Procambarus clarkii) among 21 populations in the Pearl River basin and 2 peripheral populations in the Yangtze River basin. We found the genetic diversity of P. clarkii in the Pearl River basin was somewhat lower than in the Yangtze River basin. We also found (1) there was significant genetic differentiation between populations, (2) genetic differentiation was not related to geographic distance , and (3) a Bayesian assignment analysis revealed three distinct genetic clusters and genetic admixture. Our results therefore provide evidence that human-mediated multiple introductions occurred in the Pearl River basin. Anthropogenic activities such as commercial transportation were likely responsible for the long-distance dispersal of P. clarkii. This study provides useful information for developing management strategies.Discerning the dispersal patterns of invasive species is critically important for the design of effective management strategies and the development of appropriate theoretical models predicting the spatial expansion of introduced populations. Post-introduction dispersal may occur naturally or via human transport, but for many organisms, assessing the relative contribution of each of these factors is difficult using traditional methods. Here, we explored the genetic patterns associated with the spread of red swamp crayfish ( There is an increasing need to take action to control biological invasions and thus mitigate their impacts on biodiversity, ecosystem services, and human activities. Prevention is generally far more cost-effective and environmentally desirable than post-introduction measures such as eradication or long-term containment. Before implementing appropriate prevention strategies, it is necessary to collect detailed information about the dispersal pattern and vectors of an invasion. However, traditional (mark-recapture method) quantification of actual dispersal patterns of invasive species is a huge challenge and cannot provide effective information on invasive species population dynamics4.Bio-invasions are considered a result of global trade, human transport and climate change5. Genetic tools provide information on the pathway(s) of an invasion, estimate inoculum size, determine whether an invasion consists of one or more introduction events, and show whether admixtures occur in the introduced range6. A genetic approach to the management of invasive species is gathering momentum. The information gleaned from this analysis of gene flow and population structure has broad implications for quarantine, trade and management of Bactrocera correcta7, Bemisia tabaci biotype B8, and Vulpes vulpes9. Coupled with ecological information on dispersal and/or colonization mechanisms and life-history characteristics, population genetics analyses provide a strong basis to formulate management strategies10.Fortunately, molecular systematics and population genetics have rendered new and exciting tools to better our understanding of population dynamics during biological invasionsProcambarus clarkii) is a highly successful invader originating from the south-central United States and northeastern Mexico11. According to records, P. clarkii was first intentionally introduced to Nanjing, China, from Japan in 192912 and subsequently introduced into many provinces in China. Presently, P. clarkii was detected in a new watershed in the Pearl River basin. Successful establishment of P. clarkii populations has been facilitated by their high environmental tolerance, rapid population growth rate and superior competitive ability14. This invasive crayfish is a polytrophic keystone species that can exert multiple pressures on ecosystems15. Most studies have focused on the effects of the decline in macrophytes and predation on several species , highlighting how this biodiversity loss leads to unbalanced food chains17. Furthermore, P. clarkii is a host and vector for obligate parasites such as Aphanomyces astaci and spreads contagious and lethal disease to the indigenous crayfish Austropotamobius pallipes18. Thus, expansion and establishment of P. clarkii in new areas may convey considerable risks for introducing and transmitting this disease and obligate parasites to native species.The red swamp crayfish  and two outside of the Pearl River basin , 1.0 μL template DNA, and deionized water to a final volume of 15 μL. PCR cycling conditions were: 94 °C for 2 min; followed by 35 cycles of 94 °C for 30 s, 55 °C for 30 s and 72 °C for 40 s; and final extension at 72 °C for 10 min. Amplified products were separated by 6% denaturing polyacrylamide gel electrophoresis and stained with AgNO3.We extracted genomic DNA from ~0.1 g of muscle tissue using phenol-chloroform, and total DNA was preserved at −20 °C. Seven polymorphic microsatellite loci  were amplified as previously described23. Linkage disequilibrium for each pair of loci in each population was also tested using the same software. We used Bonferroni’s correction24 to evaluate the significance of deviations from HWE or linkage disequilibrium. In addition, we used Micro-Checker software25 to test for the presence of null alleles. Finally, we found that the genotyping error rate was 0.087% in all loci of total populations. Therefore, we are confident that our genetic data were sufficiently robust to analyse genetic diversity and population structure.We performed several tests on microsatellite loci to check scoring errors and violations of assumptions in our statistical analyses. We tested deviations from Hardy-Weinberg equilibrium (HWE) for each locus in each population with 10,000 dememorizations, 5,000 batches, and 5,000 iterations per batch using Genepop on the WebHo), and expected heterozygosity (He) for each population using GenAlEx version 6.5 software26. To assess differentiation among populations and analyse the genetic relationships among populations, pairwise FST values and pairwise genetic distances were calculated using the same software, respectively. Neighbour-joining trees were constructed in Mega v6.027.We calculated the average number of alleles, observed heterozygosity  when populations experience a bottleneck29. We tested bottlenecks using both the stepwise-mutation (SMM) and two-phased (TPM) models with 10,000 replicates. Variance for TPM was set to 30, and the proportion of SMM in TPM was set to 80%. In our models, we used the Wilcoxon sign rank test to establish whether the number of loci showing heterozygosity excess was significantly greater than expected in populations at equilibrium. A mode shift in allele frequency distribution was also used as an indicator of a population bottleneck30.To detect bottlenecks, a test of heterozygosity excess was performed using the program Bottleneck 1.2.0231. In AMOVA, total variance is partitioned into separate components, each of which describes the proportion of the total variance at distinct hierarchical levels. We tested for a relationship between geographic distance and genetic distance using Mantel tests on the Isolation By Distance (IBD) Web Service v3.2332. The geographic distances between populations were calculated from the coordinates of each sample sites using gpsCalc. We conducted a IBD analyse for overall population test. In order to more precisely illustrate IBD models of populations along rivers, we conducted separate IBD analyses for sample sites: (1) populations Xing’an (XA), Lingchuan (LC), Lingui (LG), Yangshuo (YS), Lipu (LP) and Zhaoping (ZP) along the Guijiang River and (2) populations Chenzhou (CZ), Yingde (YD), Qingyuan (QY), Sihui (SH), Sanshui (SS) and Gaoming GM) along the North River. The distances between populations along Guijiang River and the North River were used river kilometre distances.We used analysis of molecular variance (AMOVA) to partition genetic variation within and among populations using Arlequin v3.5 software33. We assessed the likelihood of models with the number of clusters ranging from K = 1 to K = 11. We performed 20 independent runs for each value of K with Markov Chain Monte Carlo (MCMC) using 1,000,000 replicates and a length of burn-in period of 100,000 replicates. Based on likelihood scores among runs, ΔK of the corresponding K values were calculated according to an algorithm described by Evanno et al.34. We chose the model with the highest ΔK as the best one representing the true underlying genetic structure. The corresponding value of K was determined to be the best estimate of the number of clusters in the full data set.Finally, we evaluated the genetic structure using Bayesian assignment analysis in Structure v2.3.4We genotyped a total of 904 individuals from 23 populations with seven polymorphic microsatellite loci. Microsatellite loci were found to be robust in tests of HWE and linkage disequilibrium. The great majority of loci were found to be in HWE after sequential rounds of Bonferroni’s correction, except for locus PclG-16 in the Shatang (ST) population. No loci exhibited significant linkage disequilibrium after sequential Bonferroni’s correction.Ho ranged from 0.25 to 0.60 and He ranged from 0.289 to 0.598. The Nanjing (NJ) population had the most genetic diversity among populations. Except for two peripheral populations, the Sihui (SH) population had the most genetic diversity among populations in the Pearl River basin.We found a total of 39 alleles at the seven microsatellite loci in the 23 populations. Locus PclG-28 had the most alleles (nine) and locus PclG-13 had the least (two). The average number of alleles per locus in the population ranged from 2.57 to 4.14 Table . The aveFST was 0.13. The greatest differentiation occurred between the Xiangzhou (XZ) population and the Fuchuan (FC) population (FST = 0.41) and the smallest was between the Lingui (LG) population and the Yongfu (YF) population (FST = 0.01)  Table .Table 1AP < 0.05 or P < 0.01 respectively). Using the SMM model, it is likely that there was a bottleneck in six populations (P < 0.05 or P < 0.01)  Table .Table 2A Mantel test of IBD revealed no relationship between genetic distance and geographic distance among all populations Fig. . We founK = 3 as the best model based on a plot of estimated log likelihood for the data at each K and a plot of the ΔK statistics  within a river showed clear assignment to different clusters, whereas some geographically separated populations (such as LP and XZ) belonged to a single cluster and had substantial affinities.Based on our Bayesian assignment analysis, we determined s Figure . Structuers Fig. . Six popThe neighbour-joining tree based on Nei’s genetic distance recovers two largely distinct clades Fig. . Populatet al. revealed high gene flow in river basins and similar genetic structures during the natural dispersal of mosquitofish (Gambusia holbrooki)35. If P. clarkii naturally spreads in rivers, the genetic structure will show large similarities, and there will be a correlation between geographic and genetic distance. Contrary to the hypothesis, some populations of P. clarkii along rivers had largely divergent genetic diversity 35 and Chinese mitten crab (Eriocheir sinensis)37.The genetic pattern observed in the Pearl River basin provides an illustration of human-mediated long-distance jump dispersal. Díezdelmolino 38. Merchant shipping traffic was busy at midstream and downstream of the Pearl River basin. Greater shipping activity is expected to increase invasion success and genetic similarities at the population level by altering the diversity of discharged invasive species and the number of introduction events40. However, merchant shipping did not reflect large genetic similarities in our study. The neighbour-joining tree showed a tangled pedigree. A large genetic distance even occurred among populations distributed in a river. Additionally, various types of hydropower station are distributed in the Pearl River basin. So we speculated shipping was improbable to mediated long-distance dispersal of P. clarkii. Land transportation  was faster, more convenient and cost-effective than shipping. It might be the main vector to the dispersal of P. clarkii. This was a slightly different from dispersal vectors in the Yangtze River basin. Yangtze River basin is characterized by large basin area and many lakes. River drop is small and shipping is very busy and prosperous in midstream and downstream. Commercial shipping has likely facilitated communication among populations of P. clarkii in the Yangtze River basin and has influenced their population genetic structure and diversity21. Both long-distance jump dispersals in the two River basins were explained by anthropogenic transportation.Ship-borne, attached to ship hulls and ballast water are expected as dispersal vectors for the long-distance dispersal of some non-native freshwater species42. There were seven genetic clusters in the Yangtze River basin21, but only three in the Pearl River basin. The observed genetic differentiation pattern within the Pearl River basin suggests multiple independent introductions from genetically divergent sources out of the Pearl River basin. Similar patterns were observed for invasive Cordylophora in the Great Lakes basin4. Notably, a low level of genetic differentiation was observed between populations YF and LG, indicating high levels of population connectivity and dispersal increase homogeneity. The homogeneity of the two geographically isolated populations further supported human-mediated dispersal.Long-distance dispersal patterns are known to affect genetic structure between established populations, while also producing distinctive patterns of genetic differentiation during range expansion44. Our results supported this view. The observed large genetic variation within populations suggested that some populations might have received multiple introductions. Even though some populations  exhibited a harsh bottleneck effect, the genetic diversity was not obviously reduced. A founder effect is usually expected when a species, such as bullfrog (Lithobates catesbeianaus) and aphid (Adelges cooleyi), is introduced into a new habitat46. However, founder effects and bottleneck effects do not always appear to be a barrier to successful invasion and subsequent expansion47. Multiple introductions from genetically divergent population might dilute the founder and bottleneck effects by rescuing some alleles48. The multiple introductions associated with long-distance dispersal of P. clarkii may have played important roles in shaping genetic variation, similar to other research50.Previous research noted that multiple introductions increase genetic variation and accelerate large-scale expansion of invasive species, which increase invasion successP. clarkii is caused mainly by anthropogenic transportation, the primary management strategy to control P. clarkii should be control of anthropogenic transportation, such as the management of the common staring (Sturnus vulgaris)10 and bullfrogs51. Microsatellite evidence for the dispersal of invasive common starling provides information to produce a large-scale management strategy10. In 2005, the state of Montana designated bullfrogs (L. catesbeianus) as a prohibited species, which makes it illegal to possess, sell, purchase, exchange, or transport bullfrogs in Montana51. Since this legislation, management of which has been greatly effective in preventing bullfrogs from establishing a new population51. This study highlights the competence of genetic analysis to provide information for management of P. clarkii. Governments should create management policy to prevent dispersal. Prevention is clearly more cost-effective than post-establishment eradication or containment5. In our opinion, some simple management guidelines are: (1) governments enact constraint regulations on trade and introduction to prevent P. clarkii from escaping, or being released or cultivated and invest a large-scale education effort; (2) management agencies strictly enforce inspection at motor and railway stations in distribution area of P. clarkii to prevent transport. Deterring boaters from high-risk lakes is effective to reduce the spread of invasive species via human activities, and large-scale education efforts are more effective52.Since the dispersal of Invasions are often complex and involve biotic and abiotic factors. Therefore, it is important to incorporate information to make comprehensive management strategies to ensure that sufficient effort is placed where required, thus maximizing the likelihood for successful control outcomes.Supplementary Information


The characterization of the structure of southern African populations has been the subject of numerous genetic, medical, linguistic, archaeological, and anthropological investigations. Current diversity in the subcontinent is the result of complex events of genetic admixture and cultural contact between early inhabitants and migrants that arrived in the region over the last 2000 years. Here, we analyze 1856 individuals from 91 populations, comprising novel and published genotype data, to characterize the genetic ancestry profiles of 631 individuals from 51 southern African populations. Combining both local ancestry and allele frequency based analyses, we identify a tripartite, ancient, Khoesan-related genetic structure. This structure correlates neither with linguistic affiliation nor subsistence strategy, but with geography, revealing the importance of isolation-by-distance dynamics in the area. Fine-mapping of these components in southern African populations reveals admixture and cultural reversion involving several Khoesan groups, and highlights that Bantu speakers and Coloured individuals have different mixtures of these ancient ancestries. Northern component, which is substantially different from that characterizing populations located to the south of the Kalahari  and published genome-wide SNP data. By applying a local ancestry deconvolution approach, we highlight previously unobserved complexity in the Khoesan-related genetic variation, and generate novel insight into the genetic history of the region. We provide evidence for the presence of at least three distinct Khoesan ancestral components, and reveal a substantial degree of admixture between Khoesan groups. Our fine dissection of the Khoesan-related legacy in highly admixed populations also reveals slight differences between Coloured and Bantu-speaking populations, possibly suggesting admixture with different Khoesan sources. Finally, we demonstrate that Khoesan-related structure is highly correlated with the geographic location of populations, but not with linguistic affiliations or subsistence strategies.We generated novel genotype data for 59 individuals from seven southern African populations collected in Namibia and Lesotho. Forty-four of these individuals from four Bantu speaking groups , and a Khoesan-speaking group (NamaM), have been published previously . Additional data from outside of southern Africa were taken from populations with European, African, and Middle East ancestry, genotyped on different Illumina platforms and the Affymetrix Axiom Genome-Wide Human Origins 1 array  (The 1000 Genomes Project ConsortiumTable S1).Our analyses focus on southern African populations. We therefore merged our data with an additional 31 Khoesan-speaking, and 20 “admixed” and Bantu-speaking populations  we merged all data collected from all studies into a large dataset, which we refer to as the “Unphased dataset”. The two platform-specific datasets, comprising 250,547 (Illumina) and 498,140 (Affymetrix) markers, were merged on physical position to avoid unnecessary loss of markers due to mismatching IDs on different platforms. Following this merge, we again performed the same quality control and removal of relatives described above, obtaining a final dataset containing 1856 individuals genotyped at 63,767 SNPs.https://mathgen.stats.ox.ac.uk/genetics_software/shapeit/shapeit.html#gmap). We generated a second dataset (“The Khoesan Ancestry dataset”), by initially removing from each platform-specific dataset (Illumina and Affymetrix) non-Khoesan genomic fragments as identified using PCAdmix , to extract the Khoesan Specific Fragments (KSF) inferred from the postprocessing described above. MaskMix considers each individual as homozygous, and composed by one chromosome per pair only, from which high confidence KSFs were extracted and analyzed. This approach allows us to use chromosomal data instead of individual genotypes, maximizing the amount of genetic data suitable for the analysis, and is not expected to affect any of the analyses performed because the relative allele frequency would be unchanged. To allow the comparison between individuals genotyped using arrays from different providers, the resulting two datasets were pruned to retain markers that overlapped between the Illumina and Affymetrix datasets and that were located on Khoesan-specific genomic fragments. Finally, we removed all the individuals for which <10% of the total number of overlapping SNPs were retained. The resulting dataset is composed of a total of 63,767 markers and 787 individuals. Given the variation in Khoesan ancestry in different individuals, the average number of retained SNPs per individual was 22,442 . We refer to this final set of SNPs selected as described above as the “Khoesan Ancestry dataset”. Furthermore, we assessed the different performance of the two datasets (Illumina and Affymetrix), exploring the distribution of the tract length estimated by the approach with respect of the platform manufacturer .We used a custom-made PYTHON script . For the K ancestral components as implemented by ADMIXTURE, visualizing their distances with a heatmap using the pheatmap . For the Ne is the effective populations size, and t is the time since separation (in generations). This approach has been applied to pairwise Ne inferred over time for five Khoesan by Figure S3. PCA was performed using PLINK 1.9 (Table S3). We report all the comparisons in Table S3, while significant values are reported in X is represented by all the other populations in the unphased dataset . In order to remove the effect of recent admixture, we repeated the same test on the “Khoesan Ancestry” dataset, the Illumina Khoesan Ancestry  and the Affymetrix Khoesan Ancestry datasets . Moreover, we assessed the 4f test of the form X is one of the admixed (Basters and Coloured) or Southeast Bantu populations .We applied both model-based and nonparametric clustering approaches to describe population structure in the Unphased Dataset. First, we used the ADMIXTURE . However, the Figure S5) is smaller than the distance between the African and the other European ancestries, which could suggest that the algorithm picked a new combination of allele frequencies caused by the admixture. To test this hypothesis, we evaluated the performance of ADMIXTURE on simulated admixed samples. In details, we used the Yoruba (YRI) and British (GBR) genetic data and generated four groups of 25 admixed individuals composed by a variable fraction .As we describe in the results, at Table S1). We used a chimpanzee outgroup using genome data available in A ML tree describing the relationships between Khoesan populations was inferred using allele frequency distributions implemented in the TREEMIX software , and report the tree with the maximum support in Table S2), as estimated by the We performed 10 different runs using different random seeds  genetic distance with PLINK 1.9, correcting for missing data, and summarized relationships with a Local Ancestry Multi-Dimensional Scaling (LAMDS) plot, using the ion in R . We corradehabitatHRR package  coordinates and IBS distance matrix. We initially plotted all southern African groups with 90% utilization distribution density kernels for the three ancestries, estimated using the Kernel Utilization Distribution (UD) function in the  package . The fun package . We next package  as impleFigure S8A). The overall performance of the approach was assessed by exploring the posterior probability trace , while the ability of the model to describe the data was evaluated by analyzing the correlation between parametric vs. observed covariance matrix  and the decay of covariance vs. geographic distance for observed and inferred matrices.In order to further investigate the distribution of the genetic variability among the three Khoesan ancestries, we built a “GeoGenetic map” of populations , taking advantage of the Bayesian statistical framework implemented in SpaceMix . All the combinations were tested through a five-fold cross-validation analysis in which the dataset was split into five random subsets. Each of these subsets was then tested against the other four, and the combined error recorded and shown in a barplot (Table S4).To assess the role of geography, subsistence, and language in predicting genetic variation of Khoesan populations, we performed regression analysis of LAMDS first two components  barplot . Two altFigure S13, in which we report the estimated dates  comprising 1856 individuals from 91 populations genotyped at 63,767 autosomal markers . After kinship analysis, 25 individuals were removed, one from each of the 25 inferred pairs of highly related individuals. Ten of these pairs contained individuals genotyped in different studies: two Bantu South Africa from the HGDP . At To visualize the evolutionary relationships among the analyzed individuals, we used ADMIXTURE . More generally, almost all of the Khoesan populations show a non-negligible fraction of ancestry components that are modal in East Africa and Europe, consistent with ancient and recent migrations from these regions , analyses performed at higher values of K provide insights into the genomic history and substructure of populations, so we describe these results below. At Figure S5), which suggests that the admixture between African and Eurasian populations might have generated a novel combination of allele frequencies, which is now captured by this component . We tested this hypothesis by exploring admixture runs performed on different simulated populations composed by a variable fraction of British (GBR) and Yoruba (YRI) ancestry. In all the simulations, the ADMIXTURE software models a third component . At We noted that, although the smallest estimated cross-validation values are found for Materials and Methods). Similar methods have previously been successful in assessing the continental legacy of American populations . The mean correlation of IBS between the whole and the resampled dataset is always higher than 0.8, and reaches 0.9 with as little as 1500 markers, which suggests that the impact of different numbers of markers on IBS estimates is negligible . In addition, the tract length density for the two datasets is very similar , with the number of short chunks identified for the Affymetrix dataset being slightly larger than the Illumina. However, given that missing data are excluded in our pairwise IBS distance estimation, we do not expect this to cause substantial bias. Our LAMDS analysis reveals three main groups of Khoesan-related ancestry (Northern Khoesan) is composed of all the K’xa speaking populations located at the North of the Kalahari (Ju|’Hoan and !Xun) with the exception of the more central =Hoan. The other two groups are composed by the Nama, Khomani and Karretjie (Southern Khoesan), and all of the remaining Khoesan populations .Ancestry analysis of Khoesan populations is complicated by the fact that the genomes of most of the contemporary populations are a mosaic of multiple ancestries . The Karretjie appear more related to the Khomani than to the Nama, with the Naro acting as an outgroup to these two branches. Similarly, Hai||om and Shua form a distinct branch that splits from the other Southern Khoesan, although there is little support for this branching pattern across bootstrap runs. The split, which separates the !Xun from the JulHoan, is well supported, further emphasizing the genetic distinctiveness of these two K’xa populations  from the K’xa in the North (Northern Khoesan) and the Nama, Khomani, and Karretije in the South (Southern Khoesan). Notably, the Figure S3A) when ancestral components inferred by ADMIXTURE are used, which is broadly consistent with previous estimates , and assume a generation time of 29 years . However, interpretation of these tests is challenged by admixture events between nearby populations, which skewed the allele frequency of the “source” populations. When the same test is performed only on “Khoesan fragments,” this signature is retained only for the Naro population , which behaved as truly admixed in multiple analyses. Although a low degree of admixture between Central and Southern groups cannot be excluded, this is unlikely to explain the observed genetic structure. On the contrary, the results are compatible with a isolation-by-distance scenario .Interestingly, five out of the 11 of the Figure S13). Overall, the results are consistent with earlier work . This approach detects apparent substructure within the three clusters, such as the genetic differentiation between !Xun and Ju|’Hoan, or between Khomani and Nama. The Central group seems to be further subdivided into a Eastern and Western group. Several admixture events .We modeled the geography of population structure in Khoesan populations, taking advantage of the Bayesian statistical framework implemented in SpaceMix. The resulting geogenetic map, which summarizes the genetic structure and the admixture events among populations, is shown in Central Khoesan ancestry, the Naro and some of the Jux’Hoan occupy a position intermediate between populations characterized mostly by Central and Northern components and the Hai||om are scattered between individuals with Northern and Southern Khoesan genetic profiles. These results are consistent with our PCA analyses based on the subset of individuals in each group with >80% Khoesan ancestry. It is important to note that all the analysis converge toward a tripartite genetic structure in Southern Africa, suggesting that the error due to the “computational phasing” is negligible. In addition, it has been shown previously . None of the Central Khoesan populations show significant evidence of admixture between Northern and Southern groups. In the Khomani, the lowest Table S3). The Naro show evidence of admixture involving populations close to Ju|’Hoan and a central Khoesan population, such as Taa and G|ui. The Ju|’Hoan also show significant Northern Khoesan) and Naro . Similarly, the !Xun also show evidence for admixture with the Ju|’Hoan.PCA confirms patterns similar to those described above for the Khomani and Naro, which are spread toward groups rich in ectively . We form dataset ; among tadehabitatHR package  groups with 90% utilization distribution density kernels for the three ancestries , estimat package . Next, wCentral Khoesan)—are genetically related to the cluster defined by Nama, Karretjie, and Khomani (Southern Khoesan). We noted that admixed individuals mapping to this cluster appear to highlight a partly structured distribution, since the Bantu populations are located on the upper side of the distribution, while Coloured and Basters are on the lower side . These results are supported by the test Figure S4C). However, caution must be used in the interpretation of these tests when admixed populations are used as “Sources.”All of the other Bantu-speaking groups—with the exception of the Kwangali, who are closer to Taa and K’xa speaking groups from Botswana (wer side . To testP < 0.001), as previously observed by We performed a Procrustes analysis to test the relationship between genetic and geographic distances, and found a statistically significant correlation , West , and Khoekhoe, which is currently spoken by the Nama, Damara, and Haixxom populations . The hisFigure S3, and Figure S5); these dates are in line with previous estimates for the separation of the two Khoesan components , as well as the West and East Khoe-Kwadi speakers and the K’xa speaking population = Hoan. This further highlights the mismatch between genetics and linguistic affiliation in populations from the region , Karretjie, and Nama (Khoe-Kwadi). All of these populations are thought to have originated in the Northern Cape . All the Coloured and the Bantu populations from the Southernmost part of the continent (South Africa and Lesotho) are characterized by the Southern Khoesan component  identified four different ancestral components broadly consistent with their geographic location . SpecifiThe genetic structure of southern African populations is complicated by the existence of ancient population structure, onto which several layers of additional genetic ancestries have been overimposed over the last few centuries. Here, we demonstrate that local ancestry approaches can be used to tease apart the genetic structure of such ancient components, characterizing their relationships and current distribution, further supporting a role for widespread admixture in human history (


Sebastes thompsoni, commercial rockfish catch in the Northwest Pacific Ocean, may influence its population structure. To clarify the population genetic structure of Korean S. thompsoni and its degree of hybridization with the most close species, Sebastes joyneri, we analyzed a mitochondrial (mt) DNA control region and eleven polymorphic microsatellite (ms) loci. S. joyneri individuals were clearly distinguished from S. thompsoni by the mtDNA control region and ms loci results, with single interspecific hybridization between two species suggesting no impact on genetic structure of S. thompsoni. Analysis of mtDNA revealed no population structure within S. thompsoni, suggesting the survival of a single population in southern refugia during the glacial period. The ms loci results, in contrast, showed two genetically distinct clusters within S. thompsoni: One was predominant throughout Korean coasts ; the other was predominant at Dokdo Island in the East Sea; and both occurred in similar ratios at Wangdolcho Reef in the East Sea. A possible factor that restricts gene flow between Korean coastal and offshore populations in the East Sea may be related to the complex oceanic current patterns such as eddies and upwelling, which represent impermeable barriers to population connectivity for this species. Our findings highlight that these two populations might be representative of two separate stock within Korean waters and maintain their geographically related genetic structure.The goldeye rockfish,  Conversely, because speciation within Sebastes occurred fairly recently  and Korean rockfish (Sebastes schlegelii) are good examples to demonstrate this; both species spend their early life stages in drifting seaweed, showing very high genetic homogeneity despite the geographic distance between populations  loci found evidence of genetic homogeneity with the exception of one ms locus . Speciation event between S. thompsoni and S. joyneri was fairly recently  DNA control region and 11 polymorphic microsatellite loci.The objective of this study was therefore to clarify the connectivity among local populations of Korean 22.1S. thompsoni were collected from its main habitats throughout Korean waters, from seven locations: Eocheongdo Island (Eo), Chujado Island (Ch), Yokjido Island (Yo), Wangdolcho Reef (Wa), Sokcho (So), Dokdo Island (Do), and Jejudo Island (Je); S. thompsoni appeared infrequently in Sokcho  for the ms loci analysis, in which we analyzed at least 30 individuals per location. Samples of fish from five sampling locations  were collected during 2012–2016 by commercial fishing vessels, and samples of fish from Dokdo (Do) were collected by commercial fishing vessels and research surveys (most samples) during 2011–2014. In the remaining sampling location (Eo), samples were directly collected by fishing from a boat in December 2013 and October 2014. We also included S. joyneri samples to examine its genetic relationship with S. thompsoni. Samples of S. joyneri were collected during 2010–2016 from three locations  by commercial fishing vessels, with habitats overlapping those of S. thompsoni in Korean waters. We used 29 tissue samples of S. joyneri for the mtDNA sequence analysis and 48 tissue samples of S. joyneri for the ms loci analysis.Samples of o Figure . We usedS. thompsoni: ST; S. joyneri: SJ) and the first letters of the sampling location , joined by an underscore. The tissue samples were preserved in 99% ethanol and stored at −20°C until DNA extraction. The specimens used in this study were deposited at Pukyong National University (PKU).Sample identification was conducted according to the morphological characteristics described by Kim et al.  and Naka2.2http://www.bio-rad.com) according to the manufacturer's instructions. The region between the proline transfer RNA (tRNAPro) gene and the end of the control region in the mtDNA gene was amplified using the primer set L15876 (5′‐AAGCACTTGAATGAGCTTG‐3′)  with a reaction mixture containing 1 μl of total DNA, 2 μl of 10× PCR buffer, 1.6 μl of 2.5 mmol/l dNTPs, 0.5 μl of each primer, 0.1 μl of Ex Taq DNA Polymerase , and distilled water to a final volume of 20 μl. The cycling conditions were an initial denaturation step for 1 min at 96°C followed by 35 cycles of 1 min at 96°C, 1 min at 56°C, and 2 min at 72°C, with a final step of 10 min at 72°C. The PCR products were purified using a LaboPass PCR Purification Kit . The mtDNA was sequenced on an ABI 3730XL Sequencer  using an ABI BigDye Terminator Cycle Sequencing Ready Reaction Kit v3.1 (Applied Biosystems).DNA was extracted from muscle tissue using 10% Chelex 100 Resin . The alleles were scored with GeneMapper 3.7 (Applied Biosystems).We also selected 11 microsatellite markers from the loci designed for 2.3d) between and within species was calculated using the Kimura two‐parameter model  among S. thompsoni sampling locations, the pairwise FST values were calculated with 10,000 permutations using Arlequin 3.5.1.2 , allelic richness (Ar), and inbreeding coefficient (FIS) were calculated using FSTAT 2.9.3.2 . Observed (HO) and expected (HE) heterozygosities were estimated using Arlequin 3.5.1.2 . The model‐based Bayesian clustering procedure in STRUCTURE 2.3.4  best represented the study areas. In this analysis, an admixture model without prior sampling location information was used and allowed for correlations between allele frequencies  originated from other groups  in sampling areas. Here, we separated the Wangdolcho Reef population of S. thompsoni into two genetic clusters (Wa‐1 and Wa‐2); this decision followed criteria for the majority cluster (Cluster 2 or 3) in the STRUCTURE analysis (K = 3). The best grouping was defined by the highest differentiation among groups (FCT) and nonsignificant differentiation within groups (FSC). F‐statistics were obtained in each analysis from 10,000 permutations in Arlequin 3.5.1.2  was performed to test the best hierarchical groupings based on two alternative hypotheses in sampling areas using Arlequin 3.5.1.2  and 29 S. joyneri individuals (three sampling locations) collected in Korean waters. In S. thompsoni, 189 polymorphic sites were detected, with 183 transitions and 21 transversions. We identified 90 haplotypes from 91 individuals and detected very high haplotype (h = 0.9998) and nucleotide (π = 0.0231) diversity (Table d) within S. thompsoni ranged from 0.000 to 0.054 (average 0.024), indicating a relatively high intraspecific genetic discrepancy. In S. joyneri, 61 polymorphic sites were detected, with 56 transitions and five transversions. We identified 25 haplotypes from 29 individuals, and detected relatively high haplotype (h = 0.9901) and nucleotide (π = 0.0125) diversity, although somewhat lower than that of S. thompsoni (Table d) compared between S. joyneri individuals ranged from 0.000 to 0.026, with an average distance among all individuals of 0.013.In total, 765 base pairs (bp) of the mtDNA control region sequences were amplified in 91 S. thompsoni individuals (six sampling locations) and 48 S. joyneri individuals (three sampling locations) collected in Korean waters were screened using 11 microsatellite loci  to 9.82 (Dokdo Island). The average allelic richness (Ar) among all individuals was 7.43, with a range of 7.09 (Eocheongdo Island) to 7.89 (Dokdo Island). The expected and observed average heterozygosities of each sampling location among all loci ranged from 0.6494 (Yokjido Island) to 0.7031 (Wangdolcho Reef) and from 0.5573 (Eocheongdo Island) to 0.6495 (Sokcho Island); the average expected and observed heterozygosities overall were 0.6704 and 0.6149, respectively. Significant (p < .05) departures from HWE were found at 18 independent loci . After applying the Bonferroni correction for multiple comparisons, however, significant (p < .0009) departures from HWE were confirmed for only five independent loci . This deviation from HWE was observed with the presence of null alleles in the microchecker analysis, while no evidence of large allele dropout or scoring errors due to stuttering were shown. Some ms loci  showed significant differentiation of allele frequencies among sampling locations. In particular, some alleles in the Dokdo Island population showed a much higher  or lower (Sth91213) frequency than observed in populations at other sampling locations or did not show up  departures from HWE were found at some independent loci . However, only two independent loci  exhibited significant (p < .0009) departures from HWE after the Bonferroni correction was applied.In the microsatellite loci analysis, 215 3.2S. thompsoni and S. joyneri were clearly distinguished, except for one individual  that identified morphologically as S. thompsoni but was genetically similar to S. joyneri. The average genetic distance (d) between S. thompsoni and S. joyneri was 0.047 , collected in the Chujado and Dokdo Islands, were detected as hybrids between S. thompsoni and S. joyneri according to the admixture proportion (.1 < q < .9) . Hence, we found no evidence of genetic population structure even when using hypervariable mtDNA control region sequences. In contrast to the mtDNA analysis results, two clusters were found in the Korean S. thompsoni populations based on ms loci. Pairwise FST comparisons of the Dokdo Island population with other local populations indicated highly significant differences after the Bonferroni correction was applied (p < .0009), with a range of 0.0175 (Wangdolcho Reef) to 0.0639  in pairwise FST values with Eocheongdo (0.0145), Chujado (0.0236), Yokjido (0.0194), and Dokdo (0.0175) Island populations; however, after the Bonferroni correction was applied (p < .0009), the pairwise FST values were only significant for the Chujado, Yokjido, and Dokdo Island populations. Other evidence for population genetic differentiation within S. thompsoni was detected by the STRUCTURE analysis , reflecting large genetic differences between species , the next highest K value (K = 3) was adopted as the most suitable number of clusters. For this reason, when K is 3, STRUCTURE analysis may allow to detect at lower levels of genetic variation inferred from the Korean S. thompsoni populations.The NJ tree of p Figure , supportujado 0.036, YokjiK = 3, including S. joyneri populations (Cluster 1), confirmed the genetic divergence between two clusters (Clusters 2 and 3) within the S. thompsoni populations, separated at the boundary of Wangdolcho Reef  on the DAPC plot separated the Dokdo population from other sampling locations (except the Wangdolcho Reef population), although some individuals that were identified as Cluster 2 in the STRUCTURE analysis overlapped  observed in only one cluster were also detected in some ms loci , were significantly supported, where the ST_Eo, ST_Ch, ST_Yo, ST_So, and ST_Wa‐1 samples were observed as separate genetic groups from the ST_Wa‐2 and ST_Do samples, according to the highest FCT values  and lower or nonsignificant FSC values  using ms marker data (Table The results of AMOVA showed that the two ta Table .q < .9) between Clusters 2 and 3 in S. thompsoni  and msDNA (p > .0009) data ; Cluster 3 was predominant at Dokdo Island; and both clusters occurred in similar ratios at Wangdolcho Reef. The subdivision of the two clusters in Korean S. thompsoni, unlike Japanese S. thompsoni, may be due to the restricted dispersal of larvae and juveniles by complex oceanographic patterns in the East Sea. Furthermore, a relatively high admixture proportion between the two clusters in most sampling locations, in particular Chujado and Yokjido Island, has been identified, possibly due to disassortative mating associated with bias in occurrence ratios in each cluster.The present study found discordance between the mtDNA and msDNA loci results; the former suggested that Korean FIS of Korean S. thompsoni in this study reflect a significant deviations from HWE owing to a difference in null allele frequencies between samples. Moreover, the frequency of null allele ranges from 0.05 to 0.33, suggesting the deviation from HWE could be associated with the presence of null alleles. On the other hand, it does not seem to be a Wahlund effect because of no high migration and no IBD structure in this species. The presence of null alleles, which caused an excess of homozygotes, can overestimate the levels of genetic differentiation  of mtDNA control region of Korean S. thompsoni were detected in this study, which is higher than other Sebastes species  from 11 Sebastes species distributed in the Northeast Pacific Ocean  and Yokjido  Island populations. There are several possible explanations for such a finding: small sample size, selection, immigration, or genetic drift. Although the sample size used for analysis in all populations is similar or slightly different, low genetic diversity was confirmed only in Chujado and Yokjido Island populations. It is also unlikely that effect of selection or immigration occurred at Chujado and Yokjido Island in Korea Strait because of their main stable habitat. Thus, genetic drift within the both sites is the most likely explanation for relative low genetic diversity, implying a reduction in effective population size due to local overfishing  that showed the significant decrease in both heterozygosity and the mean number of alleles in one population (Tasman Bay) during its exploitation history  individuals using a combination of amplified fragment length polymorphisms (AFLP), mtDNA, and morphometric characters. Interestingly, this study showed that rates of hybridization varied considerably among the locations, which is associated with variations in habitat segregation, implying that the extent of habitat segregation of the two species dependent on vertical water temperature regimes determined the opportunity for hybridization. Four Sebastes species in the North Atlantic Ocean have also shown evidence of interspecific hybridization when in sympatry . Nevertheless, we found evidence of interspecific hybridization between Korean S. thompsoni and S. joyneri in only one individuals , although there was suggestion that the possibility of the hybridization of four individuals in the STRUCTURE analysis. Only one individual identified as hybrid was not only at least 10% of the genome (q) originating from another cluster in the STRUCTURE analysis . The occurrence ratio of hybridization between the two species was considerably lower than expected, despite the high likelihood of hybridization due to extensive overlap in the mating and release seasons of the two species , respectively, suggest that genetic population structure is likely a single panmictic group like this study, but showed a higher genetic diversity  rather than Korean S. thompsoni , but Cluster 3 was predominant at Dokdo Island, and both clusters occurred in similar proportions at the Wangdolcho Reef. The Dokdo Island  population was nearly identified as Cluster 3, differing from other local populations ; this reef is geographically located in a transitional zone between the two clusters. These results provide the evidence of restricted gene flow between the Dokdo Island population and other local populations. The Wangdolcho Reef population may be a mixed population, not a hybrid population. AMOVA results also suggested that Korean S. thompsoni had diverged into two , according to the F‐statistic values  (Table Unlike the results of mtDNA control region sequences (present study) and those of seven ms loci . For example, the population structure of Pacific ocean perch  based on ms loci showed the significant, geographically related genetic structure, which may be due to the limitation on the dispersal of larvae by oceanographic factors such as eddies  populations, and genetic differences among populations were significantly associated with geographic distance. In addition, patterns of population structure formation in the same geographic area have been observed in other rockfishes such as rosethorn rockfish . In spring, the strong Ulleung warm eddy (UWE), that is, the warm cyclonic eddy developed in the north or south of Ulleungdo Island, and the weak Dokdo cold eddy (DCE), that is, the cold anticyclonic eddy observed at the south of Dokdo Island, occur in the East Sea  of individuals identified in Wangdolcho Reef may be roughly separated by physical barriers caused by upwelling to the west (mainly Cluster 2) and UWE (or DCE) to the east (mainly Cluster 3). Similar to our results, the community structure of phytoplankton around Wangdolcho Reef has shown a clear division into two areas, eastern and western  with upwelling and small eddies, which occurs mainly along the western Reef  and paleoclimatic change (Kim, Hyporhamphus sajori; Yu et al., Sebastes schlegelii; Gao et al., S. tompsoni, in the similar geographic area showed no population structure based on mtDNA control region and ms loci, which may be attributable to the passive dispersal of the eggs and larvae through the Yellow Sea Warm Current. Extensive drifting seaweed, identified as Sargassum horneri, originated from the East China Sea during spring also flows into the Yellow Sea due to the effects of the Yellow Sea Warm Current and the northwestern monsoon (Hwang, Lee, Ha, & Park, Most nge Kim, . These cnge Kim, . HoweverS. thompsoni may have differentiated primarily because of characteristics of the local marine environments and their consequent effects on the dispersal of larvae and juveniles, instead of through hybridization between S. thompsoni and S. joyneri. This study carefully suggests that Korean S. thompsoni needs to be managed at least two subpopulations, and further studies of the marine biogeographic boundary in the East Sea are needed for a more comprehensive understanding of metapopulation dynamics of marine species.Our results indicate that the two subpopulations of Korean None declared.H.J.Y and J.K.K conceived of and designed the study. H.J.Y performed laboratory work and analyzed the data. H.J.Y and J.K.K wrote the manuscript.https://doi.org/10.5061/dryad.2024j.DNA sequences are accessible at GenBank accession numbers: MF817322‐MF817441. Sampling locations and microsatellite genotypes are available at Dryad  Click here for additional data file.


Tetrao urogallus) is one of Poland's most endangered bird species, with an estimated population of 380–500 individuals in four isolated areas. To study these natural populations in Poland further, more than 900 non-invasive genetic samples were collected, along with samples from 59 birds representing large, continuous populations in Sweden and Russia; and from two centres in Poland breeding capercaillie. Microsatellite polymorphism at nine loci was then analysed to estimate within-population genetic diversity and genetic differentiation among populations. The results confirmed that isolation of populations and recent decreases in their sizes have reduced genetic diversity among capercaillie in Poland, with all the country’s natural populations found to be experiencing the genetic after-effects of demographic bottlenecks. The results of analyses of genetic differentiation and structure further suggest the presence of a 'lowland' cluster , and a Carpathian cluster. Capercaillie from Sweden and Russia are also found to differ markedly. The Polish lowland populations seem more closely related to birds from Scandinavia. Our genetic analysis also indicates that the stocks at breeding centres are of a high genetic diversity effectively reflecting the origins of founder individuals, though identification of ancestry requires further study in the case of some birds.The capercaillie . This is to say that the breeding lines associated with these two sources should be maintained separately at breeding centres. The high level of genetic differentiation of birds from the Solska Primaeval Forest suggests that this population should also be assigned the status of independent MU. Tetrao urogallus, L. 1758) is one of the most intensively studied woodland grouse in Europe, whose range, subspecies, demographic trends, ecology and habitat requirements have all been described in detail, e.g. [The capercaillie , thoughil, e.g. –8. By coil, e.g. ; 4. Althil, e.g. , the speil, e.g. .The capercaillie is classified as a 'critically endangered' species (CR category) in the Polish Red Book of Animals, and is included in Annex I to the European Union's 'Birds Directive' . While tPoland's largest remaining population of the species is that inhabiting the Western Carpathians with 285–325 individuals ; 13. TheSeveral studies investigating polymorphism in the control region of mitochondrial DNA have shed light on phylogeography of the species and the delineation of its subspecies. The results have indicated no genetic differences between the majority of what were regarded as subspecies, suggesting that Europe has just two genetically identifiable lineages: the southern present in the Cantabrian, Pyrenees, and in the Balkans mountains (mainly the Bulgarian Rhodope and Rila mountains), and the boreal (northern) present in remaining parts of Europe –24.Thus far, the conservation genetics of Polish capercaillie is not well understood. However, preliminary data  confirm One of the most important steps in planning effective conservation action should be the identification of Conservation Units (CUs), i.e. administrative units that are discrete and biologically relevant . The twoThe primary goal of our investigation was to examine the genetic status of capercaillie in the relevant Conservation Units present in Poland. Additionally, we sought to compare genetic diversity and estimate genetic differentiation among the small and fragmented Polish populations in relation to the large and demographically stable populations present in Russia and Sweden. Taken together, these analyses provide insight allowing important source populations to be identified and their genetic diversity assessed.Between 2005 and 2014, samples for genetic analysis were collected from eight regions in which the capercaillie exists naturally , and from birds constituting two stocks at breeding centres (hereinafter 'farm populations'). Natural populations were sampled in five regions of Poland: the Augustowska Primaeval Forest  in the northeastern part of Poland (hereinafter PA); the Solska Primaeval Forest  in the Lublin region of eastern Poland (LUB); and in the species’ three Carpathian strongholds, i.e. the Gorce National Park (GOR) , the Polish Tatra Mountains National Park (TAT)  and Babia Góra National Park (BPN) , and beyond Poland: in two regions of Russia  and the Ukhta region of the Komi Republic ); and in the southern part of Norrbotten County in Sweden (denoted as SWE), around Arvidsjaur  . The numN = 102) and faeces (N = 814). After being collected in the field, feathers were stored in paper envelopes or plastic vials, while after delivery to the laboratory they were kept in a freezer at -4°C. Faeces were collected in 50 ml Falcon Conical Centrifuge Tubes  and covered with silica gel (distribution in Poland by Conbest) to dehydrate the sample. Following delivery to the laboratory and removal of the gel, samples were covered with a new portion of silica gel. Tubes with these contents were then stored in a freezer at -72°C. Samples from R-K were feathers, obtained from 33 birds captured in the wild  in 2014, and transported to the Głęboki Bród Forest District as part of a conservation programme . Moulted contour feathers were collected directly from boxes in which birds had been transported, being placed in tubes with 96% alcohol immediately after collection. Each bird was transported in a separate box. From the Ukhta region we also obtained 12 muscle-tissue samples from hunted birds. These were stored frozen. Feathers were collected from 14 net-captured birds from Sweden, later transported to Ruszów Forest District as part of a conservation programme for the species (Project LIFE11 NAT/PL/428). Feathers were collected directly from the boxes in which birds had been transported, and placed in tubes with 96% alcohol immediately after collection. Samples from birds from Sweden and Russia were collected with permission granted by the General Director of Environmental Protection . We obtained samples of faeces from WIS and LEZ with each bird kept in its own separate pen, providing a faecal sample that was placed in a tube and stored frozen. Only samples from birds constituting breeding stock were collected. We obtained samples from 21 birds from WIS and 24 from LEZ. Overall, a total of 1020 samples for genetic analysis were collected.Sampling in the case of all the natural populations in Poland was entirely non-invasive, involving feathers , or using NucleoSpin Tissue Kits  in line with a standard protocol. DNA extraction from muscle tissue samples was also performed with QIAamp DNA Mini Kits .DNA from faeces was extracted using NucleoSpin Soil Kits , using the manufacturers' protocol, except for the fact that double the volume of lysate was used for each sample.Several measures were taken in association with the DNA isolation process to minimize potential contamination: (i) DNA extraction was performed in a separate room specially designated for the purpose in relation to non-invasive and museum samples, and equipped with UV lamps allowing the workstation, pipettes, tubes, tips and bottles with chemicals to be sterilized. (ii) Prior to extraction, the top of the workstation was cleaned with alcohol. (iii) Each time DNA was extracted from 15 samples and one 'blind' sample  to control for the possibility of contaminated reagents. (iv) We avoided extracting DNA from samples from different populations in the course of a single extraction process. (v) Following the extraction, all pipettes and additional equipment  were cleaned with alcohol and autoclaved.Tetrao tetrix) [All extractions, including the 'blind' samples were subjected to PCR. We aimed to amplify 13 microsatellite loci, i.e. TuT1; TuT2, TuT3, TuT4 (tetranucleotide repeats) and TuD4 and TuD5 (dinucleotide repeats) , and 6 t tetrix) ; 31, i.eSeveral measures were taken to obtain reliable genotypes: (i) if, during analysis in the sequencer, the PCR product was identified in a 'blind' sample from the extraction process, this set of extracts was excluded from further analysis and the extraction of these samples was repeated; (ii) if, during analysis in the sequencer, the PCR product was identified in a 'blind' sample from the PCR process, this set of PCRs was excluded from further analysis and the PCR for these extracts was repeated; (iii) for each DNA extract all three multiplex PCR reactions were repeated twice. All extracts lacking the PCR product in the two reactions were excluded from further analysis. Similarly, extracts with two identical genotypes in both independent PCRs were classified as successfully genotyped. All the extracts showing signs of contamination  were excluded from further analysis. Two additional PCRs were performed in the case of differences between genotypes which could be explained by typical technical problems observed frequently during the microsatellite genotyping of non-invasive samples . ConsenIn the case of samples from the breeding centres , feathers collected from transporting boxes  and samples from hunted individuals (R-U) it was clear that each sample belonged to an individual bird. However, in the case of non-invasive samples collected from the natural population in Poland several samples could have belonged to a single individual. We assumed that the presence of identical microsatellite genotypes in two or more independent samples attested to samples belonging to the same individual. Although we attempted to minimize problems with 'allelic drop-out' and 'false alleles' (see PCR and microsatellite genotyping), we were aware that small differences between genotypes from different samples could still be observed, even if they belonged to a single individual. We assumed that differences at less than three loci, which could be explained by 'allelic drop-out' or a 'false allele', guaranteed that the samples belonged to the same individual. Comparisons of genotypes were performed using GenAlEx v. 6.501 ; 35, on HWE) and linkage disequilibrium (LD) were assessed using Fisher's exact test in Genepop v.4 [To identify possible 'null alleles', 'large allele drop-out' and typographic errors, we analysed microsatellite genotypes in MicroChecker v. 2.2.3 . The perepop v.4 ; 38 withP(ID)) was calculated using GenAlEx v. 6.501 [P(ID-Sibs)).For each locus within each population and for a combination of 9 loci, Probability of Identity , allelic richness—the number of alleles corrected for sample size using the rarefaction method with a sample of 12 individuals  and 'private allelic richness' , observed (HO) and expected heterozygosity . These analyses were performed using GenAlEx to calculate A, P, HO and HE, FSTAT version 2.9.3.2 [R and FIS), and HP-RARE [P1). Between-population differences in allelic richness (R) and observed (HO) and expected heterozygosity (HE) were compared using Tukey's HSD test [TukeyHSD command from the Stats package in the R environment [We calculated mean values of standard genetic indices: the number of alleles , 'privaity  and inb 2.9.3.2  (R and F HP-RARE  (P1). BeHSD test  applied ironment , with a FST [RST [FST values and their significance and overall FST with corresponding 95% confidence intervals were calculated in FSTAT. Overall and pairwise RST were calculated in SPAGeDi 1.4 [FST and RST, we also estimated standardized measures of genetic differentiation: DEST [F'ST [DEST were calculated using DEMEtics [P-values were adjusted to account for multiple comparisons, using Bonferroni correction. F'ST was calculated by dividing pairwise FST by the maximum value obtained using RecodeData v.0.1 [Genetic differentiation among populations was assessed as FST , as baseFST [RST , as baseFST [RST ). PairwiGeDi 1.4 , while son: DEST  and F'STST [F'ST . PairwisDEMEtics . Signifita v.0.1 .K). STRUCTURE was run 15 times for each user-defined K, with an initial burn-in of 50,000 and 1,000,000 iterations of the total data set. The admixture model of ancestry and the correlated model of allele frequencies were used. Sampling location was not used as prior information. We next examined ΔK statistics that identify the largest change in the estimates of K produced by STRUCTURE, as ΔK may provide a more realistic estimation of K than those based on likelihood [K = 1–8); and (ii) natural and farm populations (user define K = 1–10). To visualise the STRUCTURE results we used STRUCTURE HARVESTER 0.6.94 [The Bayesian-clustering method (STRUCTURE version 2.3.4 ) was usekelihood . STRUCTUR 0.6.94 . We thenR 0.6.94  to averaR 0.6.94  to displHWE optimisation, and as some of the sampling sites in our study were not in HWE, we also obtained an additional representation of the genetic structure using principal component analysis (PCA). We used the R package ADEGENET v1.3.4 [As the identification of genetic structure in STRUCTURE relies on T v1.3.4  to carryM-ratio method [M-ratio) in order to sample microsatellite loci to detect reduction in effective population size . The parameter values followed those recommended by the authors [M-values , we used Critical_M.exe [M value (that which 5% of simulations were below) on the basis of the number of individuals sampled and the number of loci, using 10,000 replicates. The observed M values below a critical value suggest a bottleneck. Critical values of M were calculated for three different values of θ , i.e. 0.2, 1 and 2, corresponding with long-term equilibrium population sizes of 100, 500 and 1,000, while there was a common estimate of the microsatellite mutation rate = 0.0005 mutations/generation/locus [Evidence of recent effective reductions in the sizes of populations was investigated using BOTTLENECK 1.2  in respeo method , which u authors . To testal_M.exe , which gon/locus . The peron/locus . These aAmong 916 non-invasive samples, the first two PCR reactions did not yield amplification products in 293 samples (32%). Among the remaining 623 samples, two identical genotypes were obtained in 174 cases (28%). In the case of 449 remaining samples, two additional PCRs were performed. Following comparison of genotypes from four subsequent amplifications, 30 samples were excluded from analysis due to clear contamination, while 41 samples were excluded in line with evident differences among the genotypes obtained in subsequent PCR reactions. For the remaining 378 samples, consensus genotypes from four PCR reactions were constructed. Analysis in GIMLET indicated low genotyping error: the maximum error rate per sample calculated for 378 samples genotyped four times for 13 microsatellite loci was: 0.01–0.05 for 241 samples, 0.08–0.12 for 123 samples, and 0.16–0.19 for 14 samples. The mean genotyping errors per locus were 0.01–0.062.We found 36.7% of missing data in locus TUT1, 26.3% in locus Bg14 and 23.2% in locus Bg15. The presence of missing data was not interlinked with sample type (i.e. non-invasive or invasive) and we decided to exclude these loci from further analysis. Locus Bg10 was also excluded on account of the high frequency of null alleles (18%) as indicated by MicroChecker and significant heterozygote deficiency in seven out of ten investigated populations. We therefore obtained a final set of nine microsatellite loci, successfully amplified in 656 samples , without missing data and with no indication of the presence of null alleles. Similarly, for these loci MicroChecker did not suggest problems with stuttering and large-allele drop-out.On the basis of the genotypes obtained at nine microsatellite loci, we assigned non-invasive samples to individuals. In total, we found that the natural populations in Poland supported 156 unique genotypes , which iP-value for 5% nominal level was 0.000139, 7200 permutations). Linkage disequilibrium was found in GOR between locus Bg16 and TUD5, and BG16 and TUT2, and between TTT1 and Bg18 in LEZ.For 36 locus x locus combinations there were three cases of significant linkage disequilibrium among the investigated loci after Bonferroni correction (adjusted P(ID)) ranged from 0.03 (locus TUD5 in SWE) to 0.588 (TTT1 in LUB), although in a majority of cases P(ID) was lower than 0.2 (Table C in P(ID) and P(ID-Sibs) were lower than or equal to 0.001, except in the case of LUB, GOR and BPN where P(ID-Sibs) values were 0.004, 0.003 and 0.002, respectively. Hence, the expected number of different individuals with the same genotype was very low.Per locus Probability of Identity  was found in the WIS farm population. A significant FIS value was found at BPN only in the case of locus Bg12  all nine loci were in A), mean allelic richness (R), mean number of private alleles) provided a basis for the division of the natural populations into three groups. The group of lowest genetic diversity comprises LUB and GOR . The group of moderate genetic diversity comprises PA, BPN, TAT and R-U , albeit with R-U having a clearly higher mean number of private alleles  than each of the three Polish populations . The group with the highest values for genetic diversity indices  consists of R-K and SWE. We found that mean R values were significantly lower (P<0.05) in BPN, GOR and LUB than in R-K and SWE.The indicators of genetic diversity , based oHO (<0.60) values were found in LUB, GOR and BPN, while values were moderate (0.66–0.67) in PA and TAT, and highest (>0.70) in R-U, R-K and SWE. In general, the highest genetic diversity among natural populations was found for SWE, the lowest for LUB  were not in rrection . In the FST = 0.159 (95%CI 0.124–0.196); overall RST = 0.137 (P<0.05); overall DEST = 0.376 (P<0.001)). All pairwise FST comparisons were significant with values ranging from 0.02 to 0.28  and 'Carpathian'  groups. Increased K further separates R-U and R-K from the 'lowland group' (K = 3), LUB from PA (K = 4); and dividing individuals from GOR into two genetic clusters—one of them also being present in the two other Carpathian populations (K = 5 and K = 6). The admixture of individuals from SWE, initially included in the 'lowland group' (K = 2), gradually increased, showing connections with PA and the Russian populations (K = 4 and 5) and TAT (K = 6 and 7).Our STRUCTURE analysis for eight natural populations indicated a gradual increase of mean likelihoods from erations . The obso et al.  method uK = 1 to K = 9 with high variance among iterations for K = 6–8. ΔK indicated the most likely population structure for K = 2 and 3, but an increase at ΔK = 9 was also observed. Both breeding centres were found to include individuals from the 'lowland' and 'Carpathian' clusters together with some of admixed origin (K = 2). However, for higher K (3–6) we identified individuals from the Russian and Carpathian clusters in WIS, whereas birds from LEZ were gradually included in a separate genetic cluster (K = 5–10) with some indications of the presence of the gene pool from WIS  [Identification of Conservation Units can assist conservationists and wildlife managers involved in the conservation of endangered species to focus management efforts on specifically defined areas and populations ; 64. In  alleles  which in alleles  and hazebonasia) ; 68. Thebonasia) . Alternabonasia) –24. It iK = 3, the Augustowska and Solska Primaeval Forests (PA and LUB) were grouped together with the population from Sweden, and this group was separated from Russian populations. The existence of genetic differentiation between Russian and Swedish birds was also supported by PCA. This corresponds with a previous study [K≥3, isolation of the Solska Primaeval Forest (LUB) was evident, an observation also supported by PCA. Clearly, isolation and subsequent genetic drift have led to the genetic differentiation of these birds from other lowland populations—a conclusion supported by high values for FST, RST and DEST. Hence, we suggest that the Solska Primaeval Forest and its population should be assigned to a separate MU.Our STRUCTURE and PCA analysis revealed deeper divisions of the investigated natural populations. First, when us study , indicatus study . It is pus study ; 72. As us study ), two geus study . Second,Regarding genetic diversity, our result supports a previous study , which d2) area. A low level of genetic diversity for this population was suggested previously [Surprisingly, genetic diversity was lowest in Poland’s largest population, i.e. that of the Solska Primaeval Forest (LUB) (with 100–130 birds as of 2013 ), inhabieviously  as a reseviously , 76. Alteviously .A low level of genetic diversity also characterised birds from the Gorce and Babia Góra National Parks (GOR and BPN). These are considered to join the Polish Tatra National Park (TAT) within the Western Carpathian population . Indeed,The highest genetic diversity in natural populations from Poland characterises the Augustowska Primaeval Forest (PA) and the Polish Tatra National Park (TAT). As already mentioned, TAT could be connected with the capercaille population from Slovakia, and observations suggest that current numbers are higher there than in the other Carpathians strongholds. The Augustowska Primaeval Forest is located in northeastern Poland in close proximity to other large forests like the Knyszyńska and Białowieska Primaeval Forests. These forests retain some connections because of the presence of belts of woodland. In the first half of the 20th century there were still capercaillie present, not only in the Augustowska Primaeval Forest, but also in other forests of northeastern Poland ; 78. It Four Polish breeding centres for the capercaillie have been established in recent years. Three run by the State Forests in its Wisła, Leżajsk and Głęboki Bród Forest Districts ; 79; 80 The breeding stock from the Leżajsk centre (LEZ) is also diverse genetically, though the number of microsatellite alleles and heterozygosity were clearly lower than in birds at the Wisła centre. Similarly, we detected the presence of Carpathian and lowland genetic clusters in this breeding population. Unfortunately, there is little information available about the exact origin of the founders. Until recently appropriate breeding documentation was not standard protocol. However, the large number of private alleles suggest that the majority of individuals from the Leżajsk centre originated from some other population(s) not investigated in our study.All Polish populations of the capercaillie show reduced genetic diversity and signs of genetic bottlenecks. To preserve the highest level of genetic diversity, all of Poland’s natural populations of the capercaillie should be made subject to conservation measures, as the Polish population of the species constitutes a very diverse gene pool. We found significant genetic differentiation at microsatellite markers between lowland and Carpathian populations. Hence, our genetic data indicate that lowland populations of the capercaillie in Poland  and the population from the Polish Carpathians should be assigned to separate Conservation Units (Management Units). Additionally, the high level of genetic differentiation of birds from the Solska Primaeval Forest suggests that this area should also be treated as an independent Management Unit.S1 FileTable A. Non-invasive sampling information. Collected—number of collected samples; Genotyped—number of genotyped samples, GR—successful genotyping ratio for 9 microsatellite loci  or number of sampled individuals )); FIS–fixation index (*–significant after Bonferroni correction); P(ID) by locus—Probability of Identity for each locus; All loci loci P(ID)/P(ID-Sibs)–Probability of Identity for combination of 9 loci/ Probability of Identity for combination of 9 loci, taking into account the genetic similarity among siblings. n = 260) of the capercaillie, estimated using standardized measures: DTable D. Genetic differentiation among 8 natural populations and 2 breeding flocks Click here for additional data file.