Bactrocera dorsalis, expanded throughout mainland China in the last century to become one of the most serious pests in the area, yet information on this process are fragmentary. Three mitochondrial genes  were used to infer the genetic diversity, population structure and demographic history of the oriental fruit fly from its entire distribution range in China. High levels of genetic diversity, as well as a significant correspondence between genetic and geographic distances, suggest that the invasion process might have been gradual, with no associated genetic bottlenecks. Three population groups could be identified, nevertheless the overall genetic structure was weak. The effective number of migrants between populations, estimated using the coalescent method, suggested asymmetric gene flow from the costal region of Guangdong to most inland regions. The demographic analysis indicates the oriental fruit fly underwent a recent population expansion in the Central China. We suggest the species originated in the costal region facing the South China Sea and gradually expanded to colonize mainland China, expanding here to high population numbers.The oriental fruit fly,  Bactrocera dorsalis (Hendel), is one of the most important pests on fruits and vegetables across South East Asia and the Pacific region The oriental fruit fly, The oriental fruit fly has significantly expanded its geographic distribution in the last century. Early records report its presence in 1912 in Taiwan After its initial recognition in Taiwan, the oriental fruit fly was reported in 1934 on Hainan Island, China Bactrocera dorsalis population genetics and phylogeography mostly addressed specific and/or geographically limited issues Despite the economic and ecological threats associated with the invasion of the oriental fruit fly, data on its phylogeography are scarce. An early study investigated the relationship between two laboratory and three wild populations Mitochondrial DNA, due to its accelerated rate of evolution, short coalescence time and simple maternal inheritance, has been used as a marker of choice for historical phylogeography and complements well with the information provided by microsatellite markers. Thanks to the possibility to reconstruct evolutionary relationships among haplotypes, the mitochondrial DNA is particularly informative to reconstruct historical processes, such as the identification of the region of origin of a species, the pathways of invasion and historical demography, and has been repeatedly used to study the spread of alien species With this study we specifically focus on Chinese populations, previously identified as a likely source for the species, to tackle the issue of the origin and range expansion of the oriental fruit fly. Specifically at issue are: a) a first exploration of Chinese diversity and definition of local genetic groups; b) the reconstruction of the major routes of expansion in mainland China and the definition of the demographic profile associated with the expansion, and c) the identification of the region of origin of the species and a re-evaluation of the different hypotheses proposed in the literature.Bactrocera dorsalis were collected from 12 locations covering the entire distribution range in China during years 2009–2010 using traps baited with Methyl Eugenal  were amplified from each and all individuals using primer pairs nad1-F (5′-TTTAGTTGCTTGGTTGTGTATTCC-3′)/nad1-R (5′-GAAAAAGGTAAAAAACTCTTTCAAGC-3′), cytb-F (5′-AACTCTTCACGCCAACGG)/cytb-R (5′-GGTCGTGCTCCAATTCAT-3′) and nad5-F (5′-TAACCCAATACACCTCCT-3′), nad5-R (5′-GGTAACTGCTGGGGTTTA-3′). Primers for nad1 are from Nardi et al. (2005), primers for cytb and nad5 were designed based on available complete mitochondrial genome sequences of tephritids, including B.dorsaliscytb), JF521167-JF521298 (nad1) and JF521299-JF521440 (nad5).Total DNA was extracted using the DNeasy Blood and Tissue Kit (QIAGEN) from 221 individual specimens. Three fragments of the mitochondrial genome  CT. An AMOVA hierarchical analysis of variance was performed using ALEQUIN to partition total variance in its components among groups, among populations and within populations, based on the groups inferred by the SAMOVA analysis. The correlation between genetic (FST/1-FST) and geographic distance matrices  Spatial analysis of molecular variance was performed using SAMOVA (ver. 1.0) eμ) were jointly estimated applying the Bayesian search strategy. Nem was calculated by multiplying these latter values. Four independent runs of MIGRATE, each consisting of one long chain of 100,000,000 generations with the initial 10,000 excluded as burn-in of the analysis, were conducted to assess consistency in the results, changing seed number at each run.The extent of gene flow between population pairs was studied using the coalescent-based strategy implemented in MIGRATE (ver. 3.2.7) D ad Fu's SF were calculated to test for neutrality. Population size before expansion (θ0), population size after expansion (θ1), population expansion time (τ), and sum of squared deviation (SSD) between observed and expected mismatch distributions were similarly calculated. All parameters were tested against the expected values under the hypothesis of a recent population expansion based on 1000 bootstrap replicates.The demographic history of all populations pooled together and of each of the three population groups identified by the SAMOVA analysis was studied using mismatch distributions in ARLEQUIN. Tajimas'nad1, cytb and nad5, respectively, or 164 after concatenation of the three markers for each individual.Collapsing of individual sequences led to the identification of 132, 143 and 142 unique haplotypes for genes nad1, cytb and nad5, respectively. Haplotype diversity (H) ranged from 0.9006 to 1, 0.8889 to 1, 0.8947 to 1 and 0.8947 to 1, nucleotide diversity (π) from 0.0093 to 0.0125, 0.0077 to 0.0147, 0.0105 to 0.0194 and 0.0081 to 0.0117, similarly in concatenated sequences, nad1, cytb and nad5. These figures suggest that all populations retain fairly high levels of genetic diversity.Basic descriptive statistics, calculated for each population based on concatenated sequences as well as each of the three genes independently, are shown in CT values in the SAMOVA analysis suggested 3 as the optimal number of population groups (FCT3 = 0.04231). The 12 populations were clustered as follows: Jiangjin, Wulong, Wanzhou, Xiushan, Huaxi, Nanning, Wuhan, Nanchang and Jianshui; Fuzhou and Guangzhou; Wenchang. These three groups correspond to three geographically well defined regions that we refer to as Central, South-East and Southern China in the following presentation (see Monitoring of Ftion see .CT) and within populations (FST) are highly significant, while differentiation among populations between groups (FSC) was not or marginally (nad5 only) significant  and inside populations , while genetic differentiation between populations inside each of the three groups identified is limited  . Accordinificant .nad1, cytb and nad5, respectively)   A as welctively)  B, C, D.nad1, H7 and H8 for cytb, H4 for nad5), with most remaining haplotypes that are found in one single population group, generally at low frequency and connecting to central haplotypes through few mutations. Some missing haplotypes  were inferred.Median Joining networks reconstructed from haplotypes of the three genes are shown in em, average gene flow inside population groups is 29.442 (range 0.778–51.673) and across population groups is 17.370 (0.322–56.664), i.e. high levels of overall gene flow are observed.The effective mutation scaled population size was estimated for each population and the amount of mutation scaled immigration rate in both directions was estimated for all 66 population pairs . High leD  and Fu's SF  based on concatenated sequences as well as on individual genes  as well as each individual gene  and individual genes . Individual genes (see The mismatch distribution of the 12 populations pooled together was compatible with the sudden expansion model based on concatenated sequences (Pgene see . Parametenes see . Parametenes see  are in a1) and before expansion (θ0), an estimate of the extent of population growth, is 12796× for the entire dataset and 5223× for the Central China group based on concatenated sequences. Individual genes, although with differing numerical estimates, similarly suggest a large population growth, from 33× to 7144× for the entire dataset and from 85× to actually infinite for the Central China group.The ratio between estimated effective population size after expansion  increased genetic drift associated to the temporarily reduced population size in founding colonies Chinese populations of the oriental fruit fly, nevertheless, seem to retain fairly high levels of genetic diversity, as exemplified by the high observed values of haplotype diversity. Noteworthy, this is observed even in populations such as Wuhan, Xiushan, Wulong, Wanzhou and Jiangjin that, based on collection records, established not longer than a decade ago and that display values of genetic variability actually higher than some of the oldest populations in the study set, Fuzhou and Guangzhou.Nevertheless, some characteristics of the oriental fruit fly and the ecology of the area have to be considered that could help explain why no loss in genetic variability is observed concurrent to the colonization process. Due to the high reproductive potential of this species and high dispersal capabilities, together with the relative absence of major geographical or ecological barriers to dispersal in the area, it is possible to envision how the oriental fruit fly might have gradually expanded without any significant or prolonged bottleneck. Furthermore, the relative abundance of suitable host fruits such as mango, carambola and guava, the large orange plantations in South China and Yangtze valley, and the relative uniformity and stability of a suitable tropical and subtropical monsoon climate in this area might have exerted little novel selective pressures during the range expansion in Central China.Furthermore, multiple introductions, or colonization of a given area from multiple sources, can counteract the drop in genetic variability associated with colonization or rescue a species from an actual loss in genetic diversity The oriental fruit fly may have, therefore, expanded quickly but gradually in mainland China, without the significant bottlenecks generally associated with a range expansion that takes place through invasive propagules of few individuals. This view is further in line with the high levels of gene flow and limited population differentiation observed (see below) and with the situation described by Aketarawong et al. Although some structure could be identified by the SAMOVA analysis, leading to the partitioning of the 12 populations studied in the three groups Central, South-East and Southern China, the overall level of differentiation was low. According to the analysis of molecular variance, more than 90% of variability was observed inside populations, with no or marginal differentiation between populations and some differences arising only between population groups. This is rather unexpected given the distances involved, with the area occupied by the Central group being roughly equivalent to Central Europe in size, but in line with other examples of highly vagile fruit fly species that have expanded in large areas in relatively recent times The median joining networks for the three genes, accordingly, did not describe any underlying structure, as networks are distinctively star-like and haplotypes sampled in the three regions, not to mention populations, appear randomly distributed. The Mantel test identified a significant, though not strong, correlation between genetic and geographic distance, suggesting a certain degree of isolation by distance with no major discontinuity.Taken together, these observations suggest a relative genetic uniformity of the oriental fruit fly in China. This is in line with the notion that the species has high dispersal capacities, with eggs and larvae that can disperse efficiently inside host fruits both under natural conditions  are not sufficient to interrupt or determine a significant limitation to gene flow. A similar situation has been described in the region of Yunnan, where weak genetic structure was observed among 14 oriental fruit fly populations despite the presence of three big rivers and mountain ranges running North-West to South-East in the area D and Fu's Fs values showed that the oriental fruit fly populations in mainland China do not fit a simple model of selective neutrality D and Fu's Fs estimators separately in the three groups indicated that the Central China group may have specifically undergone a regime of population expansion. The unimodal mismatch distribution The significant negative Tajiams'These results are concordant with trapping data in the area. Oldest presence of the species is reported from the area facing the island of Taiwan, where the species was first detected, and the island of Hainan. These two areas correspond to population groups South-East and South China, respectively, that based on our data show no sign of population expansion. In turn, the marked range expansion observed in the last few decades mainly interested mainland China up to the 32N parallel, a very large region corresponding to population group Central China, for which distinctive signs of population expansion could be described.Based on historical records and the genetic data presented here, it is therefore possible to reconstruct a process of fast and recent range expansion from the coastline area facing the South China sea, where the species has been likely established for a longer period of time, to a very large mainland region that has been colonized in the last few decades. Based on the high genetic diversity in the area and limited genetic structure (see above), we hypothesize that this process of colonization may be interpreted as a gradual, though fast, range expansion associated with high population numbers and population growth. The opposite scenario of a stepping stone model of repeated colonization events through numerically limited propagules may on the other the hand be excluded, as no trace of genetic bottlenecks and related drop in genetic variability is observed.The notion that local natural barriers seem to be rather ineffective in counteracting oriental fruit fly dispersal, as well as the potential distribution analysis, suggests that the process described here of population expansion in mainland China may interest other regions north of the 32N parallel in the next decades.Different hypotheses have been proposed about the geographic origin of the oriental fruit fly. While the current distributional range of the species is rather ample, from India to Hawaii encompassing all South-East Asia, historical records clearly show that marginal populations represent recent introductions, with the species being most likely East-Asian in origin. One initial hypothesis on the geographic origin of the oriental fruit fly, based on the earliest records of its presence in the island, is Taiwan Based on our extended sampling in mainland China, that greatly expands the current knowledge on local oriental fruit fly diversity beyond the region of Yunnan Further support for the costal region of Southern China being the source area for the species as a whole comes from a joint interpretation of our results and those presented by Aketarawong et al. Figure S1Scatter plots of genetic distance vs. geographic distance  for pairwise population comparisons. A: concatenated sequences, B: nad1, C: cytb, D: nad5.(TIF)Click here for additional data file.Table S1Genetic diversity indices. V, number of variable sites; n, number of unique haplotypes; H, haplotype diversity; π, nucleotide diversity; k, average number of nucleotide differences.(DOC)Click here for additional data file.Table S2Estimates of population size and effective immigration rate between populations pairs. Θ: mutation scaled effective population size; M: mutation scaled effective immigration rate. In parentheses the 95% HPD intervals. Instances of asymmetrical gene flow are indicated in bold. The source population is indicated in columns, the target population in row.(DOC)Click here for additional data file.


Exyra semicrocea (Lepidoptera: Noctuidae), using mitochondrial cytochrome c oxidase subunit I (COI) gene sequences. Examination of 221 individuals from 11 populations across eight southeastern US states identified 51 unique haplotypes. These haplotypes belonged to one of two divergent (∼1.9–3.0%) lineages separated by the Mississippi alluvial plain. Populations of the West Gulf Coastal Plain exhibited significant genetic structure, contrasting with similarly distanced populations east of the Mississippi alluvial plain. In the eastern portion of the Coastal Plain, an apparent transition zone exists between two regionally distinct population groups, with a well-established genetic discontinuity for other organisms coinciding with this zone. The structure of E. semicrocea appears to have been influenced by patchy pitcher plant bog habitats in the West Gulf Coastal Plain as well as impacts of Pleistocene interglacials on the Apalachicola-Chattahoochee-Flint River Basin. These findings, along with potential extirpation of E. semicrocea at four visited, but isolated, sites highlight the need to consider other endemic or associated community members when managing and restoring pitcher plant bog habitats.Pitcher plant bogs, or carnivorous plant wetlands, have experienced extensive habitat loss and fragmentation throughout the southeastern United States Coastal Plain, resulting in an estimated reduction to <3% of their former range. This situation has lead to increased management attention of these habitats and their carnivorous plant species. However, conservation priorities focus primarily on the plants since little information currently exists on other community members, such as their endemic arthropod biota. Here, we investigated the population structure of one of these, the obligate pitcher plant moth  Currently, global biodiversity losses are occurring at an alarming rate, exceeding anything in the geological past Pinus palustris) forests in this area pitcher plant bogs. These bogs are typically found in wet pine flatwoods or seepage slopes and are characterized by wet, sandy and low nutrient soils Sarracenia (pitcher plants) are listed in The Convention on International Trade in Endangered Species .Among the most critically endangered in the United States is the longleaf pine ecosystem, which historically occupied most of the southeastern Coastal Plain Sarracenia sp. and populations Sarracenia alata (the winged pitcher plant), which ranges from eastern Texas to western Alabama, identified a phylogeographic break and high genetic divergence centered on the Mississippi River alluvial plain, with additional population structure on either side of the break Many studies on carnivorous plants have focused on the investment in and evolution of carnivory Sarracenia sp. Exyra semicrocea (Lepidoptera: Noctuidae) ranges across the United States southeastern Coastal Plain from eastern Texas to southern Virginia Sarracenia sp. like S. alataE. semicrocea is oligophagous, feeding exclusively on Sarracenia sp., and its life cycle occurs entirely inside pitcher plant leaves, from oviposition through larval development and mating. Because of this well documented relationship, we believe that the E. semicrocea/Sarracenia complex represents an ideal system for examining ecological and biogeographical questions pertaining to pitcher plant bogs and their endemic biota. Here, we examined populations of E. semicrocea across the southeastern Coastal Plain to address the hypothesis that the genetic structure of this moth is consistent with the major finding of Koopman and Carstens S. alata, namely that a strong genetic break occurs across the geographic region occupied by the Mississippi River alluvial plain.While research and conservation efforts have primarily focused on the carnivorous plants, less attention has been given to other constituents of pitcher plant bogs. This is unfortunate because these bogs support a complex and intimately associated biotic community. For example, a number of arthropods have evolved characteristics for inhabiting This study was conducted in accordance with all United States laws and internationally accepted protocols with relevant collecting permits from The Nature Conservancy , National Estuarine Research Reserve Sites , Big Thicket National Preserve Site , and Okefenokee National Wildlife Refuge .E. semicrocea were collected across eight southeastern Coastal Plain states between May and August 2010 ; thus, samples were acquired from 11 localities. At each site, attempts were made to collect a minimum of 24 E. semicrocea individuals, however, the actual number acquired per population ranged from 3–24  gene. The primer pair for subsequent polymerase chain reaction (PCR) was LCO1490 (5′-GGTCAACAAATCATAAAGATATTGG-3′) and HCO2198 (5′-TAAACfTTCAGGGTGACCAAAAAATCA-3′) 2, 200 mM dNTPs, 1 U Taq DNA polymerase and 0.4 mM of each primer. PCR was conducted with a PTC-100 Thermal Cycler (MJ Research) using the following cycling profile: initial denaturing step of 94°C for 5 min, 15 cycles of 94°C for 45 s, 40°C for 45 s, 72°C for 60 s; 25 cycles of 94°C for 45 s, 55°C for 45 s, 72°C for 60 s, and a final extension of 72°C for 5 min. Five µl aliquots of PCR product from each reaction were electrophoresed in 1% agarose gels, stained using ethidium bromide, and viewed using shortwave (265 nm) UV to confirm PCR success. Prior to sequencing, amplification products were purified with Montage™ PCR Filter Units (Millipore) according to the manufacturer's specifications. Sequencing was conducting in both directions using Big-Dye Terminators v.3.1 and analyzed on a PRISM 3100  at the Auburn University Genomics and Sequencing Laboratory or the University of Washington Genomics Unit. Forward and reverse sequence chromatograms were assembled and any ambiguities corrected using Sequencher v4.8 . All finished sequences were aligned manually using SE-AL version v2.0a11 .Specimens of ust 2010 . Exyra sh) and nucleotide (π) diversities calculated by Nei's nn or ‘nearest-neighbour’ statistic ST statistics based on haplotype frequency and molecular diverfogence were generated using Arlequin v3.1 E. semicrocea were collected from four species of Sarracenia , Snn and pairwise ΦST statistics were also conducted to test whether E. semicrocea exhibited genetic structure between host plant species. A lack of significant structuring was detected among these comparisons (data not shown), thus the results presented here focus specifically on genetic structure between geographic sites. To quantify the spatial distribution of genetic variation in E. semicrocea, analyses of molecular variance (AMOVAs) CT, designated by the resulting population structure from pairwise ΦST and Snn statistics); (ii) among populations within groups (ΦSC); and (iii) within populations (ΦST). The significance of both the pairwise ΦST statistics and AMOVAs were assessed with 10,000 permutations. All of the above tests, when applicable, were conducted under Tamura and Nei's Estimates of population genetic diversity were obtained using haplotype (efm) versus recent divergence between pairs of E. semicrocea populations, a Markov Chain Monte Carlo (MCMC) based analytical method was utilized as implemented in mdivhttp://cbsuapps.tc.cornell.edu/). Analyses were conducted under the finite-site mutation model, which incorporates the possibility of multiple mutations per site, differences in nucleotide frequencies and the presence of transition/transversion bias. Three independent runs were conducted with the following conditions: maxM = 50, maxT = 10, length of Markov chain = 2×106 cycles, burn-in time = 5×105 cycles. Different random seeds were used in each run to check for consistency in the estimates.To discriminate between the relative effects of ongoing migration  assessed by 10,000 permutations.Networks were generated to depict relationships among Exyra semicrocea were sampled from 11 localities across a range of ∼1,700 km. Overall, 51 (8.1%) nucleotide positions across the analyzed 630 bp COI fragment were variable and no stop codons were encountered. Nucleotide differences at the majority of these sites (50) represented ‘silent’ substitutions , with the remaining one being a non-synonymous substitution to an amino acid with similar biochemical properties (data not shown). These patterns suggest that all sequences were derived from mitochondrial copies of COI rather than nuclear copies of mitochondrial derived genes (numts) A total of 221 h) and nucleotide (π) diversities ranging between 0.51–0.94 and 0.00097–0.00434, respectively  via mdiv further support this, suggesting no female  migrants (M = 0) had been exchanged between the BT and KN populations in the recent past (data not shown). An analysis of molecular variance (AMOVA) found a significant proportion of genetic variation occurring between populations . Additionally, estimates for among populations within groups and within populations were significant (P<0.01) and accounted for ∼24% and ∼21% of the total observed genetic variation, respectively  and among groups , with a small  but significant (P<0.001) component among populations within groups (r = 0.542) and significant (P = 0.013) correlation between genetic and geographical distances. Because of the distinct geographic clustering observed among specific eastern populations, a 3-way Mantel test was conducted in order to evaluate the relative contributions of contemporary isolation-by-distance versus historical isolation to the overall pattern. For this analysis, a third binary  matrix was created and the 3-way Mantel test detected a significant correlation  between genetic distance and this binary  but no significance correlation  between the geographic and genetic distance matrices. These results suggest that historical isolation, rather than geographic distances, is most likely responsible for segregating the eastern populations into two primary groups. Taken together, these results indicate a transition, approximately located in the western Florida panhandle/southern Alabama between WB and AP  between all eastern populations were conducted relative to both AP and WB . Population comparisons on either side of the potential transition zone  revealed plateaus in the distribution of posterior probabilities of M (nn and ΦST statistics for these populations (see above). Conversely, clear peaks in the distribution of posterior probabilities for M were identified in population comparisons across the proposed transition zone  also significant for Tajima's D  possess appreciable divergence from, and a clear break between, eastern populations across the Mississippi River alluvial plain. The identification of the Mississippi River alluvial plain as a significant barrier has been documented for a number of species Pinus palustris (longleaf pine), Dichromona colorata (white topped sedge), Drosera capillaris (pink sundew), Rhexia alifanus (a meadow beauty), and Utricularia sp. (bladderworts) E. semicrocea in this region  S. alata minimally overlaps with other Sarracenia sp. throughout its range E. semicrocea in Texas and Louisiana, it is not surprising that this barrier has influenced a similar pattern in both species. Interestingly, the genetic structure of microbial communities found within the pitchers of S. alata also mirrors that of the plant E. semicrocea (this study). Taken together, these patterns highlight the biological uniqueness and intimate associations among constituents of pitcher plant bog habitats in the WGCP.Populations of E. semicrocea contrasts with an absence of genetic structure among similarly (or more widely) distanced populations in the east .Another explanation for the significant structure among western E. semicrocea east of the Mississippi River alluvial plain are genetically structured into three distinct groups. These groups are situated within the East Gulf Coastal Plain , the South Atlantic Coastal Plain , and Conecuh National Forest (CF) in the proposed transition zone between the groups  exhibited in some cases weak, but significant, genetic structure  of pi River . These dtructure . One potE. semicrocea, are most likely fire adapted E. semicrocea overwintering only as 4th or 5th instar larva E. semicrocea was not found at four sites  visited for this study in spite of previously being reported as occurring at these locations (pers. comm. D. Folkerts and M. Hodges). We propose that previous winter burns conducted at these four sites in conjunction with isolation and fragmentation may have contributed to the potential extirpation of their E. semicrocea populations. This is alarming as there are other endemic pitcher plant arthropods that are considerably less common, and with potentially weaker dispersal capabilities, that may also be adversely impacted by such management techniques. This highlights the need for additional research specifically addressing the effects of fire on other constituents of pitcher plant bog communities and in the interim and until more information is gathered, it may be prudent to maintain unburned habitat during prescribed fire events (preferably during the growing season) as refugia for community members susceptible to such management practices.As mentioned previously, pitcher plant bogs of the southeastern United States Coastal Plain possess the most diverse assemblages of carnivorous plants in the world E. semicrocea has on Sarracenia spp. , this species has been one of the more studied E. semicrocea as encompassing six distinct population groups within two divergent lineages  is of importance and should be considered when developing management and conservation strategies for pitcher plant bogs in the southeastern United States Coastal Plain. In this context, the three populations of E. semicrocea west of the Mississippi River  are distinctive due to their strong genetic structure and this is further supported by the comparative phylogeographies of E. semicrocea, S. alata, and the microbial community within the pitchers representing unique and coevolving units in the WGCP (see above). Thus, steps should be taken to maintain the distinct genetic diversity at each of these sites. Similarly, the three population groups  of E. semicrocea in the eastern portion of its range deserve comparable attention. Overall, the two E. semicrocea lineages  could minimally be considered Evolutionary Significant Units (ESUs) of the species. The recognition of ESUs, defined as one or a set of populations with a distinct evolutionary heritage, can aid managers in prioritizing sites and activities related to conservation Although pitcher plants have received attention with several species protected and others listed as endangered, threatened or of special concern, their arthropod inhabitants have received little or no conservation consideration. Because many of these arthropods are obligate associates of pitcher plants, they have likely suffered equivalent declines. Despite the fact that none have official protection status and the roles and importance that these endemic arthropods play within these habitats are poorly understood, conservation efforts should ideally strive to protect all components of threatened ecosystems. Due to the obvious effects that Table S1Distributions of Exyra semicrocea haplotypes by sampling localities/populations across the southeastern United States Coastal Plain.(DOC)Click here for additional data file.


To investigate the distribution of 17 Y-short tandem repeat (STR) loci in the population of the Cukurova region of Turkey.In the period between 2009 and 2010, we investigated the distribution of 17 Y-STRs in a sample of 249 unrelated healthy men from the Cukurova region of Turkey. Genomic DNA was extracted with InstaGene matrix and Y-STRs were determined using the AmpFISTR Yfiler PCR amplification kit. Gene and haplotype diversity values were estimated using the Arlequin software. To compare our data to other populations, population pairwise genetic distances and associated probability values were calculated using the Y Chromosome Haplotype Reference Database Web site software.st = 0.025, P = 0.018). Comparisons were also made with the neighboring populations using analysis of molecular variance of the Y-STR loci genetic structure and our population was nearest to Lenkoran-Azerbaijani  and Iranian Ahvaz population , followed by Greek  and Russian  population. Other countries like Portugal, Spain, Italy, Egypt, Israel , and Taiwan showed a high genetic distance from our population.At 17 Y-STR loci we detected 148 alleles. The lowest gene diversity in this region was 0.51 for DYS391 and the highest 0.95 for DYS385a/b. Haplotype diversity was 0.9997 ± 0.0004. We compared our data with haplotype data of other Turkish populations and no significant differences were found, except with Ankara population (ΦOur study showed that Y-STR polymorphisms were a powerful discrimination tool for routine forensic applications and could be used in genealogical investigations. Human Y chromosome short tandem repeats (Y-STR) are tandemly repeated regions of 2-7 base pair units in the non-recombining region of human Y chromosome. Since human Y-STR markers are inherited without recombination, they are transmitted from father to son unchanged. This makes the study of Y chromosome polymorphisms very useful, especially in population histories, forensic applications, and paternity analysis ,2.www.yhrd.org). The International Society of Forensic Genetics recommended the use of regional Y-STR haplotype databases to verify that no population sub-structure exists before pooling data from different regions . These amplification reactions were performed using a GeneAmp PCR System 9700 (Applied Biosystems). The ABI 3130 Genetic Analyzer (Applied Biosystems) was used for genetic typing. Allele designations were based on the comparison with the allelic ladders included in the AmpFISTR Y-filerTM kit (Applied Biosystems), using the Gene Mapper software, version 3.2.Blood samples were collected from 249 unrelated healthy and voluntary donors from the Cukurova region of Turkey. We obtained the samples from the Balcali Hospital staff and from the casework in our forensic laboratory. Genomic DNA was extracted from 6-µL whole blood samples by using recommended protocol with InstaGene matrix  or Qiagen DNA micro kit . Polymerase chain reaction amplification was performed following manufacturer’s instructions . The loci DYS19, DYS389I, DYS389II, DYS390, DYS391, DYS392, DYS393, DYS385a/b, DYS438, DYS439, DYS437, DYS448, DYS456, DYS458, DYS635, and Y-GATA H4 were analyzed using the AmpFiSTR Y-filerBlind testing and evaluation was performed according to the quality assessment scheme provided by the YHRD. After a successful certification, the data in this study from Turkish populations were submitted to YHRD, and YA003668 accession number was received. The study was approved by the Ethics Committee of the University of Cukurova, Faculty of Medicine.st) and associated probability values  were calculated using analysis of molecular variance (AMOVA), with an online YHRD tool /πt, where πt is the nucleotide sequence diversity across the entire set of populations and πs is the average nucleotide sequence diversity within populations . The DYS385 marker was not included in the population comparisons, because two alleles were not assigned to the individual locus . Significance level was set at P < 0.05.Gene and haplotype diversity values were estimated using the Arlequin software ,7. In orHRD tool . Pair-wiulations ,10. To tWe detected 148 alleles at the 17 Y-STR loci in 249 samples. Using the present set of Y-STR markers, 240 different haplotypes were identified, 232 of which were unique and 7 were found in 14 individuals  and 1 was found in 3 individuals. The observed haplotype diversity was 0.9997 ± 0.0004.The most polymorphic marker was DYS385a/b with a genetic diversity (GD) value of 0.95 and the least polymorphic was DYS391 with a GD value of 0.51. Apart from DYS385, the loci DYS458, DYS635, and DYS389II showed the highest GD values. Different loci had a different number of alleles, with the most recorded alleles at DYS458.A number of intermediate alleles was seen at DYS458 – 12.2, 16.2, 17.2, 18.2, 19.2, and 20.2. The frequency of intermediate alleles at DYS458 was 16.8% of the samples. Only one duplicate allele  was observed at DYS19 locus. One null allele and one intermediate allele (17.2) were observed at DYS448 .st = 0.033, P = 0.025) and the Cukurova region   were available for comparison. When these data were compared using AMOVA with each other and with our results from the Cukurova region, there was no significant difference among the results from Bulgaria, Germany, and the Cukurova region. When they were compared to the results from the Ankara region, there was a significant difference between Ankara population and the groups from Bulgaria (Φ= 0.018)  11-13).-13.st = st = 0.012, P = 0.068) and Iranian-Ahvaz  than Greek  and Russian  population samples. A high genetic distance was observed between our population and other geographically more distant countries like Portugal, Spain, Italy, Egypt, Israel , and Taiwan. Egypt  was closer than Spain , Portugal , Italy , Israel , and Taiwan  , Italy, Spain, Iran , Greece, and Taiwan, for which the same set of Y-STR loci was available ,14-21. A = 0.00) .Our analysis of genetic polymorphisms of 17 Y-STR loci in the Cukurova population showed a high degree of haplotype diversity in the Cukurova population. It also found DYS385 to be one of the most informative markers.In the last fifteen years, genetic structure of Y chromosome has been determined in several countries -24. A feIt seems more complex to construct a Y-STR database than that of unlinked autosomal markers, since the whole haplotype must be a type for each sample. The practical value of Y-STR databases will be greatly increased with typing of each individual at as many loci as possible, as opposed to typing a great number of individuals at a small number of loci. In order to increase the discrimination power of Y chromosome haplotypes, an increased number of Y-STRs is used. Our results are important since we examined a large number of loci and had a big sample size. Such information is of crucial importance for genetic epidemiology and population genetic purposes ,29.The most striking locus in our population data was DYS458, at which a number of intermediate alleles were observed. Intermediate alleles of DYS458 were most frequently found in Northern and the Eastern Africa and the Caucasus ,30,31. TWe compared our results with 3 Turkish population groups  -13. We cWe also compared it to the neighboring populations by using 15 loci haplotypes. The greatest similarities were found with the Lenkoran-Azerbaijani and Iranian-Ahvaz population, followed by Russian and Greek population. This similarity may be a result of historical and demographic reasons. Lack of similarity with other neighbors may be a result of genetic differences and/or a small number of studied individuals. Significant differences were observed from nearly all populations from the Far East.This study does not reflect the genetic structure of whole Turkey, which could be considered a limitation. Furthermore, the comparison with the other populations was based on the samples available in the YHRD, which were occasionally rather small and thus perhaps not representative of their entire populations. Further studies are needed to determine the genetic structure of all Turkish populations, so that these data can be used for forensic and genealogical investigation and to gain understanding of some of the major demographic and historical events in this region. However, the analysis of Y-STR polymorphisms in our population using Yfiler provides a powerful discrimination tool for routine forensic applications and may serve in genealogical investigations.


Drosophila melanogaster and its close relatives have been extremely important model species in the development of population genetic models that serve to explain patterns of diversity in natural populations, a major goal of evolutionary biology. A detailed picture of the evolutionary history of these species is beginning to emerge, as the relative importance of forces including demographic changes and natural selection is established. A continuing aim is to characterise levels of genetic diversity in a large number of populations of these species, covering a wide geographic area. We have used collections from five previously un-sampled wild populations of D. melanogaster and two of D. simulans, across three continents. We estimated levels of genetic diversity within, and divergence between, these populations, and looked for evidence of genetic structure both between ancestral and derived populations, and amongst derived populations. We also investigated the prevalence of infection with the bacterial endosymbiont Wolbachia. We found that D. melanogaster populations from Sub-Saharan Africa are the most diverse, and that divergence is highest between these and non-Sub-Saharan populations. There is strong evidence for structuring of populations between Sub-Saharan Africa and the rest of the world, and some evidence for weak structure amongst derived populations. Populations from Sub-Saharan Africa also differ in the prevalence of Wolbachia infection, with very low levels of infection compared to populations from the rest of the world. Understanding the forces that shape patterns of genetic diversity within and between population and species is a major goal of evolutionary biology. Contemporary patterns of genetic variability result from a combination of evolutionary processes in the history of a population or species, including demographic size changes and the action of natural selection Drosophila melanogaster and its close relatives have been important model species in the development of population genetic models that serve to explain patterns of genetic diversity in natural populations D. melanogaster populations from around the species range, and models that include the effects of population size changes and the impact of both positive and negative selection have been developed to attempt to explain patterns of diversity and to determine the evolutionary history of populations , 101 for Ghana , 63 for France , 18 for Morocco  and 38 for Sussex . The D. simulans sample from Georgia consisted of 19 lines and 22 additional females, giving a final sample size of 41. We also included a single female from each of five D. simulans isofemale lines from the French population, which were originally identified as D. melanogaster. Although the sample size for this population is small and thus estimates of diversity and divergence should be treated with caution, five individuals provide information about ten wild alleles, and were therefore considered worth analysing. Species identification for each of the 323 females included in the study was confirmed using a species-specific PCR.Five populations of Wolbachia infection using PCR primers to amplify part of the wsp gene 2), 0.5 µL of dNTPs (10mM), 1 µL of each primer (10 µM) and 0.1 µL of Taq polymerase . Cycling conditions on a G-Storm thermal cycler were 94°C for 5 minutes followed by 35 cycles of 94°C for 30 seconds, 55°C for 45 seconds and 72°C for 1 minute, with a final extension step of 72°C for 5 minutes. Amplification for all samples, and for known positive and negative control samples, were checked by running on a 1% agarose gel. A subset of individuals were tested twice to confirm their infection status.All the females included in the study were assayed for the presence of D. melanogaster genome, of which three were X-linked and six were autosomal (two on the second chromosome and four on the third chromosome). These loci were a subset of those developed by Orozco-terWengel et al 2 (25mM), 0.4 µL of dNTPs (10mM), 0.2 µL of each primer (20 µM) and 0.4 µL of hot start Taq polymerase . PCR products were analysed on an ABI3730 DNA Analyzer (Applied Biosystems) and alleles were scored using GeneMapper software version 4.0 (Applied Biosystems).All females were genotyped for nine microsatellite loci from around the MSA), version 4.05 HE), or gene diversity, was calculated both using the full dataset and by taking the average of 200 resampled datasets in which one of the two alleles at each locus is randomly discarded. This was done in order to examine whether there had been any impact on genetic diversity of the five generations of inbreeding in the samples that came from isofemale lines. Allelic richness was estimated using the minimum number of individuals successfully genotyped per locus MSA was also used to calculate pairwise estimates of FSTP-values were adjusted for multiple testing using a Bonferroni correction Several measures of population diversity and divergence were calculated using Microsatellite Analyser   = 1 to K = 8. In order to assess whether the burn-in and run lengths were sufficient, we checked that key parameters such as FST and alpha (the Dirichlet parameter representing the degree of admixture) converged before the end of the burn-in period, and that the variation in log likelihoods across multiple runs of the program at each K value was smaller than the variation between runs at different K values. Following the removal of the Ghana population from the analysis , there were highly statistically significant differences between D. melanogaster populations (P<10−16). This pattern is not driven entirely by the results for the Ghana population (see below); when this population is removed there is still statistically significant evidence for heterogeneity in infection levels (P = 0.026).D. simulans sample showed the highest level of infection, with over 90% of individuals from Georgia, USA being infected. In addition, all of the French D. simulans were infected, although there were only five individuals in this sample, so this prevalence level should be interpreted with caution. These values are close to the expected equilibrium frequency for the Wolbachia strain that infects D. simulans, wRi, which is predicted to be around 94% infection D. simulansThe D. melanogaster sample, Wolbachia was highly prevalent in all of the non-Sub-Saharan Africa populations, with around 50–80% of individuals being infected. This is somewhat higher than previous estimates of Wolbachia prevalence in D. melanogaster, which suggest around 30–45% of stock centre lines are infected Wolbachia in Ghana, suggesting that prevalence is less than 1% in this population. Although estimates from sub-Saharan Africa are relatively scarce, this is in disagreement with prevalence estimates from stock centre lines of African origin Wolbachia is present in all of our sampled populations Within the Wolbachia causes only weak cytoplasmic incompatibility in the wild, the infection is predicted to be lost from populations, unless it also confers a fitness benefit to the host Wolbachia infection on fitness in D. melanogaster, although these effects vary depending on a number of factors, including the sex and the genetic background of the host Wolbachia-induced cytoplasmic incompatibility also varies with age, and can be much stronger when infected males mate very young, which has also been postulated to explain the persistence of infection in wild populations Wolbachia infection vary between populations, or if there are differences in mating behaviour, such that males tend to mate at different ages in different populations, this might lead to marked differences in infection level between populations. Differences in the relative levels of genetic diversity found on the X chromosome and autosomes of D. melanogaster from African and non-African populations Wolbachia infection within these populations.Since infection status has not been assayed in the Ghana population before, it is unclear whether this low level of infection is a result of a prior infection having been lost, or because the population has not previously been infected. Given that D. melanogaster populations could be infected with different strains of Wolbachia, although surveys of long-term lab stocks originating from five continents  suggest that one of these strains has replaced all others over the last ∼50 years wsp gene has been shown to be uninformative in distinguishing between different Wolbachia strains One additional factor that should be considered is that the D. melanogaster and two populations of D. simulans, a total of almost 3000 genotypes. For comparison, we added data on genetic diversity at the same loci for the Victoria Falls Zimbabwe (Zw) population studied by Orozco-terWengel et al We generated microsatellite data for nine loci in 323 individuals from five populations of D. melanogaster sample, the Ghanaian population consistently shows the highest levels of genetic diversity for all measures, exhibiting a mean expected heterozygosity almost double that of the least diverse population, Morocco. The mean number of alleles per locus and the allelic richness is also markedly higher in Ghana than in all other sampled populations, and whilst this population does have somewhat lower gene diversity than the East African Zimbabwe population, its allelic richness is very similar and the mean number of alleles per locus is higher than in Zimbabwe, probably reflecting the large sample size for the Ghanaian population. This is in general agreement with previous studies showing that West African populations have levels of diversity higher than populations outside Sub-Saharan Africa As expected, within our D. melanogaster population has a diversity level intermediate between the European and Sub-Saharan Africa populations, but similar to D. simulans from the same location. This is somewhat surprising given that D. simulans is generally thought to have a larger effective population size than D. melanogaster, based on reports that it has higher levels of genetic diversity D. simulans sample does indeed exhibit higher diversity than the French D. melanogaster sample, although this is based on a small sample size for D. simulans. It should also be noted that the microsatellite markers used were developed in D. melanogaster, and thus were selected on the basis of being polymorphic in that species. They therefore may not be expected to be as polymorphic in another species, although comparison of the expected heterozygosity for individual loci in the two species from the Georgia population shows that D. melanogaster exhibits higher diversity at five loci, and D. simulans at four (data not shown).The North American FST estimates between all pairs of populations. Significant levels of differentiation were found between all pairs of populations except between the two D. simulans populations , and between the French and Moroccan D. melanogaster populations. We found varying levels of divergence between different populations of D. melanogaster; FST estimates are highest between Ghana and the European/North African populations (∼0.24), but are considerably lower between Ghana and the North American population (0.18), suggesting that North American populations are more closely related to ancestral African populations than European and North African populations are. This has been reported previously and is thought to result from continued or more recent admixture from Africa into North American populations Estimates of genetic differentiation between populations are shown in D. melanogaster are consistent with several previous estimates of divergence between African and European populations FST estimates between the Moroccan population and the Ghana population are also higher than some previous estimates of population differentiation between Sub-Saharan African and North African populations  from 1 to 8. K. Whilst there is a large increase in log likelihood between the model run under K = 1 and K = 2, for values of K greater than 2, the increase in likelihood is much smaller, particularly when K is greater than 3. The Structure manual suggests that the best estimate of the true value of K is to be found at the point where the likelihoods begin to plateau, suggesting that subdivision of our samples into two or three genetically distinct clusters allows us to capture the major patterns of structure in the data. FST results reported above.We investigated these patterns in more detail using a Bayesian approach to detect any genetic structure between the five populations of FST results suggest that there is low but significant genetic differentiation between individuals from different locations. It is therefore possible that there is weak structure that the standard models implemented in Structure are unable to detect. For example, the results above suggest that there may be differences between the USA and European/North African populations, and some previous studies find that these represent separate populations based on similar Bayesian analyses of population structure .We therefore used the LOCPRIOR model to investigate this further, by using information about the sampling locations of individuals to improve the ability of the program to detect weak population structure. This is a recent development of the program that allows the sampling location of each individual to be included in the model and, in the event that the location is informative in terms of detecting population structure, to make use of this information ters see . When K K = 3, we see more pronounced differences between these populations. Although there is not strong statistical support for division of these populations into three clusters, there are some individuals very strongly assigned to some clusters, and the proportion of individuals assigned to each cluster is asymmetric, and these are indications that there may be real population structure. If indeed there are three distinct clusters, one cluster again makes up around 45% of the ancestry of individuals from Georgia, 5% for individuals from Morocco and the UK and 1% for individuals from France. A second cluster makes up around 40% of the ancestry of most French individuals, 15–20% of the ancestry of individuals from Georgia and Morocco, but only about 5% of the ancestry of UK individuals. The final cluster makes up the remaining ∼60% of ancestry of French individuals, 75% of ancestry in Morocco, ∼40% of ancestry in the USA population and approximately 90% of the ancestry in individuals from the UK population. These patterns actually agree reasonably well with the FST results reported earlier, with the Georgia population being the most distinct, little genetic differentiation between France and Morocco, and the UK population falling in the middle. Thus, by using information about the sampling locations of individuals in the sample, we find clear evidence that the population from Georgia is distinct from the remaining non-Sub-Saharan Africa populations, and patterns suggestive of there being further differentiation between the UK population and another group comprising the French and Moroccan populations.However, when we examine the ancestry assignments for D. melanogaster and two of D. simulans spread across Sub-Saharan Africa, North Africa, Europe and North America. We also examined the prevalence of Wolbachia infection in each of these samples. We found very high rates of infection with Wolbachia in D. simulans, consistent both with other estimates of infection prevalence and theoretical predictions of the equilibrium frequency of infection in this species D. melanogaster, we find infection levels of 50% or greater in populations from North Africa, Europe and the USA, consistent with some previous estimates from wild populations Wolbachia infection in the Ghana population, contrary to previous estimates from other Sub-Saharan African populations Wolbachia infections, which are known to change very rapidly  FST results and with previous studies, we can be relatively confident in their reliability. In general, our results are consistent with previous studies, in that we find that Sub-Saharan Africa individuals cluster into one population that is distinct from the samples from the rest of the world (Structure methods FST results. Thus there may be significant genetic structuring of populations outside of Sub-Saharan Africa and amongst populations in Europe.Finally, we used a Bayesian analysis to detect any genetic structure between the five populations of he world . We also


Many native Atlantic salmon populations have been invaded by domesticated escapees for three decades or longer. However, thus far, the cumulative level of gene-flow that has occurred from farmed to wild salmon has not been reported for any native Atlantic salmon population. The aim of the present study was to investigate temporal genetic stability in native populations, and, quantify gene-flow from farmed salmon that caused genetic changes where they were observed. This was achieved by genotyping historical and contemporary samples from 20 populations covering all of Norway with recently identified single nucleotide polymorphism markers that are collectively diagnostic for farmed and wild salmon. These analyses were combined with analysis of farmed salmon and implementation of Approximate Bayesian computation based simulations.2 = 0.47 P < 0.001. Genetic isolation by distance existed within the historical and contemporary data sets, however, the among-population level of divergence decreased with time.Five of the populations displayed statistically significant temporal genetic changes. All five of these populations became more similar to a pool of farmed fish with time, strongly suggesting introgression of farmed fish as the primary cause. The remaining 15 populations displayed weak or non-significant temporal genetic changes. Estimated introgression of farmed fish ranged from 2-47% per population using approximate Bayesian computation. Thus, some populations exhibited high degrees of farmed salmon introgression while others were more or less unaffected. The observed frequency of escapees in each population was moderately correlated with estimated introgression per population RThis is the first study to quantify cumulative introgression of farmed salmon in any native Atlantic salmon population. The estimations demonstrate that the level of introgression has been population-specific, and that the level of introgression is not solely predicted by the frequency of escapees observed in the population. However, some populations have been strongly admixed with farmed salmon, and these data provide policy makers with unique information to address this situation. Salmo salar L.) was started in Norway in the early 1970′s, and now represents a globally significant industry. Each year, hundreds of thousands of farmed salmon escape into the wild [Aquaculture production of Atlantic salmon  located on 35 commercial farms spanning from the south to the far north of Norway, in addition to eight escapees captured in the sea. These samples were picked from approximately 6000 farmed fish that had been previously genotyped with microsatellites in association with a forensic service conducted by the Institute of Marine Research to identify the farm of origin for escaped salmon for the legal authorities ,38-41. TDNA was extracted in 96-well format using Qiagen DNeasy blood and tissue kit. Each plate contained two or more negative control wells. DNA aliquots of these samples were sent to the Centre for Integrative Genetics (CIGENE) in Norway for SNP (n = 99) analysis using a Sequenom platform. A list of the markers, their NCBI assay details and linkage map positions are available . It is furthermore important to note that seven of the wild populations used in the marker identification study conducted by Karlsson et al. (2011) overlap with populations in the present study . While this can potentially cause ascertainment bias, this has been considered when interpreting results. In addition to the 70 diagnostic SNPs, a further 29 SNPs were also genotyped. These were selected as putatively neutral SNPs that are known to be polymorphic in Norwegian salmon.Seventy of the SNP markers genotyped here were selected from the panel of SNPs that have been suggested to be collectively diagnostic for farmed and wild Norwegian salmon . These 7In order to investigate temporal genetic stability in the 20 populations, and the direction of any potential changes, the data set was organized into the historical and contemporary samples. The pool of farmed salmon was only used for specific tests and the simulations to quantify gene flow (see below). For all computations, the data sets were divided into the loci that are collectively diagnostic between farmed and wild Atlantic salmon and the randomly selected loci.ST values  and compute significance levels associated with these tests using the Fisher’s exact method as implemented in the program. FST values were all computed using the Weir and Cockerham estimator [ST values for the historical and contemporary sets of samples were computed from the distribution of 1000 FST values calculated from 1000 bootstraps where 35% of the individuals from each population were randomly re-sampled. This latter test was computed in the program R (R development team).Genotypic data was first organized in the program MSA, coding the nucleotides A, C, G and T as alleles 1–4 . MSA wasstimator . ConfideP < 0.05, P < 0.001 and Bonferroni corrected threshold levels.Population-specific summary statistics, i.e., numbers of alleles, heterozygosities, numbers of deviations from Hardy Weinberg Equilibrium (HWE), and the numbers of times that linkage disequilibrium (LD) was observed between pairs of loci within each sample were computed in the program GENALEX v6.  using prST values. This was conducted using the program’s default values in order to investigate spatio-temporal population genetic structure in addition to the direction of any observed temporal changes in relation to the pool of farmed fish. Isolation by Distance (IBD) analyses on the historical and contemporary set of samples was conducted using the Mantel test as implemented in the R package “Vegan”. The test was computed with input data from a matrix of pair-wise FST values, and, the pair-wise distances between river mouths in kilometers.GENALEX v6. was also used to create principal component analysis plots (PCA) based upon a matrix of FP < 0.05 and P < 0.001 for exclusion. Bayesian clustering analysis was computed in the program STRUCTURE [Genetic assignment tests, using direct assignment and exclusion and different combinations of samples and loci sets, were conducted in the program GENECLASS2 , using aTRUCTURE ,48. ThisST between two groups of 100 randomly selected fish from within each of the groups (wild and farmed) was plotted following 1000 bootstraps. This was conducted for farmed vs. wild, wild vs. wild and farmed vs. farmed using the diagnostic markers and the randomly selected markers. Next, the assignment power of these sets of loci was examined in STRUCTURE. Due to the fact that there were more diagnostic loci than randomly selected loci, and that assignment power is influenced by the number of loci, a sub-set of the diagnostic loci  was also used to compute these assignment tests. The following routine was repeated 1000 times for each set of SNPs: 100 farmed fish were sampled as the learning farmed sample, 100 wild fish among all populations were sampled as the learning wild samples, then one fish was sampled randomly to constitute the unknown individual to identify. STRUCTURE was then run with 50 000 burn-in and 500 000 iterations, at K = 2, using population information and population flag, so that population information is only used for the learning samples, but not for the unknown individual. The accuracy of assignment of this individual to either the farmed or the wild salmon groups was compared for the different sets of SNPs using the threshold probability of 0.6. The re-sampling simulations were performed in R, and the STRUCTURE runs with ParallelStructure R package  [In order to investigate the statistical power of the different sets of genetic markers implemented here, several tests were computed. First, the distribution of pair-wise Fucture/) .ST between each wild population’s historical and contemporary sample. The simulations were coded and executed in R using previously published scripts to simulate realistic genetic introgression using gametes sampled randomly from the donating and recipient populations [NE) of 200 for all populations except those displaying an NE less than this in which case the observed NE, as reported previously [Two alternative methods were employed to quantify the amount of gene flow that would be required from an alien population to cause the observed temporal genetic change within each wild population, computed as Fulations . Averageeviously , using aeviously , was useM ). 2. Simulate n scenario of introgression where the value of M is sampled from the prior distribution, and compute the FST between historical and each simulated population. 3. Calculate the vector s of the n differences between simulated and observed FST: s:  where si = (FSTO- FiST). 4. Solve the linear regression: M= α+βs+ϵ . The estimate of α gives the expected value of M when FSTO- FiST = 0. 5. Update the prior distribution of M with estimated distribution of α. 6. Repeat step 2–5 until α converges to a stable estimate.First, the posterior point estimate of migration rate (M) was inferred by an ABC algorithm . The rouST between historical and contemporary sample in the estimation of M, the posterior distribution of M was also estimated with an alternative “fixed migration rate” approach that consisted of the following steps: 1. Simulating genetic introgression from alien population with a fixed migration rate M per generation. Tested values of M were from 1 to 80%, every 1% between 1 and 20, and every 5% between 20 and 80%. 2. From the set of simulated populations , selecting 1000 among those that gave an FST that fit within the 95% confidence interval of the observed FST. 3. Mean and standard deviation of migration rate from the 1000 selected scenario provide a posterior distribution of (M).To account for the standard deviation of the observed FFor both ABC and fixed migration rate approach, two alternative scenarios were tested. First, the alien population considered for the simulations was a pool of farmed salmon, and second, to account for possible straying, the alien population was the historical population of the geographically nearest neighbour river. The R package ADEgenet  was usedLoci that displayed technically unreliable genotype clustering, or a genotyping coverage <90% in the entire data set, were excluded prior to all statistical analyses. This stringent quality control reduced the total number of loci from 99 to 72. Among the remaining loci were 47 SNPs selected from the collectively diagnostic panel for farmed and wild salmon  , and a sE) over all 72 loci was very even among wild populations, ranging from a low of 0.33 for both the historical and contemporary samples for Berbyelva, to a high of 0.38 for the historical sample representing the river Ørstadelva. None of the populations displayed clear increases or decreases in this parameter between their historical and contemporary samples.A range of population genetics parameters are summarized for all wild populations and the pool of farmed salmon  at P < 0.05, LD was observed from a low of 70 times (2.6%) in Bondalselva contemporary sample, to a high of 243 (9.2%) in the historical Vestre Jakobselva sample. The pool of farmed salmon displayed similar summary statistic values to the wild populations, although increased frequency of HWE and LD was observed in this sample due to it being a mixture of fish from multiple sources , Bondalselva (25), Ørstaelva (94), and Opo (57) for the historical samples, and Vestre Jakobselva (75) and Berbyelva (67) for the contemporary samples. The upper and lower 95% confidence intervals surrounding all estimates were large however, with the upper boundary often reaching infinity.The effective population size for each wild population is presented  randomly re-sampled from the data set 1000 times, 47d displayed approximately double FST than 25r  and 3 for 25r (12%)  was higher for 47d than 25 randomly re-sampled loci from 47d (“25resam”), and from 25r . Together, these tests demonstrate that the diagnostic loci contain far greater statistical power to differentiate farmed and wild salmon than the randomly selected loci.P < 0.05 and P < 0.001 was 11 and five respectively. For either 47d or 25r at P < 0.05  using 47d and 25r (R2 = 0.36 P = 0.0049)  . Several trends can be extrapolated from these comparisons . This is consistent with the results of the section above dedicated to comparing the statistical properties of 47d and 25r. Second, a geographic trend to the pair-wise difference between each wild population’s historical sample and the pooled farmed sample was present. Populations from the far north and south of Norway displayed much greater pair-wise FST estimates to the farmed sample than populations from mid- and western Norway displayed to the farmed sample. This was detected in 47d and 25r, but more pronounced in the former. These observations suggest that a population in northern Norway is likely to display a greater genetic change compared to a population from mid Norway for the same level of farmed salmon introgression.Computed pair-wise, all historical and contemporary samples representing the wild populations displayed statistically significant differences to the pool of farmed salmon, using both sets of SNPs separately, and following adjustment for multiple testing , but not for 25r   represents a sub-set of the markers recently identified as collectively diagnostic for farmed and wild Norwegian salmon . The panST between each wild population and the pool of farmed salmon showed a clear geographic trend  in Danish rivers [Admixture between hatchery fish released deliberately into the wild and native populations has been computed in other species and systems, for example brown trout  and is deemed to be less accurate to estimate the posterior mean of M than the ABC routine that converges gradually to an optimum value. We therefore present the results from the two methods as complementary. Other alternative approaches could also have provided reliable estimates of posterior distribution of M, such as rejection based Bayesian Inference [M, taking into account the uncertainty around the observed FST. However, algorithms that estimate the value of a parameter based on its posterior likelihood are sensitive to the shape of the likelihood curve or “likelihood landscape”; a leptocurtic curve would result the algorithm to converging rapidly to the optimum value of the estimated parameter, whereas a platicurtic curve would result in the algorithm converging slowly. In the present data, FST between historical and contemporary samples were close to zero with large standard deviations for some populations. Such combination of small mean value and large standard deviation represents a flat likelihood landscape where the algorithm is slow to converge, and often lacks precision.Introgression of farmed salmon was estimated here using the simulation approach. As it is implemented, the ABC routine finds the point estimate of migration rate nference ,62, thatSome of the challenges to quantify introgression of farmed salmon in native populations have been described in detail in the introduction. Briefly, these include the complicated logistics associated with the distribution of genetic material within the aquaculture industry , the facIn order to investigate the direction of genetic change in wild populations and quantify introgression, a pool of 375 farmed salmon, sampled over a five year period from 48 cages located on 35 farms spanning the Norwegian coastline were included in the present study. While this pool of farmed salmon does not necessarily accurately represent the allele frequencies of escapees entering all of these rivers for the entire study period, they clearly permitted elucidation of the direction of the genetic changes in wild populations  and contemporary (red–right bar) sample.Click here for filePrincipal component analysis for each geographic region based upon 47d and 25r.Click here for fileST values among all samples included in the present study.Matrix of pair-wise FClick here for filePrincipal compont analysis for the historical and contemporary samples for all 20 rivers and the group of farmed salmon based upon 47d and 25r.Click here for file


Feral sheep are considered to be a source of genetic variation that has been lost from their domestic counterparts through selection.KRTAP1-1, KRT33, ADRB3 and DQA2 in Merino-like feral sheep populations from New Zealand and its offshore islands. These genes have previously been shown to influence wool, lamb survival and animal health.This study investigates variation in the genes All the genes were polymorphic, but no new allele was identified in the feral populations. In some of these populations, allele frequencies differed from those observed in commercial Merino sheep and other breeds found in New Zealand. Heterozygosity levels were comparable to those observed in other studies on feral sheep. Our results suggest that some of the feral populations may have been either inbred or outbred over the duration of their apparent isolation.The variation described here allows us to draw some conclusions about the likely genetic origin of the populations and selective pressures that may have acted upon them, but they do not appear to be a source of new genetic material, at least for these four genes. It is thought that livestock genetic variation has decreased through breed substitution and crossing of local and global breeds . AccordiNew Zealand (NZ) has eleven feral sheep populations either on the mainland, or on offshore islands . The maiKRTAP1-1 , KRT33 , ADRB3  and DQA2  protein DQA2).In this study, the level of genetic variation of four genes was determined in order to ascertain whether the isolation of these flocks had preserved greater genetic diversity compared to their commercial counterparts in NZ. These four genes are located on three different chromosomes i.e. Previous studies have reported that variations in the keratin and keratin-associated protein genes, including the ones above, influence many wool properties including fibre diameter , staple ADRB3 has been associated with survival in various sheep breeds [Neonatal lamb mortality, particularly in Merino sheep, represents a large loss to the NZ sheep industry. Allelic variation in p breeds , thus itIt has been reported that feral sheep may have an increased resistance to a number of diseases. This resistance could imply that variation in key immune function genes such as the highly polymorphic MHC genes is important, as it plays a role in the immune response to pathogens and parasites -21.Collectively the four genes chosen here cover a variety of different animal traits that could be associated with variation in the ability to survive in remote and potentially more severe environments, and where feed availability was probably reduced relative to farmed sheep.Ten feral flocks and two reference flocks  were investigated in this study Table . GenomicPCR amplifications and genotyping approaches were carried out using previously described methods ,24-26OH), expected heterozygosity (E Hwith a Levene's correction) and coefficient of inbreeding (FIS) estimates based on the method of Weir and Cockerham [FIS estimates were calculated across all the populations and genes  and for individual populations and genes. Allelic richness, a measure of genetic diversity at a single locus, was determined using FSTAT version 2.9.3 [Allele frequencies, number of alleles, observed heterozygosity  and one from Pitt Island (Mohaka sheep), and allele C absent in the populations from Herbert Forest and Chatham Island.All genes investigated in this study were polymorphic and allele frequencies for each gene varied among the studied flocks Table  and 3. NKRT33 alleles [D and E were found in all the populations whereas alleles A and B were absent in the sheep from Arapawa Island and the Mohaka populations, allele C was absent in those from Mohaka and alleles B and C in those from the Pitt Island and Hokonui, respectively.Previous studies have reported five  alleles , all of ADRB3 alleles were detected in the feral sheep. The lowest diversity was observed in the Mohaka population with only three alleles while it was greatest in the sheep from Pitt Island with six alleles. It is interesting to note that alleles D and H, which occur at relatively low frequencies in other commercial breeds in NZ [G is low in NZ commercial sheep and was only found at a low frequency in the sheep from Pitt Island and Hokonui.Six different ds in NZ , were abDQA2 alleles varied considerably among populations with some alleles completely absent in some populations. The lowest diversity was observed in the sheep from Mohaka with only two DQA2 alleles. Conversely, thirteen DQA2 alleles were present in the sheep from Pitt Island.The distribution of KRTAP1-1 alleles of sheep from Chatham Island (p = 0.113), Woodstock (p = 0.434), Herbert (p = 0.055) and Mohaka (p = 0.098) were not significantly different from those of the Merino reference flock, and those of sheep from Mohaka (p = 0.673), Campbell Island (p = 0.084) and Hokonui I (p = 0.454) were not different from those of all breeds and (2) frequencies of ADRB3 alleles of sheep from the Arapawa Island I did not differ from those of either reference flock .For all four genes, in most cases allele frequencies in the feral populations differed significantly (p ≤ 0.025) from the frequencies in the reference flocks, most of the differences being highly significant (p < 0.001). The following exceptions were found: (1) frequencies of OH) and expected (EH) levels of heterozygosity and coefficient of inbreeding (FIS) are shown in Table ADRB3 gene (1.59) in the sheep from Arapawa Island II while the greatest number of alleles was found for KRT33 (6.12) in the Hokonui II sheep. Allelic richness was highest for KRT33 and lowest for ADRB3 in all feral populations except for the Mohaka sheep.Allelic richness, observed , while Mohaka sheep had the lowest mean estimate for Ho and He . Allele sharing was high between animals originating from Campbell Island and Pitt Island for KRTAP1-1 and among the Arapawa II flock of feral sheep for KRT33 and lower among the Arapawa I flock for DQA2. Finally, allele sharing among sheep from Woodstock was very low for DQA2.Observed and expected heterozygosity values ranged from a low of 0.06 observed for This is the first report describing DNA variation in feral sheep from NZ. The genes investigated in this study were chosen because they had previously been shown to influence wool traits , cold suDQA2 suggesting this flock is outbred, although other loci would need to be typed to confirm this. This is most likely due to the ongoing introduction of new genetic material from other Merino sheep which are typically farmed in areas adjacent to this population.Although the feral sheep sampled were chosen so that they were representative of their populations, there is no guarantee that the farmers who maintain these populations on the NZ mainland have been able to maintain genetic diversity, especially because the flocks sizes are small compared to the original populations. Allele sharing among four offshore island flocks  was significant for one gene but not necessarily for the same gene. Sheep populations from both Pitt and Campbell Islands, have undergone extensive size reduction before being relocated to the mainland and it is surprising that the level of inbreeding is not higher. In contrast, among the mainland Woodstock sheep, many different alleles are detected for Sources of genetic variation in the feral sheep populations include founder effects, random drift, balancing selection, genetic bottlenecks, or combinations of these. Each will be discussed below.Genetic drift may have affected these feral populations . HoweverIn some cases, allele frequencies in the feral populations were not "Merino-like" and tended to show greater similarity to allele frequencies in other common farmed sheep in NZ. This provides support for the anecdotal contention that these feral sheep have at times interbred with farmed non-Merino sheep.There is evidence of genetic differences between groups of sheep on remote yet neighbouring islands. Chatham and Pitt Island sheep are thought to be descendents of the same founding Merino sheep, yet they show quite different allele frequencies for many of the genes studied here. Pitt and Chatham Island feral sheep have distinct wool colours but whether this is a result of the differences in the genes studied cannot be ascertained here.ADRB3 variants D and H are rare in farmed NZ sheep [Founder effects may influence the genetic diversity of feral populations . It is aNZ sheep  and theyADRB3 alleles have been lost in the feral populations because they provide no selective advantage. This is called balancing selection and it reflects the situation where alleles are retained in a population by forms of selection such as heterozygote advantage, frequency-dependent selection [ADRB3 alleles A and E are associated with cold survival, alleles C and F are linked to cold-related mortality, and allele D has a strong association with cold-related mortality and total mortality [ADRB3 allele D in the feral populations could be due to the fact that these flocks were exposed to cold climatic conditions during lambing and death of lambs carrying the allele.An alternative explanation to the founder effect is that particular election  or selecelection . ADRB3 aortality . The comA number of studies have suggested that feral sheep show few signs of susceptibility to infection by ectoparasites ,12 and fTeladorsagia circumcincta imposes a selection pressure in the Soay sheep of the island of Hirta in Scotland, and that this is reflected in the temporal divergence of the MHC genes over a relatively short period between 1988 and 2000. In the context of the results reported here, while the MHC allelic richness is at times low, in the absence of any data or evidence of on-going disease challenge it would be speculative to attempt to draw any conclusions. It should be noted that for DQA2, allele sharing was high within one island population but low within the mainland feral population, suggesting that the island population may have undergone some selection pressure.Charbonnel and Pemberton  have sugKRT33 and KRTAP1-1 was typically low suggesting the flocks may be outbred. Allele richness was highest for KRT33 indicating that the level of genetic diversity has remained quite high in these feral sheep populations. Feral sheep populations have some unique wool characteristics including at times a hairy birth-coat type, which has been shown to offer some advantage in improving lamb survival [Allele sharing at survival -43, the survival , tightlysurvival  and varisurvival . The genGenetic bottlenecks can cause loss of genetic diversity . Like foThe authors declare that they have no competing interests.DQA2 genotype data. NM provided the genotype data for the keratin genes used in this study. RF developed the ADRB3 genotyping methodology and generated the allele frequency data for the Merino and all breeds reference populations used in this study. She also helped revise this manuscript. JA applied for and was granted the funding that underpinned the collection of blood and data from the owners of these sheep. She designed the study and collected the blood samples from the different sheep populations identified. She was involved in typing KRTAP1-1 and assisted draft the manuscript. JRS provided completed parts of the statistical analysis and provided useful discussion on the results obtained from this study. He also assisted in the production of the final manuscript. JH helped develop the project in his capacity as research leader, provided comments on the grant proposal, and drafted the final manuscript. All authors read and approved the final manuscript.GM supervised the collection of the genotype data, completed most of the analyses and drafted the manuscript. HZ and QF generated the