As a major crossroads between Asia and Europe, Romania has experienced continuous migration and invasion episodes. The precise routes may have been shaped by the topology of the territory and had diverse impacts on the genetic structure of mitochondrial DNA (mtDNA) in historical Romanian provinces. We studied 714 Romanians from all historical provinces, Wallachia, Dobrudja, Moldavia, and Transylvania, by analyzing the mtDNA control region and coding markers to encompass the complete landscape of mtDNA haplogroups.We observed a homogenous distribution of the majority of haplogroups among the Romanian provinces and a clear association with the European populations. A principal component analysis and multidimensional scaling analysis supported the genetic similarity of the Wallachia, Moldavia, and Dobrudja groups with the Balkans, while the Transylvania population was closely related to Central European groups. These findings could be explained by the topology of the Romanian territory, where the Carpathian Arch played an important role in migration patterns. Signals of Asian maternal lineages were observed in all Romanian historical provinces, indicating gene flow along the migration routes through East Asia and Europe.Our current findings based on the mtDNA analysis of populations in historical provinces of Romania suggest similarity between populations in Transylvania and Central Europe, supported both by the observed clines in haplogroup frequencies for several European and Asian maternal lineages and MDS analyses.The online version of this article (doi:10.1186/s12863-017-0487-5) contains supplementary material, which is available to authorized users. Romania is located in Southeastern Europe as part of the Central basin of the Black Sea and lower Danube basin, and the country is divided in the center by the orographic Carpathian-Balkan system. As a segment of the Danube basin, this territory was a major crossroads between Asia and Southeastern, Central, and North Europe and one of the direct eastward routes linking to the North Pontic steppe. The Romanian language is a part of the Eastern Romance languages that evolved from spoken Latin during the process of Romanization followed by a period of Slavic influence at the beginning of the sixth century .14C BP [14C BP) [The earliest anatomically modern human fossils in Europe attributed to the end of the Upper Paleolithic period were found in Romania in the Peştera cu Oase (the cave with bones) dated ~35 ka 14C BP  and PeștPopulation movement during the Neolithic and Bronze Age shaped genetic variation in the present-day Romanian population, especially during the Middle Neolithic . During st to 2nd centuries AD), with the exception of current regions of Moldavia, northeastern Transylvania, and eastern Wallachia, was followed by a period of colonization by various groups from the Roman Empire  who settled mostly in Transylvania [The partial conquest of Dacia by Romans  into three different political structures: Wallachia, Moldavia, and Transylvania. Dobrudja was, during most of its medieval history, under Ottoman rule. During the next several centuries, the historical provinces of Romania were continuously under the suzerainty of the Byzantine, Austro-Hungarian, Habsburg, Ottoman, and Russian Empires. However, these empires had no major demographic impact, except in Transylvania, which underwent a massive colonization by Székelys, Saxons, and Hungarians under the domination of the Kingdom of Hungary between the 10th and 13th centuries [The current territory of Romania was divided in the Middle Ages  haplogroups. MtDNA variation in Romanian populations has mainly been analyzed in multinational studies characterized by a relatively small number of samples collected over the entire Romanian territory to encompass the complete mtDNA haplogroup landscape. Previous studies have revealed that Romanian populations exhibit genetic similarity with other Europeans –11, whilThe maternal lineages of Balkan Peninsula populations exhibit genetic uniformity, with region-specific characteristics . It is wTo address the scarcity of information and to improve our understanding of mtDNA variation in the context of European and Middle East populations, we analyzed 714 Romanians belonging to the four historical provinces. We purpose to reveal the specific genetic differences between populations from the historical provinces that could experience demographic combinations as a consequences of past migrations, local admixture and colonization events in their past.Written informed consent was obtained for the collection of samples and subsequent analyses before patients were entered in the study, in accordance with protocols that comply with the Declaration of Helsinki, and the study was approved by the ethics committee of the “Carol Davila” University of Medicine and Pharmacy in Bucharest, Romania.n = 226), Dobrudja (n = 46), Moldavia (n = 235), and Transylvania (n = 207). The samples were randomly collected from maternally unrelated subjects with known genealogical information for at least three generations (parents and grandparents). For all samples, the precise affiliations to the Romanian counties (n = 41) that constitute the administrative divisions of present-day Romania were known . For all samples, the hypervariable segments HVS I (positions 16024–16416) and HVS II (positions 1–410) of the mtDNA control region were sequenced using two pairs of primers, as previously described .2, 0.2 μM each primer, 0.2 U of AmpliTaq Gold DNA Polymerase , and approximately 30 ng of DNA. The amplification products were purified using the QIAquick PCR Purification Kit  and sequenced with the Big Dye Terminator v3.1 Cycle Sequencing Kit using the ABI 3130XL Genetic Analyzer . To resolve the ambiguously classified samples, a hierarchical system based on a PCR restriction fragment length polymorphism (PCR-RFLP) protocol using 18 coding region positions was used for haplogroup assignment, as described elsewhere [PCRs were performed in 25 μL of reaction mixture including 1× PCR buffer , 200 μM each dNTP, 2.5 mM MgClThe sequences were edited using ABI PRISM SeqScape v.2.5. The sequences of the HVS I and HVS II regions for each individual were aligned manually using MEGA 6 and compared with the revised Cambridge Reference Sequence rCRS , 20.We amplified the two HVSI and HVSII segments in different PCRs  and corroborated the haplogroup assignation of two researchers to reduce the probability of the artificial recombination. The sequences were corroborated based on the forward and reverse sequencing results.Haplogroup assignments were based on the HVS I and HVS II sequences of all samples in this study and were determined using online programs, i.e., HaploGrep , MitomapThe control region sequences of the 714 subjects in this study are available at GenBank under the following accession numbers: KT945272–KT945497 , KT945940–KT945993 (Dobrudja group), KT945705–KT945939 (Moldavia group), and KT945498–KT945704 (Transylvania group).D [Gene diversity parameters, including the number of sequences, haplotype diversity, nucleotide diversity, number of polymorphic sites, and mean number of pairwise differences, were estimated using DnaSP version 5. To assess neutrality, Tajima’s D  and Fu’sD  were calD .FST values between populations was calculated with ARLEQUIN version 3.5.2.2 (based on mtDNA haplogroup frequencies) and visualized by a multidimensional scaling (MDS) plot using SPSS 17.0 . Statistical significance of FST values was assessed using 10000 permutations.The distance matrix generated by pairwise Romanian populations were compared with published data for two groups corresponding to European and Middle East populations . The MJN was obtained to better differentiate the haplotype frequencies among the Romanian provinces. Different mutation weights were applied in accordance with the previous papers [A principal component analysis (PCA) was performed based on mtDNA haplogroup frequencies using the same software for comparisons among populations. A median joining network (MJN) for certain haplogroups was generated to infer phylogenetic relationships among the populations from the four Romanian provinces using Network v4.5.0.0  was used to graphically represent the geographical distribution of haplogroup frequencies  in the European region by applying the Kriging algorithm for the surface interpolation map.We analyzed 714 samples to estimate genetic diversity in each of the four Romanian provinces . In addition to length heteroplasmy, we found two sequence heteroplasmies in HVSI at position 16084 T/C in one sample and at position 16237 T/C in a second sample.The resampling analysis for the Dobrudja sample using a 1000 replicate bootstrap indicated that the mean statistics for our data in the vector that we analyzed was 3.833. The bootstrap bias was only -0,0152 and its standard error was about 1.1364. The 95% confidence for mean statistic: the lower bound was 2.08 and the upper bound was 6. The histogram of men of bootstrap samples was nearly normal considering the great number of bootstrap replications, as depicted in the Additional file As expected, we were able to classify the huge majority of individuals from the four Romanian populations into nine Eurasian mitochondrial haplogroups . All mtDNA data are summarized in Additional file The Romanian populations also exhibited sequences that belonged to the most frequent Asian haplogroups  and African haplogroup L. We detected haplogroups A, C, D, and I in the Romanian sample, with an overall frequency of 2.24%, consistent with the frequency in other European populations. We observed a relatively high frequency of Asian haplogroups M and N in Wallachia, Dobrudja, and Moldavia, but not in Transylvania, which also lacked the M haplogroup. The haplogroup X, entirely represented by subhaplogroup X2, was present at the highest frequency in Transylvania.The overall frequency of haplogroup H (40.98%), the most common haplogroup in Europe , was conOur data indicated that haplogroup U is the second most frequent in all analyzed populations but is noticeably less frequent in Transylvania than in other areas Fig. . In our In Walachia, Moldavia, and Transylvania, we detected similar HV haplogroup frequencies to those observed in other European countries, but we observed the highest frequency in the Dobrudja population. With respect to haplogroups N, M, X, and V, we found different frequency distributions between Transylvania and the other three provinces, as depicted in Additional file To explore the genetic affinities of Romanian populations with neighboring populations, we conducted a PCA based on the frequencies of mitochondrial haplogroups Fig. . The firThe high frequencies of the haplogroup M (excepting Transylvania) and U5, as well as the lower frequencies of subhaplogroup U1 reveal a pattern that could explain the distribution of Romanian populations on the PCA plot. Component loadings of PC2 indicated a high correlation coefficient for X, which is consistent with the observed high frequencies of the haplogroup X in the Wallachian, Dobrudjan, and Moldavian provinces.FST based on the mitochondrial haplogroup frequencies for 19 neighboring populations, including Romanian mtDNA data from previous studies, or 41 populations from all of Europe and the Near East , indicating no differentiation among Romanian provinces  between these provinces and the Caucasus, Egypt, or Turkey populations.To further visualize the relationships among Romanian populations analyzed here and European and Near Eastern populations, we estimated pairwise Based on MDS plots including the geographical neighbors or 41 populations, we found that Transylvania is more closely related to Central European populations than to the other Romanian provinces, which are more closely related to the Balkan populations , suggesting that the Carpathians correspond with a genetic boundary, as evidenced by a discontinuity of NRY markers between the East and West of the Carpathian ridge, highlighted by the peculiarities of samples from Central Transylvania compared to Moldavia , 36.The networks that show the general topology of control sequences of Romanian samples mainly indicate overlapping haplotypes between Moldavia-Wallachia and Moldavia-Transylvania, suggesting gene flow between these provinces. The substantial workforce movement from Moldavia towards these two provinces throughout the communist period supports these observations. The geographic proximity and recent population movement explain the common haplotypes observed in Dobrudja, Moldavia, and Wallachia.The differences in the haplogroup frequency distributions and haplotype diversity in Dobrudja can also be explained by its smaller sample size compared to that of the other populations included in the study.FST values, which indicate statistically significant differentiation between Wallachian, Moldavian, and Transylvanian populations and the Asian population included in the analysis. The Dobrudjan population did not show the same patterns; a larger Asian influence shaped genetic variation in this province during its history.The presence of Asian mtDNA lineages among Romanian populations, albeit at low frequencies, indicates the flow of maternal lineages along migration routes through East Asia and Europe during different time periods, namely, the Upper Paleolithic period and/or, with a likely greater preponderance, the Middle Ages , 38. It th century.As for haplogroup L, we found two samples with L3, one in Wallachia and the other in Dobrudja (0.28%), consistent with the low frequencies found in European populations , except The different genetic affinities observed here could be explained by the topology of the Romanian territory, which influenced admixture among the Transylvania population and neighboring populations over time, together with the distinct historically attested migration  and coloFST values strictly in the case of Dobrudja, and this result could be easily explained by the greater Asian influence in this area throughout history.In our mtDNA analysis, we observed a uniform distribution of the majority of haplogroups among Romanian provinces with the populations in the Wallachia, Moldavia, and Dobrudja provinces tended to cluster together within the Slav population based on the MDS scatterplots, while Transylvania was more closely related to Central European populations. We confirmed gene flow from Southwestern Asia based on pairwise population Our findings suggesting a closer affinity of Transylvania to central Europe are supported by the observed haplogroup frequency clines for several European and Asian maternal lineages and could be attributed to different migratory routes shaped by the Carpathian Arch.The mitochondrial DNA data in this study and the analysis of differentiation among historical regions represent a considerable extension of our knowledge of mitochondrial diversity in Romania.


Pontorporia blainvillei, is an endemic cetacean of the Atlantic coast of South America. Its coastal distribution and restricted movement patterns make this species vulnerable to anthropogenic factors, particularly to incidental bycatch. We used mitochondrial DNA control region sequences, 10 microsatellites, and sex data to investigate the population structure of the franciscana dolphin from a previously established management area, which includes the southern edge of its geographic range. F‐statistics and Bayesian cluster analyses revealed the existence of three genetically distinct populations. Based on the microsatellite loci, similar levels of genetic variability were found in the area; 13 private alleles were found in Monte Hermoso, but none in Claromecó. When considering the mitochondrial DNA control region sequences, lower levels of genetic diversity were found in Monte Hermoso, when compared to the other localities. Low levels of gene flow were found between most localities. Additionally, no evidence of isolation by distance nor sex‐biased dispersal was detected in the study area. In view of these results showing that populations from Necochea/Claromecó, Monte Hermoso, and Río Negro were found to be genetically distinct and the available genetic information for the species previously published, Argentina would comprise five distinct populations: Samborombón West/Samborombón South, Cabo San Antonio/Buenos Aires East, Necochea/Claromecó/Buenos Aires Southwest, Monte Hermoso, and Río Negro. In order to ensure the long‐term survival of the franciscana dolphin, management and conservation strategies should be developed considering each of these populations as different management units.The franciscana dolphin,  PCR amp2.2.2Ten microsatellite loci developed for other cetacean species (Table S2) and already utilized for franciscana dolphins  for unrelated individuals and for siblings (PID‐SIB) for the combined set of loci was assessed using GENALEX v6.5 : When the putative relationship was parent–offspring, the alternative relationship was full sibling; when the putative relationship was full sibling, the alternative relationship was half sibling. Considering that no parent–offspring relationship was found and none of the putative full sibling pairs first observed  were supported after the hypothesis test, we included the 69 individuals collected to perform the subsequent analyses.Since first‐order relatives can lead to an overestimation of population structure, to avoid this potential bias in our analyses we used the maximum‐likelihood software ML‐RELATE  for departure from HWE and LD were corrected for multiple comparisons with a modified false discovery rate (FDR) procedure (hereafter B‐Y) . ARLEQUIN v3.5  and expected heterozygosities (HE). Allelic richness (AR) as an unbiased measure of the number of alleles adjusted by the sample size was estimated using FSTAT 2.9.5  and linkage disequilibrium (LD) between all locus pairs  were analyzed using GENEPOP v4.1 Rousset, . Signifi Goudet, .m) among the four sampled localities , BAYESASS v1.3  with 1 × 107 iterations discarded as burn‐in were performed. Since more reliable results are obtained from runs when the number of proposed changes for allele frequencies (a), inbreeding coefficient (f), and migration rate (m) is between 40 and 60% of the total chain length  between males and females; within populations, r should be greater in the philopatric sex than in the dispersing one. The third test compared FIS statistics between sexes; a sex‐biased dispersal should be reflected in a statistically significant higher FIS for the dispersal sex  to determinate the probability of a genotype originating from the population in which the individual was collected  were tested using 8,000 nonparametric permutations and corrected with the B‐Y  using ARLEQUIN v3.5 , individuals in the data set are probabilistically assigned to one or more populations. Since individuals within a population may have mixed ancestry, we used the admixture model. K ranging from 1 to 7 was evaluated performing 30 independent Markov chain Monte Carlo (MCMC) runs of 5 × 106 iterations following a burn‐in period of 1 × 106 iterations for each value of K. The method of Evanno, Regnaut, and Goudet (K (∆K), was used to determine most likely value of K over multiple runs, as implemented in STRUCTURE HARVESTER  and the logarithm of the geographic distance between localities. Using a geographic information system (GIS) in ArcGIS software, geographic distances between localities were calculated as the minimum distance by sea between each other. The rejection of the null hypothesis of a flat or negative slope between genetic and geographic distances was used as evidence of IBD.In order to test for isolation by distance (IBD), a Mantel test was performed. IBD v3.23  and nucleotide diversity (π) of the data set.The data set used for mtDNA analysis consisted of 100 sequences: 64 sequences previously published by Gariboldi et al.  for NC, M = 2Nem). The finite sites (HKY) model was used. Ten independent runs of 2 × 106 iterations each and a burn‐in of 5 × 105 iterations were performed. For each parameter, likelihood values with the highest posterior probability were accepted as the best estimates.By using a MCMC approach, migration rates between the sampled locations were estimated with MDIV  were assessed using 8,000 nonparametric permutations and corrected with the B‐Y .An AMOVA was performed to analyze the spatial structure among our sampling locations using ARLEQUIN v3.5 , with the exception of RN (1:0.4).3.2The microsatellite loci studied varied with respect to the number of alleles observed, from 7 to 13. Although putative populations had different sample sizes, the mean number of alleles was similar between them . Additionally, we did not find significant LD between all pair of microsatellite loci within putative populations after B‐Y correction . Therefore, we considered that all loci were genetically independent.CL, MH, and RN showed similar levels of heterozygosity, and the mean RN Table . We did FST = 0.050; p < 10−5); the greatest source of variation was found within putative populations (95.01%). All pairwise comparisons were statistically significant, except the one between CL and NC . In the case of CL‐NC, twenty‐two individuals (55%) were assigned to a common cluster.In accordance with the AMOVA results, the Bayesian model‐based clustering method showed a clear population subdivision. Considering a range between 1 and 7 populations, the analysis suggested the existence of three populations genetically differentiated Figure . AssuminEstimated migration rates were low between all locations, except from CL to NC Table . The latThe entire tests conducted to detect sex‐biased dispersal were not statistically significant Figure . Therefop = .333).A positive relation was observed between the geographic and the genetic distance between putative populations Figure a. Howeve3.3n = 2), L15 (n = 1), and L22 (n = 1), were found in RN and another haplotype previously described in Gariboldi et al. (n = 1), was found in MH. When combining our samples with those previously published . As for microsatellites, the greatest source of variation was found within putative populations (91.92%). Results of the pairwise comparisons showed no significant differences between NC and CL, NC and RN, or between CL and RN; all other pairwise comparisons were statistically significant  and, to a lesser extent, between CL and RN (m = 11.04). All other population pairs showed values lower than m = 2.28.In accordance with the mtDNA population structure results, higher levels of gene flow were observed between NC and CL (p = .373)  Figure b.4Differentiation among groups of individuals is a fundamental topic in population genetics. In this sense, the use of multiple molecular markers may provide valuable information about the processes that shape the population structure of a species. In this study, we have extended the knowledge of population structure of the franciscana dolphin, the most threatened small cetacean in the southwestern Atlantic Ocean Secchi, . Materna4.1FST estimates between populations  versus 10 nuclear markers (microsatellite loci) (Larsson, Charlier, Laikre, & Ryman, Nevertheless, only the microsatellite data set revealed a significant differentiation between RN and all other localities Table . ConsideLoligo sanpaulensis (Paso Viola, Among putative populations, migration is a central driving force in evolution that reduces local adaptation (Abdo, Crandall, & Joyce, Loligo sanpaulensis and MH majority preys are Cynoscion guatucupa and Artemesia longinaris (Paso Viola, In the case of MH, in agreement with its previously reported separation as a genetically different population (Gariboldi et al., Additionally, although the genetic population structure of many species may be characterized by a pattern of IBD (e.g., Ansmann, Parra, Lanyon, & Seddon, K (Evanno et al., Bayesian clustering analyses such as STRUCTURE (Pritchard et al., Finally, based on mtDNA and microsatellite loci data, Méndez, Rosenbaum, Subramaniam, et al.  reported4.4In this study, we have reported a fine‐scale genetic structure for the franciscana dolphin over the southernmost portion of the species range, uncovering a new genetic distinct population, RN. Based on these results and previous studies (Gariboldi et al., Incidental annual mortality of the franciscana dolphin in Argentina represents up to 2%–5% of its abundance in the area (e.g., Cappozzo et al., None declared.Relevant data are within the paper and its Supporting Information file. Click here for additional data file.


Primula veris has shown that plants from grassland and forest habitats showed pronounced differences in phenology and flower morphology, suggesting limited gene flow between habitats. To test this hypothesis, we sampled 33 populations of P. veris consisting of forest and grassland patches and used clustering techniques and network analyses to identify sets of populations that are more connected to each other than to other sets of populations and estimated the timing of divergence. Our results showed that spatial genetic variation had a significantly modular structure and consisted of four well-defined modules that almost perfectly coincided with habitat features. Genetic divergence was estimated to have occurred about 114 generations ago, coinciding with historic major changes in the landscape. Overall, these results illustrate how populations adapting to different environments become structured genetically within landscapes on small spatial scales.Genetic divergence by environment is a process whereby selection causes the formation of gene flow barriers between populations adapting to contrasting environments and is often considered to be the onset of speciation. Nevertheless, the extent to which genetic differentiation by environment on small spatial scales can be detected by means of neutral markers is still subject to debate. Previous research on the perennial herb  When species are distributed across large areas, geographical isolation can limit gene flow and cause strong genetic differentiation, ultimately leading to ecotype and even species formation. This process can take place either along an ecological gradient or across isolated, well-defined habitat entities6. Strong genetic differentiation can also occur at much smaller local scales as a result of contrasting environmental features that affect gene flow and population genetic structure10. It has for instance been observed that recently diverged sister species originate from habitat-specific adaptations following colonization events in new environments14. Similarly, landscape heterogeneity can lead to formation of locally adapted populations with reduced gene flow between habitats.Understanding how the environment shapes species is a central question in evolutionary biology16. Prezygotic barriers such as differences in flowering time significantly reduce gene transfer between neighboring populations growing under contrasting environmental conditions or in different habitats21. Additionally, differences in pollinator communities between habitats can shape floral morphology and therefore significantly impact on gene flow by pollen transfer25. Different habitats also demand different adaptations in a way that the right alleles are required to thrive in a specific environment26. This restricts certain genotypes to settle or flourish in a given environment characterized by specific soil characteristics, predators, or light availability28. Selection against immigrants due to maladaptation hampers integration of genes of less fit genotypes into the native genotype and can for this reason be considered as an additional legitimate barrier against gene flow and thus ultimately a driver for genetic divergence among populations32.Several reproductive barriers that prevent gene flow can arise when a species occupies contrasting environments within the same geographical region, which in turn can elicit genetic structuring21. Because ecological divergent selection can give rise to barriers to gene flow33, which in turn may reduce the homogenizing effect of gene flow among habitats, this can cause genetic divergence at neutral loci between habitats due to genetic drift33. Demonstrating isolation by adaptation with neutral markers is not always possible since this process largely depends on the stochastic nature of genetic drift or the presence of weakly linked genomic regions under divergent selection33. Nonetheless, several studies have recently used neutral markers to assess population genetic structure and its relation to natural selection35. Direct comparison between neutral microsatellite markers and a genetic marker directly influenced by natural selection has shown that microsatellites are better suited to determine population genetic structure as a consequence of neutral processes such as gene flow36. Furthermore, a simulation study showed that ecological differences can cause a reduced gene flow at neutral markers resulting in genetic differentiation among populations37.However, how these aspects affect gene flow at neutral loci between diverged populations has only been studied recentlyPrimula veris has shown clear evidence for habitat-specific differentiation in phenology and flower characteristics between neighboring populations growing in forests and grasslands38. Due to increasing shade during the growing season, plants in the forest habitat flower about three weeks earlier compared to neighboring populations growing in open grasslands, restricting the chances of gene flow between populations of both habitats38. The size of the flowers and positioning of the anthers and stigma also differed significantly between grassland and forest plants. In particular, the L-morph flower of forest plants showed strong deviation from anther-stigma separation38. Deviations from reciprocal placement of sexual organs can hamper pollen deposition on compatible stigmas25 and therefore restrict pollen flow between grassland and forest populations. Due to these differences, we hypothesized that restricted gene flow between forest and grassland populations has created genetically structured groups within a mosaic landscape consisting of both grassland and forest habitats and therefore may present the very first signs of ecological speciation.Forests and grasslands represent two contrasting habitats that largely differ in several environmental conditions, such as light availability, humidity and temperature, which in turn may select for different traits and result in ecological divergence. Recent research on the distylous P. veris using 12 polymorphic microsatellite loci. We used Bayesian clustering techniques and a landscape genetic analysis based on network theory to test whether grassland and forest populations can be divided into clusters that act as independent evolutionary units in the landscape and to see whether these clusters can be brought back to specific characteristics of the habitats they were sampled from. Additionally, coalescent-based approximate Bayesian computation (ABC) was used to make inferences about population history and to estimate the timing of divergence between populations growing in grassland and forest populations.To test this hypothesis, we investigated patterns of genetic variation and structure in a large set of populations of P > 0.05) difference in population size between grassland (average size: 345 ± 26.35) and forest populations (207 ± 167.81) after omitting one outlier. In total, 119 different alleles were found across 12 microsatellite marker loci in 792 sampled plants. The number of alleles per locus ranged from four to 23 (mean: 9.92). Measures concerning genetic diversity are summarized in Table P = 0.09, t = 1.73, df = 31) and allelic richness  were not significantly different between both habitats. Expected (He) and observed (Ho) heterozygosity ranged from 0.325 to 0.606 and from 0.385 to 0.589 in grassland and forest populations respectively and did not differ significantly between habitats . The mean values for Fis were positive for both habitats , indicating slight inbreeding. The inbreeding coefficient did not differ significantly between habitats . All but two loci showed significant heterozygote deficiency across all populations. Linkage disequilibrium (LD) was found in 7 out of 66 possible locus pairs following Bonferroni correction. Significant linkage for at least one locus pair was present in three grassland and three forest populations .Population sizes ranged from 60 to approximately 12 000 flowering individuals  (Table P < 0.01) (Table FST = 0.087 and FST = 0.052 respectively with P < 0.001). Additionally, traditional Mantel tests showed a significant positive relationship between genetic and geographic distance for forest populations , whereas no such relationship was detected for grassland populations  , whereas 14% of variation was due to differences between populations and 4% due to differences between habitats. All values were highly significant with 01 Table . Overall%, whereaK39. The inferred clusters strongly corresponded with the habitats from which individuals were sampled, confirming differentiation by environment . The average Q values for grassland populations were 0.705 and 0.295 for the red and blue cluster respectively, and 0.321 and 0.679 for the forest populations. The value for LnP(D) reached a plateau at K = 4 and corresponding cluster plots are shown in Fig. K = 26.2 for K = 2, second largest ∆K = 10.1 for K = 4, third largest ∆K = 5.8 for K = 3).Bayesian clustering Fig.  revealedClustering of the populations belonging to the two habitats was also visually confirmed by the PCoA analysis Fig. . FurtherP. veris populations . Four different modules were detected . The MIGRATE-N analysis showed that the mutation-scaled immigration rate M was highly directional with migration occurring mainly from the grassland into the forest habitat (M = 1043) and far less from the forest into the grassland habitat (M = 278).Results from ABC showed that grassland and forest populations diverged ca. 114 generations ago (95% CI: 126 y - 1030 y). This is 228 y before present assuming a generation time of 2 years. Model checking indicated that summary statistics from the posterior predictive distribution of the simulated datasets correctly estimated parameters. More specifically, none of the summary statistics of the 1% closest simulated datasets deviated significantly from those of the observed dataset  between grassland and forest populations of the perennial herb P. veris translated into genetic structuring and the occurrence of well-defined genetic entities in the landscape.Identifying the factors that promote genetic differentiation is of interest for understanding the processes initiating the early stages of speciation. Gene flow among populations inhabiting different environments can be reduced by geographical distance or by divergent selection resulting from local adaptationFST = 0.069), with forest populations showing the largest differentiation. The analysis of molecular variance showed that 4% of the total genetic variation could be attributed to differences between habitats, indicating that environmental factors significantly contributed to partitioning of genetic variation and that gene flow seems to be restricted even if populations are located close to each other. In the related Primula merrilliana, Shao et al.43 documented a much higher value of variation that could be attributed to different habitats (13.30%). In this study, however, both contrasting habitats (foothill versus mountain habitat) were clearly spatially separated with extreme differences in growing conditions, both of which can be expected to have a considerable impact on genetic variation. On the other hand, Jacquemyn et al.44 found lower amounts of variation (<4%) that were associated with patch age in the related forest herb Primula elatior. A very similar study on the epiphytic orchid Fumellea rossi that grew in different forest types reported a value of 2.1% for the part of the total genetic variation that could be ascribed to habitat 40. A comparable value of 2.3% was reported by Andrew et al.35 in the sunflower Helianthus petiolaris occurring in dune and non-dune populations, which they denoted as different ecotypes.Genetic differentiation across all populations was moderate  and was visualized by a Bayesian cluster analysis and a PCoA, which revealed two distinct groups of populations largely overlapping with the two P. veris habitats, indicating a strong concordance between the type of habitat and the partitioning of gene diversity in the landscape. This result is similar with findings of Mallet et al.40, where the number of clusters equaled the number of different habitats. The network analysis of spatial genetic variation largely confirmed these results and showed that genetic variation was spatially distributed across four modules that almost entirely overlapped with populations belonging to the grassland or the forest habitat. Populations within habitats were also more connected to each other than to populations of the other habitat, suggesting that gene flow between forest and grassland populations was restricted. Similar results were shown by Lowry et al.15, who showed significant genetic clustering between coastal and inland races of M. guttatus. However, in contrast with this study, the forest and grassland populations investigated here occurred interspersed within the landscape. The detection of four different evolutionary units by our module finding algorithm therefore also suggests exploration for clustering at a deeper level than the cluster analysis implemented in STRUCTURE48.Differentiation between habitats was considerable  in P. veris occurs at the beginning of April, when plants start flowering, and declined during the growing season52. Primula veris thus shows an increased carbon gain early in spring and exploits the high spring light phase before expansion of the vegetation canopy. This high light phase largely coincides with flower and fruit production, and a substantial carbon gain in spring could guarantee the initiation of flower primordia and/or increase the proportion of dormant flower buds developing52. Flowering earlier in the year in forest habitats, before canopy closure, can therefore be considered as a reproductive strategy of P. veris to escape competition for light.The presence of significant modularity points to the existence of independent evolutionary units that may form the basis for further adaptation and ultimately ecotype formation. Several adaptations to specific environmental features linked to the habitats in which 53. Whereas pollinator communities in grasslands are dominated by the hairy-footed flower bee Anthophora plumipes, this species was absent in forest populations. Here, bumblebees  and several Lasioglossum species were the most frequent insects visiting P. veris flowers. Separation of flowering in time and pronounced differences in pollinator communities therefore most likely act as reproductive barriers that limit gene flow between habitats54.Field observations have also indicated that pollinator communities differ between grassland and forest populations, with little overlap between them55. Grasslands and forest are strongly different habitats, demanding different sets of adaptations which can directly compromise the ability of a plant to germinate, grow or reproduce in a non-native environment56. For example, previous research has shown that compared to the related Primula elatior and P. vulgaris, P. veris is least well-adapted to survive moderate shade, as a consequence of its relatively low quantum efficiency, high light saturation point and high dark respiration52. The pronounced differences in specific leaf area and stomatal density point to habitat-specific adaptations to light conditions57 or water status58, which may restrict establishment of grassland plants in forest habitats and vice versa.Furthermore, successful establishment after immigration to the opposing habitat could be hampered by immigrant unviability because of maladaptationth century. This period coincides with large alterations of the landscape60. In 1775, the date of first map of the study area, the entire area was widely covered with calcareous grasslands and old-growth forest was limited. These grasslands are known for their high diversity in flowering plants61 and attract a wider variety of insects because they can supply copious nectar for pollinators62. It is therefore reasonable to assume that pollinator diversity was highest in calcareous grasslands and that gene flow occurred predominantly from grassland to forest. This is in line with our results that showed that historical migration rates were about 4 times larger from grassland to forest populations than from forest populations to grassland populations. However, in the 18th and especially the 19th century, many calcareous grasslands were abandoned60 or planted with high-productivity forests that mainly consisted of Pinus nigra. These secondary forests are unsuitable to support P. veris populations and this may have initiated increased isolation between forest and grassland populations. Assuming that, due to the increased isolation, less pollen was dispersed from grasslands into forest populations, forest and grassland populations gradually started to diverge and phenotypic and genotypic differences between forest and grassland populations started to increase.The time of divergence between grassland and forest populations was estimated to occur at the end of the 18P. veris formed clear genetic entities in the landscape that were related to the habitat from which they were sampled. Historic changes in landscape configuration and spatial isolation and the associated changes in gene flow probably have gradually induced phenotypic and genotypic differences between plants from grassland and forest populations. Future research is needed to examine whether the observed phenotypical differences, such as specific leaf area or stomatal density, are the result of plasticity or are in fact evolutionary adaptations63 and to identify genomic regions that are subject to adaptation.To conclude, our results demonstrated that populations of Primula veris L. (cowslip) is a herbaceous, spring flowering perennial plant species that usually grows in calcareous grasslands, but that can also be found in old growth forest and hedgerows46. This rosette forming hemicryptophyte can be found throughout most of temperate Europe and the British Isles until the western Russian border. In early spring, P. veris forms a rosette of leaves and produces flowers that grow in umbels. Since P. veris prefers a warm microclimate, it has a preference for sunny slopes and open forest patches. Selfing is prevented by a diallelic self-incompatibility system and heterostyly with two reciprocal flower morphs (pin and thrum)64. Pollen flow is mainly accomplished by early Hymenoptera (mostly bumble bees and bees) that are able to reach the nectar46. Seed dispersal is restricted to only a few meters from the maternal plant since there is no mechanism for long-distance seed dispersal46.38. Cowslip flowers about three weeks earlier in forest habitats than in grasslands46, possibly as a way to take advantage from the higher light availability in spring before canopy closure49. Additionally, P. veris shows habitat specific variation in flower morphology, with forest populations generally producing larger flowers that show strong deviations in stigma-anther separation, particularly in the L-morph38. This deviation is mainly driven by variation in stigma height, resulting in high and asymmetric reciprocity indices and the occurrence of several short-styled homostylous plants38. In contrast, flowers of grassland plants show clear distyly with low and symmetric reciprocity indices at both the lower and upper level38. Analysis of vegetative traits further shows that plants in grasslands produce more, but shorter flowering stalks. Interestingly, grassland plants have a lower specific leaf area and a higher stomatal density than forest plants , also suggesting habitat specific adaptations to cope with the different environmental conditions imposed by forest and grassland habitats65.Previous research has shown some remarkable differences between forest and grassland plants2  managed farmlands throughout the region  were located in open, old growth forests, whereas the grassland populations (n = 18) were selected in species-rich calcareous grasslands or grassy road edges. Grazing was common practice in the management of calcareous grasslands in the past and is still applied today60. Patches containing P. veris populations ranged from approximately 40 m2 in size for roadside populations up to several hundreds of square meters in grassland populations. For each population, population size was assessed by counting the total number of flowering plants. Additionally, a large healthy leaf was taken from 24 plants per population and was immediately stored within silica gel until further analyses in the lab.In spring 2015, a total of 33 populations on the northern side of the Viroin River were sampled  and 2 µl of RNAse-free water. Forward primers were fluorescently labeled with one of the four available colours . A 2720 Thermal Cycler  was used for the polymerase chain reaction with the following program: an initial denaturation at 94 °C for 3 min, then 10 cycles of 30 s at 94 °C, 60 s at 57 °C and 30 s at 72 °C. This was followed by a further 28 cycles of 94 °C for 30 s, 55 °C for 60 s and 72 °C for 30 s, finishing with a final extension phase at 72 °C for 7 min. After the fragment elongation, capillary electrophoresis was performed using an ABI3130 instrument  using a GeneScan 500 LIZ  size standard. Microsatellite peaks were visualized and scored using GeneMapper® Software v4.0 (Applied Biosystems).20 mg of dry leaf material was weighted and homogenized with the use of zirconia beads and a FastPrep-24 Instrument , after which DNA was isolated using the NucleoSpin® Plant II DNA extraction kit . DNA quality and concentration were evaluated using a NanoDrop ND-2000 spectrophotometer . For genotyping, 12 primer pairs were used , allelic richness (Ar), observed (Ho), expected heterozygosity (He) and the inbreeding coefficient (Fis) for each population. Linkage disequilibrium was checked with GENEPOP 4.072. Conformance to Hardy–Weinberg equilibrium was determined by assessing the significance of the Fis values by means of Fisher’s exact tests implemented in GENEPOP 4.0, with specified Markov chain parameters of 5,000 dememorization steps, followed by 1,000 batches of 5,000 iterations per batch. The sequential Bonferroni correction was applied to obtain critical confidence limits for multiple comparisons, with an initial α of 0.05. To test for significant differences in population genetic parameters between forest and grassland populations, a two-tailed t-test or Mann-Whitney U test was performed in R in accordance to assumption fulfillment.Micro-Checker was used to check for null-alleles, stutter and large allele dropout 73. Individuals were assigned to two groups reflecting the two habitats. Significances were determined using permutation tests.Assessment of total genetic diversity partitioned among both habitats (forest vs. grassland), among populations within one habitat, and within populations was done by carrying out a hierarchical analysis of molecular variance (AMOVA) on Euclidean pairwise distances among individuals implemented in GENALEXFST (adegenet70), Hedrick’s standardized measure for GST and Jost’s D  were calculated (5000 permutations). A mantel test was used as a nonparametric equivalent of ANOVA to test the hypothesis that genetic differentiation among populations within one habitat was significantly different from differentiation among populations from different habitats75. Therefore we constructed a dissimilarity matrix with zeroes in the within group submatrices and ones in the between groups submatrices. If differences between habitats are greater, then the ones in the design matrix will be associated with larger differences. This ANOVA-like Mantel test was performed for the three different measures for differentiation and 5000 random permutations were used. Furthermore, a Mann-Whitney U test was performed to test for significant differences for all three differentiation parameters among habitats. The combined information of different differentiation measures enables a more profound and reliable understanding of the relations between populations81.Parameters for pairwise genetic differentiation GST) and geographical distances82. Significance of the observed relationships was obtained by using a Mantel test84. A total of 5000 random permutations were performed. The same test was also used to look for isolation by distance separately among populations in grasslands and among populations in forests.Evidence of isolation by distance among populations was obtained by examining correlations between matrices of genetic distances  using the ape package in R85.We also investigated whether the two habitats translated into distinct genetic clusters using a Bayesian clustering method applying Markov Chain Monte Carlo estimation implemented in STRUCTURE 2.3.4 software86. Population graphs go beyond describing the traditional F-statistics and provide better insights into the overall population genetic structure by visualizing links of significant genetic similarity between populations87 and providing quantitative measurements of link density and strength between and within habitats as estimates of population connectivity. Each node represents a population and can be connected to several other populations by edges or links. In this way, a graphic representation of the identified modules in the network can provide insights into the factors  that define the assignment of a population to a cluster of genetically similar populations. To perform the network analysis, first a genetic distance matrix was constructed and converted into a correlation matrix in several steps following Fortuna et al.87.We applied a network approach to spatial genetic variation known as population graphs89. Edge exclusion was calculated as:N is the number of individuals in the entire data set, and rij is the partial correlation coefficient between sites i and j. This statistic asymptotically follows a chi-square distribution. A link among populations i and j is removed if the value of its deviance (theta) is less than 3.84 (the 5% threshold of the chi-square distribution with df = 1). For further details on graph theory of genetic structures see Dyer and Nason86 and Fortuna et al.87Some populations are only poorly connected to others and therefore several links can be excluded in order to simplify the model without losing information about the genetic covariance structure among sites. Based on conditional independence it is possible to detect these redundant edges. Edge exclusion deviance is a theoretic measure to calculate whether an edge should be excluded from a fully saturated population graph90, M, can be written as:ei is the number of edges or links within module i, km (kn) is the degree of node m (n) and di is the sum of the degrees of all nodes or patches in module i.We applied a modularity analysis on the network of spatial genetic variation to detect modules or sets of populations that are more genetically related to each other than to populations belonging to other modules. Newman’s modularity91 to find high level modules. In this study, we ran 100 replicates of the Guimera and Amaral algorithm91 for M and then obtained the maximum value of M. In order to test to what extent the value of modularity departs significantly from random expectation we ran 1000 randomizations of the network of genetic variation keeping exactly the same number of links per node, but reshuffling them randomly using a local rewiring algorithm92. The P-value was estimated as the fraction of random networks with a modularity value equal to, or higher than, the value obtained for the empirical network. The final population graph displaying the different modules was constructed using the igraph package93 in R 3.3.071.Multiple techniques consist of maximizing modularity of a complex network by exploring different possible states of the population structure. Simulated annealing is an optimization techniques that explores low cost configurations of community structure94. This software simulates one or several user specified scenarios of historic and/or demographic events and compares the observed data with summary statistics of the simulated data to calculate posterior distribution of demographic parameters94. Our goal was to determine the time of divergence between grassland and forest populations. Therefore, we constructed a scenario with a bifurcation event between both groups and compared it to our data. We set wide priors (10–10000) for al parameters and used 300000 simulated dataset to infer parameters. The 1% datasets with summary statistics closest to the observed data were used for approximate Bayesian computation of parameters by using regression rejection steps of the algorithm following logistic regression of the parameter values. We evaluated the goodness of fit of the model-parameter posterior distribution with the model checking function implemented in DIY-ABC version 2.094. Each of the 14 summary statistics of the observed dataset is ranked against the distribution of the corresponding summary statistics from the posterior predictive distribution and deviations are checked using the estimated P-value.To estimate the timing of divergence between populations growing in grassland and forest patches, we used the coalescent-based approximate Bayesian computation (ABC) algorithm implemented in the program DIY-ABC version 2.095. MIGRATE-N uses maximum likelihood or Bayesian inference to jointly estimate historical migration rates and effective population sizes. To compensate for fluctuating population sizes at each site, we ran a simplified migration model with populations grouped per habitat type. Bayesian inference was used with one long chain of 100000 steps and a sampling increment of 1000. The default static heating scheme was used. The burn-in was set to 100000.In addition, past migration rates were estimated using MIGRATE-N 3.6


Castor fiber. We sequenced seventeen 492 bp partial tRNAPro and control region sequences from Late Pleistocene and Holocene age beavers and included these in network, demographic and genealogy analyses. The mode of postglacial population expansion from refugia was investigated by employing tests of neutrality and a pairwise mismatch distribution analysis. We found evidence of a pre-Late Glacial Maximum ancestor for the Western C. fiber clade which experienced a rapid demographic expansion during the terminal Pleistocene to early Holocene period. Ancient British beavers were found to originate from the Western phylogroup but showed no phylogenetic affinity to any one modern relict population over another. Instead, we find that they formed part of a large, continuous, pan-Western European clade that harbored little internal substructure. Our study highlights the utility of aDNA in reconstructing population histories of extirpated species which has real-world implications for conservation planning.Establishing true phylogenetic relationships between populations is a critical consideration when sourcing individuals for translocation. This presents huge difficulties with threatened and endangered species that have become extirpated from large areas of their former range. We utilise ancient DNA (aDNA) to reconstruct the phylogenetic relationships of a keystone species which has become extinct in Britain, the Eurasian beaver  Identification of suitable source population/s from which to select founding members for the restoration area is a critical factor in increasing the likelihood of success4. Structured differences in the distribution of phenotypic traits and adaptive genetic variation arise between populations as the result of divergent evolutionary processes and adaptation to local conditions6. These variations in genetic architecture create differential responses to novel environments, stressors and pathogens and create variation in the reintroduction potential of populations4. Assessment of genetic parameters across source population/s is, therefore, now a chief objective in translocation planning3.Reintroduction programs are an essential tool in the restoration of viable, free-ranging populationsCastor fiber, is the largest Eurasian rodent with an ecology heavily reliant on riparian woodland ecosystems7. It is considered a keystone species and ecosystem engineer, capable of providing multiple wetland ecosystem services and promoting biodiversity8. Castor fiber was once widespread and abundant throughout Eurasia9 but over-hunting and habitat loss led to severe demographic reductions and the extirpation of the species from vast areas of its former range11. By the start of the 20th century, the species was on the brink of extinction with the total Eurasian population estimated to contain only 1,200 individuals12. Remaining individuals were distributed between eight scattered and highly isolated relict populations , sourced from Telemark, Norway, were released into freshwater lochs in Argyll as part of the Scottish Beaver Trial16. Unlicensed releases and/or escapes have also occurred along the River Tay, Scotland17, , and along the River Otter in East Devon, England (from an unknown origin)18. Additionally, in 2011, the Enclosed Beaver Project was initiated, which saw a mating pair, of unknown/unpublished origin, released into a captive site in Western Devon19. The success of these populations, combined with favourable feasibility studies21, indicate that large-scale reintroductions to Britain may be initiated soon.The beaver, 22. Therefore, no natural recolonisation, admixture of populations and/or external gene flow can occur as on mainland Europe. Determining the most appropriate source population/s for reintroduction is, however, confounded by the complex demographic history of the species. Relict European populations passed through severe bottlenecks of between 30 and 300 individuals23. These populations are genetically impoverished with low within-population genetic diversity, some monomorphic populations and strong phylogeographic structuring25. Recent evidence from ancient DNA has uncovered a huge decrease in genetic diversity within C. fiber, which is wholly attributed to anthropogenic influences and which has created the artificial pattern of strong population structure observed in remnant extant populations26. A lineage split exists for two clades, representing Western and Eastern Europe, with contact zones in Eastern Europe and NW Scandinavia26. Durka et al.24 proposed these lineages as Evolutionary Significant Units (ESUs) and recommended that they not be mixed. The divergence of these most likely occurred during isolation in refugia during the Last Glacial Maximum  after which re-emergent populations experienced mixing of divergent haplotypes via contact zones.Selection of founding members for a British beaver population should be carried out with great care. Britain was physically separated from the European landmass by sea level rise around 8.2 kya1, i) the pre-existing adaptation strategy or, ii) the adaptive potential strategy. In the former, population/s that bear either the closest phylogenetic similarity to the extirpated population and/or populations that survive under similar environmental conditions are selected. In the latter, individuals are selected from population/s which show high levels of heritable genetic variation that maximizes their potential to adapt to new environments. Any British beaver reintroduction program must, therefore, consider the ancestral relationship of the source population/s to the original British population in addition to factors such as, i) the relative merits of selecting founders from single versus multiple populations, ii) mixing individuals from disparate clades, iii) levels of pre-existing genetic diversity in the source population/s and iv) the potential for inbreeding or outbreeding depression. Reintroduction programs often aim to restore species to some, presumably arbitrary, historic baseline. However, species are not static through time and respond to a suite of biotic and abiotic influences29. Baselines are, therefore, constantly shifting and the use of heterochronous ancient DNA data permits the construction of evolutionary frameworks with the spatio-temporal complexity to reflect this. Significantly, ancient DNA allows the sampling of populations that have become regionally extinct and thus cannot be included in studies that utilize DNA from extant populations alone31 as is the case with British populations of C. fiber.Selection of source populations generally follows one of two broad strategies14C radiocarbon dating to the ancient British beaver population, for the first time, with an aim to inform conservation decision making on the appropriate source population/s for the reintroduction of beavers to Britain. We address the following questions: i) what is the phylogenetic relationship between ancient British beaver populations to modern and ancient European populations? And, ii) what was the mode and timing of post-LGM population expansion?In this study, we employ ancient DNA analysis and c. 12,900–11,700 cal BP), a brief cold phase of the Terminal Pleistocene immediately prior to Holocene warming. The second specimen from Gough’s Cave, MM004 , also falls within the Younger Dryas range, although the range on the date extends into the earliest Holocene. All other samples dated within the range of the mid – late Holocene. The new dates are significant because they imply that beavers survived a period of cold-climate conditions in Britain without retreating to more temperate regions in the south. There was a small amount of overlap in the 14C probability distribution for MM004 and MM005 and these samples came from different skeletal elements, raising the possibility they originate from one individual. A chi-square test showed that these are significantly different from each other based on the probability distributions of 14C evidence . While this is not conclusive, it does strongly suggest that the specimens are from two different individuals.Five samples in total were submitted to the Oxford Radiocarbon Accelerator Unit, all of which returned successful radiocarbon dates . Levels of genetic divergence between the British data and the rest of the Western clade were Fst = 0.122, which suggests a moderate amount of genetic differentiation, despite the absence of marked phylogenetic distinctiveness.A total of 17 samples successfully generated partial (492 bp) tRNA-Pro - CR sequences, and these were all located in the SW and SE of England Fig. . We wereC. fiber haplotypes were highly congruent in both the network  of the western clade .The topology of the BEAST MCC tree Fig.  was highD statistic and Fu’s F returned negative and significant values for the Western clade  and R2 returned a significant and positive value . A mismatch distribution, using a generalised least-squares approach and based on the sudden demographic expansion model, found that this clade fitted a unimodal pattern of demographic expansion  p = 0.96  populations, such as has been reported for Arvicola terrestris32, as our dataset did not contain any C. fiber specimens of Lateglacial Interstadial age. However, even under a scenario where Eastern European beavers contributed to Lateglacial Interstadial recolonisation of Britain, these populations are likely to have been wholly replaced by a second of wave of Early Holocene recolonisation from Western Europe. Therefore, the geographical source area for the Holocene British population was unquestionably Western Europe.British samples did not show any phylogenetic affinity with Eastern ESU haplotypes, therefore there was almost certainly no Postglacial recolonisation of Britain from the east. We cannot discount an Eastern European origin for earlier, Lateglacial Interstadial  specimens, MM004 and MM005, did not show any notable degree of divergence from Holocene haplotypes, suggesting that this population was contiguous with later populations and possibly contributed genetically to the establishment of these. Large polytomies in a phylogeny can be interpreted as in indicator of a rapid population expansion. This conclusion is supported here by the population expansion tests, which all strongly supported a pattern of exponential population growth for the Western clade with a tMRCA mean age of 47,529 cal BP. Given the evidence presented, we conclude that the ancient British beaver population formed part of a large, continuous, pan-Western European population. This population likely expanded rapidly from glacial refugia after the LGM, had high levels of gene flow and low levels of regional divergence within the tRNAPro-control region loci. Moreover, out of 17 haplotypes described here, 10 were unique and none were shared with extant relict populations. This adds further support to the conclusions drawn by Horn et al.26 that ancient beaver populations harboured much greater levels of genetic diversity than are seen today in post-bottleneck populations.The British data all grouped unambiguously within the Western clade and showed no supported spatio-temporal substructure, either internally within British populations or between the British haplotypes and the continental Western European data. The two Late Pleistocene  use of the geographically closest, extant, beaver relict population, ii) mixture of individuals from two or more Western populations or, iii) release of individuals of multiple origins, regardless of ESU assignment. The lack of strong phylogeographic structure observed in this study within the Western European clade, and the low-moderate Fst value between ancient British beavers and the Western clade, suggests that ancient British beavers did not possess significant phylogenetic distinctiveness from mainland European ancient beavers  or France (ga haplotypes). Given the extremely low levels of divergence in mitochondrial control-region DNA among extant Western Europe populations, all extant Western populations show similar levels of genetic similarity to the ancient British population. Evidence from Senn et al.14 highlighted that the partition between Western and Eastern ESUs is not as well-defined as originally thought and argued that this division should be abandoned. Pending the adoption or rejection of this recommendation, the fact that the British ancient beaver population does not show close phylogenetic affinity with the Eastern clade leads us to recommend that scenarios i) or ii) from Halley38 should be adopted.Halleyers Figs ,3. Nor dC. fiber are already present and persisting in Britain40. The Tay beaver population has largely been allowed to expand naturally with minimum intervention thus far. From an unknown founder number, the population now stands at around 38 family groups  that appear to be thriving17. Genetic screening has established that these individuals descend from the Bavarian region in Germany41, a highly admixed population with individuals of Scandinavian, Russian and French origin25, i.e not only of mixed Western European ancestry but also originating from different ESUs. European beaver populations with admixed ancestry generally tend to have elevated levels of genetic diversity relative to unmixed relict populations and display no obvious signs of outbreeding depression25. Therefore, it may be advantageous for future beaver reintroduction projects to consider selecting founder stock that maximise genetic diversity and levels of adaptive potential above achieving close phylogenetic relatedness to the historical British population. The results presented here suggest that this would be the most appropriate path to follow given current information. Future studies may benefit from the inclusion of ancient nuclear DNA. However, as beavers do not show strong sex-biased patterns of dispersal, live in family groups and are purported to maintain a monogamous mating strategy42, we would expect mitochondrial and nuclear DNA to show broad phylogenetic congruence.It should be kept in mind that there are other genetic considerations involved in choosing source populations for translocation purposes that may be given more weight than phylogenetic history. Populations of We sourced 40 bone samples of putative Terminal Pleistocene  and early-late Holocene  age from museum collections to represent a range of sites and chronological periods across the UK, including Scotland. We targeted dense and compact parts of the bone for both radiocarbon and aDNA sampling.14C dating. All sampling was done in situ with a hand-held drill, on sterilised surfaces. These were: MM004 (Gough’s Cave), MM005 (Gough’s Cave), MM033 (Cambridgeshire Fens), MM015 (Allermoor) and MM061 (Cambridgeshire Fens). Between 0.5 – 1 g of bone was sent to the Oxford Radiocarbon Accelerator facility (ORAU). Two of the age estimates returned for the Gough’s Cave individuals, MM004 and MM005 were very similar, which raised the possibility that they may represent a single individual. We therefore utilised the probability distributions of the 14C age estimates in a chi-square analysis to ascertain whether the two dates fall outside of each other’s 95 percent confidence ranges43.We attempted to obtain age estimates for all material that returned full-length DNA sequences. However, we could only obtain curatorial sampling permission and sufficient quantity of bone to submit five samples for AMS et al.44. We targeted a partial tRNA-Pro - control region (CR) section of the mitogenome to take advantage of a large, pre-existing database of both modern and ancient sequences. We amplified the target loci in overlapping fragments with primer pairs custom designed for this study in addition to primers designed by Horn et al.26  had been UV irradiated prior to use. Sampling was done with a hand-held drill with speeds below 1000 RPM. Between 10 and 50 mg of finely powdered bone was used as the basis for DNA extractions following the protocol outlined in Dabney 45 followed by shotgun sequencing on an Illumina NextSeq500 platform. Illumina paired-end reads were merged with a mismatch cost of 2 and gap cost of 3 and were mapped to the reference consensus sequence with length and similarity fractions set at 0.94. Mapped reads were then re-aligned to a consensus sequence along with PCR generated contigs. All contigs and full-length sequences were subjected to Basic Local Alignment Search Tool (BLAST46) searches to rule out contamination. All sequences were translated to ensure no nonsense base calls.For 4 individuals, consistently poor-quality reads were produced. For these samples, we used Next Generation Sequencing (NGS) from DNA extracts to fill gaps. We carried out single-index DNA library builds based on the protocol by Meyer & KircherC. fiber tRNA-Pro – control region sequences. Due to the complex translocation history of beavers, many admixed populations contain individuals from mixed ESUs, which can potentially lead to inaccurate phylogenetic placement25. Therefore, when selecting modern sequences for phylogenetic analyses, we only included data from individuals known to originate from a putative relict population.To search for new haplotypes in our dataset, we aligned all sequences generated in this study with all available published n = 78) were subjected to analyses using Bayesian Evolutionary Analysis Sampling Trees47(BEAST). For sequences we generated in this study, all ages were determined by AMS 14C radiocarbon dating. In the ancient data from Horn et al.48, sample ages were inferred from both radiocarbon ages and associated zoo-archaeological information. All other samples included were of 21st century age; Ducroz et al.23, Durka et al.24 and Senn et al.14. We included identical sequences in the BEAST run if they possessed accurate age information and the final dataset contained 78 sequences  constant population size and, ii) a coalescent exponential pattern of population growth. For each separate BEAST run, we also carried out a ‘null’ simulation where sampling is performed only from the prior and not the data. Two independent MCMC chains were run for 1 × 108 iterations each, sampling trees and model parameters from the posterior every 10,000 iterations. The first 10% was discarded as burn-in. Path sampling was used to estimate marginal likelihoods (MLEs) for each model. These were run on the parameters of four independent MCMC chains comprised of 100 steps of 1,000,000 generations following a burn-in of 50 percent. We then calculated Bayes Factors from the MLEs and determined model preference using the criteria of Kass & Raftery50. Finally, we generated a maximum clade credibility (MCC) tree, visualised using FigTree v.1.3.151.For network construction, we used all available ancient and modern tRNA-Pro and control region sequences, resulting in a database consisting of 95 sequences (see Supplementary Table i) the Western  and Eastern (n = 27) phylogroups and, ii) the British data , and the remainder of the Western clade (n = 51). Fst values, based on the methods of Hudson et al.52 were calculated in DNAsp v.5.053. When applied to mitochondrial DNA these are based on differences in haplotype frequencies and assume the island model of population structure54.Levels of genetic divergence were estimated between, n = 68. These were Tajima’s D, Fu’s F55 and R256. Both the Tajima’s D and Fu’s F values are expected to be equal to zero under a null hypothesis of population equilibrium and selective neutrality; significant negative values for Fu’s F and Tajima’s D and significantly positive values for R2 can be interpreted as a signature of population expansion. We assessed the significance levels of tests via the generation of 1,000 random samples. The Western clade was also examined for evidence of a population experiencing exponential growth by carrying out a mismatch distribution in Arlequin v 3.557 based on the sudden demographic expansion model. If populations have passed through a demographic expansion, this distribution will fit a unimodal curve59.To test whether the data are consistent with a neutral model of evolution or, alternatively, show a signature of rapid population expansion, we performed three tests of neutrality for the Western clade only, with a total sample size of https://www.ncbi.nlm.nih.gov/genbank/ under accession numbers MG773796-812.The datasets generated during the current study are available in the Genbank repository Supplementary Information 1Dataset 1


The population of Zulu sheep is reported to have declined by 7.4% between 2007 and 2011 due to crossbreeding. There is insufficient information on the genetic diversity of the Zulu sheep populations in the different area of KwaZulu Natal where they are reared. The study investigated genetic variation and genetic structure within and among eight Zulu sheep populations using 26 microsatellite markers. In addition, Damara, Dorper and South African Merino breeds were included to assess the genetic relationship between these breeds and the Zulu sheep. The results showed that there is considerable genetic diversity among the Zulu sheep populations (expected heterozygosity ranging from 0.57 to 0.69) and the level of inbreeding was not remarkable. The structure analysis results revealed that Makhathini Research Station and UNIZULU research station share common genetic structure, while three populations  had some admixture with the exotic Dorper breed. Thus, there is a need for sustainable breeding and conservation programmes to control the gene flow, in order to stop possible genetic dilution of the Zulu sheep. Human history has been completely transformed by the domestication of animals and plants over the past 10,000 years , 3. ArchSheep play an essential role in the livelihood of people around the world as they are a source of meat, milk, wool, hide and manure, especially in developing countries  8] 9]. [9].8] [. [9].8] th and 17th centuries. Some of the migrants settled along the way, while the other group travelled further. This gave rise to the division of Nguni people. The Zulu people, who took their name from their first ruler or king, ''Shaka Zulu'', settled in the green plains of KwaZulu Natal [Zulu sheep are one of four ecotypes of the Nguni sheep breed   16]. Th. Th16]. lu Natal . The farlu Natal . This Nglu Natal . Moreovelu Natal . Howeverlu Natal  21] wit witth anlu Natal .There is currently a gap in information available on the genetic variation among Zulu sheep populations. The research published so far by Kunene et al.  and HlopThe aims of this study were two-fold: (i) to confirm or disconfirm the structuration among eight Zulu sheep populations revealed by morphological analysis, using molecular tools; (ii) to assess the phenomenon of crossbreeding, in particular with Dorper and Merino sheep, using microsatellites. Microsatellites are characterised as co-dominant inheritance, highly distributed throughout the genome, showing a high mutation rate and a high level of polymorphism  25] [26 [2625]  28] [29 [2928]  31]. Th. Th31]. The molecular information obtained in the present study will serve as a guideline for management and breeding strategies (reducing inbreeding and crossbreeding) for better utilisation and conservation of Zulu sheep; this is recommended to avoid unscientific conservation decisions made at political level .In total, 323 alleles were detected across the 28 microsatellites loci in the studied Zulu sheep populations and three exotic breeds with a mean of 11.54 alleles per locus . The mosThe mean number of observed alleles (MNA) ranged from 3.84 (UZ) to 6.64 (NQ) . After aO) was detected in ME (0.67), while JO showed the lowest (0.53). The highest expected heterozygosity (HE) (with the exception of the exotic breeds) was observed in NQ (0.69), with the lowest (0.57) in ES , whereas distribution of HE was significantly different between populations .The highest observed heterozygosity (H7) in ES . DistribFIS) estimated ranged from -0.01 (ES) to 0.16 (JO) (The inbreeding coefficient (.16 (JO) .P<0.05) in ES (0.005), UZ (0.003) and MS (0.03).The two-phase mutation model under Wilcoxon sign rank tests was utilised to find out recent bottlenecks (heterozygosity excess) in the Zulu sheep populations. The heterozygosity excess obtained (data not shown) were significant (FST) among populations is shown in FST genetic distance estimate values revealed that the closest populations were NQ and NG (0.056) and MS and UZ (0.070), while the longest distance (0.235) between the Zulu sheep populations was realised between ES and MS. DO was more genetically distant from UZ and MS  than to the other Zulu sheep populations. The NQ population was genetically the closest population to the DO sheep breed.Pairwise genetic differentiation (The neighbor-joining tree obtained from Reynolds weighted genetic distance  showed tK) present in the populations, permitting the identification of differences among populations and hidden substructures within them. The highest ΔK value was detected at K = 4 , the second cluster , the third cluster (UZ and MS) and the fourth cluster (DA and ME). However, DA and ME contained some individuals associated with other eight Zulu sheep populations (not well differentiated), while DO related to NG, ES, UL and NQ. The lower ΔK peak was detected for K = 9, showing that the Bayesian analysis was able to distinguish more substructure in the dataset. At this level JO and MT appear to be different, whereas NG and UL were still not distinguished and admixed, especially with NQ. The admixture between these groups  was further investigated in a substructure analysis  statistic generated by Discriminant Analysis of Principal Components (DAPC) indicates that the optimal number of clusters in the data set is K = 9 , showingK = 9  showed eK = 9 presented in Moreover, the DAPC results are related to those of STRUCTURE at Analysis of molecular variance (AMOVA) was performed to assess the variation within and between only Zulu populations. AMOVA revealed a high variance component within individuals (87.58%), followed by among populations (8.99%), and among individuals within populations (3.43%) .A survey of Zulu sheep population size over a period of five years (2007 to 2011) revealed that population size decreased by 7.4% due to crossbreeding . ConsequO and HE of the Zulu sheep were 0.58 and 0.63 respectively. In comparison, these values are closer to those revealed by Soma et al. ..K = 9, gOur study has shown that Zulu sheep are threatened by crossbreeding with exotic breeds, especially with the Dorper breed. Although some of the studied populations were affected by the admixture phenomenon there is still genetic diversity among populations. It can also be concluded from this work that Zulu sheep have some uniqueness among populations. Thus, there is a need for sustainable breeding and conservation programs to control the gene flow, in order to stop their possible genetic dilution.All experimental procedures were reviewed and approved by the University of Zululand Research Ethics Committee, Reg No: UZREC 171110–030 PGM 2015/227.A total of 207 blood samples of Zulu sheep were randomly collected from eShowe (ES), Makhathini research station (MS), Mtubatuba (MT), Jozini (JO), Nongoma (NG), Nquthu (NQ), Ulundi (UL) and UNIZULU research station (UZ) . Data onThe eight populations used in the study were selected based on the availability of Zulu sheep in these areas. The details of the studied sheep populations are reported in Blood samples were collected from each animal with the Vacutainer® system, in tubes with the addition of EDTA as anticoagulant, and stored at -20°C until analyses were performed. Analysed animals can be considered as a representative sample of the populations as they were chosen from different flocks, trying to avoid closely related individuals.Due to probable crossbreeding of Nguni Zulu populations with exotic breeds which are common in the areas of KwaZulu Natal where the Zulu sheep are found; a total of 53 Dorper (DO) and South African Merino breed (ME) individuals were included in the dataset. In addition, 29 Damara (DA) animals were included as out-group.The GenElute Blood Genomic DNA kit  was used to extract the genomic DNA. Twenty-eight microsatellite loci  were selAllele frequencies, mean number of alleles, polymorphic information content (PIC) for each microsatellite loci, and the observed and expected heterozygosity in the eleven populations were estimated using the MICROSATELLITE TOOLKIT .To calculate average allelic richness (R) and the richness of private alleles (PR) for each population, the rarefaction method  implemenFIS for each population was calculated via bootstrapping using 1,000 replicates with GENETIX software version 4.05 [FST value. The index of pairwise FST of Weir and Cockerham [The ion 4.05 . The extockerham  between ockerham . Bottlenockerham  utilisinockerham  and Peerockerham .The Reynolds’ weighted genetic distance  among thThe hierarchical analysis of molecular variance (AMOVA) was performed using ARLEQUIN software version 3.5  in orderK ≤ 13 (K = number of clusters) to estimate the most likely number of clusters present in the dataset. The algorithm of Evanno et al. [K. Moreover, in order to investigate population substructures, the most interesting cluster identified with STRUCTURE was re-analysed using the same settings and assuming K = 2 to K = n+3 (n being the number of populations included in each cluster). STRUCTURE HARVESTER [Population structure across the entire dataset was analysed using a Bayesian approach implemented in STRUCTURE software version 2.3.4  to asseso et al.  was adopARVESTER , a web-bARVESTER .a posteriori group assignments by inferring the most likely number of genetic clusters (K) using the find.clusters function in the ADEGENET package. This function utilises K-means clustering to calculate a Bayesian information criterion (BIC) value for each potential value of K  and delineates individual group assignments for DAPC.To further investigate the genetic structure of each population when adopting an approach without assumptions about HWE or linkage disequilibrium, Discriminant Analysis of Principal Component (DAPC) was carried out with the method implemented in the ADEGENET package  within tS1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 TableThe data file has individuals organised in rows, with name of population in the first column. The subsequent columns correspond to microsatellite data (two columns per locus). Missing data are coded by zero.(CSV)Click here for additional data file.S1 Fighttp://www.d-maps.com and adapted for illustrative purpose only.JO, Jozini: latitude: 27° 42' 94"S longitude: 32° 06' 57"E; MT, Mtubatuba latitude: 28° 40' 59"S longitude: 32° 21' 43"E; NG, Nongoma latitude: 27° 89' 43"S longitude: 31° 64' 54"E; ES, Eshowe latitude: 28° 89' 47"S longitude: 31° 46' 28"E; UL, Ulundi latitude: 28° 29' 97"S longitude: 31° 43' 42"E; NQ, Nquthu latitude: 28° 30' 08"S longitude: 30° 80' 39"E; UZ, UNIZULU research station latitude: 28° 85' 24"S longitude: 31° 84' 91"E; MS, Makhathini research station latitude: 27° 39' 53"S longitude: 32° 17' 64"E; DO, Dorper latitude: 29° 80' 00"S longitude: 30° 65' 00"E; DA, Damara latitude: 25° 16' 74"S longitude: 29° 39' 87"E; ME, South African Merino latitude: 29° 60' 06"S longitude: 30° 37' 94"E and latitude: 29° 98' 25'' S longitude:30° 92' 17''ESource: figure taken from (TIF)Click here for additional data file.S2 FigK plots of STRUCTURE analysis averaged over ten repetitions at K = 1 to 13 (a). Bayesian information criterion (BIC) values plotted for number of clusters ranging from K = 1 to 40 derived from Discriminant Analysis of Principal Components (DAPC) (b). Delta K distribution of sub-STRUCTURE analysis (c) Delta K distribution of sub-STRUCTURE analysis obtained from NG, UL, NQ and DO.Delta (TIF)Click here for additional data file.