Avena ventricosa Balansa ex Coss. is considered the C-genome donor of the cultivated hexaploid oat and is a ‘priority’ species for conservation, since it has limited geographic distribution and the only recorded populations in Europe are present in Cyprus. The current study attempts to characterize the genetic structure and fragmentation of the species via the application of genotypic markers. It was revealed that the genetic variety was mainly allocated among the populations collected, since clustering obtained was according to the geographic origin of the samples and the habitat. Species distribution modeling showed that the most important climatic variable defining A. ventricosa distribution is the mean diurnal temperature. Furthermore, significant association of the genetic structure to environmental variables was detected; overall, a negative association to precipitation was confirmed, while significant correlations of genetic structure and the temperature at the time of anthesis and germination were established. The safeguarding of this valuable genetic resource is discussed. Cyprus is the third island in size of the Mediterranean basin and is distinctive for its rich biodiversity. This is primarily due to its geographical location, unique edaphological and geological landscapes, relative isolation from continental regions, diverse climate conditions, as well as, human intervention that has uninterruptedly shaped ecological habitats since Neolithic Period . ConsequAmong the plants that constitute this great taxa wealth, primarily stand out endemic and Wild Crop Relative (WCR) species. WCR epitomize the regional adapted genotypes and constitute a genepool of desirable traits (including genes for biotic and abiotic tolerance), as well as including progenitors of cultivated species.A. sativa L. and A. byzantina K. Koch, both AACCDD genome) are considered as important cereal crop globally. The preservation of their WCRs have been prioritized since 2008 via the Integrated European in situ Management Work Plan [A. ventricosa Balansa ex Coss. [in situ conservation. Till present, there is a complete absence of information regarding the genetic diversity and population structure of A. ventricosa in Cyprus, which is the basis for any breeding or conservation project. The need for studying the genetic diversity of A. ventricosa is also underlined by the globally low numbers of accessions kept in gene banks [ex situ conservation of Avena spp [A. ventricosa has been proposed as the C-genome donor in cultivated hexaploid oat species, a fact that gives additional significance for its conservation [Cultivated hexaploid oat species . In addition, species A. longiglumis Durieu accession Cc4719 (AsAs genome), A. eriantha accessions JIC 2087, Cc 7065, PI367381 (CpCp genome) and A.clauda accession Cc 7044 (CpCp genome) were also included as outliers. Seeds were germinated and total DNA was extracted from 2-week-old leaves using the Nucleospin Plant II kit  following the manufacturer’s instructions. The quality and quantity of DNA was defined using Nanodrop 1000 Spectrophotometer (Thermo Scientific) and confirmed in a 0.8% agarose gel electrophoresis.Single spikelets were collected from  as previously described [. PCR products were mixed with 0.1 μL molecular size standard  and fractioned on a Genetic Analyzer ABI Prism 3130 (Applied Biosystems). Data were analyzed using GeneMapper .SSR reactions were set up in a 10 μL volume of a mixture containing 25 ng of genomic DNA, 0.75 U Taq , 1X reaction buffer, 100 μM dNTPS and 0,1 μM of each primer escribed . SSR marhttp://taxonomy.zoology.gla.ac.uk/rod/treeview.html). Popgene [All fragment sizes were transformed into binary data (presence/absence) and organized in a single matrix table. FreeTree  was empl Popgene  was used Popgene . The sigAvena ventricosa populations, with the Structure 2.3.4 software [http://royfrancis.github.io/pophelper/).Bayesian model-based clustering approach was used in order to identify the genetic structure of the software . The StrA. ventricosa on the Island of Cyprus. Species Distribution Modelling (SDM) can identify zones with analogous locations to those where a species has previously been detected. SDM, based on climatic information from the database of WorldClim—Global Climate Data (http://www.worldclim.org/), was performed to define A. ventricosa habitat. The climatic envelope occupied by the species is described through 19 environmental variables, listed in S2 Table. These information include biologically relevant temperature and precipitation layers shaped by interpolating data from weather stations around the world at a resolution of circa 1 km (30 arc-seconds), under current conditions. Furthermore, minimum, mean and maximum monthly temperature and precipitation data, were included.The initial objective was to outline the ecological niche of http://biodiversityinformatics.amnh.org/open_source/maxent). Random null distributions were produced to assess the significance of the SDM. For this test, we built a SDM using 27 occurrence points for A. ventricosa ecotypes (S3 Table) retrieved from the eurisco database (https://eurisco.ipk-gatersleben.de) and the National Genebank of Cyprus (http://arinet.ari.gov.cy). The 19 Bioclim layers for current climate conditions, were used with 75% training data and a cross-validation with 25% of the remaining localities (test data) which enabled the software to compute a testing versus training data association. Specifically, an Area Under the Curve (AUC) value was calculated, providing a measure of the extrapolative power of the specified model. AUC values should exceed 0.70, in order to provide acceptable predictive power [The maximum entropy model, implemented in MAXENT 3.4.1 algorithm was used . Consequently, correspondences among population clustering, environmental factors and genetic diversity indexes were measured using Pearson’s correlation as implemented in the SPSS 22.0 software (S4 Table).In order to examine the genetic structure of A. ventricosa individuals, signifying the rather narrow genetic diversity detected with microsatellites. On the contrary, AFLPs generated a larger set of molecular markers producing 184 sites, with 41% of them being polymorphic. The combined data were used to calculate the genetic similarities among and within populations. Genetic relationships among the different oat genomes  and the plant material used were depicted in the UPGMA dendrogram . From the five diploid accessions included in the analysis as outliers, A. longiglumis Cc4719 (having an Al genome) was clustered as an outgroup, while the Cp genome A. clauda (Cc 7044) and A. eriantha  accessions were clearly separated from the core of A. ventricosa genotypes (Cv genome). Furthermore, all A. ventricosa individuals were clustered according to the region collected, reflecting inter-, as opposed to, intra-population variability. The six populations sampled were organized in three distinctive clusters. Population ARI00-848 (Agios Sozomenos), ARI00-837 (Lithrodondas) and ARI00-844 (Koutrafas) formed the largest group and had genetic affinity with the second cluster composed of ARI00-839  and ARI00-854 (Dekelia) populations. Finally, population ARI00-853  was the most isolated and genetically distinctive.A total of 10 SSR alleles were identified in the Na), the Number of Private Bands (NPB), the Percentage of Polymorphism (%PP) and the Shannon Index (SI) (Table 2). Between populations the number of loci detected was similar, ranging from 145 (population ARI00-844) to 161 loci (ARI00-854). Within each population at least one unique locus was detected; population ARI00-854 had a maximum of six. Relatively low values were calculated for the percentage of polymorphism and the Shannon index. This is however expected in strictly self-fertilized species, such as A. ventricosa. Nevertheless, populations ARI00-854 and ARI00-839 were the most polymorphic (14%) (Table 2). The radial dendrogram of genetic distances based on the allelic variability of the combined loci for all six populations of A. ventricosa is presented in Fig 2B. UPGMA analysis revealed that entries from two A. ventricosa populations formed unique clusters, while three other populations were grouped in another cluster. Both groups were interrelated with the intermediate population ARI00-853.In order to study the intra-population variability of the collected individuals, several indexes of descriptive statistics were calculated; among them, the number of Alleles  was between populations, while the intra-population variance was restricted to 15% (Table 3). The statistically significant genetic discrepancies among the Cypriot A. ventricosa populations were also demonstrated by the high ΦPT value . AMOVA also permitted us to calculate the ΦPT paired values among populations, summarized in Table 3. The comparison among pairs confirmed the genetic distinctiveness of the ARI00-853 population, that had the least genetic affinity with the ARI00-844 population . This was in accordance with the previous analyses that underline the unique genetic background of that population.The clustering of Fig 3). As a result, the accessions used in the present study were successfully assigned into three groups . In addition, a lower value at K = 8 (ΔK = 7.553) was identified. Interestingly, the majority of genotypes had a uniform genetic identity, while in few cases admixed genotypes were recorded. The Bayesian analysis revealed that populations ARI00-837, ARI00-844 and ARI00-848 were homogenous (q = 0.99), genetically affiliated and formed the largest cluster. All individuals of the ARI00-853 population were also homogenous (q = 0.99) and constituted a distinctive second cluster. Finally, genotypes of populations ARI00-854 and ARI00-839 were genetically associated shaping the third cluster. Interestingly, population ARI00-839 had a large fraction (four out of 11) of mixed genotypes .In order to further elaborate the genetic structure of the populations, a Bayesian inference analysis was conducted using the Structure 2.3.4 software. We investigated the range from K = 1 to K = 10 and calculated the posterior probability for each value of K using the estimated log likelihood of K. The apparent optimal number observed was for K = 3 , identifying a circular band of most ideal habitat on the plains and coastal areas of Cyprus Island, and emphasizing the continental to coastal suitable habitat (warmer colors correspond to areas with a higher probability of habitat suitability for A. ventricosa). AUC values exceeded 0.70 in all analyses (0.826 ± 0.097), indicating that MAXENT models had more than acceptable predictive power. Throughout the predictions, the highest contributing climate variables were always related to mean diurnal temperature range . Taken together, the SDM analyses indicated that environmental factors clearly govern the presence of A. ventricosa in Cypriot habitats, with mean temperatures being also particularly important predictors of species presence in different areas of the island.MAXENT predictions agreed well in all models . Significant positive correlation was detected among population structure and the average temperatures in March  and in November . The Shannon index and the percentage of polymorphism were also positively correlated to mean temperature of July . On the other hand, a general pattern of negative correlation to monthly precipitation was detected, eventhough significant at the P>0.05 level.A. ventricosa populations in Cyprus and to further examine the existing genetic structure and its association with landscape, and climatic variation. Using a combined molecular markers approach (SSRs and AFLPs), the results of the current study show a relatively low genetic diversity percentage present at the intra-population level . In particular, the AMOVA distributed the genetic diversity mainly among the populations (85%), supported by the high value of the ΦPT index , measuring the genetic differentiation of populations among species. Eventhough, comparison of genetic diversity estimations amongst different markers can be ambiguous, nevertheless parameters that calculate the intra- and inter-population variation, such as, the ΦPT index, are generally comparable among different techniques [PT indexes have been correlated to the fixation of homozygous alleles within subpopulations and the reproduction mode of species (cross-pollinated or self-pollinated), with self-fertile species having higher values than outcrossing species [A. ventricosa populations can be attributed to the reproductive biology of the species that is a central component on how genetic variability is distributed within and among populations and defining the genomic evolution of a species [A. ventricosa is an annual plant, the short life cycle could be an additional parameter justifying the low percentage of genetic diversity. Finally, small populations frequently encompass less within-population genetic variation related to larger populations, owed to the loss of alleles produced by inbreeding and genetic drift [The objective of the present study was to reveal the genetic variation of chniques ,19. Furt species . Moreove species . Further species . Since Aic drift .Table 3), as expected for self-pollinated species [A. ventricosa putative habitats in Cyprus. The distinct nature of appropriate environments corresponded to a central belt (the plain between the Troodos and Kyrenia Mountains) of the island, where the probable niche of A. ventricosa populations would be generally moderate warm and dry. The evading of rainier zones is reinforced also by earlier observations of demarcation between western and northern (wetter) zones of the island [A. ventricosa habitats partitioned crosswise local ecozones, rather than a simple boundary intersecting northern and western regions. For instance, Koutrafas and Lithrodondas (populations ARI00-848 and ARI00-837) are areas of relatively high altitude, at the margins of Troodos Mountain. The regional flora consists mainly of open forest and shrub land . In these areas A. ventricosa coexist with species such as Pinus brutia Ten., Pistacia terebinthus L., Pistacia lentiscus L., Cistus creticus L., Levandula stoechas L., Thymbra capitata L. and Sarcopoterium spinosum L. On the contrary, the regions of Dekelia and Athalassa National Park (where populations ARI00-854 and ARI00-839 were collected) are hot and dry climate territories. The most characteristic plants are Thymbra capitata and Sarcopoterium spinosum. Finally, the population from Larnaka Salt Lake (ARI00-853) was recorded and collected from phryganian type vegetation just on the margins of the lake.Nevertheless, moderate genetic differences were recorded at an inter-population level . To quantify the significance of environmental variables imprinting to the genetic structure of A. ventricosa populations, we employed Pearson correlation tests. These established the presence of robust correlations among environmental and genetic distances among topographical populations and further suggested that climate patterns are important elements of genetic structure. Indeed, climate seems to further explain genetic variance in addition to the topography. Significant and positive correlation was accounted among the genetic structure (and diversity) of populations and the mean temperature of November and March, while a general negative correlation to precipitation was detected (S4 Table). Of great interest, is that the above monthly temperatures correspond to the germination and anthesis, respectively, of A. ventricosa in Cyprus, biological stages that are highly important in terms of adaptability and survival. It seems that after long-term exposure to the adverse environments, flora that grow under these conditions develop adaptive mechanisms and thus increasing sub-differentiation and genetic diversity [A. ventricosa populations. Furthermore, a significant positive correlation of the environment (excluding the effect of geographic distance) is suggestive of the interaction of neutral dynamics and natural selection to impartial procedures of gene flow among variable population densities, as well as, genetic drift [Given the strong genetic diversity among populations between sites, we wanted to relate possible signals of significant correlation among environmental and genetic dynamics at each region. According to the SDM, the mean diurnal temperature range variables was highly essential in predicting suitable habitat for iversity . Furtheriversity , as it iic drift . In concic drift ,32. As sic drift .A. ventricosa accessions from Cyprus in international collections is not adequate and are derived from a restricted number of populations [in situ conservation and further support the application of suitable sampling practices for future ex situ germplasm acquisitions. Based on the current study, populations ARI00-848, ARI00-839 and ARI00-853 should be monitored and maintained in the areas from which they were collected, and could further be the targets for future studies, since they present distinct genotypes, belonging to different clusters . The ex situ preservation of seeds, is further advised in order to maximise the possibility of retaining as many as possible distinct genotypes. Still, a more intensive special and temporal sampling would be ideal in order to get repeatable and even more robust results for a conservation plan to apply.Natural populations have been reported to harbor great neutral genetic diversity, and in addition present considerable range of disease resistance and quantitative traits of agronomic importance . Still, ulations ,35. Furtulations . Our porS1 Table(DOCX)Click here for additional data file.S2 Tablehttp://www.worldclim.org/) are derivative from tmean, tmin, tmax and prec .Variables ((DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.


Camaena cicatricosa was analyzed in this study based on mitochondrial gene  and internal transcribed spacer (ITS2) sequences in 347 individuals. This snail is the vector of the zoonotic food-borne parasite Angiostrongylus cantonensis and one of the main harmful snails distributed exclusively in China. The results revealed significant fixation indices of genetic differentiation and high gene flow between most populations except several populations. An isolation-by-distance test showed no significant correlation between genetic distance and geographical distance among C. cicatricosa populations, which suggested that gene flow was not restricted by distance. The levels of haplotype and nucleotide diversity of C. cicatricosa were generally high, except those in some special populations, according to the mt DNA and ITS2 data. Furthermore, the phylogenetic trees and asteroid networks of haplotypes indicated nonobvious genetic structure, the same as results got based on the synonymous and non synonymous sites of 347 sequences of the COI gene. All lines of evidence indicated that climatic changes and geographical and human barriers do not substantially affect the current population structure and distribution of the investigated snails.The phylogeographic structure of the land snail  However, the population phylogeography of an organism can be affected by various factors, such as climate, geographical conditions, the ecological environment, and historical processes as well as human activities etc.4. The influence of physical barriers and environmental variations on genetic structure has been investigated in many organisms, such as Sula leucogaster, Alectoris chukar and Adelphocoris suturalis7. An increasing number of researchers are now studying population genetics using cytoplasmic and nuclear genomes, due to the large amount of evolutionary information they provide9. The genetic structure of camaenids is currently poorly understood because of the difficulty of collecting specimens. In land snails, gene flow among different populations appears to be limited because of their poor migration ability11. Additionally, the morphology of the shell shows great intra-specific variation in the family Camaenidae, in terms of shell size, shape, color, spiral bands, growth lines, aperture, and the umbilicus, and these aspects may be affected by both the local environment and genetic evolution9. Therefore, investigation of the population genetic structure and phylogeography of camaenids will significantly enhance the current understanding of the geographic differentiation and historical biogeography of land snails.Molecular phylogeographic analyses can provide valuable information on specific genetic structures, genetic variation and population formationCamaena cicatricosa   is an important and harmful terrestrial mollusk found in southern China. It not only damages crops, leading to reductions in yield and quality, but also spreads zoonotic food-borne parasitic disease, and causes substantial damage to human and animal health12. The classification of this species is rather confusing, and its scientific name has been repeatedly revised. Different scholars present diverse viewpoints20. Shell morphological characteristics of C.cicatricosa are variable, and four synonymous names, which are C. c. ducalis , C. c. inflata , C. c. obtecta  as well as C. c. connectens  have been proposed. However, these subspecies are different each other in shell shape and size, openness of umbilicus, sharpness of peripheral angel, convexity of whorls and the presence of a hump beside umbilicus. These taxa previously were treated as synonyms, varieties or subspecies of C.cicatricosa based on only comparative shell morphology20. In 2016, the sinistral were revised and upgraded to species by Ding et al. according to shell morphology, anatomical as well as molecular characteristics21. Some studies have indicated that this snail is mainly distributed in Guangdong, Guangxi, Yunnan, Guizhou, Hunan, Hainan and Vietnam23. However, researchers recently clarified the phylogeny and taxonomy of sinistral camaenids, and found that this camaenid snail is distributed only in the provinces of Guangdong and Guangxi21. Previous studies on this snail have mainly focused on its taxonomy and mitochondrial genome23, but the phylogeography and population structure of C. cicatricosa have not been thoroughly understood.The snail COI), 16srRNA and nuclear internal transcribed spacer II region (ITS2) sequences. The main goals of this study were to (i) analyze the genetic diversity and structure of C. cicatricosa and (ii) examine the geographical pattern of its haplotypes. Genetic variation and gene flow were also evaluated, and factors influencing genetic variation were investigated.In the present study, the genetic structure of this species was assessed based on mitochondrial cytochrome c oxidase subunit 1 (COI (559 bp) and 16srRNA (302 bp) genes, with the following nucleotide content: 37.56% T, 13.80% C, 29.79% A and 18.85% G. A total of 46 polymorphic sites were identified including 7 singleton variables and 39 parsimony-informative sites. No insertions or deletions were detected among these fragments. The average number of nucleotide differences ranged from 0.000 to 6.725, with an average of 2.513. Haplotype diversity ranged from 0.000 to 0.848, with an average of 0.454, and nucleotide diversity from 0.000 to 0.008, with an average of 0.003 (Table 2 = 0.0423) among all of the populations, and the results indicated that gene flow was not restricted by distance  among all of the populations  was located at the center of the network, being distributed in populations CH, GM, QY, ST, TH, ZS, NN and ZP, including 81 individuals. Therefore, Hap1 was considered the major haplotype. The other haplotypes, including Hap3, Hap5, Hap13 and Hap20, were comprised of 27, 25, 28 and 25 individuals, respectively. One exclusive haplotype (Hap33) and two missing haplotypes had mutated from Hap1, and other haplotypes had mutated from these two missing haplotypes. Hap15, which appeared 20 times, had mutated into 10 other haplotypes and 1 missing haplotype. The star-like network suggested that Hap33, Hap29 and Hap19 were different from most other haplotypes by only few mutations Fig. . FurtherC. jinpingensis and C. menglunensis were chosen as outgroup here as well. In these two trees, populations from Guangdong and Guangxi provinces were again mixed together and did not exhibit an evident structure  were also common and were distributed in additional populations. The major haplotype Hap3 appeared 215 times and generated 12 other haplotypes as well as 4 kinds of missing haplotypes. The rest of the haplotypes were likely generated from the major haplotype two or three times. Additionally, 21 exclusive haplotypes were connected with the major haplotypes through a mutation, 15 of which were located at the end of the network. The haplotype Hap3 was found in the most individuals, except the YD population from Guangdong. The populations in Guangdong included all of the shared haplotypes except Hap26. Only five haplotypes were distributed in Guangxi populations, which included only five individuals, four of which were located at the edge of the network  hypothesis32. An important novel point of the MGD hypothesis is its emphasis on the internal system or physiology of a species45. Because of its adaptation to mankind-disturbed environments, such as farmland and forest ecotone, this species has a large distributional range. The probability to be passively transported far distances and to establish a new population through human activities is also very high47. Besides, the snail C. cicatricosa is hermaphroditic creature mating with other conspecific individuals, which can laid more eggs (10–25 eggs each clutch), and has shorter gestation period (5–36 days) between the last copulation and the first egg-laying48. Organisms having higher fecundity and abundance tend to be more competitive49. The complex topography and geomorphology, varied physical conditions and a wide diversity of ecosystems in mountainous areas may result in allopatric and sympatric speciation51. Based on the dominance of C. cicatricosa in these areas, we must analyze physiological and ecological sections of this species in future research.There are many factors that affect genetic structure and population distribution. An isolation-by-distance test showed no significant correlation between genetic distance and geographical distance among C. cicatricosa populations. These findings were confirmed by phylogenetic trees based on synonymous and non synonymous sites. Though the populations in Guangdong and Guangxi still can not be separated in the phylogenetic tree based on synonymous sites, individuals from the same population or province have closer relationships than those from different populations or provinces. The phylogenetic tree based on non synonymous sites suggested equal relationships except ZQ16 and ZQ20. That is to say the phylogenetic tree based on synonymous sites could reflect further information and make more sense. The results of this study suggested that the cause of homogeneity is the high level of gene flow, as demonstrated in other animals39. It is inferred that climatic changes and geographical barriers do not substantially affect the current population structure and distribution of this snail. In the future, more samples and a broader distribution of sampling locations are the necessary first step to study the genetic distribution patterns of C. cicatricosa. Most importantly, physiology, biology and ecology should not be overlooked.In the present study, the phylogenetic trees and networks of haplotypes based on the obtained datasets showed a lack of genetic structure among 53. The geographic coordinates were recorded using a GPS. Live adults were drowned in water for 12–24 hours and then euthanized in hot water to ensure their death. Their soft bodies were preserved in 75% or 95% ethanol and stored at −20 °C. The empty shells were cleaned, dried and preserved at room temperature. Samples were deposited in the State Key Laboratory of Molluscan Quarantine and Identification, FJIQBC.This study was based on 347 individuals collected by the authors from 20 locations in China in 2013–2015 years Table 21,52,53.COI and 16srRNA genes, and the total sequence of ITS2 were amplified via PCR using the primer pairs , and stored at −20 °C for further use. Partial fragments of the mitochondrial rs Table , reactio54. ITS2 sequences were annotated using HMMer55 and the ITS2 Database56. The alignment of mitochondrial protein-coding genes was inferred from the amino acid alignment and examined for the presence of stop codons and other indicators. Sequence alignments were generated using ClustalW implemented in MEGA 657. Sixty-seven haplotype sequences were generated and deposited in GenBank under accession numbers KU927017–KU927046 for COI, KX365248–KX365255 for 16srRNA and KU958515–KU958543 for ITS2.After sequencing, the raw sequences were proof-read in chromatograms and aligned into contigs using BioEdit 7.257. The number of polymorphic sites (S), haplotype diversity (Hd), nucleotide diversity (Pi), average number of nucleotide differences (K) and number of haplotypes (Hap) were calculated using DnaSP 5.058 or Arlequin 3.559. The gene flow was estimated using DnaSP 5.058 too.The nucleotide composition, mutation sites and base substitution were analyzed in MEGA 6ST values . Arlequin 3.559 was employed to calculate the genetic distance matrix to test for the presence of IBD in the dataset. Google Earth (http://earth.google.com) was used to estimate the linear geographic distance (km) between the sampling locations. Significance was tested with the Mantel test employing 1000 randomizations in IBDWS 3.2360.The genetic differentiation among populations was further estimated based on pairwise FCOI gene were employed to build phylogenetic trees. The node support values were assessed via bootstrap resampling using 1000 replicates. C. jinpingensis and C. menglunensis were employed as outgroup due to their close relationship with C. cicatricosa. The haplotype networks of the ITS2 and mt DNA data were constructed in Network 4.661 with the median-joining algorithm.Two datasets of mitochondrial and nuclear haplotypes were analyzed through both neighbor-joining (NJ) and maximum likelihood (ML) analyses using MEGA6 with default settings. Furthermore, the synonymous and non synonymous sites of 347 sequences of the Supplementary information


Culex quinquefasciatus collected in Uganda, where no vector control interventions directly targeting this species have been conducted, was used as a model to determine if it is possible to detect heterogeneities in selection pressure driven by insecticide application targeting other insect species.Vgsc-1014F of the voltage-gated sodium channel target of pyrethroid and DDT insecticides, and Ace1-119S of the acetylcholinesterase gene, target of carbamate and organophosphate insecticides. No significant differences in genetic diversity were observed among populations by microsatellite markers with HE ranging from 0.597 to 0.612 and low, but significant, genetic differentiation among populations . By contrast, the insecticide-resistance markers display heterogeneous allelic distributions with significant differences detected between Central Ugandan (urban) populations relative to Eastern and Southwestern  populations. In the central region, a frequency of 62% for Vgsc-1014F, and 32% for the Ace1-119S resistant allele were observed. Conversely, in both Eastern and Southwestern regions the Vgsc-1014F alleles were close to fixation, whilst Ace1-119S allele frequency was 12% .Population genetic structure was assessed through microsatellite analysis, and the impact of insecticide pressure by genotyping two target-site mutations, Culex.Taken together, the microsatellite and both insecticide resistance target-site markers provide evidence that in the face of intense gene flow among populations, disjunction in resistance frequencies arise due to intense local selection pressures despite an absence of insecticidal control interventions targeting  Culex quinquefasciatus is a primary vector of arboviruses such as West Nile virus (WNV) and St. Louis encephalitis virus (SLEV) in temperate regions, and in many tropical/sub-tropical regions is implicated in the transmission of lymphatic filariasis (LF). Insecticide-based approaches are one of the most important interventions to mitigate disease burden; nevertheless, increased resistance of vectors to insecticides poses a challenge for sustainability and effectiveness of both current and future vector control interventions. Here, Cx. quinquefasciatus collected across a transect from Eastern to South Western Uganda were utilized to infer the likely pattern of the evolution of insecticide resistance among populations through a combination of putative selective loci (the target-site mutations Vgsc-1014F and Ace1-119S) and 26 neutral microsatellite markers. Taken together, the results yielded by these markers provide evidence that Ugandan Cx. quinquefasciatus mosquitoes are under heterogeneous selection pressure imposed by insecticides from distinct classes. Contemporary evolution of insecticide resistance in mosquitoes, which threatens the effectiveness of insecticide-based approaches to reduce the burden of vector-borne disease in endemic regions, results from a complex interaction of biological traits (e.g. genetic mechanisms of resistance) and operational implementation of control programs .Ace-1) or the voltage-gated sodium channel (Vgsc) gene, as well as metabolic resistance mediated by upregulation of, or mutations in, detoxification gene families such as the cytochrome P450 (P450s), esterases and glutathione S-transferases (GSTs) are the most well studied genetic mechanisms underpinning resistance . To. ToK) in7 MCMC, 106 burn-in, chain sampling every 2,000 generations, and all mixing parameters set as default (0.10), with the exception of both allele frequencies (DA) and inbreeding coefficient (DF) runs set at 0.30 as suggested by the author. Parallel runs differed only in initial seed value. Mixing and convergence of MCMCs were visually assessed using TRACER 1.6. Among the five independent runs, we chose the one with lowest Bayesian deviation and constant convergence to assess migration patterns.To infer levels of gene flow among populations we utilized a Bayesian approach implemented in BAYESSASS 3.0 . InitialFST outlier approach. This method evaluates the relationship between FST and HE (expected heterozygosity) under the assumption of neutrality. The test was performed under an island model and infinite allele model as implemented in LOSITAN [FST” to allow the program to run once to determine a first candidate subset of selected loci in order to remove them from the computation of the neutral FST, and then the option “Force mean FST” was also used to get a simulated mean FST close to the observed one found in the real dataset. All other options or settings were left as default and 50,000 simulations were run.The 26 microsatellite loci studied were tested for selection based on the  LOSITAN . AnalyseFST outlier method was run, with all populations considered separately and with Central Ugandan populations (largely urban) combined. Thus, four simulations were performed as described above for: (a) all populations, (b) populations defined here as Central Uganda  vs Tororo, (c) Central Uganda vs Kanungu and (d) Kanungu vs Tororo. Where signatures of genomic selection in the vicinity of likely candidate microsatellite selected markers were detected, we examined the number of genes present in the supercontig encompassing the microsatellite markers and their GO terms using the BioMart tools available at VectorBase (www.vectorbase.org).To investigate if differing selection strength is detectable between geographic locations the Ace1-119S and Vgsc-1014F, was conducted utilizing TaqMan allelic discrimination and pyrosequencing assays, respectively  was observed in all populations with a frequency ranging from 12–32% (He (0.323 ± 0.065) was observed with a marked excess of heterozygotes (FIS = -0.362), whilst within populations, genotype frequencies were not significantly different from Hardy-Weinberg expectation for both the Kanungu and Tororo populations (P = 1.0) and between Kanungu and Tororo (P = 1.0).Genotyping of two target-site mutations, ectively . The Acem 12–32% . Despitem 12–32% . The frem 12–32% . Among tulations . AdditioVgsc-1014F locus in Cx. quinquefasciatus has two alternative non-synonymous substitutions (TTT and TTC) at the third position of the codon (L1014F) [Vgsc-1014F mutations we detected a high frequency of resistant alleles ranging from 62–100%  . For the 62–100% . The pyrrganisms . Mosquitrganisms . Excludirganisms .Vgsc resistance allele frequencies were detected between all populations with the exception of the Jinja and Kampala comparison (P = 0.63). The TTC resistant allele was observed at a high frequency (41–69%) in all populations with the exception of Kanungu, where a higher frequency of the TTT resistant allele (55%) was detected, coupled with an absence of the wild-type allele  with nine haplotypes detected displaying a total of 10 polymorphic sites , revealing deviation in the direction of heterozygote deficiency. Analyses performed with Micro-Checker indicated a high frequency of null alleles at loci MCQ28 (0.12) and MCQ33 (0.22), which could likely be responsible for the heterozygote deficiency at these loci. Although loci MCQ1 and MCQ34 were within HW expectation in at least one population, these loci failed to amplify in more than 10% of the samples also suggesting the presence of null alleles. We therefore excluded four markers  of the 30 tested. The microsatellite exclusion level in this study (four of 30 microsatellites or 13%) is consistent with previous studies which have examined genotypes in family lines as an additional verification of utility  showed departure from HW equilibrium across all populations . The numty e.g. , 61)..FIS), reHE) and allelic richness within populations were similar across populations (Rs = 5.17) detected in Kampala. Nevertheless, no significant difference in the index of genetic diversity was observed among the populations , while the pairwise FST values between populations ranged from 0.0071 to 0.0293  based on microsatellite markers  compared to Tororo and Kanungu. No signal of selection was observed for MCQ21 when comparing only Tororo and Kanungu  in Ugandan Cx. quinquefasciatus populations no vector control programme targeting this species has been conducted. Therefore, these results support a hypothesis that the evolution of insecticide resistance in these populations is driven by control interventions targeting other vector species or pest insects. Indeed, previous studies have suggested that the evolution of insecticide resistance in Cx. quinquefasciatus on the African continent might result from intervention approaches targeting sympatric Anopheles species [Cx. quinquefasciatus from Zambia showed a significant increase of the Vgsc-1014F mutation and increase of detoxification enzyme activity assays after introduction of ITNs for malaria control [Markedly, although our study demonstrates high frequencies of both target-site mutations , 41.6% (Mid-eastern) and 32.8% (South-western) between 2006 and 2009 [Markedly, we have also noticed a distinct pattern of ITN usage among UDHS, which differ by 14% between Central-South western and 8.7% Central-Mid-eastern, despite a similar ITN coverage of around 58% for all three regions . Additioand 2009 .Culex populations. Indeed, we recognise that pinpointing the particular source underlying the evolution of resistance remains a difficult task. For example, increased resistance in Cx. quinquefasciatus populations from Ghana and Benin, with high frequencies of Ace1-119S was attributed to the use of organophosphates and/or carbamate insecticides by farmers for pest control or pollutants in mosquito breeding sites [Cx. quinquefasciatus populations from Ghana [Nevertheless, with our study design it is not possible make a direct association of the UNMCP  interventions with the evolution of resistance in ng sites , 67. Heaom Ghana .Cx. quinquefasciatus from Uganda could also be modulated by variation in the repertoire of adaptive genetic mechanisms, as in the studied population we identified gene duplication for both target-site loci and allelic variation of the Vgsc resistant allele. Interestingly, we identify two alternative resistant alleles (TTT and TTC) in the Vsgc-1014 codon observed at high frequency in all populations, contrasting with previous reports on the geographic distribution of Vgsc-1014F in Culex mosquitoes worldwide [Cx. quinquefasciatus from Sri Lanka, which was previously argued as the most likely geographic origin [Cx. quinquefasciatus from Zanzibar [Vgsc it was not possible to determine if heterozygote TTT/TTC increases a mosquito’s level of resistance, or offsets possible deleterious effects.Regardless of the strength of selection imposed by diverse insecticide-based approaches such as ITNs, IRS, pesticides and environment pollutants , the evoorldwide , which eorldwide –76. To dc origin , and wasZanzibar . AlthougVgsc-1014F resistant alleles among populations, with the TTC variant being predominant in all populations with the exception of Kanungu. We do not have a conclusive explanation for the difference in frequency of TTT and TTC among Ugandan populations and between Uganda and other African populations. Nevertheless, a few potential mechanisms could explain this. One possible explanation involves a colonization process with the TTC allele introduced to Uganda from another geographical region with a large founder effect followed by genetic drift that shifted the TTT and TTC allele frequency, although a de novo origin and dispersion cannot be discounted. Alternatively, it is also possible that the TTC allele arose on the African continent but has not been detected in other populations due to the application of specific Culex species Vgsc-1014 genotyping assays, which were mostly designed for detecting the TTT and TTA alleles [We also identify a significant difference in frequency of the  alleles .Ace1-119 and Vgsc-1014 variants also indicated the presence of gene duplication for both genes, suggesting that CNV could be among the mechanisms driving the evolution of insecticide in these populations. Indeed, gene duplication of insecticide-associated genes such as Ace-1, esterase estα21 and estβ21, Rdl (resistance to dieldrin) and detoxification genes  have been reported as adaptive resistance mechanisms in diverse arthropod species including Tetranychus urticae, Aphis gossypii and A. gambiae [Our target-site genotyping of the  gambiae , 37, 78.Vgsc was indicated by the presence of 6.25% of mosquitoes genotyped by pyrosequencing displaying three alleles simultaneously for Vgsc-1014. For Ace-1, duplication was evidenced by a haplotype analysis, which identified three out of 12 individuals exhibiting either 3 or 5 distinct haplotypes simultaneously. Whilst contamination or DNA from spermatheca could be mistaken for duplications we have shown that the duplication of Vgsc is present in males [The likely duplication of the in males  and we sAce-119S homozygotes in all four populations, was not surprising as the same pattern has been reported previously in both Culex and Anopheles populations [Ace-1 gene involving resistant and susceptible alleles, creating a permanent heterozygosis to partially normalise AChE enzyme activity levels [Ace-1 duplication to overcome deleterious effects and fitness disadvantage of the resistant Ace1-119S [Vgsc duplication in Cx. quinquefasciatus from the USA  and in A. aegypti from Brazil [The absence of ulations , 20, 80.y levels , 81. In m Brazil  is curreCx. pipiens from California, USA [Cx. quinquefasciatus from Recife, Brazil after vector control intervention with Bacillus sphaericus, a bio-larvicide [Our study also detected a contrasting pattern between microsatellite and insecticide selected markers. For both markers linked to resistance, it was not possible to identify a clear spatial geographic pattern, but we did detect heterogeneity in allelic frequencies when comparing Ugandan Central (largely urban) to Eastern and Southwest populations . By contrast, no significant difference in genetic diversity indices was observed at neutral markers among the populations, although these data did indicate a structured pattern. Although the low levels of genetic variation of microsatellites across Ugandan populations from distinct geographic locations have also been reported in nia, USA , heterogarvicide . As indiarvicide , 85.Culex mosquitoes using molecular markers as diverse as ISSR (Inter-Simple Sequence Repeat) and microsatellites, which reported that population structure reflects geoclimatic, geographic distance and environmental variables [The microsatellite data also indicated significant genetic structure among populations with the three clusters identified by the STRUCTURE and DAPC analyses corresponding to Eastern, Central and Southwest Uganda regions. The level of population structure among our study sites was consistent with findings from similar studies in ariables –88. AddiAedes rusticus, comparisons among areas treated and not-treated with Bacillus thuringiensis israelensis (Bti) show more intense IBD among treated than among non-treated sites [The distinct pattern of IBD observed for microsatellite markers and selected markers, also indicated that factors other than geographic distance, such as environmental heterogeneity as well as serial sequential founder effects, may also influence the pattern of spatial autocorrelation of resistance-associated markers . For exaed sites . These aAce-1, Vgsc or genes encoding detoxification enzymes . However, this analysis was limited by the incomplete Cx. quinquefasciatus genome assembly, which restricted the genomic window capable of being applied in the analysis. Due to the contrasting patterns of neutrality for marker MCQ21, we speculate that this marker could be hitchhiking with an insecticide-associated gene nearby this marker, with a genomic location beyond the supercontig resolution.Therefore, despite the intense gene flow identified in the studied populations, it is possible that local differences in strength of insecticide selection might have created heterogeneous “insecticide adaptive islands” across the geographic regions. Analysis of microsatellite locus neutrality indicated that all markers indeed adhered to a neutral model with the exception of the MCQ21 across all populations and MCQ11 when comparing only Ugandan Central to Eastern populations. For both markers, further analysis of the genomic location of markers does not indicate likely linkage with known insecticide associated genes such as Cx. quinquefasciatus occurring despite the absence of vector control intervention targeting this species. This study demonstrates that although it is challenging to pinpoint the direct source of insecticide exposure in a background of selection pressure, studies of the evolution of insecticide in non-target species can provide important information about the nature of the insecticidal challenges to which vectors are exposed.In conclusion, our study shows that the contemporary pattern of local selection is driven by the evolution of insecticide resistance in Ugandan S1 Methods(PDF)Click here for additional data file.S2 Methods(PDF)Click here for additional data file.S1 Results(XLSX)Click here for additional data file.S2 Results(XLSX)Click here for additional data file.S3 Results(XLS)Click here for additional data file.S4 Results(XLS)Click here for additional data file.S1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 Fig(PDF)Click here for additional data file.S4 Fig(PDF)Click here for additional data file.S5 Fig(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 TableAce-1.Polymorphic sites identified in a 535 bp partial fragment (intron 2 and exon 3) of (PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.S6 Table(PDF)Click here for additional data file.S7 Table(PDF)Click here for additional data file.


The investigation of genetic clusters in natural populations is an ubiquitous problem in a range of fields relying on the analysis of genetic data, such as molecular ecology, conservation biology and microbiology. Typically, genetic clusters are defined as distinct panmictic populations, or parental groups in the context of hybridisation. Two types of methods have been developed for identifying such clusters: model‐based methods, which are usually computer‐intensive but yield results which can be interpreted in the light of an explicit population genetic model, and geometric approaches, which are less interpretable but remarkably faster.snapclust, a fast maximum‐likelihood solution to the genetic clustering problem, which allies the advantages of both model‐based and geometric approaches. Our method relies on maximising the likelihood of a fixed number of panmictic populations, using a combination of geometric approach and fast likelihood optimisation, using the Expectation‐Maximisation (EM) algorithm. It can be used for assigning genotypes to populations and optionally identify various types of hybrids between two parental populations. Several goodness‐of‐fit statistics can also be used to guide the choice of the retained number of clusters.Here, we introduce snapclust performs comparably to current gold standards for genetic clustering as well as hybrid detection, with some advantages for identifying hybrids after several backcrosses, while being orders of magnitude faster than other model‐based methods. We also illustrate how snapclust can be used for identifying the optimal number of clusters, and subsequently assign individuals to various hybrid classes simulated from an empirical microsatellite dataset.Using extensive simulations, we show that snapclust is implemented in the package adegenet for the free software R, and is therefore easily integrated into existing pipelines for genetic data analysis. It can be applied to any kind of co‐dominant markers, and can easily be extended to more complex models including, for instance, varying ploidy levels. Given its flexibility and computer‐efficiency, it provides a useful complement to the existing toolbox for the study of genetic diversity in natural populations. Quantifying the magnitude of the population subdivision, assessing whether the genetic differentiation matches with the spatial repartition of subpopulations or not, and, identifying from which genetic units individuals belong or come have been the focus of attention of population geneticist from the inception of population genetics Wright, . SpecifiWhile there is no single taxonomy of methods, a natural separation can be made between “model‐based” approaches, which use a population genetics model to compute a likelihood, including maximum‐likelihood (ML) and Bayesian methods  equilibrium to compute the likelihood of a given clustering solution. Rapid convergence to ML estimates of clusters is achieved by combining geometric approaches  and j indexes loci , so that xi,j is a vector of allele counts for individual i at locus j. The likelihood of our model is defined as the probability of observing these data given a clustering solution g = {g(i)}, where g(i) defines the group of individual i, with groups indexed by k = 1, …, K. Under the HW model, this likelihood is defined as:M is the probability mass function of the multinomial distribution, fg(i),j is the vector of allele frequencies in group g(i) at locus j, and π is the ploidy of the organism considered. Allele frequencies within a group are directly computed as the relative frequencies of each allele in this group. Assuming independence between loci, the likelihood term for the genotype i is given by the following:fg(i) = {fg(i),1 ,…, fg(i),J} and xi = {xi,1, …, xi,J}. If we further assume independence of individuals conditional on their group memberships, the general likelihood is given by the following:f = {f1 ,…, fK}. In practice, we will consider the log‐likelihood of a clustering solution defined as follows:snapclust considers a constant ploidy across individuals and loci, the formula above can readily be extended to varying ploidy, in which case π will become an individual‐ or locus‐specific term.We consider a dataset of allelic profiles p(g(i) = k) that an individual i belongs to a group k is defined by the standardised likelihood:Assuming that all clusters have been sampled, the probability 2.1.2A and B. The allelic composition fH,j(w) of a hybrid population H at locus j is defined as a mixture of the allele frequencies of two parental populations, fA,j and fB,j. This mixture is defined by the hybridisation coefficient w, which indicates the proportion of the genomes of the hybrid population coming from the parental population A, so that:w is very flexible, as it enables the specification of any kind of hybrids between A and B. For instance, first‐generation hybrids (F1) correspond to w = 0.5, while first‐ and second‐generations backcrosses with A that correspond to w = 0.25 and w = 0.125 respectively. The likelihood of a hybrid is defined as before, but using the allele frequencies mixture as follows:The clustering model above can be readily extended to accommodate the presence of hybrids. For simplicity, we consider a case where hybrids are obtained from two parental populations 2.1.3, snapclust achieves fast likelihood maximisation using the EM algorithm define initial group assignments expectation step) update allele frequencies f within each group, computed as the relative frequencies of alleles amongst individuals of this group; compute group membership probabilities p(g(i) = k) for all individuals i and groups k(maximisation step) update the group definition g: based on group membership probabilities computed in step 2, assign each individual to their most likely group.Here2.1.4K‐means in R . As a consequence, it is unfortunately also prone to being trapped in local maxima, yielding suboptimal results for some starting points. To avoid this issue, we implemented several options to define the initial clusters used as starting point of the algorithm. The first strategy, borrowed from the original implementation of 2.1.5L’ the estimated maxima of LL(g), the AIC of our model is computed as:P being the total number of alleles in the dataset across J loci. The complexity reflects the fact that for each of the K groups, (P − J) independent allele frequencies are estimated, so that the total number of free parameters of the model is (K (P − J)). We also implemented the variant of the AIC for small sample sizes, defined as . The function snapclust.em implements the basic method, including different options for defining the initial state of the EM algorithm and the model for hybrids classification. The functions AIC, AICc, BIC and KIC implement the respective goodness‐of‐fit statistics. The function snapclust.em.choose.k derives clustering solutions for increasing numbers of clusters and computes the associated goodness‐of‐fit statistics, so that it can guide the choice of the optimal number of clusters. The method is documented in a dedicated online tutorial available by typing adegenet::adegenetTutorial(‘snapclust’) in a R session. Code and documentation are released under GPL ≥ 2 license.Jombart,  version 2.22.2.1quantiNEMO , first‐generation backcrosses (BC1: F1 × P1 and F1 × P2), and second‐generation backcrosses (BC2: (F1 × P1) × P1 and (F1 × P2) × P2). Each simulated dataset was formed by 100 individuals from P1 and P2 each, and 10 individuals from each hybrid class . While arbitrary, these sample sizes yielded a sufficient number of hybrids to analyse while retaining enough individuals to characterize the genetic makeup of parental populations.The simulated datasets used for the clustering of hybrids were derived from the previous simulations, by sampling two parental populations  at random in each of the 360 simulated datasets described before. For each, hybrids were simulated using the function 2.2.3snapclust to existing standard for the assignment of individual genotypes to groups (rather than inferring the true number of clusters). Therefore, the number of clusters was fixed to the known number of populations within the simulated dataset for all presented analyses. The clustering of individuals in absence of hybrids was performed using the snapclust, structure 2.3.4 . STRUCTURE analyses were carried out, using an admixture model with correlated allele frequencies between populations and no a priori information on population membership. The program was run ten times for result consistency purposes, with MCMC length of 500,000 after a burn‐in of 100,000 iterations. Individuals were assigned to the cluster for which their posterior assignment probability was the highest. For BAPS, we performed a “mixture clustering” analysis. Finally, we ran the function find.clusters retaining 90% of the total variation in the initial dimension reduction step.Our simulation study focussed on comparing true positive rate (TPR) was defined as the proportion of individuals belonging to the same population which were indeed clustered together by the method. The true negative rate (TNR) was defined as the proportion of individuals which did not belong to the same population and were adequately placed in different groups by the method. Note that the Rand index , so that the present analyses should give a more detailed account of clustering results than the Rand index alone. The impact of the different simulation parameters on TPR and TNR was assessed using separate multivariate linear regressions. As classical linear regression is designed to predict a response variable which can take any positive or negative values, a logit transformation was applied to the proportions, so that log(TPR/1 − TPR) and log(TNR/1 − TNR) were used as response variables. We preferred a linear regression on these rates to a binomial generalized linear model (GLM) for practical reasons. The calculation of TPR and TNR being based on all pairwise comparisons of individuals within simulated datasets, a binomial GLM would have required millions of observations to be included, leading to prohibitive computations. Instead, 360 TPR and TNR values were analysed, that is, one per simulation.As clusters identified in these previous analyses are not labelled, it was impossible to judge if individuals were assigned to their true cluster. To assess the quality of the results and compare the different methods, we used pairwise comparisons of individuals instead, examining whether pairs of individuals where adequately placed in the same, or different clusters. We used two complementary measures to do so calculated on each of the 360 simulated dataset analysed. The ex Rand, , which cFst between simulated populations, the number of populations, and the clustering method. We also investigated potential two‐way interactions between the clustering method and the four simulation parameters we varied  and second (BC2)‐generation backcrosses . The NEWHYBRIDS analysis was carried out, using Jeffreys's prior and setting the burn‐in period to 100,000, with a MCMC length of 500,000 iterations. Ten repetitions were carried out for each simulated dataset. Unlike the previous comparison, parental and hybrid classes are labelled, so that it was possible to compare the methods by directly examining how well they assigned individuals to their actual hybrid group, using the mean correct group assignment, computed as the proportion of individuals whose type  was correctly identified. In addition, we also examined the group membership probability calculated by each method for the true group, later referred to as the “support” for the true group. As before, the impact of the different simulation parameters on the performance of the methods was assessed, using multiple linear regression on logit probabilities, with separate models for the mean correct group assignment, and the support to the true group. In both cases, the following predictors were included: number of loci, dispersal model, Fst, as well as the hybrid class , and the clustering method. Interaction were investigated between the method and the simulation parameters, and between the number of loci and the Fst. As before, variable selection was achieved using backward stepwise selection based on AIC and likelihood ratio tests, using a Bonferroni correction to account for multiple testing with a target type 1 error of 1%.The clustering of individuals in the presence of hybrids was carried out using 2.3microbov” dataset in adegenet. Parental populations were obtained by sampling 30 individuals from the Lagunaire and 30 from the Salers populations. Hybrids were simulated using the function hybridize, to obtain 30 F1 hybrids, and then 30 of each first and second backcrosses, resulting in 210 individuals. While arbitrary, these numbers replicate a situation where hybrids are more numerous than parental populations, as could be the case in nature when studying large hybridisation zones.To complement the simulation study which assessed the overall performances of our method, we illustrated its practical application by reproducing a typical analysis of microsatellite markers data, starting with the identification of the most likely number of clusters, followed by the assignment of individuals to groups, and the description of relationships between groups. We simulated hybrids from an empirical dataset of 30 microsatellite markers typed for 15 breeds  in order to confirm that K = 2 parental populations was the optimal solution. We then looked for potential hybrids (function snapclust.em), using hybridisation coefficients corresponding to F1 (0.5), first‐generation backcross  and second‐generation backcross individuals . Group membership probabilities were visualised using the function compoplot. As a complement, we also explored the diversity between hybrid classes using a discriminant analysis of principal components  and near perfect TNR, showing that clusters present in the simulated dataset were overall well recovered by all approaches  values  for find.clusters to 99.0% (IQR: 98.5%–99.7%) for BAPS, with 98.0% (IQR: 97.1%–99.5%) for STRUCTURE and 98.1% (IQR:97.3%–99.6%) for snapclust. Similar results were observed for TNR values, although the model explained a smaller fraction of the variance  for find.clusters, 98.9% (IQR: 98.6%–99.7%) for BAPS, 98.1% (IQR: 97.1%–99.5%) for STRUCTURE, and 98.1% for snapclust (IQR: 97.3%–99.5%).Multivariate linear regression captured a large fraction of the variation in logit. Increased number of loci  and stronger Fst  generally improved group prediction, although a significant yet negligible saturation effect was observed between the two . While hybrid classes were on average harder to identify than parental populations, with the lowest success observed for deeper backcrosses, the two methods behaved very differently: NEWHYBRIDS seemed to recover parental populations more efficiently, but snapclust exhibited improved performances for the identification of hybrids in deeper levels of hybridisation  compared to NEWHYBRIDS. Results were qualitatively identical when examining the support to the true group , the average differentiation between BC2 and the “neighbouring” groups (closest parent and BC1) was negligible (Fst < 0.01). This lack of differentiation was confirmed by a DAPC retaining 20 dimensions  include a natural measure of uncertainty in the form of distributions of group membership probabilities for each individual. For snapclust, an alternative approach to assess statistical uncertainty may be to use bootstrap, in which case the method would be to run a large number of times (e.g. 100) on datasets obtained by random resampling (with replacement) of the loci. Such approach would provide a distribution of group membership probabilities for each individual (one per run), and thereby a measure of uncertainty. Bootstrap on loci can readily be implemented, using existing tools for genetic data handling .In our simulations, the number of loci and levels of genetic differentiation varied independently, so that the The approach described here is flexible, as it can accommodate any type of co‐dominant markers including microsatellites and SNPs, and can readily be extended to varying ploidy levels. Interestingly, it can also be extended to other genetic models as well, including potentially more complex ones. Contrary to Bayesian approaches which need hundreds of thousands or even millions of iterations to reach mixing and provide a representative sample from the posterior distribution, our fast likelihood maximisation using the EM algorithm converges in a few iterations—typically less than 10 in our simulations. As a consequence, our approach could have great potential for addressing more complex population genetics model, as long as their likelihood is tractable or can be reasonably approximated. One potential obstacle to such extensions is that group memberships need to be treated as a discrete variable, where individuals essentially belong to one group. This will exclude mixture models in which individuals effectively have multiple origins. A workaround for this issue may be to model “mixed groups” explicitly, as we have done in our hybridisation model.adegenet (Jombart, snapclust in the same environment as a wealth of other tools for population genetics (Archer, Adams, & Schneiders, Our method is implemented in the R package Jombart, , which sJombart, , revealiJombart, , or inveJombart, . InteropJombart, . We hopeadegenet: https://cran.r-project.org/web/packages/adegenet/index.htmlThis work does not involve empirical datasets. The dataset microbov, used in our illustration, is distributed with the R package  Click here for additional data file.


Tamarix taklamakanensis is an endangered shrub endemic to arid basins of northwestern China. Based on seven microsatellite loci, we assessed the genetic diversity and structure of 15 populations in the Tarim Basin, China. We found that the total gene diversity was moderate, whereas the allelic diversity was low; we also found high levels of gene flow among populations of T. taklamakanensis in the center of Tarim Basin, but higher levels genetic differentiation in geographically outlying populations. Our study will provide useful information for conservation and restoration. Tamarix taklamakanensis is an endangered shrub endemic to arid basins of northwestern China. This species serves to stabilize soils in this region, but has seen substantial loss in its abundance due to depletion of ground water. The populations of this species have become small and fragmented, warranting conservation. Seven microsatellite loci were used to assess the genetic diversity and structure of 15 populations in the Tarim Basin, China. Among populations, the expected heterozygosity and total gene diversity were both moderate , however the allelic diversity was low (A = 2.4). Eleven populations were detected to have experienced recent bottlenecks using Wilcoxon’s test and a model-shift test. Most populations of T. taklamakanensis in the centre of Tarim Basin showed low levels of genetic differentiation, but higher levels in geographically outlying populations. Genetic structure based on Bayesian assignment, the neighbour-joining network and principal coordinates analyses produced similar results, supporting five groups in the Tarim Basin. Gene flow was high among Bayesian groups based on historical gene flow estimated by private alleles. The genetic structure of T. taklamakanensis supports a pattern where gene flow principally occurs along river corridors through hydrochory of seeds and insect-mediated pollination. Populations upstream have contributed to a more diverse mixture of populations near the confluence of several rivers near the centre of Tarim Basin. This pattern of genetic structure could be influenced by the flow of water from different river systems. Conservation efforts should focus on fostering the regeneration of this species, maintaining genetic diversity and preserving the extant genetic structure. Conservation efforts are contingent upon maintaining ground water and streamflows in this arid basin.Population genetic information can provide valuable insight for the conservation and management of threatened and endangered plant species.  Fragmentation has been recognized as one of the factors increasing the vulnerability of many plant species . It reduHabitat fragmentation can affect plant species differently depending on their life history traits. The erosion of genetic diversity caused by habitat fragmentation depends on multiple life traits, including population size, distance between populations, time since isolation, seed and pollen dispersal distance, and generation time  What is the level of genetic diversity among populations of T. taklamakanensis? (ii) Is there significant genetic differentiation among populations? (iii) What evolutionary factors influence genetic diversity and genetic structure? These data will be helpful to infer the dominant evolutionary forces responsible for the observed genetic patterns, and will be of importance to devise ongoing conservation and management strategies for T. taklamakanensis.Genetic information of T. taklamakanensis within the Tarim Basin. A total of 12–16 individuals were sampled at each of fifteen populations. GPS coordinates were recorded for each population  and using the protocol reported by T. taklamakanensis, PCR was conducted with 30 samples from 6 populations. PCR products were screened by gel electrophoresis using 2 % agarose gel that was run for 2 h. Primer sets that produced bands of the expected length  at the University of Wisconsin DNA-sequencing facility. Allele lengths were analysed with Geneious v7.0 using the package Plugin (HO) and unbiased expected heterozygosity (HE) within each locus and population were calculated using GenAlEx 6.5 software (FIS) at different significance levels (P = 0.05 and 0.001) and F-statistics value  within each locus over all populations were calculated with FSTAT 1.2  for the significance level. Null alleles were tested with MICRO-CHECKER 2.2 , Nei’s gene diversity index (h), observed heterozygosity  was tested with the software 2MOD, estimating the relative likelihoods of fthe two models using an Markov chain Monte Carlo (MCMC) procedure as described in FST) were calculated as the variance components (FST matrix was used to construct a neighbour-joining (NJ) network in MEGA 6.0 (FST) was also used to perform principal coordinate (PCO) analysis, implemented in GenAlEx 6.5 (K), we first simulated a total of 10 000 MCMC iterations for the burn-in period, followed by a run length of 10 000 iterations. For each value of K (K = 2–10), three independent runs were performed to assure convergence and homogeneity among runs. We used deltaK to select the best K. Each run yielded a log likelihood value, Ln Pr (X/K), which had a corresponding deltaK. The highest Ln Pr(X/K) corresponded to the highest deltaK, and the maximum was chosen to determine the optimal number of genetically distinct clusters. DeltaK was calculated in Structure Harvester (http://taylor0.biology.ucla.edu/structureHarvester/). The probabilities of ancestor assignment were calculated for each pre-defined population (A), observed heterozygosity (HO) and unbiased expected heterozygosity (HE) corrected for the sample size, were calculated within the groups.To assess the partitioning of total genetic variation among and within populations, analysis of molecular variance (AMOVA) was implemented in ARLEQUIN v.3.01  was 0.4, total gene diversity (hT) was 0.432. The mean expected heterozygosity (HE) per locus was 0.392 (HO) per locus was 0.573 (FIS) ranged from −0.094 to −0.913  showed a significant deviation from drift-mutation equilibrium under the assumption of IAM model, three populations  under the assumption of SMM model, and seven populations  under the assumption of TPM model. In addition, seven populations  showed a shifted pattern in allele frequency distribution with mode-shift test . These rT. taklamakanensis, with slightly higher likelihood than that of gene flow model (P (gene drift) = 0.51, Bayes factor = 1.03), indicating the presence of genetic drift but generally ample gene flow within the species. Sufficient gene flow was also supported by low genetic differentiation measures (FST) in most pairs of the populations, discussed below.The migration-drift equilibrium analysis revealed a genetic drift model in P < 0.001). However, a moderate amount of the variation was found to occur among populations .The majority of total genetic variation was found to occur within populations  using STRUCTURE. The analysis indicated five inferred genetically distinct groups in T. taklamakanensis. At K = 5, two populations HBS1 and WSX were largely assigned to their own unique groups with moderately high assignment probabilities of 0.649 (HBS1) and 0.816 (WSX). Individuals from populations MF1 and MF2 were assigned into a group (Group 2) with probabilities of 0.765 and 0.724, respectively [see Supporting Information—Table S1]. The remaining 11 populations were assigned to admixed groups 4 and 5  ranged from 0.226 in group 3 to 0.442 in group 2, and the observed heterozygosity (HO) ranged from 0.398 in group 3 to 0.819 in group 2. Genetic structure was assessed without  4 and 5 . The res 4 and 5  and PCO  4 and 5 . In the  4 and 5 . These rT. taklamakanensis has maintained a moderate level of genetic diversity, despite the decline in abundance and increasing fragmentation seen in this species over the past few decades (T. taklamakanensis (HE = 0.392) was lower than that of T. chinensis  was higher than that Reaumuria soongorica (hT = 0.312) , compared with those reported in some tree species, such as Changiostyrax dolichocarpa , whereas the other three groups, located around the perimeter of the Tarim Basin, all had low levels of allelic diversity , 2 (MF1 and MF2) and 3 (WSX), which exhibited higher assignment probabilities and greater genetic structure , and likely beyond the flight range of pollinators or seed dispersal. Thus, given that the areas surrounding river drainages are barren, gene flow could be restricted to river corridors. Hydrochory has been shown to be an important dispersal mechanism of plant propagules  mitigate the loss of genetic diversity and (ii) preserve the extant genetic structure. Priority should be given to disjunct populations with lower allelic diversity, in particular population in groups 1 and 4. In situ conservation and restoration efforts of T. taklamakanesis are dependent on conserving the groundwater in the basin. Unless water consumption from river diversion and well drilling is curbed, work from conservation efforts will likely be undermined. Given these circumstances, ex situ conservation should also be considered. A germplasm nursery of T. taklamakanensis has been established in Turpan Botany Garden. However, this ex situ nursery is lacking the genetic information revealed in our study, and currently captures only a small part of the total genetic variation. Given that severe drought could devastate entire populations of this species or climate change may necessitate assisted migration, comprehensive germplasm storage would be essential for population recovery.Our study provides valuable insight into the conservation and management of tlenecks  and obsetlenecks  suggest This research was supported by grants from National Natural Science Foundation of China (31400561), the Western Doctoral Project  of Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences.Z.H.S. wrote the article; B.A.R. designed the experiment and revised the article. All authors contributed to execution of the experiment.None declared.


Macrobrachium nipponense) is mainly distributed in East Asia. The phylogeography, population genetic structure and historical demography of this species in the East Asia were examined by using partial sequences of the cytochrome oxidase subunit I (COI) and 16S rRNA in mitochondrial DNA. Ten populations that included 239 individuals were collected from Taiwan , mainland China , Japan  and Korea . The nucleotide diversity (π) of all individuals was 0.01134, with values ranging from 0.0089  to 0.01425 . A total of 83 haplotypes were obtained, and the haplotypes were divided into 2 main lineages: lineage A included the specimens from BLJ, KLJ, CLR, MTR, TLC, MRC and JRC, and lineage B comprised the ones from HRK, SRC, SMR, MTR, TLC, MRC and JRC. Lineage A could be further divided two sub-lineages (A1 and A2). Individuals of lineage A2 were only from TLC. Demographic expansion was observed in each lineage, starting within the second-to-latest interglacial period for lineage A and within the last glacial period for lineage B. All FST values among the ten populations were significantly different, except for the values between MRC and JRC, and SMR and SRC. The phylogeography and genetic structure of M. nipponense in East Asia might be influenced by Pleistocene glacial cycles, lake isolation and human introduction. The possible dispersal routes of M. nipponense in the East Asia were also discussed.The oriental river prawn ( The complex geological events and climatic history of various regions helped shape current phylogeographical patterns , 2. TherThe gene flow of inland freshwater between populations is obviously lower than the one between populations in estuaries or oceans, and thus historical phylogeographical analyses of freshwater species permit strong inferences regarding the biotic and geological evolution of a region , 9. MoreMacrobrachium nipponense) originated in mainland China about one million years ago . Th. Th32]. 1  , and this indicated all populations have the same ancestor. Populations with ancestral genotypes tend to preserve higher nucleotide and haplotype diversities because of the long-term accumulation of mutations , 41, 42.2 (TLC population) were close to BLJ haplotypes, and this might result in the secondary back contact from BLJ populations.Significant genetic differences were found in most pairs of the ten populations , and theM. nipponense from SRC to SMR for aquaculture or the maintenance of genetic diversity [There was no significant genetic divergence between MRC and JRC, which may result from frequent gene flow because the geographical distance between JRC and MRC is very close and their environments were similar . The traiversity  may part1, A2 and B of the oriental river prawn M. nipponense have experienced population expansion since the Pleistocene glacial cycles in East Asia . The possible dispersal routes of M. nipponense in the East Asia were also speculated. Lineage B could have experienced expansion more recently than lineage A. The phylogeography and genetic structure of M. nipponense in East Asia might be influenced by land bridges during Pleistocene glacial maximums and by human colonization.Our study indicated a high level of genetic structure among the oriental river prawn populations in East Asia. Two main lineages (A and B) were found, and lineage A was older than lineage B. A rough estimate of the ages of lineages A and B were obtained and were approximately 490,000 and 253,000 years old, respectively. Lineages A, AS1 Dataset(FASTA)Click here for additional data file.S2 Dataset(FASTA)Click here for additional data file.S3 Dataset(FASTA)Click here for additional data file.