Cimex lectularius L., was recently found to form two host‐associated lineages within Europe: one found with humans  and the other found with bats . No unequivocal evidence of contemporary gene flow between these lineages has been found; however, it is unclear whether this is due to an inability to produce viable “hybrid” offspring. To address this question and determine the extent of compatibility between host‐associated lineages, we set up mating crosses among populations of bed bugs based on both their host association (human—HA vs. bat—BA) and geographic origin (North America vs. Europe). Within‐population fecundity was significantly higher for all HA populations (>1.7 eggs/day) than for BA populations (<1 egg/day). However, all within‐population crosses, regardless of host association, had >92% egg hatch rates. Contrary to previous reports, in all interlineage crosses, successful matings occurred, fertile eggs were oviposited, and the F1 “hybrid” generation was found to be reproductively viable. In addition, we evaluated interpopulation genetic variation in Wolbachia among host‐associated lineages. We did not find any clear patterns related to host association, nor did we observe a homogenization of Wolbachia lineages across populations that might explain a breakdown of reproductive incompatibility. These results indicate that while the HA and BA populations of C. lectularius represent genetically differentiated host‐associated lineages, possibly undergoing sympatric speciation, this is in its incipient stage as they remain reproductively compatible. Other behavioral, physiological, and/or ecological factors likely maintain host‐associated differentiation.As populations differentiate across geographic or host‐association barriers, interpopulation fertility is often a measure of the extent of incipient speciation. The bed bug,  Cimex lectularius L., was recently found to form two host‐associated lineages within Europe: one found with humans  and the other found with bats . No unequivocal evidence of contemporary interlineage gene flow between these lineages has been found; however, it is unclear whether this is due to an inability to produce viable “hybrid” offspring. Our results show these lineages to be reproductively compatible, suggesting other behavioral, physiological, and/or ecological factors likely maintain host‐associated differentiation.The bed bug,  In R. pomonella, there is a clear divergence among host lineages, with some preferentially attracted to apples and others to hawthorn , intracellular α‐proteobacterium, as an obligate nutritional mutualist that provisions the bed bug with riboflavin and biotin  resulted in fewer eggs oviposited than in the reciprocal cross  investigated the relationship between assayed populations using mitochondrial markers previously found to reveal host‐lineage differentiation; (b) conducted reproductive crosses that spanned geographic locations, and both within and between the two host‐associated lineages; and (c) investigated the patterns of genetic variation of Wolbachia among populations for evidence of interpopulation homogenization.To better understand reproductive compatibility between different host‐associated lineages of 22.1C. lectularius were used in this study. Collections were made from bat roosts and homes, and from both the United States and Europe. A full description of each population is provided in Table Six populations of 2.2https://www.qiagenbioinformatics.com). As in Balvín et al.  sequencing in order to compare with patterns of host‐lineage association previously observed by Balvín et al.  and Bootn et al.  and Bootn et al. , genes wn et al. . Sequencn et al. .2.31 generation to produce offspring. This measure was not quantitative, so viability is reported only as yes/no at the population level.Reproductive compatibility was assessed using the methods described by DeVries et al. . BrieflyAll assays were female‐centric, so each comparison was between females that mated with males from their own population and females that mated with males from a different population. Also, the large number of crosses would create substantial within‐population temporal variation. To minimize this variation, all crosses were run in a 2 × 2 matrix design. Thus, in a cross between populations A and B, females of population A were mated to males of populations A and B, and females of population B were mated to males of populations A and B. Although this design resulted in homogeneous crosses  being repeated, it ensured that each cross had concurrent within‐population positive controls , which were used to normalize all crosses involving that population . All assays were conducted between May 2014 and April 2016.2.4t test because each 2 × 2 design included within‐population positive control crosses. Hatch rate in interpopulation crosses was assessed using the Kruskal–Wallis test. All analyses were performed in SAS 9.4 (SAS Institute).Fecundity was compared among all within‐population crosses using ANOVA, with means compared using the Tukey–Kramer multiple comparison test. Egg hatch rate (percentage of eggs resulting in 1st instars) was compared among all within‐population crosses using the Kruskal–Wallis test. Fecundity was compared in crosses between populations using Student's 2.5Wolbachia was assessed at three single nucleotide polymorphisms (SNPs). In a preliminary screening of a subset of samples collected across Europe (~100 infestations split evenly between BA and HA), these SNPs were found to be able to discriminate between BA and HA lineages; no U.S. samples have previously been screened to determine whether this pattern holds true . PCR amplification was performed using the following primers: SNP‐1: forward—CGGTAATCCTTGGGTGCAAT, reverse—TCCAATAACGCTATCTGAAAGTCT; SNP‐4: forward—CCCTGTGTAATGGGAATTGG, reverse—GCAACTTCTACCACGGGATT; and SNP‐12: forward—GGGTTACAGTGGCCAGAATG, reverse—TGCTGATAAGCCACGTTTACC. PCRs were performed in 20 µl volumes each containing 1× PCR buffer, 2 mM MgCl2, 100 mM dNTPs, 2.5 mM of each primer, 0.5 U Taq DNA polymerase , 50 ng of DNA template, and ddH2O to make 20 µl. PCR cycling conditions included an initial denaturation stage of 5 min at 95°C, followed by 35 cycles each consisting 1 min at 95°C, 1 min at 58°C, and 1 min at 72°C. This was followed by a final extension stage of 72°C for 5 min. PCR products were run on 2% 1× TBA agarose gels (with ethidium bromide) to confirm amplicons of the expected sizes . PCR products were purified using ExoSAP‐IT (Affymetrix Inc.), bidirectionally sequenced using the BigDye Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems), and run on an ABI 3130xl Genetic Analyzer (Applied Biosystems). Sequences were visualized and edited in CLC Main Workbench v.7.6.2 .Interpopulation genetic diversity of 33.1Bat‐associated samples from the Czech Republic  were found to be identical to two BA haplotypes identified previously in Balvín et al.  and Boot3.2F5,377 = 96.17, p < 0.0001, Figure H5,365 = 7.8037, p = 0.1674)  and bat‐associated  populations are shown.bAll percentages are relative to female fecundity in within‐population crosses (patterned cells set to 100%), and thus reflect the increase or decrease in fecundity when mated to males from another population.cn represents the homogeneous sample size followed by the heterogenous sample size.*Significant differences (p < 0.05) according to Student's t test (2‐tailed), with the test statistic and sample size (combined for the homogenous and heterogenous crosses) listed below each percentage .Hatch rate in the interpopulation crosses ranged from 72.4% to 111.1% of the respective within‐population hatch rates Table . Only fiaHuman‐associated (HA) and bat‐associated  populations are shown.bAll percentages are relative to hatch rate in within‐population crosses (patterned cells set to 100%), and thus reflect the increase or decrease in hatch rate when mated to males from another population.cn represents the homogeneous sample size followed by the heterogenous sample size. Note: Only those crosses that produced eggs were used to calculate hatch rate. Therefore, sample size is lower than reported in Table *Significant differences (p < 0.05) according to the Kruskal–Wallis test, with the test statistic and sample size listed below each percentage .1 offspring of all interpopulation crosses (including interlineage crosses) were reared to adults, and examined for fertility. Combined progeny of interpopulation crosses produced offspring (F2 generation), indicating reproductive compatibility among all populations.The F3.4Wolbachia, and thus determine whether cross‐colony homogenization of Wolbachia lineages has occurred, three Wolbachia SNP variants were screened. Human‐associated samples from Beroun and Winston Salem exhibited Wolbachia SNP variants previously found to be associated with the human lineage in Europe  . Interestingly, the U.S. HA‐JC sample exhibited Wolbachia SNP variants seen in the European bat‐associated lineages, and both the BA Moravicany, and the HA Oslo samples proved heterozygous for these SNPs , hematophagy, and close association of all life stages with the host make it a particularly attractive model for investigations of incipient speciation. Two host‐associated lineages co‐exist in Europe: human‐associated (HA) and bat‐associated (BA) populations , and when reared to adults, produced offspring (F2 generation).Similar levels of reproductive compatibility have been reported in other host races of other species. In the case of the apple maggot fly, C. lectularius remain unclear, but in the absence of reproductive incompatibility, ecological  and behavioral  factors likely play a role. Fidelity to lineage‐ or population‐based aggregations was dismissed as a behavioral isolating mechanism  responsible for preventing gene flow between human‐ and bat‐associated lineages of C. lectularius. Although there were some differences in fecundity among lineages, all human‐ and bat‐associated populations tested were fully compatible with each other under laboratory conditions. Future work should focus on ecological factors , chemosensory specialization , and morphological adaptations  which may maintain genetic isolation. The recently completed genome sequence of C. lectularius, and particularly its well‐annotated chemosensory genes ; Formal analysis ; Investigation ; Methodology ; Writing‐original draft ; Writing‐review & editing . Richard G Santangelo: Investigation ; Writing‐original draft ; Writing‐review & editing . Warren Booth: Conceptualization ; Formal analysis ; Funding acquisition ; Investigation ; Methodology ; Resources ; Writing‐original draft ; Writing‐review & editing . Christopher Lawrence: Investigation ; Writing‐original draft ; Writing‐review & editing . Ondřej Balvín: Writing‐review & editing . Tomáš Bartonička: Writing‐review & editing . Coby Schal: Conceptualization ; Formal analysis ; Funding acquisition ; Methodology ; Resources ; Writing‐original draft ; Writing‐review & editing .Data S1Click here for additional data file.


Haliotis fulgens) supports an important artisanal fishery in the west coast of the Baja California Peninsula (BCP), Mexico, which has shown a declining tendency despite the several management measures. Thus, the aim of this study was to characterize the spatial patterns of neutral genomic variation of green abalone along the BCP to test whether the genomic structure patterns support the current green abalone management areas. To test this hypothesis, a set of 2,170 putative neutral single nucleotide polymorphisms discovered by a double digest restriction-site associated DNA approach was used on 10 locations along the BCP. The results revealed a population structure with three putative groups: Guadalupe Island and northern and southern BCP locations. The contemporary gene flow might be explained by local oceanographic features, where it is bidirectional within the southern region but with a predominant southward flow from the northern region. These findings indicated that the administrative areas did not match the biological units of H. fulgens fishery; hence, the stock assessment and management areas should be revised.Effective fishery management strategies should be based on stock delimitation and knowledge of the spatial scale at which species are distributed. However, a mismatch often occurs between biological and management units of fishery resources. The green abalone ( Effective fishery management includes stock delimitation and requires knowledge of the spatial scale of those biological units . NeverthThunnus alalunga, Salmo salar L., Thunnus albacares, Novel genomic approaches enable discovering and genotyping a large number of loci in non-model species, which allow coping with previous technological limitations  both incHaliotis fulgens) almost fully supports the artisanal abalone fishery along the Baja California Peninsula (BCP)  . Its disla (BCP)  and occula (BCP) . The repla (BCP)  and wintla (BCP) ; neverthla (BCP)  that migWhile the abalone fishing activity in the USA has been completely banned , in MexiAdministrative zones are extensive areas in agreement with the latitudinal decreasing pattern in maturity size length from north to south along the BCP  where abH. fulgens—spanning ~10° in latitude—belongs to the temperate-tropical transition zone, characterized by a significant seasonal gradient influenced by the California and the North Equatorial Currents, with heterogeneous oceanographic and oceanic circulation conditions  derived from double-digest RAD Sequencing (ddRAD-seq). Therefore, the aim of this study was to characterize the spatial patterns of neutral genomic variation among populations of the green abalone n = 185) were collected under SAGARPA permits PRMN/DGOPA-011/2017 and PRMN/DGOPA/016-2018 by hookah diving during fishery evaluation surveys performed by the cooperative fishers. Epipodial tentacle fragments were taken without sacrificing the animals and preserved in 95% ethanol until DNA extraction  and MspI (NEB®). A Pippin Prep device (Saga Science) was used to select fragment sizes from 376 bp–408 bp for all the libraries. Two ddRAD libraries were completed, each one with two pools; each pool comprised 48 individuals with a different barcode, and a unique index. A 4-index combinatorial and 48 barcodes described by Double-digest restriction-associated DNA (ddRAD) libraries were prepared following process_radtags pipeline in the STACKS software v1.30 , and a maximum of three nucleotide mismatches among stacks within individuals (−M = 3). To build the catalog, a maximum of four mismatches between sample loci between individuals (−n = 4) were allowed.Reads were assembled de novo using the terature  (Supplemterature . The finr = 0.8) in all of the localities 100% (−p = 10) and with a minor allele frequency MAF > 0.05. To prevent physically linked loci for subsequent analyses, the write_single_SNP option was used to retain only the first SNP from each RAD tag. Individual samples with >10% missing data across loci estimated with vcftools v.3.0  q-values .To identify and eliminate potential candidate loci under selection, two independent methods were used. The first one employed a Bayesian approach to estimate the population specific software , which isoftware . The dec.Hardy–Weinberg equilibrium (HWE) was estimated with the software Arlequin v3.5.2.2  by exactAr), observed heterozygosity (Ho), expected heterozygosity (He) and Inbreeding coefficient (FIS) were estimated with the package PopGenReport v.3.0.4  method. To estimate the number of principal components (PCs) to be retained, the function optim.a.score was used.A discriminant analysis of principal components (DAPC) was performed in the R Package  v.2.1.1 . This meK (groups or putative populations), based on the admixture ancestry model and correlated allele frequencies. Runs consisted of an initial burn-in of 10,000 Markov Chain Monte Carlo (MCMC) iterations followed by 100,000 iterations for each inferred cluster (K 1 to 10 with five replicates) and the locprior model. The delta K method was used to determine an optimal K  three groups based on DAPC and the unweighted pair group method with arithmetic mean (UPGMA) results; and (E) four groups based on visual division on the DAPC results.Analyses of molecular variance (AMOVA) using Arlequin v3.5.2.2 software  were perAn UPGMA tree was constructed using the program Phylip  based ondivMigrate function ]  including all locations; (2) using only coastal locations ; and (3) within the central-south locations . To test whether the correlations between genetic and geographic distances were the result of a continuous or distant patchy cline of genetic differentiation, a 2-dimensional Kernel density estimator was applied with the function kde2d using the MASS package  was tested in the R Package t v2.1.1  using a t v2.1.1  by corre – FST)]  vs. geog package  in R v.3 package .process_radtags program of STACKS , 96.8% of the reads were retained, resulting in an average of 7,458,559 reads per sample (range: 0.33 M–23.3 M). Individual samples with less than 1 million reads were discarded (n = 8). The remaining samples were standardized to 1 million reads and initial catalog of 116,443 RAD loci with 353,332 SNPs was built with the denovo_map.pl module in STACKS, from which the populations module extracted a dataset of 2,216 loci with an average read depth of 32X. Individuals with >10% of missing genotypes also were excluded (n = 2). After the FDR correction, no evidence was observed for significant linkage disequilibrium between loci (p > 0.05). which resulted in a global database of 175 individuals and 2,216 SNPs.A total of 1,420,020,148 pair-end raw reads were obtained from two sequencing lanes. After applying the quality filters with the p < 0.05) in more than half of the locations and it was removed from the database, leaving a neutral data set of 2,170 SNPs. Genotyping accuracy was evaluated using individual replicates, and an average genotyping error rate of 0.04% was recorded.Forty-five loci outliers were identified by Arlequin and five by Bayescan (five were common in both methods), leaving a dataset of 2,171 SNPs  were the least diverse, while Punta Eugenia, Cedros island, and Tortugas South were the sites with the highest diversity (FIS was 0.004 (p = 0.38) and did not show significant values across locations . Pairwise comparisons showed significant differences between the oceanic Guadalupe island (~250 km away from the coast) and the rest of the localities (p < 0.001). Aditionally, the northern coastal San Jerónimo island had significant FST estimates with all locations (p < 0.001) with the exception of Faro San José. Sites within the southern region showed no significant genetic divergence values among localities except for Faro San José with Tortugas South (p < 0.001).The population substructure was significant (K = 2) based on the delta K statistic of Evanno, grouping all individuals from Guadalupe Island separately from the rest of the coastal locations , and the third by coastal central-southern localities  . The Bayocations . Howevers . However, only the last grouping (E) that consider Guadalupe Island, San José Island, Faro San José and the rest of the localities as separated groups, the differentiation among populations within groups  was no longer significant  , D , and E  resulted in a significant variation among groups (nificant . Neither > 0.05) .The UPGMA tree identified two to three major groups with a high bootstrap value (100%). The most robust topology separated the northern sample sites  from the rest .divMigrate  . This pattern of IBD remains significant without the most isolated location, Guadalupe Island , but it was no longer significant when only the central-south locations were analyzed . This pattern was further explored using 2-dimensional kernel density plots. The dataset—including all locations—showed a discontinuity indicating a patched pattern of genetic differentiation among populations (The Mantel test showed a strong relationship between linearized = 0.001) . The regulations . Howeverulations  and S7C.The population structure results of green abalone along the BCP supports three regional genetic groups showing a clear differentiation between the oceanic island  and the rest of the coastal localities; the second group is formed by the northern coastal localities, which include San Jerónimo Island and Faro San José; and the third group includes the southern coastal localities .H. fulgens and it was able to detect a significant population structure between northern and southern coastal locations.Differentiation between coastal localities and Guadalupe Island and panmixia along the central west coast of the BCP had already been reported  supportiH. fulgens along the northernmost part of the species distribution  using a panel of 1,209 putative neutral SNPs. However, this previous study was performed in a reduced area of the species distribution range and differences might exist between the USA and Mexican locations, as studies have reported in H. corrugata using microsatellites  is an extensive region with sandy habitats  may have been promoted within the central and southern zones of the BCP. The seasonal oceanographic circulation also might enhance larval dispersal between regions during the spawning season when the California Current is more intense (winter) with a southward direction, but the large habitat discontinuity north of Punta Eugenia might have a crucial role limiting larval connectivity between regions. Nevertheless, a seascape approach is needed to test the effect of ocean currents in neutral green abalone population structure.An essential prerequisite of a sustainable fishery management is the match of biologically meaningful entities and management units. Assessing population status or developing a management strategy might not be possible without first knowing where a population begins and ends . UnfortuH. fulgens’ reefs within and between zones .Significant values are shown in bold after Bonferroni correction for multiple comparisons  three groups based on DAPC and UPGMA results, and (E) four groups based on visual division on the DAPC results. Significant values are shown in bold (p < 0.05).AMOVA to examine the partitioning of genetic variance at four hierarchical levels among regional groups of populations identified by the previous analysis, and with different hypothesized structures: (A) administrative zones, (B) two groups based on STRUCTURE results, (C) three groups based on Click here for additional data file.10.7717/peerj.9722/supp-6Supplemental Information 6This tree is constructed by the UPGMA method from distance chord. Numbers at the nodes represent the percentage of a group’s occurrence in 5,000 bootstraps. Only those with values higher than 60% are reported. This is an unrooted tree. GI= Guadalupe Island, SJI= San Jerónimo Island, FSJ= Faro San José, CI= Cedros Island, PE= Punta Eugenia, TN= Tortugas North, TS= Tortugas South, AN= Asunción North, AS= Asunción South, Bo= Bocana.Click here for additional data file.10.7717/peerj.9722/supp-7Supplemental Information 7FST values against population pairwise distance for the 10 localities of H. fulgens based on 2,170 SNPs among 10 sample sites with a 2-dimensional kernel density estimator. (A) using all the sampling locations, (B) without Guadalupe Island, and (C) using only the central-south locations.Isolation-by-distance plots of linearized Click here for additional data file.10.7717/peerj.9722/supp-8Supplemental Information 8Click here for additional data file.


Gavialis gangeticus) is a critically endangered crocodylian, endemic to the Indian subcontinent. The species has experienced severe population decline during the twentieth century owing to habitat loss, poaching, and mortalities in passive fishing. Its extant populations have largely recovered through translocation programmes initiated in 1975. Understanding the genetic status of these populations is crucial for evaluating the effectiveness of the ongoing conservation efforts. This study assessed the genetic diversity, population structure, and evidence of genetic bottlenecks of the two managed populations inhabiting the Chambal and Girwa Rivers, which hold nearly 80% of the global gharial populations. We used seven polymorphic nuclear microsatellite loci and a 520 bp partial fragment of the mitochondrial control region (CR). The overall mean allelic richness (Ar) was 2.80 ± 0.40, and the observed (Ho) and expected (He) heterozygosities were 0.40 ± 0.05 and 0.39 ± 0.05, respectively. We observed low levels of genetic differentiation between populations . The bottleneck analysis using the M ratio  suggested the presence of a genetic bottleneck in both populations. The mitochondrial CR also showed a low level of variation, with two haplotypes observed in the Girwa population. This study highlights the low level of genetic diversity in the two largest managed gharial populations in the wild. Hence, it is recommended to assess the genetic status of extant wild and captive gharial populations for planning future translocation programmes to ensure long-term survival in the wild.The gharial ( India harbours three crocodylian species: the gharial , the mugger or marsh crocodile , and the saltwater crocodile (Crocodylus porosus). The gharial has the narrowest distribution range and is the most threatened species among all other crocodylian species occurring in the Indian subcontinent. Until the early twentieth century, the gharial was widely distributed in the Indus, Ganges, Mahanadi, Brahmaputra, Kaladan and Irrawaddy River systems spanning across Pakistan, India, Nepal, Bangladesh, Bhutan, and Myanmar4. Over the years, the gharial has suffered a population decline of over 80% and substantial range contraction due to habitat loss, poaching, and mortalities in passive fishing7. The species is now extinct in Myanmar, Bhutan and Pakistan. The extant gharial population is restricted to a few major river systems in India, Nepal and Bangladesh. It is presently listed as ‘critically endangered’ on the IUCN Red List of threatened species and is highly conservation dependent7.The order Crocodylia is represented by 27 species and three families: Alligatoridae, Crocodylidae and Gavialidae2. Due to the alarming decline in the gharial populations throughout its range, the Government of India listed it in Schedule I of the Indian Wild Life (Protection) Act, 1972 to provide enhanced protection. Subsequently, conservation translocation programmes, including reintroduction and restocking, were initiated in 1975 to restore its population in the wild5. As a part of these programmes, eggs collected from the wild were hatched and reared in rehabilitation centres, and individuals after attaining a length of 1.2 m were translocated into suitable habitats within the newly created protected areas6.In India, by the early 1970s, the gharial was restricted to few isolated locations in the Ganga, Mahanadi and Brahmaputra River systems9. Although there is no record, the hatchlings from these two rivers were likely intermixed while rearing. The hatchlings produced in Kukrail and Deori were also translocated to several other rivers, viz. Son, Ken and Ramganga, some of which have started breeding10. Eggs were also collected from the Narayani River in Nepal, and hatchlings were released in the Girwa river. Additionally, conservation breeding was initiated in several zoos, such as the Nandankanan Biological Park and Madras Crocodile Bank, which contributed stocks for translocation11. A male gharial from the Frankfurt Zoo, Germany, was obtained, which also contributed to developing stocks in Odisha for translocation12. All translocation sites are now protected as wildlife sanctuaries under the Indian Wild Life (Protection) Act, 1972. In the last four decades, over 5000 gharial individuals have been translocated in more than 12 rivers in India, with over 3500 individuals in the Chambal and Girwa Rivers12. At the beginning of the restocking programme, the gharial population in the Chambal River was 107 individuals, and now, it is approximately 1675 individuals of different ages and sexes13. The Chambal River within the National Chambal Sanctuary (425 km) has been a stronghold of the gharial population since the initiation of the translocation programme. The Girwa River originally had a small population of approximately 34 individuals of different ages and sexes11 and now has 43 adult individuals restricted to a 20 km stretch of the Girwa River falling within the Katerniaghat Wildlife Sanctuary. Presently, these two populations hold nearly 80% of the extant wild populations of gharial7.In India, the head-start gharial conservation programme was initiated at the Chambal, Girwa and Mahanadi Rivers, where adult breeding populations existed. Initially, four facilities were established, viz. (1) Gharial Rehabilitation Centre, Tikarpada; (2) Gharial Rehabilitation Centre, Katerniaghat; (3) Gharial Rehabilitation Centre, Kukrail; and (4) Deori Gharial Rearing Centre, Morena targeting the Mahanadi, Girwa and Chambal populations. Hatchlings reared at these centres were released at the targeted rivers. In the Kukrail Centre, gharial hatchlings from 240 eggs collected from Chambal and 38 eggs collected from Girwa were reared during 197516. The effectiveness of any translocation programme is also influenced by the level of genetic diversity of the source population17, affecting individual and population fitness, resilience against environmental change and long-term persistence18. However, most translocation programmes rarely evaluate this factor17. Therefore, understanding the genetic status of the translocated population is vital for determining the effectiveness of translocation. Since the initiation of the gharial conservation programme, several studies have been carried out to determine their ecological features21. Nevertheless, studies on the genetic status of wild and captive gharial populations are limited23.Effective translocation programmes of endangered species essentially depend on prior knowledge of the ecological role, availability of suitable habitat, climatic requirements, behaviour, life-history traits, adequate post-release monitoring, demographic, behavioural and ecological aspectsIn this study, we aim to assess the genetic diversity, population genetic structure and evidence of genetic bottlenecks using nuclear microsatellite loci and the mitochondrial control region (CR) of the two largest managed populations of gharials inhabiting the Chambal and Girwa Rivers Fig.  to answeOut of the 27 nuclear microsatellite loci (11 species-specific and 16 cross-species) screened, 18 loci showed successful amplification, and nine loci failed to amplify using known gharial samples  in Chambal and 0.89 ± 0.01 in Girwa  was 4.84 × 10–2. We identified a total of 228  distinct individuals using multilocus genotype data. The genetic diversity estimates for the Chambal and Girwa populations are summarised in Table F) was − 0.03 for both populations. None of the loci showed significant deviation from HWE following Bonferroni correction.The cumulative probability of identity (PID biased) value of the panel of seven polymorphic markers was 1.74 × 10K = 2) optimum number of clusters inferred by the likelihood distribution L(K) and delta K estimates  of 0.97, and Chambal samples were assigned to cluster-II, with q = 0.82 ; G’ST = 0.058 (P < 0.05) and allelic differentiation index Jost’s D = 0.016 (P < 0.05).The Bayesian approach implemented in Structure v2.3.4 identified two (K = 20) showed an overlapping pattern with no clear structuring  was 0.12 (95% CI 0–0.26) from Chambal to Girwa River and 0.02 (95% CI 0–0.05) from Girwa to Chambal.The discriminate analysis of principal component (DAPC) identified 20 genetic clusters associated with the lowest Bayesian information criterion (BIC)  using the stepwise mutation model (SMM)  and the two-phase model (TPM)  failed to detect the signature of a genetic bottleneck. We also obtained a normal L-shaped allelic distribution for both populations.The 22, whereas H2 was unique to the Girwa population. The haplotype diversity was (mean ± SD) 0.148 ± 0.057, and nucleotide diversity was 0.00029 ± 0.00011 in the Girwa population.We observed two mitochondrial CR haplotypes H1 in 58 individuals (92%) and H2 in five individuals (8%) with a single parsimony-informative site (at the 375-nucleotide position) in the Girwa population. Haplotype, H1 was shared by both the Girwa and Chambal populations23. A low level of heterozygosity is not rare in wild crocodylians. Similar levels of heterozygosity have been reported in other crocodylians, including Alligator sinensis24, Alligator mississippiensis25, Caiman yacare26, Crocodylus siamensis27, Crocodylus mindorensis28, Crocodylus moreletii30, and Crocodylus palustris32, as well as in other vertebrate species that have experienced demographic bottlenecks33. Moreover, the Girwa population exhibits two and Chambal population exhibits a single mitochondrial CR haplotype22. The presence of extremely low variation at a hypervariable locus is surprising because, in other crocodylian species, the reported haplotypic diversity at mitochondrial CR is considerably higher38. The genetic diversity of a species is influenced by its geographic range, abundance, demography, and life-history traits. Narrow-ranging, less abundant and long-lived species with demographically challenged population history tend to have lower genetic diversity than widely distributed, abundant species with stable demographic history41. The low abundance, narrow distribution range, complex life-history traits, and unstable demography of the gharial may have resulted in the observed low level of genetic diversity.This study is the first comprehensive genetic assessment of the critically endangered gharial and provides crucial baseline information essential for guiding the ongoing conservation efforts. The genetic diversity observed at seven polymorphic nuclear microsatellite loci was low in the gharial populations of Chambal and Girwa. Both populations had similar mean heterozygosities, and the allelic richness (Ar) was marginally higher in the Chambal population Table . The oveF), primarily measures the deviation from HWE (where F = 0 indicates that the population is at HWE) and is often only weakly correlated with inbreeding and fitness43. The estimate is likely to be affected by several factors including use of a limited number of loci, closely related individuals, genetic bottleneck, gene flow and admixture46. Additionally, populations that experience demographic bottlenecks do not necessarily become inbred. Our estimate of F ≈ 0, shows that the populations are at or near HWE.The coefficient of inbreeding  but not by multivariate analysis DAPC  were lower than the critical value (MC = 0.68), confirming the signature of genetic bottlenecks in both populations. In contrast, the HET failed to detect the signature of a genetic bottleneck. This discrepancy between the results could be due to the limited statistical power of the two analyses. The M ratio can effectively detect genetic bottlenecks for a greater amount of time (up to 50 generations), whereas HET can efficiently detect bottleneck that occurred within a short time, 0.2–4.0 Ne (effective population size) generations49. The gharial population has suffered two instances of range-wide population decline during the early-1970s and mid-1990s, when the population dwindled to less than 200 adult individuals in the wild20. The average generation time estimates for gharial individuals is 25 years7, suggested that the latter decline in population occurred very recently (approx. 1–1.5 generation ago). Hence, the undetectability of the bottleneck through HET could be due to the recent bottleneck event. Additionally, bottleneck detection can be masked due to various confounding factors including time of occurrence, duration, magnitude, gene flow, pre-bottleneck genetic variability, mating system, sample size and a limited number of loci52. Hence, there is a need to investigate both of these populations with extensive microsatellite loci to achieve high statistical power54.The estimates of the In conclusion, low polymorphism was observed in species-specific and cross-species microsatellite loci; it remains crucial to develop novel microsatellite loci for population genetic studies. We acknowledge that our study might have been influenced by the inclusion of closely related individuals from each nesting site. However, the sampling was conducted from a large number of nests spread across 19 nesting sites. Therefore, we believe that our sampling strategy potentially represents the level of genetic diversity of the studied populations and minimises the bias arising from closely related individuals.12 in terms of increasing the abundance of the species in the wild. However, despite a long history of conservation efforts, limited information on the genetic status of the gharial has hindered, if not precluded, effective conservation planning. Gharial populations inhabit different biogeographic zones of India and are adapted to specific environmental conditions, yet translocation programmes have not considered the effects of genetic intermixing among populations. Such intermixing of populations without prior knowledge of the genetic structure can potentially threaten the genetic integrity and distinctiveness of the resident populations14. Our study augments the existing knowledge on the genetic status of the critically endangered gharial, which is vital for future translocations and research. This study highlighted low levels of genetic diversity and admixed structure in the two largest managed populations of gharial in the wild, which are considerably lower than the previously reported estimates of captive populations23.Gharial translocation is considered as one of the most successful species recovery programmes in the worldMoreover, the gharial is a highly conservation-dependent species, and translocations without assessing the genetic status of the source populations may further deteriorate the level of genetic diversity in the translocated populations. Hence, we recommend limiting the interpopulation release of individuals to prevent further intermixing of the gene pool until information on the genetic status of the extant wild and captive gharial populations using an extensive dataset of mitochondrial and microsatellite markers is available. Information on the genetic status will assist in the identification of potential source populations and maintain adequate levels of genetic diversity to secure the continuing persistence of gharials in the wild.The Forest Department of Madhya Pradesh (Letter No. SL/Tech-1/8200), Rajasthan (Letter No. 1399) and Uttar Pradesh (Letter No. 3093 and 3778(A)) provided all required permissions for the survey and collection of the biological samples. As all samples were collected from either dead remains of hatchlings or hatched eggshells, no animal ethical clearance was required for this study.55. Cotton swabs were used when chorioallantoic membrane was unable to separate from eggshells.Sampling of a large number of adult individuals required for population genetic assessments has its own ethical and logistical constraints, especially when the species in question is critically endangered. Considering these restrictions, we collected tissue from the remains of dead hatchlings and chorioallantoic membranes from hatched eggshells shortly after hatching. Samples were collected from 16 nesting sites along the Chambal River and three nesting sites along the Girwa River during 2017 and 2018, respectively Fig.  online. 23. However, only six of the eleven loci were found to be polymorphic. Hence, we conducted an exhaustive literature survey to identify the potential cross-species loci used across families Alligatoridae, Crocodylidae and Gavialidae. We found a total of 424 previously described microsatellite loci and listed the loci based on successful cross-species transferability, allele diversity, heterozygosity, polymorphic information content and allele range. Finally, we selected 16 cross-species loci developed in eight different crocodylian species for screening. Out of 16, a total of 12 loci were selected based on successful cross-species transferability in other crocodilian species. The remaining four loci were selected based on a number of alleles ≥ 4, Ho and He ≥ 5, PIC ≥ 0.5 and allelic range < 300 bp. The list of microsatellite loci screened in the current study is provided in Supplementary Table We initially screened 11 of the 18 gharial specific microsatellite loci described by Jogayya et al.56.Polymerase chain reactions (PCR) were performed in 10 μL reaction volumes containing 5 μL of 2 × QIAGEN Multiplex PCR master mix, 2 μL of 5 × Q-solution , and the labeled forward primer at 0.15 μM and the reverse primer at 0.15 μM and 2 μL of DNA template. The thermal profiles included an initial denaturation at 95 °C for 15 min, followed by 35 cycles at 94 °C for 40 s, Ta at 56–62 °C  of DreamTaq DNA polymerase (Thermo Scientific). The thermal profile was 95 °C for 5 min, followed by 35 cycles at 95 °C for 35 s, 56 °C for 40 s, 72 °C for 45 s, and a final extension of 72 °C for 10 min. The amplified products were visualised using a 2% agarose gel. The positive amplicons were cleaned up with Exonuclease-I (Thermo Scientific) and Shrimp Alkaline Phosphatase (Applied Biosystems), and sequenced using forward primers in 3500XL Genetic Analyzer (Applied Biosystems).We selected a 520 bp partial fragment mitochondrial CR to assess the genetic variation in gharials. We used primers (L15637 5′-GCATAACACTGAAAATGTTAAYATGG-3′ and H16258 5′-CTAAAATTACAGAAAAGCCGACCC-3′) described by Oaks (2011) to amplify the selected fragment56. We scored each repeat with ‘1’ if the genotype was identical to the consensus and ‘0’ if the genotype did not match the consensus due to any errors such as no amplification, allelic dropout, or false allele56. We estimated the average amplification success as a percent of positive PCR amplification. We estimated the frequency of genotyping errors due to allelic dropout (ADO) and false allele (FA) following Broquet et al.58. The frequency of null alleles was estimated using FreeNa59 and occurrences of large allelic dropout using Micro-Checker v2.2.360.We obtained a consensus genotype and estimated the quality index for each locus genotyped following Miquel et al.We used samples with a quality index above 0.67 per locus  and a mean quality index across loci above 0.75 for analyses.61. We estimated summary statistics  using GenAlEx v6.062 and deviations from Hardy–Weinberg equilibrium (HWE) for each locus using Bonferroni correction in Cervus v3.0.763. We also estimated allelic richness using a rarefaction approach implemented in HP-Rare v1.1 to account for the uneven sample size of the two populations64.We calculated the probability of identity PID (biased) and PID (sibs) using Gimlet v1.3.3FST65,G’ST66 and allelic differentiation index Jost’s D67 using the strataG package68 implemented in R studio. The estimates were calculated using 103 bootstrap iterations. We inferred the population genetic structure using the Bayesian approach implemented in Structure v2.3.469. Structure is a systematic model-based Bayesian clustering approach that uses allele frequencies at each locus to infer the population structure. The analysis was performed for 1–10 clusters (K). For each K, ten iterations were run under the admixture model with correlated allele frequencies and sampling location as a priori. The LOCPRIOR (sampling location as a priori) model performs well when no clear signal of a structure is detected or when there is low genetic differentiation, limited loci or a limited sample size70. The simulations were run for 105 burn-in and 106 Markov chain Monte Carlo iterations (MCMC). The optimum number of K was inferred using the likelihood distribution L(K) and the delta K71, which were estimated using the web version of Structure Harvester, v0.6.9472. The assignment plot was prepared using the program Distruct v1.173. Additionally, we used the DAPC, a multivariate nonmodel-based approach, to identify and describe genetic clusters47. The optimal number of the clusters was estimated based on the lowest associated BIC. The analysis was performed using the adgenet in R studio.We have estimated the genetic differentiation using fixation indices m) between Chambal and Girwa populations74. The simulations (n = 3) were run with different seed numbers using 107 MCMC iterations and 106 burn-in periods.We used BayesAss v3.0 to estimate the recent migration rate (M ratio) implemented in Arlequin v3.175 and (b) the HET approach implemented in Bottleneck v1.2.0276. The M ratio estimates the ratio of the observed number of alleles to the size of allele range based on the assumption that in a recently reduced population, the ratio is expected to decrease due to random loss of alleles in a population. The calculated M ratio was then compared with the critical value (MC = 0.68). An M ratio below MC is considered as a signature of genetic bottleneck77. The HET approach assumes that in a recently reduced population, an excess of the gene diversity under Hardy–Weinberg equilibrium is expected relative to gene diversity under mutation-drift equilibrium. We used one-tailed Wilcoxon test to determine the presence of a significant number of loci with excess heterozygosity. The estimates were calculated under two mutation models: the SMM and TPM. The TPM tends to be the most appropriate mutation model for microsatellite loci78. The TPM was carried out at 95% SMM (variance at 12), and the simulations were run for 104 iterations76.We estimated demographic changes using two qualitative approaches: (a) the Garza-Williamson index . We also obtained previously published mitochondrial CR sequences of the Chambal population (n = 103) from GenBank (Accession No. MT458816–MT458918)Supplementary Information.


Both classical and recent genetic studies have unanimously concluded that the genetic landscape of South Asia is unique. At long distances the ‘isolation-by-distance’ model appears to correspond well with the genetic data, whereas at short distances several other factors, including the caste, have been shown to be strong determinant factors. In addition with these, tribal populations speaking various languages add yet another layer of genetic complexity. The Kol are the third most populous tribal population in India, comprising communities speaking Austroasiatic languages of the Northern Munda branch. Yet, the Kol have not hitherto undergone in-depth genetic analysis. In the present study, we have analysed two Kol groups of central and western India for hundreds thousands of autosomal and several mitochondrial DNA makers to infer their fine genetic structure and affinities to other Eurasian populations. In contrast, with their known linguistic affinity, the Kol share their more recent common ancestry with the Indo-European and Dravidian speaking populations. The geographic-genetic neighbour tests at both the temporal and spatial levels have suggested some degree of excess allele sharing of Kol1 with Kol2, thereby indicating their common stock. Our extensive analysis on the Kol ethnic group shows South Asia to be a living genetics lab, where real-time tests can be performed on existing hypotheses. This diversity has mainly arisen, in part, through long term human settlement, social customs and genetic drift5. Broadly, Indian populations can be categorised as the castes, tribes, linguistic and religious communities. Presently, India counts hundreds of tribal groups, belonging to four major language families; Austroasiatic, Dravidian, Indo-European and Tibeto-Burman7. Kol is one of them, with their major concentration in Central India  clustering with the Munda speakers. However, in the principal component analysis (PCA), both of the studied Kol groups aligned along the South Asian cline with clusters formed by a large number of Indo-European and a few Dravidian speakers  speakerers Fig. . Althougers Fig. . At the ers Fig. . Kol1 aners Fig. . It is n25 e.g. the Southeast/East Asian components among Mundari and Tibeto-Burman speakers25. However, we also see a South Indian component which was nearly fixed in Irula and is geographically widespread amongst other South Asian populations with a frequency gradient from east to west or south to north  we omitted this sample from the pool.We further investigated one outlier sample of Kol which showed high level of East/Southeast Asian ancestry. In the PC analysis, this Kol individual (Kol outlier) aligned along the Trans-Himalayan cline5 Fig. . In term5 Fig. , this inf3 test  in the populations29  sequences of the HVS-I (hypervariable segment I) and selected coding regions. Both of the Kol groups shared M2, M3, M18, M30 and R5 haplogroups  populations.In conclusion, contrary to what is suggested by their name, we found no recent common genetic ancestry of these two Kol groups with the Austroasiatic (Mundari) speakers. The genetic structure of these Kols is more akin to the North Indian Indo-European scheduled caste population known as the Harijan. This finding matches our recent finding that Harijans and Kols shared short IBD  segments with Indian Mundari speakers26. The present study presents what appears to be the first genetic evidence for such a collective ethnolinguistic identity reassignment.Thus, our detailed analysis on one of the major South Asian tribal populations, support a deeply rooted endogamy, which not only exist among caste populations, but also present among tribal populations. Particularly in this case, our sampled Kols lived side-by-side with the Mundari populations. Our finding leaves us with the question as to whether the sampled ‘Kol’ populations could represent the remnant of ancestral Kol before the ancestors of Munda were linguistically assimilated by incursive Austroasiatic speakers. Since antiquity and even in modern times, in the social climbing process, entire ethnic groups and language communities have been known to pass themselves off as another caste or linguistic group that happens to rank higher in the caste hierarchy9, in the case of the linguistically assimilated young Kols whom we sampled, we double-checked their ethnolinguistic identity with linguistic expert involved in the study. Since language shift has previously been reported amongst Central Indian tribes32, we presume that this is also the case with the Kols sampled in the present study. However, we note that a similar model did not appear to apply to the Gond in our previous studies20. Therefore, in this study we used large number of autosomal and mitochondrial DNA markers to investigate the conflicting association of Kols as well as their inter and intra population affinities . It was striking that, in our survey to the sampling regions Fig. , we did 33. We further selected 17 high-quality samples (seven Kol1 and ten Kol2) and generated Illumina 650 K genotype data. This data was released in our earlier publication23. All the 55 samples were sequenced for the mtDNA HVS-I region  was sampled from the geographic borders of Uttar Pradesh and Madhya Pradesh states and second group Kol2) was collected from Maharashtra state Fig. . Both of was coll34 for quality control and data management. We merged the data of the 17 Kol samples with the 1756 samples belonging to 119 world populations  that can affect both principal component analysis (PCA) and ADMIXTURE, we thinned the data set by removing one SNP of any pair in strong LD r2 > 0.4, in a window of 200 SNPs (sliding the window by 25 SNPs at a time). We performed PC analysis using the smartpca programme of the EIGENSOFT package37 with the default settings to capture genetic variability described by the first ten components. We ran unsupervised ADMIXTURE v1.338 with a random seed number generator on the LD-pruned data set 25 times from K = 2 to K = 15. The best supported clustering was shown at K = 1223. Given the result of the PC and ADMIXTURE analysis, we removed one outlier sample from the Kol2 group for further population-based analysis. The outgroup f3 statistics39 were calculated as f3 = , where X was any other population and Yoruba served as an outgroup. To investigate the pan-Kol ancestry, we performed D statistics by taking African Yoruba as an outlier D = , whereby X was the other Indian populations. For haplotype-based comparison ChromoPainter v126 and fineSTRUCTURE v126 were used to perform an MCMC iteration, using 10 M burning runtime and the same MCMC iterations. We first phased our samples with Beagle 3.3.240 and modelled haplotype sharing among studied individuals by using ChromoPainter. The ChromoPainter creates a co-ancestry matrix where each and every individual share chunkcounts and chunklength with each other26. Thereafter, fineSTRCUTURE algorithm cluster them in to subgroups based on the pattern of co-ancestry matrix. The data of fineSTRUCTURE were used to construct the maximum likelihood (ML) tree using MEGA41. Runs of homozygosity (RoH) were performed to investigate the inbreeding and ancestral homozygous component sharing. For RoH estimation, we applied window size 1,000 kb, a minimum of 100 SNPs per window allowing one heterozygous and five missing calls per window27.For autosomal data we used PLINK1.9Supplementary Information.


Tullbergia bisetosa is substantial among  islands, reflecting low dispersal and historic population fragmentation, while COI patterns reveal ancestral signatures of postglacial recolonization. This pronounced geographic structure demonstrates the key role of allopatric divergence in shaping the region's diversity and highlights the vulnerability of indigenous populations to genetic homogenization via human transport. For the invasive species Hypogastrura viatica, nuclear genetic structure is much less apparent, particularly for islands linked by regular shipping, while diverged COI haplotypes indicate multiple independent introductions to each island. Thus, human transport has likely facilitated this species’ persistence since its initial colonization, through the ongoing introduction and inter‐island spread of genetic variation. These findings highlight the different evolutionary consequences of human transport for indigenous and invasive soil species. Crucially, both outcomes demonstrate the need for improved intraregional biosecurity among remote island systems, where the policy focus to date has been on external introductions.Human‐mediated transport of species outside their natural range is a rapidly growing threat to biodiversity, particularly for island ecosystems that have evolved in isolation. The genetic structure underpinning island populations will largely determine their response to increased transport and thus help to inform biosecurity management. However, this information is severely lacking for some groups, such as the soil fauna. We therefore analysed the phylogeographic structure of an indigenous and an invasive springtail species (Collembola: Poduromorpha), each distributed across multiple remote sub‐Antarctic islands, where human activity is currently intensifying. For both species, we generated a genome‐wide SNP data set and additionally analysed all available COI barcodes. Genetic differentiation in the indigenous springtail  They are among the world's most remote islands and have substantial conservation value , which represent a substantial component of sub‐Antarctic soil communities and include a large and concerning number of alien invasive species on these and other islands , and the highly invasive Hypogastrura viatica  (Hypogastruridae). Both of these species occur on at least four sub‐Antarctic islands and have been confirmed not to comprise cryptic species  or by Tullgren funnel extractions of soil or vegetation cores (<30 cm2 width), and were immediately preserved in ethanol.We sampled two springtail species from the Order Poduromorpha: the indigenous 2.2TM technology. DArT‐SeqTM is a reduced representation next‐generation sequencing method, which employs a combination of restriction enzymes to digest genomic DNA, followed by the ligation of adaptors to both restriction enzyme overhangs, to facilitate sample identification and Illumina sequencing  Pty. Ltd.  for genomic DNA extraction and sequencing using DArT‐Seq2.3p‐values where necessary and generally refrain from heavy emphases on statistical significance, acknowledging growing concern over the appropriate adjustment for multiple testing in genomics , our results are adjusted for multiple tests using the most common approach in the genomic literature . For all other analyses, we only calculated uncorrected FST outlier approach and a false discovery rate of α = 0.01. Each SNP locus was also tested for deviations from Hardy–Weinberg equilibrium (HWE) in each population, using exact tests with a Bonferroni correction, conducted in dartR.The final set of SNPs for each species was tested for evidence of selection in BayeScan 2.1  was used to visualize the clustering of genetically similar groups within each species and was carried out using the “gl.pcoa” command in dartR. Additionally, 3D PCAs  were created using the “pca3d” package Weiner,  in R. WeK value from K = 1 to K = 15, using the simple prior model. The number of populations that best fit the data was determined using the “chooseK.py” function, and these population assignments were visualized using Distruct v.2.3  were amalgamated. This was performed iteratively until a final set of “pseudo‐populations” remained, each discernable by having a significant number of fixed differences from one another.Finally, each data set was tested for fixed allelic differences , which can indicate a complete lack of contemporary gene flow Slatkin, . This ap2.4T. bisetosa and H. viatica were collated from GenBank (http://www.ncbi.nlm.nih.gov/genbank) and the Barcode of Life Data System (“BOLD”: http://www.barcodinglife.org), by searching for public records within their respective Barcode Index Number (BIN) on BOLD. BINs employ a Refined Single Linkage algorithm to cluster sequences that are highly likely to represent the same species . Thus, outlier SNPs putatively under selection or deviating from HWE were not removed from the main data sets. However, we prepared a conservative data set for each species with all of these SNPs removed  than Macquarie, Heard or Marion islands , with FST estimates as high as 0.677 between sites on different islands and estimates ranging from 0.027 to 0.346 among sites within an island  tree for t Figure b. Each iK = 7) were identified by fastSTRUCTURE to best represent the data for T. bisetosa, although the resultant structure plot showed five clusters to be most explicit , or missing data occurring at the same SNPs.Seven clusters . Populations consistently exhibited high levels of inbreeding, ranging from FIS = 0.334 to FIS = 0.498  were also identified by fastSTRUCTURE  of T. bisetosa populations , populations on Macquarie Island were separated by up to 21 km, vastly exceeding the distances between differentiated Marion Island populations (1–3 km). Differences in local population structure of T. bisetosa may therefore reflect the unique geological history of each island. South Georgia and Marion Island have undergone numerous pulses of glaciation (and volcanism on the latter), which would have promoted population differentiation over time through physical fragmentation , as would be expected if populations had been seeded by a single colonization of few individuals. Indeed, COI data indicated that multiple introductions have occurred on each island, a process known to maintain genetic diversity in other invasive populations  if different lineages within islands are yet to completely interbreed (see Kolbe et al., Most of the genomic variation within H. viatica, PCoA and fastSTRUCTURE plots did discern clearly between three main island groups, with only Kerguelen and Possession island populations relatively indistinguishable. Population structure was also weakly evident between the two sites on South Georgia and the most distal sites on Macquarie Island, revealing that some degree of genetic drift has occurred within and among islands, since the introduction of H. viatica. That this geographic differentiation remained only moderate, however, explaining such a small amount of total genetic variation, likely reflects the limited evolutionary time over which the species has existed in the region, meaning it is unlikely to be at migration–drift equilibrium (Slatkin, H. viatica across the sub‐Antarctic. Very similar findings have also been made for invasive aphids from Kerguelen and Possession Islands (Lebouvier et al., Although nuclear genomic structure was not particularly pronounced for Slatkin, . DespiteSlatkin, , a link H. viatica occurred on all four sub‐Antarctic islands and most other sampled countries, indicating potentially rapid global spread of a successful genotype (Le Roux, Wieczorek, Wright, & Tran, H. viatica. The occurrence of two haplotypes on South Georgia shared with Norway and Greenland directly accords with Norway's whaling history on the two islands, which saw substantial shipping transport between them (Tønnessen & Johnsen, H. viatica occurs on several more islands across the Southern Ocean than were sampled in this study, for which the colonization history may well differ.One COI haplotype for H. viatica imply that movement of these individuals between islands is unlikely to disrupt evolutionary trajectories or biogeographic patterns to the same degree as for T. bisetosa. Rather, intraregional transport may help to spread beneficial alleles introduced by the unique colonizations of each island, an important prerequisite to realizing any advantage from the diversity provided by multiple introductions (Barker et al., H. viatica beyond what might be achieved through natural range expansion. This is likely to apply to other invasive soil species, as natural dispersal is often limited belowground (Cicconardi et al., H. viatica, additional introductions may continue to contribute new genetic variation, as evidenced by differentiated COI lineages. While genetic diversity is not always a prerequisite for invasion success (see Dlugosch et al., In the context of increased human activity throughout the sub‐Antarctic, the relatively weak geographic structure and negligible fixed allelic differences for 4.3Soil is a key pathway by which humans unknowingly transport species beyond their natural range Hulme, ; therefoOur work also indicates that repeat introductions of existing invasives are an important management consideration for remote island systems, with invasive spread likely promoted by intraregional transport. Again, this is in line with recent findings for soil ecosystems elsewhere (e.g. Novo et al., None declared. Click here for additional data file.


Sinotaia quadrata, a widely distributed freshwater snail species in East Asia, was used to investigate this issue. It is unclear whether S. quadrata in Japan was introduced from China and how different human uses and varying geographic patterns affect the contemporary population genetics between the two regions. Thus, we investigated the demography of S. quadrata and detected its genetic structure in Japan and continental East Asia.Anthropogenic factors potentially affect observed biogeographical patterns in population genetics, but the effects of ancient human activities on the original patterns created by natural processes are unknown. Sinotaia quadrata populations first naturally migrated from continental East Asia to Japan, which is associated with the ancient period in Japanese geohistory . They were then artificially introduced in association with agriculture expansion by human movements in two recent periods . Populations in different parts of Japan have their own sources. Natural migration in the ancient period and artificial introduction in the recent period suggest that the population distribution is affected by both the geohistory of East Asia and the history of human expansion. In the background of the historical migration and introduction, contemporary populations in the two regions show different genetic patterns. Population divergence levels were significantly correlated with geographical patterns in Japan and significantly correlated with human interventions variables in continental East Asia, suggesting that long‐term geographical isolation is likely the major factor that shaped the contemporary population genetics in Japan, while modern human uses are likely the major factor in continental East Asia.Our preliminary results show a complex demography and unusual genetic patterns in the contemporary populations for a common freshwater snail and are of significance to determine the historical formation and contemporary patterns of biogeography in Japan and continental East Asia. Sinotaia quadrata in Japan and continental East Asia. Natural migration in the ancient period and artificial introduction in the recent period suggest that the population distribution is affected by both the geohistory of East Asia and the history of human expansion. Long‐term geographical isolation is likely the major factor that shaped the genetic structure of contemporary populations in Japan, while modern human uses are likely the major factor in continental East Asia.We document the population history and genetic history of the common freshwater snail  Comparing studies of population genetics between or among the origin and introduced areas can clarify the migration, gene flow, mutation, and introduction history  and Bellamya purificata    , but fossil records in other parts of Japan (Honshu) are more recent about 4,000–1,000 years ago  and Vietnam , China , and Vietnam   Table , to extr6) Table , based o2.2S. quadrata, which were developed by Gu et al. . In the PCR protocol, we used normal forward primers and fluorochrome‐labeled forward primers, and added a 7‐bp PIG‐tail (5′‐GTTTCTT‐3′) to the 5′ end of reverse primers to enhance genotype specificity . Thermocycling conditions were as follows: 95°C for 5 min + 30 ×  + 60°C for 30 min . PCR products were genotyped using an ABI 3130 genetic analyzer (Applied Biosystems), and allelic sizes were scored by GeneMapper v4.0 (Applied Biosystems) using an internal size standard . Questionable specimens and poor amplification with hard‐to‐read genotypes were re‐extracted and reamplified. Genotypes that have less than 20% of missing data were considered for all analyses.After pretesting several microsatellite markers for u et al. ,2012b, w2.3STF for each locus and the pairwise population STF using FreeNA  and the pairwise population ST_HG, which was estimated with null alleles included , we found that null alleles had no significant impact on subsequent analyses. Therefore, we reported subsequent results using the full microsatellite dataset. We estimated the average number of alleles (AN) and the observed (OH) and expected (EH) heterozygosities, and we tested the analysis of molecular variances (AMOVA) using Arlequin v3.5 . We also identified individual assignments using STRUCTURE v2.3.4  as were used in DAPC. In the second round, we analyzed the structure of Japan and continental East Asia separately by choosing the range of K that was equal to the number of populations in each group. All independent runs were computed using an admixture model, allele frequencies that were correlated with a length of 10,000 burn‐in period, 50,000 Markov chain Monte Carlo replications after burn‐in, and ten iterations for each run for K. The optimal number for K was selected using Structure Harvester Web v0.6.94, according to the highest value of Evanno's delta K  using the microsatellite data to detect the overall genetic structure, and also that in Japan and continental East Asia using the adegenet v2.1.0 package in R Jombart, . We firsFrancis, .2.5ST_HG  using the Mantel tests with 10,000 permutations and a linear regression model to estimate the isolation by distance at each group level. We detected the local historical gene flow within Japan and continental East Asia using the maximum likelihood algorithm with Migrate 3.6.11  and μM = m/m , and calculated the effective number of migrants (4Nm = θ × M). We ran ten short chains , two long chains , burn‐in 10,000 initial trees, and combined long chains for estimates. The starting values for θ and M were estimated from the STF values. All individuals were included, and three independent analyses were performed.To provide a better explanation for the genetic diversity . Human population density (PopD) data were collected from the Population Density, v4.11 (2015) with a 2.5 arc‐minute resolution   using the function getData in the raster v2.9–5 package in R  using NASA's Earth Observation system Data and Information System (EOSDIS)  using all the following variables: human effect, bioclimate, longitude, and latitude. All predictor variables were standardized and tested using the randomForest package in R  were separated from others and were defined as the JPK group. The other populations were structured corresponding to the geographical patterns and were grouped into two parts: JP1 and JP2. The JP1 lineage was consistent with the usual area division in Japan called Tohoku (meaning Northeast) and Kanto (meaning East). The JP2 lineage was consistent with the usual area division in Japan, which is called Kansai (meaning West) and Shikoku (one of the four main islands of Japan), and had admixtures with populations on continental East Asia (CN). Populations in the continental East Asia (CN) were considered to be the source .We inferred the population history across East Asia using the Approximate Bayesian Computations (ABC) method with microsatellite loci data to check population history models. We estimated the population divergence, admixtures, and population size changes in the demographic population history using DIYABC 2.1.0  in JPK (Appendix S4). We defined the historical parameters by performing the pilot run with wide extensive parameters and all summary statistic values 2 distance for two samples). We set all of the microsatellite mutation models to the default settings. Then, we compared the different scenarios by computing their relative posterior probabilities by checking linear discriminant analysis on summary statistics using a logistic regression method from 10% of the simulated data sets that most closely resembled the observed data set. Under the most likely scenario, as determined by the logit transformation of parameters, we estimated the posterior parameter distribution in 10% of the simulated data sets that most closely resembled the observed data set. Then, we used the option of model checking with principal component analysis to evaluate how well the posterior predictive distribution of the model fit the observed data for the best scenario. Finally, using linear discriminant analysis on summary statistics, we evaluated the confidence in the scenario choice by computing the scenario‐specific prior‐based error using historical parameters that were drawn from the prior distribution and 1% of simulated data sets that most closely resembled the observed data set using the logistic approach with other parameters set to default settings.To balance the sample size of lineages and to determine the computing time, we randomly choose 60 individuals in lineages from JP1, JP2, and CN, and retained all individuals (33.1OH ranged from 0.4715 (Sichuan) to 0.7350 (Yilan) in China, from 0.2282 (Kagoshima) to 0.6588 (Nara) in Japan, and from 0.4716 to 0.6433 in Vietnam  and among populations in different groups  to 0.9632 (Kagoshima‐Kanagawa pair); and ii) JD is from 0.1258 (Shiga‐Biwako pair) to 0.9372 (Kagoshima‐Shizuoka pair).The average observed heterozygosity of continental East Asia 0.6149) was higher than that of Japan (0.4919)  for the overall populations  in the Japan group and three clusters  in the continental East Asia group. Structures illustrated by the suboptimal K values  in round II  between population pairs was higher among regions of continental East Asia than regions in Japan  and bioclimatic variables, geographical longitude and latitude, and major variables of human intervention in the environment  at time T3 (95% CI: 2.11 × 104–1.81 × 105). Eastern Japan  was mixed with CN and JPK and diverged at 7.91 × 103 generations  ago at time T2 (95% CI: 1.83 × 103–3.87 × 104). Western Japan  was mixed with CN and JP1 and diverged at 1.16 × 103 generations ago  at time T1 (95% CI: 74.5–7.81 × 103). Expansions for the corresponding populations occurred at T2.1  and T1.1   Figure . Previou44.1S. quadrata in Japan migrated from continental East Asia at about 70,000 years ago (T3). This period is consistent with the hypothesis of an East China Sea land bridge as a “dispersal corridor”  probably contains both migration from the continent after it had geographically separated (best model of scenario 11) and ancestral divergences from the unknown ancestors before it had geographically separated . This would be illustrated by the independent origins of the Japanese archipelago that originates in southwestern Japan, which is related to the coast of mainland China, but the origins of northeastern Japan are related to coast of eastern of Siberia , which indicated that populations in Kyushu had the same unknown ancestors as those in continental East Asia. Except for the origin of the Kyushu populations, scenarios 7 and 11 illustrate the identical process for populations in the other parts of the Japanese archipelago Figure . Our res4.2S. quadrata spread throughout Japan are not simply a spread from west to east, but are a multiple processes of migrations and admixtures. These episodes of population spreading and mixing within Japan and migration from the continent occurred beginning 8,000 years ago are most likely to be caused by human movements with agriculture expansion  and mixed with some populations that migrated from Kyushu. Then, the migration continued from the continent to western Japan approximately 1,200 years ago (T1), and admixture with some populations that migrated from eastern Japan occurred Figure . PopulatS. quadrata to undergo repeated colonization–extirpation processes, which caused periodic losses in genetic diversity and multiple founder effects. This is consistent with the above results, which showed that the population size of Kyushu historically decreased , Honshu, Shikoku, and Kyushu from east to west. The largest island of Honshu is usually divided into two regions of Kanto (east) and Kansai (west) by mountains between Lake Biwa and Nagoya, and the Kanto region also contains an area of Tohoku (northeast)  Figure . Our res) Figure  where th) Figure . In addi) Figure . AlthougS. quadrata populations historically had two main approaches to migration from continental East Asia to Japan. One approach is natural migration that is associated with the geohistory of Japan in ancient times, and the other approach is the artificial introduction that is associated with agriculture expansion by human movements in recent times. Populations in Kyushu were initially derived from the continent probably through the East China Sea land bridge during the last glacial period  as the first approach. Populations in eastern Japan originated recently  from continental East Asia and Kyushu at the beginning of the agriculture expansion through human movement from continental East Asia to Japan. Populations in western Japan, however, originated more recently  from continental East Asia and eastern Japan when the socioeconomic and culture was comprehensively exchanged between continental East Asia (such as China) and Japan. Natural migration in the ancient period and artificial introduction in the recent period suggest that the population distribution of S. quadrata is affected by both the geohistory of East Asia and the historical human expansion. Moreover, in the background of the historical migration and introduction, contemporary populations in the two regions may experience different biogeographic processes, which are manifested in different genetic structures in the two regions. Populations in Japan that have complex geographical barriers are more highly structured and isolated by distance than that in their source populations in continental East Asia where there is more human use of this snail, suggesting that long‐term geographical isolation is likely the major factor that shapes the genetic structure of contemporary populations in Japan, while modern human uses are likely the major factors affecting the genetic structure in continental East Asia. Although further studies are required, our preliminary results reveal a complex population history and unusual genetic patterns in the contemporary populations for a common freshwater snail in Japan and continental East Asia. By emphasizing the effects of natural processes and humans, our study is of significance to research on the historical formation and contemporary patterns of biogeography in East Asia.In conclusion, The authors declare that they have no competing interests.Bin Ye: Conceptualization (lead); Data curation (lead); Formal analysis (lead); Resources ; Software (lead); Validation (lead); Visualization (lead); Writing‐original draft (lead); Writing‐review & editing (lead). Takumi Saito: Conceptualization ; Data curation ; Investigation (lead); Resources ; Software ; Writing‐original draft ; Writing‐review & editing . Takahiro Hirano: Conceptualization ; Data curation ; Investigation (lead); Resources ; Software ; Writing‐original draft ; Writing‐review & editing . Zhengzhong Dong: Investigation ; Resources ; Writing‐original draft ; Writing‐review & editing . Van Tu Do: Investigation ; Resources ; Writing‐original draft ; Writing‐review & editing . Satoshi Chiba: Conceptualization (lead); Data curation ; Formal analysis ; Funding acquisition (lead); Project administration (lead); Resources ; Writing‐original draft ; Writing‐review & editing .Supinfo S1Click here for additional data file.Supinfo S2Click here for additional data file.Supinfo S3Click here for additional data file.