wild ungulates being main wolf prey64 – red deer (Cervus elaphus), roe deer (Capreolus capreolus) and wild boar (Sus scrofa), with less numerous moose , locally reintroduced European bison (Bison bonasus), and chamois (Rupicapra rupicapra) restricted to mountains. There are also isolated populations of alien species introduced for hunting purposes, such as fallow deer (Dama dama), sika deer (C. nippon) and mouflon (Ovis musimon), as well as a large population of the Eurasian beaver (Castor fiber).In central Europe the wolf co-occur with two other large carnivores – more widespread Eurasian lynx , a region that was found to host a distinct wolf subpopulation based on previous mtDNA analysis16. To compare the genetic structure of Polish wolf population with adjacent regions, we analyzed also samples gathered in Lithuania, Belarus, Ukraine (Chernobyl zone), Slovakia and eastern Czech Republic.Our main goal was to obtain a dense, representative sampling of previously unstudied areas west of the Vistula river that were recolonized by wolves over the last decade14 for more details). We also gathered tissue and hair samples from wolves killed in traffic accidents (N = 97), illegally shot or snared (N = 29) or found dead due to diseases and other natural causes (N = 16). Additionally, we analyzed blood and hair samples of animals injured in traffic accidents or by poachers (N = 9). Lastly, we analyzed tissue samples from wolves hunted legally in Lithuania (N = 63) and Slovakia (N = 23). No animals were specifically killed or captured for this study. Distribution of sampling locations is shown on Fig. 21 or, in case of regions unstudied before, dynamics of their recolonization by wolves17. As a presence of dog-specific alleles can potentially interfere in downstream population structure analyses, we aimed at the elimination of all putative wolf-dog hybrids from our dataset. Thus, additionally, 50 dog samples  were collected from veterinarians and private owners and from individuals killed by vehicles and used as a reference group to detect possible wolf-dog hybrids. Dog owners granted us permission for the use of these samples in research. Details concerning this procedure can be found in Supplementary Methods.Non-invasive samples N = 2110  were collected from 2011 to 2018 by authors and trained volunteers all year round, during long-distance wolf tracking on forest roads, tourist trails and around known wolf dens and rendezvous sites  were kept at room temperature in dry paper envelopes containing desiccant. Urine samples were mixed with 2 volumes of 96% ethanol and sodium acetate  and kept at −20 °C.DNA isolation from non-invasive samples was performed in a separate cleanroom to avoid contamination. DNA from scats was isolated either with QIAamp DNA Stool Mini Kit (Qiagen) or Exgene™ Stool DNA Mini kit (GeneAll Biotechnology), while for hairs, swabs and FTA cards we used Exgene™ Genomic DNA Micro kit (GeneAll Biotechnology) or QIAamp DNA Investigator Kit (Qiagen). DNA from tissues and precipitated urine samples was isolated with Exgene™ Tissue SV kit (GeneAll Biotechnology).67 and H1649868. This domain contains a 230 bp fragment rich in parsimony-informative sites, enabling its use for a range-wide wolf haplotype classification21. PCR was performed in 15 μl reaction volume containing 1X DreamTaq Green PCR Master Mix (ThermoScientific), primers at concentration of 0.33 μM each, 0.17 μg/μl BSA and 3 μl DNA extract . The PCR reaction started with an initial denaturation at 95 °C (3 min) followed by 35 cycles of 94 °C (15 s), 54 °C (30 s) and 72 °C (60 s), and a final elongation of 72 °C (10 min). PCR products were purified using exonuclease I and FastAP alkaline phosphatase (ThermoScientific) and sequenced on ABI3730/xl Genetic Analyzer (Applied Biosystems). Chromatograms were analyzed and edited in FinchTV 1.4 and compared to sequences deposited in the NCBI database.To analyze the matrilineal genealogy we amplified the left variable domain of the mitochondrial control region using primers L1599569, vWF70, PEZ1771 and CPH572. Additionally, DBX intron 6 and DBY intron 773 were used as sex markers. Loci were amplified in three 10 μl multiplex reactions, each containing 5 μl Multiplex PCR Master Mix (Qiagen), 0.25 μg/μl BSA, primers at concentration 0.2 μM each and 4.2 ul DNA extract. PCR was started with initial denaturation  followed by 4 cycles of 94 °C (30 s), 60 °C (90 s) and 72 °C (60 s); another 5 cycles of 94 °C (30 s), 58 °C (90 s) and 72 °C (60 s), 5 cycles of 94 °C (30 s), 56 °C (90 s) and 72 °C (60 s); another 5 cycles of 94 °C (30 s), 54 °C (90 s) and 72 °C (60 s), 25 cycles of 94 °C (30 s), 50 °C (90 s) and 72 °C (60 s), and a final elongation 30 min at 72 °C. PCR products were separated by electrophoresis using an ABI3730/xl Genetic Analyzer with the internal size standard GS600 LIZ™ (Applied Biosystems) and allele sizes were binned using PeakScanner 1.0 software. To avoid allele dropout and genotyping errors we utilized a multitube amplification approach74. However, as the reliability of a genotype could be predicted based on sample quality75, the number of multitube replicates varied depending on amplification quality. Initially each non-invasive DNA sample was amplified twice. At this stage low quality samples (>35% missing data) were discarded and good quality genotypes  were accepted. For the remaining samples two additional PCR repetitions were performed and consensus genotypes were reconstructed from tetraplicates.To infer population structure we analyzed 13 polymorphic microsatellite loci: FH2001, FH2010, FH2017, FH2054, FH2087L, FH2088, FH2096, FH2137, FH2140, FH216176. We accepted one mismatching allele to consider genotypes as belonging to the same individual. Evaluation of allele frequencies, expected and observed heterozygosity, identification of private alleles, calculation of RST values and estimation of probability of identity of multilocus genotypes between unrelated individuals (P ID) and siblings (P ID-sib) were also done in GenAlex. Tests for the presence of false and null alleles were done in Microchecker77. Rarefied allelic richness (Ar), inbreeding coefficient (FIS) and fixation index (FST) were estimated using FSTAT78.Multilocus genotypes were compared and collapsed in GenAlex79 implemented in GeneAlex, 2) parentage and sibship analysis in software Cervus 3.080 and Colony 281 and 3) manually by direct genotypes comparison.Closely related individuals were identified by a combination of methods: 1) estimation of relatedness between pairs of individuals using the estimator of Lynch & Ritland82 and spatial clustering algorithms used by GENELAND 4.0.8. In STRUCTURE, 106 MCMC iterations were run following initial burn-in of 105 steps using the admixture model with correlated allele frequencies and no prior population information. As STRUCTURE runs are stochastic and may produce different outcomes for replicate runs, even when the same choice of model and parameters is used, ten independent repetitions were run for each K value (varying from 1 to 12). The Evanno method30 applied in STRUCTURE HARVESTER83 was used to infer the most likely number of clusters. Separate STRUCTURE runs were aligned and merged in CLUMPAK84 application using implemented there DISTRUCT85 and CLUMPP LargeKGreedy86 algorithms, respectively. As one-step STRUCTURE analysis may not always reveal substructures in datasets containing highly divergent populations, we utilized also a multi-step approach that has been proven useful in identifying hierarchical population structure88. For each of the main clusters identified in the first step, a separate analysis was rerun using the same parameters. In case of GENELAND, we performed analyses using both the uncorrelated and correlated allele frequency models, as the latter may be more powerful at detecting subtle structure, but on the other hand is more sensitive to deviations from model assumptions and thus more prone to algorithm instabilities than the uncorrelated model90. Each model was run ten times with 5 × 105 MCMC iterations, a thinning of 100, a maximum rate of Poisson process fixed to 100, a spatial coordinates uncertainty fixed to 0.1°, a maximum number of nuclei in the Poisson–Voronoi tessellation fixed to 500 and a range of tested K values from 1 to 15. Posterior probabilities of population membership for each pixel and individual were calculated with a burn-in of 1000. In case of inconsistency between multiple runs results, best runs were selected based on mean probability density values.Genetic clusters were detected using two Bayesian approaches: the non-spatial model implemented in STRUCTURE 2.3.432 implemented in the R package adegenet91. DAPC is a model-independent method, and therefore free of population genetics assumptions such as Hardy-Weinberg equilibrium. Separate analyses were performed for the whole dataset divided a priori into groups corresponding to geographic regions from Fig. Additionally, we analyzed microsatellite data using a multivariate method DAPC 36. Ten independent runs were performed, each for 107 iterations with a burn-in of 106 and sampling every 100 iterations. Rough 95% confidence intervals were calculated as mean ± 1.96 × SD, as recommended by the software’s manual.Contemporary migration rates between regions and identified genetic groups were estimated using BayesAss 3.0Supporting Information


Anopheles koliensis (Owen). Using mitochondrial and nuclear sequence data, as well as microsatellites, we show the evidence of geographically discrete population structure within Papua New Guinea (PNG). We also confirm the existence of three rDNA ITS2 genotypes within this mosquito and assess reproductive isolation between individuals carrying different genotypes. Microsatellites reveal the clearest population structure and show four clear population units. Microsatellite markers also reveal probable reproductive isolation between sympatric populations in northern PNG with different ITS2 genotypes, suggesting that these populations may represent distinct cryptic species. Excluding individuals belonging to the newly identified putative cryptic species (ITS2 genotype 3), we modeled the genetic differences between A. koliensis populations through PNG as a function of terrain and find that dispersal is most likely along routes with low topographic relief. Overall, these results show that A. koliensis is made up of geographically and genetically discrete populations in Papua New Guinea with landscape topography being important in restricting dispersal.New Guinea is a topographically and biogeographically complex region that supports unique endemic fauna. Studies describing the population connectivity of species through this region are scarce. We present a population and landscape genetic study on the endemic malaria‐transmitting mosquito,  Anopheles koliensis through Papua New Guinea (PNG), we use ribosomal DNA genotyping, nuclear microsatellites, along with mitochondrial and nuclear DNA sequencing to reveal genetic and geographically district populations through PNG and one reproductively isolated cryptic species was identified in northern PNG. Using landscape genetic methods, we show that this geographic structure observes through PNG is best explained by landscape topography, with slope (or elevation) presenting as a significant factor.In a population genetic study of the malaria mosquito  Its western margin forms the boundary between the Oriental and Australian faunal regions. The complex geological makeup of New Guinea is due to it being the amalgamation of numerous geological units. The island was formed by the accretion of an Australian craton—the southern part of the island and numerous Pacific terranes and island arcs—forming the northern part of the island  using the PCR‐RFLP species diagnostic tool with a sensitive acrylamide gel size separation suggested A. koliensis in Papua New Guinea. We initially aim to verify the existence of the three previously described rDNA ITS2 RFLP variants within A. koliensis and assess reproductive isolation between individuals with these genotypes. We use additional nuclear loci (DNA sequence and microsatellite) as well as a mitochondrial locus to achieve this. We use this sequence and microsatellite data to describe the population genetic structure of A. koliensis throughout PNG and hypothesize that the complex geological history and landscape topography of New Guinea have shaped population structure in this mosquito. Finally, we use recently developed methods to assess whether landscape topography plays a role in maintaining this genetic structure, providing the first contribution to the literature on landscape genetics of a species in New Guinea.In this study, we further investigate the population genetics of 22.12‐baited light traps, or through larval collections with the larvae being bred out to adults. Using these sampling techniques, A. koliensis was collected throughout PNG  of the ITS2 gene are often present within individual genomes and PCR products. These intragenomic sequence variants often cannot be directly sequenced using Sanger DNA sequencing as paralogues containing indels can collapse the chromatogram. For this reason, three individuals from sympatric populations from sites 1,330, 1,109, and 1,136  were also amplified and directly sequenced following methods of Ambrose et al. . For the Tajima,  and Fu's Tajima,  for each Tajima, . Haploty Tajima,  under a  Tajima, , and nuc Tajima, , pegas , we also performed a discriminant analysis of principal components (DAPCs) using the R package adegenet Jombart, . This is2.7A. koliensis. The most eastern population in the northern Papuan Peninsula in Figure We conducted a landscape genetic analysis to investigate the possible effects of landscape topography on genetic differentiation in We assessed four different hypotheses for connectivity among the northern, southern, and combined populations. The first hypothesis was the null hypothesis, and under this hypothesis, pairwise measures of genetic variation were fit to a constant. The second hypothesis described the connectivity between a set of populations as a function of the geographic distance between them Wright, . The thiSince landscape topography may be nonlinearly related with dispersal cost  were used to assess the relative support for each hypothesis . We als33.1A. koliensis by PCR‐RFLP of the ITS2 followed by restriction digest using Msp I ; however, no sites were identified with all three genotypes present. As expected, ITS2 sequences clustered into three genetic groupings . Neutrality tests using all sequences suggest an excess of low‐frequency polymorphisms present (significant test for Tajima's D (p < .05) of −2.01 and Fu and Li's D and F test statistic (p < .02) of −4.19 and −3.84, respectively. The phased 386‐bp‐long rpS9 sequence revealed 60 pseudohaplotypes from 107 individuals . Neutrality tests were mixed with a nonsignificant Tajima's D: (−1.56) but significant (p < .02) Fu and Li's D and F test statistic of −3.54 and −3.21, respectively. For both markers, these results indicate an excess of singletons. This is reflected in the haplotype networks for both loci which are displayed in Figure The COI sequencing of 463 bp from 177 individuals revealed a high diversity with 99 unique haplotypes  with which they are sympatric. Again, the Sepik and Madang/Lae populations, while somewhat distinct, do overlap suggesting admixture or incomplete sorting. The southern New Guinean population appears most closely related to the Sepik population.3.4A. koliensis populations at the broadscale , and this model was able to explain a large proportion of the variation in genetic differences between populations (0.7R2m). This model also had the most support in 97.52% of the bootstrap replicates, showing that the support does not hinge on a specific combination of populations. Under this model, where surface topography was transformed using an inverse–reverse monomolecular function , and was also reasonably robust against uncertainty, having received the most support in 70.93% of the bootstrap replicates. Here, surface topography was transformed using a monomolecular function .Population structure is evident between north and south PNG in COI but not rpS9. The high haplotype diversity in both mtDNA COI and rpS9, and an excess of singleton sequences suggest that the species has a long history in the region. The higher effective number of haplotypes and te Table  togetherThe analysis of the 11 microsatellites provided enhanced detail in regard to the spatial separation of populations through PNG. The population in southern PNG seen in the COI marker were now clearly identified, and the northern Papuan Peninsula population could be discriminated. Within northern PNG, microsatellites appeared to pull apart the Sepik region populations to the west from the Madang/Lae populations in the east—an area that could be regarded as continuous.4.2A. koliensis populations could be better explained when considering the topography of the region as it is supported by both our model selection analysis and the bootstrap analysis for the combined set of populations. The least‐cost path model for mosquito dispersal suggests that movement between populations occurs along distinct routes, rather than a diffusive model for dispersal where individuals take multiple routes between each pair of populations. This phenomenon has also been observed in montane amphibian populations  variant (G2 in this study) starts to blood feed later in the night, where MW (G1) and W (G3) were actively seeking a host as early as 6 p.m. It would be reasonable to hypothesize that genes for circadian or other rhythmic actives may be positioned near the centromere on the X chromosome.Microsatellite data suggest that individuals of ITS2 genotype G3 were reproductively isolated from individuals of the more common G1 and G2 ITS2 genotypes. Individuals of ITS2 genotype G3 appear to be spatially restricted to the western Sepik region of northern PNG, while individuals of ITS2 genotype G2 seem to be restricted to the Madang/Lae region, which supports the observed gene flow restriction between the Sepik and Madang/Lae populations. The ITS2, being part of the rDNA gene family, is tandemly arranged multicopy gene family in metazoans, and the evolutionary machinery maintaining sequence fidelity between copies in this array is not well described and does not follow traditional Mendelian rules of inheritance (Bower, Cooper, & Beebe, A. koliensis may well be more than one species in PNG. Individuals carrying the G1 and G2 genotypes show no evidence of genetic structure at other loci despite appearing to have different host feeding initiation times (Benet et al., Individuals of ITS2 genotype G3 are spatially restricted to the Sepik region of northern PNG and form a clearly separate group in the microsatellite clustering analyses, despite being sampled from the same sites as individuals of ITS2 RFLP genotype G1. The distinct microsatellite profile of these genotypes despite their overlapping ranges provides evidence that LA completed laboratory work and much of the genetic analyses and manuscript writing; JOH performed the landscape genetic analyses and wrote these sections in the manuscript; CR assisted with the genetic analyses and contributed to writing of the manuscript; WX performed a subset of the molecular genotyping, nDNA, and some mtDNA sequencing, while SLF initiated a substantial amount of the genotyping and mtDNA COI sequencing; RDC was responsible for the field collections; and NWB designed the project, contributed to field collections, and assisted in developing the manuscript. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Dugong dugon) across its original range using museum material from 14 natural history museum and university collections. The mitochondrial DNA control region was successfully amplified from samples of bone or tooth powder from 162 individuals. These samples range from 1827 to 1996 and span the historical distribution range of the dugong. We were able to successfully amplify overlapping fragments of the D-loop region of the mitochondrial DNA (mtDNA) resulting in sequences of a 355 bp fragment for 162 individuals for the final analyses. This included a new sequence (189 bp) from a previously unidentified piece of skin of the extinct Steller’s sea cow , as an outgroup. The resulting dugong sequences match those from previous studies of dugongs from Australia and Indonesia, but revealed several new and divergent mtDNA lineages in the Indian Ocean. One mtDNA lineage includes most specimens from the Western Indian Ocean, with another distinct lineage isolated to nearby Madagascar and Comores. There is little geographic structuring detectable among other populations in the Western Indian Ocean and all populations from that region appear to have historically contained comparatively low levels of genetic diversity. The genetic diversity of several Indian Ocean samples collected after 1950 was lower than that of the samples collected earlier from similar locations, a result coincident with the anecdotal reductions in population size. The new lineages and potential loss of diversity highlight the particular conservation importance and vulnerability of dugong populations in the Western Indian Ocean.We investigated the phylogeography of the dugong ( Dugong dugon) is one of four extant species of sea cow (Sirenians). It has the largest extent of occurrence of any Sirenian, extending over some 128,000km of coastline across 37–44 countries in the Indo-Pacific region, including tropical and subtropical coastal and island waters from east Africa to Vanuatu [The dugong , but it appears that the only viable populations are located in the Bazaruto Archipelago off Mozambique , off Qatpers comm.). Since genetic analysis can provide information that is not available by any other means, the analysis of aDNA samples of dugong throughout its historical range can assist in determining genetically isolated populations and thus aid in delineating management units.The extraction of mitochondrial DNA (mtDNA) from tissues containing only low levels of genetic material, such as hair, faeces, eggshells, teeth and bone  assumed to be from the extinct Steller’s sea cow  to provide a closely related outgroup. The date of acquisition or geographical location for this sample was unknown; however, given the description of the species in 1741 and subsequent rapid extinction a few decades later, the age of the sample is likely around 240 to 280 years old.Ideally, an assessment of the genetic distinction of populations, delineation of management units, and evaluation of gene flow between them is carried out as a conservation tool before a species becomes vulnerable or even endangered , 42. HowThe aim of the present study was to determine the genetic diversity and phylogeographic structuring of the dugong throughout its historical range based on these museum samples, in order to identify historically significant management units for effective conservation planning and implementation. Exhaustive comparisons with all known existing dugong mtDNA sequences await the imminent publication by other research groups of several additional mtDNA data sets from newly-sampled regions in the West Pacific.Samples of bone and tooth powder were obtained from a total of 177 individual dugongs located in 14 collections  of bone or tooth powder was sufficient for extraction. A new 1.5mm drillbit was used for each sample and an effort was made not to drill more than 5 or 6 samples in the same location to avoid cross-contamination between samples. Drilling was restricted to skulls and teeth/tusks only, to prevent accidental resampling of the same specimen. The evacuation and relocation of European collections during World War II may have resulted in skulls being separated from the remainder of the skeleton, thus sampling of both skulls and skeletons could potentially have resulted in unknowingly sampling the same individual specimen twice.H. gigas), which was detected in the collection of the Überseemuseum in Bremen, Germany. The sampling method was the same as for dugong samples.In addition, for use as an outgroup, a sample was taken from a putative piece of skin from a Steller’s sea cow DugCR 3F 5’- ACA CCA TCC TAT GTA TAA TCG TGC A-3’ (present study)DugCR 93F 5’- TGC ATT ACA CTA CTT ACC CCA TGC-3’ (present study)DugCR 114F 5’- GCA TGG GGT AAG TAG TGT AAT GCA-3’ (present study)DugCR 137R 5’- ATT GGA GGT GAT AAG CGT GTT GA-3’ (present study)DugCR 322R 5’- GCT GGT TTC TCG AAG CTT GGT A-3’ (present study)DugCR 359R Taq DNA polymerase (Invitrogen) and 10x reaction buffer. Reactions generally contained 2 mM MgCl2, 0.4 mg/ml bovine serum albumin (BSA), 0.25 mM dNTPs, 0.4 μM forward and reverse primers, and 2μl of DNA template. The PCR conditions were as follows: 95°C for 5 min, 50 cycles of , and 72°C for 10 min.PCR reactions were conducted in 25 μl reaction volumes, using Platinum Hydrodamalis gigas: MH 717817). Several other sequences purportedly from Steller’s sea cow mitochondrial control region exist on GenBank (acc. nos. KP134338-42), but do not align well with our sequence from this species, or with the other sirenian control region sequences, and therefore we believe these to be inaccurate.PCR products were purified using Exonuclease I and Shrimp Alkaline Phosphatase , sequencGENEIOUS v. 7.0.6. [Network [Phylogenetic tree construction was undertaken on the alignment of sequences using the program . 7.0.6. . We empl[Network . New, prST and FST measures were calculated. The effect of multiple simultaneous pairwise tests was accounted for through FDR  correction [An Analysis of Molecular Variance (AMOVA) was carried out in ARLEQUIN v.3.5.1.3  to examirrection .Beast 2.4.4 [Hydrodamalis + Dugong 28.6MYA (28.1–29.9), Sirenia 41.6 (41.3–42.2), Proboscidea 7.2 (6.8–8.1), Tethytheria 65.0 (63.9–65.8)). An MCMC chain length of 100 million generations was used, with sampling every 1000 generations and 10% burn-in. To ensure adequate mixing, effective sample sizes (ESS) greater than 200 were maintained for all model parameters. Convergence was ensured by using multiple independent runs. A maximum clade credibility tree was constructed from the resulting Bayesian trees using TreeAnnotator 2.4.4 and FigTree 1.4.2. The same analysis parameters were used in Extended Bayesian Skyline Plot analyses [Estimation of the dates of dugong lineage divergence were undertaken using st 2.4.4 , based ost 2.4.4 , which ist 2.4.4 , 51 . This specimen came from the Überseemuseum in Bremen, Germany, and had been previously unidentified, but was suspected to be Steller's sea cow skin; no further information regarding the geographical origin or date of the sample was available. Phylogenetic analyses , or are distinguished genetically from other populations only by differing frequencies of mtDNA lineages (as seen among the eastern populations) Figs  and 5.ST of 0.57 and φST of 0.51 (both P<0.0001). All geographic regions are significantly differentiated from each other (FST with P<0.01), with the exception of East Africa, Red Sea, and the Arabian/ Persian Gulf, which show no significant difference from each other. The highest pairwise FST is found between Madagascar and any of the other regions, ranging from 0.59 to 0.93  indicated an overall F to 0.93 .Genbank reference sequences from all closely related species in order to estimate approximate times of divergence between lineages, based on known calibrated fossil divergence dates [The dugong mtDNA control region sequences were aligned with ce dates . BayesiaThe overall genetic diversities within the population samples from Indonesia and Australia were similar to those previously reported from those populations . HoweverThe samples were also analysed for evidence of significant changes in population size over both short and long time-frames, using a range of parameter estimates  and the Madagascan lineages. These are distinct from all of the previously described lineages from the eastern part of the species’ range  and therLittle geographical structuring is detectable among the other populations in the Western Indian Ocean region. This may not reflect the true current state of connectivity among these somewhat isolated populations and may simply be the result of low detectable genetic variation. Further analyses of additional, longer mtDNA fragments are likely to reveal additional variation that would prove useful in better determining the degree of isolation of these populations.In particular, the population off Mozambique, while comprising the same mtDNA haplotype as other localities in the WIO region, such as Kenya and Tanzania, should also be considered for a special conservation status, as it presents (to our knowledge) one of the few remaining viable populations  in the WIO region. As such it may represent a valuable ‘source population’ for the region.The South Indian/ Sri Lankan population, although not unique genetically, does contain a unique mtDNA lineage, and is genetically divergent from all other populations, enhancing its conservation status. This population appears to be genetically intermediate between the WIO and Indonesian/Australian populations, a pattern that has also been observed in two species of dolphin sampled from the Bay of Bengal .H. gigas). Our new mtDNA control region sequence from the extinct Steller’s sea cow enabled a robust dated phylogenetic analysis of the Sirenian lineage.Unfortunately, there are no reliable estimates of the rate of evolution within the dugong (or Sirenian) lineage. Previous estimates for the mtDNA control region (D-loop in vertebrates) have ranged from a “standard” mtDNA rate of 1% per MY to 28% per MY , 57, 58.Our Bayesian estimation of the times of divergence among dugong lineages should be a reliable measure of the relative ages of the dugong lineages. However, the specific age estimates should be regarded more cautiously. Although excellent for dating within-species divergences, the mtDNA control region is not the best locus to use for dating divergences back to the ancestors included in this analysis (> 20MY), due to its rapid rate of evolution, relatively unconstrained pattern of evolution, and potentially rapid approach to saturation . AnotherThe dated phylogeny  suggestsOf note is the relatively old age of the major dugong lineages, which appear to be of the same order of magnitude as the age of manatee and elephant species . This suThis dataset offers the opportunity to examine genetic and demographic changes over both a long, evolutionary timescale  and a short, ecological timescale . Previous coalescent analyses of Australian samples of dugong had provided evidence of recent significant increases in effective population size within at least the “Widespread” Australian lineage over the last few thousand years . Our anaOn the ecological timescale, there was some evidence of a drop in genetic diversity in a number of locations from the older samples (collected before 1950) compared to the more recent samples (collected after 1950), which may reflect an accompanying drop in dugong population sizes. This certainly matches the known reports of major population reductions since the 1960s , 5. GiveThis study has revealed unique, previously unidentified lineages of dugong present in the Indian Ocean, and suggests that valuable genetic diversity has already been lost in this region over the past 150 years. The phylogeographic analyses of our historical samples indicate low levels of gene flow between most biogeographic regions, highlighting in particular the isolation of dugongs from Madagascar and the Comores. In contrast, there is little genetic diversity or population divergence along the rest of the Western Indian Ocean coastline.ex-situ sampling approach employed here and the resulting data will contribute towards a more comprehensive population genetic comparison of the dugong throughout its entire range. Such an analysis awaits the imminent publication by other research groups of several additional mtDNA data sets from newly-sampled regions in the Western Pacific. Together with on-going analyses of nuclear DNA markers in many of these samples, the analysis will contribute to a regional assessment of the dugong's conservation status. This in turn would assist in the design and implementation of sound and effective in-situ conservation and management strategies, aiding in the successful conservation and management of the species. Further genetic analyses of historical and contemporary dugong samples are likely to reveal additional insights into the conservation status of the Western Indian Ocean populations, including their rate of loss of genetic diversity. In this respect, further efforts to locate additional material, particularly from contemporary animals  would assist in a more detailed assessment of population structure and connectivity in this region.It is expected that the S1 TableBeast analysis using prior divergence dates of outgroup species (see Data are based on cies see . (HPD–hi(PDF)Click here for additional data file.S2 TableAll samples within each region are presented first, followed by samples within each region divided into collection periods before 1950  and after 1950 . Samples without collection date are excluded from the dated population samples.(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S1 Figet al. 2014.Previously known and new lineages are indicated. Collection locations are indicated by colour where known. Support values for all major lineages are shown (neighbor-joining bootstrap / maximum likelihood bootstrap / Bayesian posterior probability). A reference Genbank sequence (EU835761) is included. All previous Genbank sequences from the Western Pacific fell into the lineages reported in Blair (PDF)Click here for additional data file.S1 Protocol(PDF)Click here for additional data file.


Salamandra salamandra) has been proposed to represent an early stage of ecological speciation, driven by differential habitat adaptation through the deposition and development of larvae in streams versus ponds in the Kottenforst near Bonn (Germany). We set out to test this hypothesis of ecological speciation in an area different from the one where it was raised and we took the opportunity to explore for drivers of genetic differentiation at a landscape scale. A survey over 640 localities demonstrated the species’ presence in ponds and streams across forests, hilly terrain and areas with hedgerows (‘bocage’). Genetic variation at 14 microsatellite loci across 41 localities in and around two small deciduous forests showed that salamander effective population sizes were higher in forests than in the bocage, with panmixia in the forests (Fst < 0.010) versus genetic drift or founder effects in several of the small and more or less isolated bocage populations (Fst > 0.025). The system fits the ‘mainland-island’ metapopulation model rather than indicating adaptive genetic divergence in pond versus stream larval habitats. A reanalysis of the Kottenforst data indicated that microsatellite genetic variation fitted a geographical rather than an environmental axis, with a sharp transition from a western pond-breeding to an eastern, more frequently stream-breeding group of populations. A parallel changeover in mitochondrial DNA exists but remains to be well documented. The data support the existence of a hybrid zone following secondary contact of differentiated lineages, more so than speciation in situ.Adaptation to different ecological environments can, through divergent selection, generate phenotypic and genetic differences between populations, and eventually give rise to new species. The fire salamander ( Divergent evolution and reproductive isolation are the primary elements of the speciation continuum, but many have recognized that reproductive isolation is usually a signature effect rather than a primary cause of speciation. Whereas the mechanisms underlying reproductive isolation are by now mostly well understood , biologists continue to struggle with understanding how and why these evolutionary processes cause the disjoined genetic connections that are integral to the emergence of new species, in particular in conditions of sympatry6. Organisms that are organized in deme-structured metapopulations, with good population sizes and small to moderate dispersal capabilities, such as many amphibian species, are good models for population genetic research and convincing examples involving the early stages of ecological speciation in this group would be particularly welcome. Unfortunately, amphibians also appear particularly prone to severe losses in recent times e.g.8 and this includes the fire salamander Salamandra salamandra Linnaeus, 175810.Adaptation to different ecological environments can, through divergent selection, generate phenotypic and genetic differences between populations. These changes may eventually give rise to new species. The speciation process is often quantitative in nature, as illustrated by numerous studies showing that divergence during speciation varies continuously, and the sequence of genetically-based changes that occur as two lineages on the pathway to reproductive isolation diverge from one another has been coined the ‘speciation continuum’13. It would be important to determine whether similar processes can be uncovered in other regions where the species is distributed today. Other regions may serve as replicates despite the fact that each region may have had its own contingent evolutionary specificities. To perform a replicate study, we choose the ‘département’ (department) Mayenne in the west of France where we found the fire salamanders to deposit their larvae in streams as well as in ponds.The fire salamander represents a remarkable study system because from studies in the Kottenforst, near Bonn in Germany, it figures as an example of local ecological and genetic differentiation and adaptation, with pond- and stream-breeding populations possibly representing the first step in the speciation process15. The protection of genetic diversity, along with that of habitats and species per se is thus a pillar to nature conservation17. The landscapes of the French coastal zone represent the archetypal dense hedgerow configuration known as ‘bocage’. From the perspective of biodiversity conservation prime assets of hedgerows are that they act as shelter areas for species unable to exist elsewhere in farmland and that they are usually interconnected into networks which facilitates dispersal across the landscape18. The network of hedgerows possibly supports the presence and dispersal of the fire salamander and that of other primarily forest-dwelling species. The Mayenne study area therewith affords the opportunity to explore other landscape processes in this system, such as supported by continuous forest versus a mosaic of hedgerows. It is important to document if species are reliant on the bocage because a decline through the thinning of the hedgerow network could fragment otherwise continuous species distributions. Accordingly, in the present study we aimed to determine whether adaptive divergence in pond- and stream-breeding populations of the fire salamander could be identified in the west of France. We expect genetic differentiation to be more strongly associated with a pond- versus stream-breeding habitat than with the wider geography. A second aim was to quantify the importance of the bocage as a constituent to the fire salamander habitat and to evaluate what role it plays in the population dynamics of the species. Finally, informed by our new findings, we reanalyzed the Kottenforst data in an explicit geographical context.The standing genetic variability of populations represents the adaptive potential to changing environmental conditions and acts as a buffer against stochastic and catastrophic eventsS. salamandra were present in 251 localities (39%) and absent in 389 localities (61%). Localities are listed in a .kml file for use with e.g. Google Earth (Supplementary Information Ps) is estimated by the equation )). The fit of the model is expressed by the ‘area under the curve’ statistic AUC = 0.721 ± 0.020, indicating that we achieved a good description of favourable versus unfavourable fire salamander terrestrial habitats. When habitat parameter values are standardized the formula is )), indicating the relative contributions to the model in the order forest (a = −0.910), altitude (b = −0.288) and hedgerows (c = −0.275). Extrapolating the model over the entire department suggests that habitats for the fire salamander outside forests are widespread in the hilly bocage landscape in the northeastern and northwestern corners of the department Mayenne. Low habitat suitability is found in the flat and deforested southern part of Mayenne, in particular the southwest , the number of alleles per locus (average 9.4) and the proportioning of the samples over habitats, in our case sample size in streams and ponds (285/449 = 0.63) or forest versus outside forest (294/440 = 0.67). Under equation 1 of Jorde et al.  the threshold for a power of 10% is Fst~0.004, which is well below the observed Fst-value of 0.024.The number of alleles observed per locus ranged from four in locus SalE5 to 15 in locus Sal3. A single locus significantly deviated from Hardy-Weinberg expectations and there were four instances of pairs of loci showing linkage disequilibrium. The overall Fst/(1-Fst)) and the logarithm of geographical distance , indicating the absence of isolation by distance over the study area. The observed amount of genetic differentiation is substantial but does not have a bimodal distribution as is illustrated by the UPGMA-dendrogram on Fst . At Fst > 0.025 just non-forest populations are added. No significant genetic differentiation was found for pond- versus stream inhabiting population . Analyses with principal components revealed a wide overlap for the forest and non-forest groups. However, the forest populations form a tight group whereas the non-forest populations are more widely scattered over the bivariate plot . No significant difference was found for the pond- versus stream-breeding classification . The analysis with Structure yielded flat distributions with undifferentiated Q-values  were significantly higher for forest than for non-forest populations  whereas they were not significantly different for pond and stream populations . Among just pond populations, those from the forest were larger than those from outside the forest  (Table Estimates for effective population size N5) Table . No sign11. Sequences are equivalent to Genbank accession number KU24998920 with an indel at position 599. The distribution of the two mtDNA types bears no correspondence to the forest/non-forest inhabiting or to the stream- versus pond-breeding habitat classification  conforms to ‘type 1b’ and the longer one  to ‘type 1a’et al.21. All the investigated populations are situated inside the forest. Fire salamander larvae were observed in a variety of aquatic habitats  and significant in the reduced data sets .Analyses with Colony software suggested that many of the sampled individuals were full siblings. With only one representative per family group population sample sizes went down by 1628 under monogamy (63.5%) and by 1194 under polygamy (46.6%). Colony also revealed a high potential for false positives since inferred siblings were frequent among larvae from different localities . Results for genetic (dis)equilibria, genetic bottleneck effects and isolation by distance were markedly different for full and the sibling-excluded data sets. In the former we found 72 cases of significant deviation from Hardy-Weinberg expectations and 941 pairwise locus combinations with significant linkage disequilibrium . Average effective population sizes for the Kottenforst were not significantly different from those in Mayenne , but showed a wider range in the Kottenforst than in Mayenne . This signal is stronger than the parallel separation in pond- and stream-breeding populations . Populations joining the dendrogram at higher Fst levels are from either forest section. This set of populations is characterized by particularly low Ňe , suggesting that a founder effect or genetic drift underlies the genetic differentiation of small populations. Analyses with principal components supports the population allocation of two spatial groups, in which the western and eastern group show no overlap along the first PC-axis if indeed small populations are excluded . In line with these results, the plot of the PC1-scores over a west to east axis describes a genetic transition in a sigmoid curve of the type reminiscent to those analyzed in the classical hybrid zone literature e.g.22. The cline that best fitted the data has a central position at km 365.3 of the longitudinal axis of the Universal Transverse Mercator coordinate system grid (UTM) and a width of 3.95 km , derived with Structure software24. The cline description that best accommodates these values has a central position at km 365.1 and a width of 1.11 km  indicating that they are more or less isolated and that genetic drift and founder effect operate more effectively in these small populations. In metapopulation terminology, the fire salamander system in Mayenne fits the ‘mainland-island’ model28 in which forests are the mainland and the bocage represents an archipelago of islands. In Spain, genetic differentiation was observed to increase from Fst < 0.05 among most continental S. salamandra populations to Fst~0.10 for peninsular populations separated for at least 2000 years, to Fst~0.19 for insular populations separated for 6–13 Kybp (thousands of years before present) . This system, literally fitting the mainland-island model and at a comparable spatial scale to Mayenne, confirms the propensity for genetic drift in small and isolated fire salamander populations.The results of our habitat suitability modelling are in line with the general habitat preference of the fire salamander in Central Europe, namely mixed deciduous forest at 200–400 m a.s.l.The two forests in Mayenne may be interconnected by dispersal through the dense but patchy and declining bocage. While population connectivity operating through the network of hedgerows is likely, the study area does not offer a good setting for testing this hypothesis. First, we noted no consistent spatial genetic signal and it will be difficult to assess if this can be attributed to the counteracting forces of isolation by distance and connectivity by habitat. Second, to be able to disentangle these effects requires an explicit spatial configuration. For example, to test for fire salamander dispersal along hedgerows, the hypothesized corridor is to deviate from a straight line. If not, genetic differentiation will, by default, be associated to geographical, not ecological distance.33. This area is currently devoid of forests  show no forest at the localities where fire salamanders were actually observed, suggesting that extant populations persisted in the bocage over the centuries and providing ample opportunity for genetic drift to operate. While we could not detect significant departures from panmixia in forests, the bocage populations are more or less isolated. Yet the bocage localities may serve as ‘islands’ or ‘stepping stones’. Even a loose network may help to preserve population connectivity at a wider spatial scale and eventually promote the genetic exchange between forest populations, such as those of Forêt de Bourgon and Bois de Hermet  of the cline can be predicted from a diffusion model as a function of dispersal distance (d) and the length of time since contact (t), as w = 2.51d √(t)38. Generation time is reported as six years for both sexes39 and also as three years for males and four years for females40. At an average dispersal of 200 m per generation, which is at the lower limit because it just covers the regular home range, cline widths would exceed the measured width in a couple of generations and at higher dispersal, such as distances of up to 2000 m24, the collapse of the cline would be near-instantaneous. However, the Kottenforst genetic transition is sharp, suggesting that intrinsic selection operates against the two lineages’ mixed offspring. The center of the transition is further characterized by a paucity of material studied, presumably caused by lack of aquatic habitat suitable for the deposition of larvae. This observation fits classical theory where a hybrid zone may be ‘captured’ where an area of low population density acts as a barrier to further movement42. If dispersal is high, selection against hybrids is to be strong. Evidence for genetic incompatibility of the lineages may have been overlooked by Caspers et al.13. These authors were surprised to find that offspring numbers in the Kottenforst increased with the degree of genetic relatedness between females and their sires. Puzzling as this observation might be for within-lineage data, in the context of selection against hybrids it makes direct sense.With an average minimum distance of neighbouring populations of ca. 400 m the grid of localities studied in the Kottenforst is tighter than in Mayenne. Yet, unlike Mayenne, the pond- and stream-breeding populations appear to be spatially, behaviourally and genetically separated. This separation takes effect along a roughly longitudinal axis. The by approximation sigmoid shaped transition is 1.11–3.95 km wide, with inflection points that are 730–1440 m apart  precludes measuring position, width and shape of the mtDNA transition east of the Kottenforst. With microsatellite data Steinfartz et al.49 also found that the Kottenforst populations are the most closely related in the wider area. This conclusion, however, depends on the position of the ‘root’ of the graph (which is not provided) and the supporting evidence is not unambiguous given the graph’s short internal branches. Future research might reveal a continuation of the fire salamander contact zone outside the Kottenforst and possibly show that the western and eastern lineages represent the currently described subspecies S. s. terrestris Lacépède, 1788 and S. s. salamandra. A wider phylogeographic survey is required to solve these issues.An argument against the secondary contact scenario may be that the Kottenforst fire salamander populations are more similar to one another than to other, nearby populations for mitochondrial56. We analyzed microsatellite genetic data in two metapopulations of the fire salamander in the northwest of Europe, with contrasting results. In France we documented small, isolated, persisting and genetically differentiated populations in the bocage versus panmixia in two small deciduous forests. This population structure fits the classical mainland-island model. A mainland-island population structure may have been paramount to the fire salamander prior to the recent intensification of agriculture, the field size enlargements that came with it and the widespread conversion from pasture for cattle-breeding to arable for growing crops. We did not find a bimodal distribution coinciding with stream- and pond-reproduction as detected in the Kottenforst57. Accordingly, the fire salamander system in France offers no support for habitat driven genetic differentiation other than through drift and founder effects operating in more or less isolated bocage populations. In Germany we found small and genetically differentiated populations scattered over the Kottenforst, in addition to larger populations occupying ponds in the western section and ponds and streams in the eastern section of the forest. The finding is somewhat puzzling, given the unrestricted gene flow that we documented for the French forest populations and the rampant migration documented for the Kottenforst per se, with large home ranges and frequent long-distance dispersal24. An ad hoc explanation, supported by the high frequencies of full-siblings and low effective population sizes, is that these localities represent a particularly intensive sampling scheme, with larvae included from unusual aquatic habitats such as puddles and wheel ruts  that together constitute a metapopulation. The deme structure in streams is less obvious, but clearly not all sections of a stream are equally suitable for reproduction, for example in sections with and without predatory fish, or sections falling dry. Yet, for consistency, we consider the amphibian sites that we study to represent local populations. Locality data on altitude , forestation and hedgerows (percent cover) were extracted from digital IGN maps  with a 25 m spatial resolution, provided by department officials. The area considered around each pond or stream sampling locality had a radius of 200 m. This scale should represent a typical fire salamander home range because 200 m is the median distance travelled by adults in a capture-recapture study in the Kottenforstet al.59 and the loci B11, C2, C3, E11, G6, G9, IA6, IIA6 were studied following Hendrix et al.60. Locus SalE5 is a dinucleotide microsatellite locus and the others are composed of tetramer motifs. After a quality check (see Results) data for the locus C2 were excluded so that 14 markers remained. A total of 129 individuals from 14 localities was sequenced for the mitochondrial D-loop  as described in Steinfartz et al.61. Nuclear genetic data from the German Kottenforst involved 2563 larval fire salamanders studied at 17 polymorphic microsatellite loci and were retrieved from Hendrix et al.21. The 47 Kottenforst sampling localities were classified in two spatial groups  that represent opposite sections of a sharp genetic transition (see Results).The area selected for population genetic research is situated around two small, largely deciduous forests and positioned at the transition from flat and deforested in the south of Mayenne, to hilly with a bocage landscape in the north Fig. . Larvae 62 to analyze for the presence of null-alleles. FreeNA was run with 1000 replicates using the EM algorithm. GenePop version 4.263 to estimate Fst-values and analyze for Hardy-Weinberg and linkage (dis)equilibria. GenePop was run with dememorization number 1000, 100 batches and 1000 iterations per batch. Linkage disequilibrium was determined using the log likelihood ratio statistic. Hardy-Weinberg equilibrium and linkage disequilibrium results were interpreted under the Benjamini-Hochberg correction for multiple comparisons. Statistical power for the detection of genetically differentiated yet sympatric populations was estimated following Jorde et al.64. The genetic diversity among populations was summarized by clustering with the unweighted pair group method with arithmetic mean with Primer-e software (UPGMA65) and by principal component analysis (PC) with Adegenet version 2.0.0 following the manuals67. We also carried out a Bayesian assignment analysis with Structure68, to which we adopted the program settings for the similar analyses by Hendrix et al.21. The parameter K (the number of genetic groups suggested by the data) was evaluated under the ‘Evanno-criterion’ that is implemented in StructureHarvester69. We used Colony version 2.0.6.270 to analyze family groups and to estimate effective population size (Ňe) from the frequency of siblings. Settings for Colony were both sexes either monogamous or polygamous (see below), no allelic dropout or typing errors, diploid, codominant markers, no inbreeding, scaled full sibship, unknown population allele frequency, ten ‘medium long’ or ‘very long’ runs under full-likelihood with ‘medium’ precision and a ‘strong/optimal’ sibship prior for effective population size. Results for Ňe reported are under α is zero, assuming that deviations from Hardy-Weinberg equilibrium are negligible. We used Bottleneck71 to test for reductions in effective population size by considering that alleles are generally lost faster than heterozygosity and thus, populations that have experienced a reduction in effective population size are expected to have excess heterozygosity relative to that expected under mutation-drift equilibrium. Samples smaller than for ten individuals were ignored. Statistical relevance of the results were determined with the Wilcoxon signed-rank test. We applied the two phase model with 93.6% single step mutations, variance 30 and 10,000 replications. The proportion of single-step mutations was determined with Misat version 1.072 with dimer code 2, tetramer code 4, gridsize 40, moments estimated under the one-step model, 100,000 runs through the Markov chain, with continuously updating the value of θ0. We used HZAR73 to formalize the geographical cline suggested by the first PC-axis for the Kottenforst. The chain length was 100,000 with a burn-in of 10%, randomized seeds and model selection based on AICc scores. For details on the cline fitting procedure and model selection see e.g.74. Mitochondrial DNA sequences were aligned with ClustalW version 275 under default settings. Matrix correlations were done with Primer-e65 under 10,000 permutations. Other statistical analyses were with SPSS v. 2076.Computer programs employed for the genetic data were as follows. FreeNA49 and we explored both possibilities. Including siblings in analyses of population diversity and structure can introduce a variety of biases (77 and references therein). Clutch sizes in the fire salamander are ca. 3027 and the more or less frequent sampling of full- and half-siblings in our study cannot be excluded. However, excluding all but one members of a family group is not exempt from problems neither and will cause other biases79. Considering the moderate number of markers and alleles in either of the two data sets we tested for the presence of full-siblings in the material at two levels. Firstly, we reconstructed family groups per population. The numbers of inferred full-siblings were substantial. Secondly, we compared all individuals across localities. Although female fire salamanders may deposit larvae in more than one water body13 this phenomenon will be rare in our sampling of disparate localities with average minimum distances of ca. 1.1 km in Mayenne and 400 m in the Kottenforst. Consequently, inferred full-siblings from different localities possibly qualify as false positives. It is altogether not straightforward to determine the balance between redundant information from siblings included versus independent information from false positives excluded. We here present results from the full data set not to hamper comparability with published results24, with the parallel results for the siblings excluded data set in Table 80.Reproduction in the fire salamander may be monogamous or polygamous. The available data indicate that both systems occur in nature about equally frequentMethods were carried out in accordance with the relevant guidelines and regulations. The tissue sampling protocol was approved by the Research Coordination Office of Naturalis Biodiversity Center. Fieldwork was carried under out under license from the ‘Service Aménagement Environnement-Chasse, Direction départementale de l’Agriculture et de la Forêt, Mayenne arrêt no. 2003-A-207.Suplementary information.