Copernicia prunifera (Miller) H. E. Moore is a palm tree native to Brazil. The products obtained from its leaf extracts are a source of income for local families and the agroindustry. Owing to the reduction of natural habitats and the absence of a sustainable management plan, the maintenance of the natural populations of this palm tree has been compromised. Therefore, this study aimed to evaluate the diversity and genetic structure of 14 C. prunifera populations using single nucleotide polymorphisms (SNPs) identified through genotyping-by-sequencing (GBS) to provide information that contributes to the conservation of this species. A total of 1,013 SNP markers were identified, of which 84 loci showed outlier behavior and may reflect responses to natural selection. Overall, the level of genomic diversity was compatible with the biological aspects of this species. The inbreeding coefficient (f) was negative for all populations, indicating excess heterozygotes. Most genetic variations occurred within populations (77.26%), and a positive correlation existed between genetic and geographic distances. The population structure evaluated through discriminant analysis of principal components (DAPC) revealed low genetic differentiation between populations. The results highlight the need for efforts to conserve C. prunifera as well as its distribution range to preserve its global genetic diversity and evolutionary potential. Habitat reduction and deforestation resulting from human activities have had adverse effects on forest populations, contributing to high rates of species extinction, particularly in the Neotropical region . With thCopernicia prunifera (Mill.) H. E. Moore , known as carnaúba, generally forms monodominant populations known as “carnaubais” [Tangara palmarum) [The palm tree naubais” . The spenaubais” . Flowerinaubais” . Fruits almarum) .C. prunifera is endemic to the Caatinga biome [2 in northeast Brazil. The climate in this region is characterized by a long dry season with irregular rainfall, representing a xeric, semi-deciduous shrubland and forest vegetation [ga biome , which iga biome . The Caagetation . This pagetation ,11. Caatgetation .Local populations use this species as a source of employment and income. Leaf extraction is responsible for sustaining several families during the period of drought that extends from July to December in northeast Brazil . The fruC. prunifera populations show signs of intense exploitation with visible signs of anthropogenicity, such as fire, extraction and cutting of leaves, soil impacted by livestock, and low or absence of regeneration  in Brazil using SNP markers to provide information that can help in the design of efficient strategies for the conservation and sustainable use of this species.Genetic diversity studies based on molecular markers of natural populations of l scarce ,28–30. IC. prunifera were evaluated. Out of the samplings collected, 10 populations came from the Caatinga  and four from the Restinga  regions in the states of Ceará and Rio Grande do Norte, Brazil  with the number A411583.Small pieces of leaves were cut using a tree trimmer, placed in plastic tubes containing 2 mL of hexadecyltrimethylammonium bromide (CTAB 2X), labeled, and stored in a freezer at -20°C until DNA extraction. This study was conducted according to the recommendations of the Brazilian Ministry of the Environment and registered in the National System of Management of Genetic Heritage and Associated Traditional Knowledge . The library was enriched by PCR (Polymerase Chain Reaction) using the following amplification program: 95°C for 30 s, followed by 16 cycles of 95°C for 10 s, 62°C for 20 s, and 72°C for 30 s, and ending at 72°C for 5 min. Finally, the library was purified using a QIAgen® QIAquick PCR Purification Kit. The Agilent DNA 12000 kit and Agilent® 2100 Bioanalyzer System were used to verify the average size of the DNA fragments. Sequencing was performed using the Illumina® HiSeq 2500 Mid Output Kit v4 (50 cycles)  in a single-end configuration.To obtain SNPs, genomic libraries were developed using the genotyping-by-sequencing technique (GBS) with two restriction enzymes, according to the protocol described by Poland et al.  with modprocess_ radtag module. In the absence of a reference genome for C. prunifera, the DeNovo Stacks pipeline was used, starting with the ustacks module to identify putatively homologous read stacks (putative loci). This step was performed for each sample separately using the following parameters: minimum stack depth (-m = 3) and maximum distance between stacks (-M = 2). The loci of each sample were grouped into a catalog using the cstacks module, allowing a maximum distance of two nucleotides (-n 2) between the loci of each sample. Loci with lower probability values (—lnl_lim -10) were eliminated using the rxstacks correction module. Finally, the population module was used to filter the SNP markers using the following parameters: only one marker per sequenced tag, frequency of least frequent allele (MAF ≥ 0.01), minimum stack depth 3X, and minimum occurrence in 75% of saplings in each population.The identification of SNPs was performed using Stacks software v.1.42 ,34. The ST values that were excessively high or low compared with what was expected under neutrality. The analysis was performed using the fsthet package [E and FST as outlier SNPs. This test was performed by considering the estimates of FST in two different scenarios: i) comparing the Restinga and Caatinga populations and ii) comparing the samples of the white morphotype with those of conventional morphotype. The final set of SNP markers hypothetically under selection consisted of loci identified as outliers in at least two of the three tests performed. Thus, the outlier SNP loci may reflect the action of selection on different types of vegetation.Two complementary tests were performed, pcadapt and fsthet, were performed to detect outlier loci . The pcadapt method  was used package  on the R package  by consiSequences containing outlier SNPs were searched with the BLASTX tool against the genomic data set of the National Center for Biotechnology Information (NCBI) using blast2go . This anp), observed heterozygosity (HO), and expected heterozygosity (HE). Inbreeding coefficients (f) were also estimated, and their confidence intervals were obtained using 1000 bootstraps. Estimates of diversity and inbreeding were obtained using the diveRsity [C. prunifera was evaluated using analysis of molecular variance (AMOVA), and its significance was tested with 10,000 permutations using the PoPPr program [Genetic diversity was estimated based on the number of alleles (A), number of private alleles  common to other methods used to detect population structure, and as it is based on principal component analysis, this method can analyze genomic datasets relatively efficiently [Genetic differentiation was estimated using pairwise  package  of R sof package . The pop package ,42 for R package . This aniciently .Genetic relationships and divergence between individuals were investigated by constructing a dendrogram generated based on the distance of Nei using the neighbor-joining method . The finSequencing of the genomic libraries resulted in 566,922,165 reads, and after quality control, the total number of reads retained was 397,047,980. In total, 1,013 SNPs (average depth of 21X) were identified. A total of 391 outlier SNPs were identified using the pcadapt method, 70 using the fsthet method to compare morphotypes, and 47 using the fsthet method to compare vegetation types . Of thesE) ranged from 0.201 to 0.265 (E = 0.265) and the highest number of private alleles (Ap = 40) compared with that of the other populations. The inbreeding coefficients (f) were similar and negative for all populations, indicating an excess of heterozygotes.The genomic diversity estimates were based on 929 neutral SNP markers. The number of alleles (A) ranged from 1,059 to 1,497. The IPG population (white carnaúba) had the lowest number of alleles, probably because of the small sample size of the population. Expected heterozygosity . In the present study, FST estimates suggested low to high genetic differentiation between populations of C. prunifera (ST ranged from 0.118 to 0.20). In addition, the SMG and IPG populations showed moderate levels of differentiation. A low genetic structure was observed for the populations from LGP and SER (0.18) and AR1 and AR2 (0.19), suggesting a genetic flow between these localities.Frunifera . In geneST was also observed in DAPC, which retained 28.7% of the total variation in the first two principal components .Analysis of molecular variance (AMOVA) indicated that most of the variation was found within populations (77.26%), and the genetic differentiation between populations was high and significant (φ = 0.227) . The ManAccording to the dendrogram , the MACC. prunifera populations were evaluated based on the type of vegetation (Caatinga and Restinga), similar levels of genetic diversity were observed (When the genetic diversity and structure of the observed .f) are both similar and negative. In addition, the FST estimates suggested low genetic differentiation between the Caatinga and Restinga populations (FST= 0.008). When considering only two morphotypes of C. prunifera (white carnaúba and common carnaúba), similarities were observed in the estimates of diversity in addition to low genetic differentiation (FST= 0.008)  compared to the Restinga populations and this result is probably associated with the sample size. The inbreeding coefficients (= 0.008) . Analysi= 0.008) .C. prunifera has no annotated reference genome, and probably for this reason, most sequences with outlier loci are similar to uncharacterized proteins. Regarding the results obtained from the annotation, most loci are associated with genes involved in metabolic processes, which have been regularly found under selection in a variety of organisms because the gene functionality correlates with environmental stressors [The large number of SNP markers obtained in this study allowed for the identification of loci with deviations from the expected neutral behavior, which are putatively under selection (outlier loci). The identification of outlier loci is an important step in understanding local adaptation and evaluating the evolutionary potential of a species . The paltressors .C. prunifera because the palm trees grow in different environments such as seasonally flooded areas in the semi-arid region [Interestingly, some annotated loci were associated with genes of transposable elements . Accordid region . In addiC. prunifera. Small sample sizes belonging to populations with relatively small geographical distances, which enable gene flow to quickly spread new adaptations to surrounding areas, reduce the capacity to detect recent evolutionary changes [It is important to highlight that the analyses performed in this study are unable to indicate associations between genomic and functional variation; therefore, it is not possible to associate generic molecular functions or biological processes with any adaptive traits involved in the diversification of the evaluated populations. Therefore, studies with larger sample sizes with better representation of the different geographical habitats are needed to generate information on the evolution and diversification of  changes . However changes .C. prunifera. The GBS approach used in this study produced a large number of SNP loci for the genomic evaluation of this palm tree without the need for a reference genome. This has resulted in robust estimates of diversity and patterns of genetic structure.Genetic diversity is one of the three classes of biodiversity recognized as a global conservation priority and plays a decisive role in conservation efforts. Genetic diversity has a substantial effect on both individual fitness and the adaptive capacity of the population, playing a vital role in maintaining the capacity of species to withstand various biotic and abiotic stressors and evolve under altered environmental conditions . The pref value, suggesting limited inbreeding with reduced self-pollination capacity under environmental conditions. Therefore, individual plants are less related than expected under conditions of random mating. Genetic diversity and population structure is influenced by biological characteristics of the species, including the mating system [C. prunifera is mixed and preferably allogamous [The results of genetic diversity and population structure were similar based on the results of the analysis according to population , type of vegetation (Caatinga and Restinga), and morphotype (common carnaúba and white carnaúba). In all situations, the populations showed a negative g system . Therefologamous , which fE). High levels of genetic diversity led to an increase in long-term survival of a species; therefore, a strong positive correlation exists between heterozygosity and population fitness, which is important for populations to adapt to new environmental conditions [Although the evaluated populations were susceptible to anthropogenic threats, it is possible that they had high genetic diversity  and the higher proportion of genetic diversity within populations with only fewer partitions between them could result from the combined effect of different factors, such as cross rate, reproductive system, and high genetic flow rate in this species. The Mantel test corroborates this result. Since geographically close populations tend to be genetically similar, this indicates a pattern of isolation by distance. However, the MACE population had the lowest level of diversity (HE = 0.201) and the highest degree of structuring, being the most genetically divergent population compared with the others. This differentiation was supported by the FST value, which is an indirect estimator of the population connectivity between subpopulations  and aim to maintain the genetic diversity of species over time, preserving the evolutionary processes and adaptive potential of populations [ex situ approaches have the potential to conserve much of the biological diversity, they do have a limitation of being more suited and efficient for conservation in plants that have orthodox seeds. Therefore, in situ conservation is recommended for C. prunifera because this species contains recalcitrant seeds [in vivo seed banks and the promotion of natural regeneration, can prevent the decrease of population size, loss of genetic variability, and ensure long-term conservation [Conserving genetic diversity within a population should be the cornerstone of any conservation strategy aimed at ensuring the long-term persistence of species and habitats . In situulations . Althougnt seeds . Howeverervation .C. prunifera indicates the need for large areas of land dedicated to in situ conservation for capturing the existing genetic diversity of these populations. FST values estimated in the present study could help in recommending the optimal number of populations for sampling, including populations that had the highest estimates of diversity and the largest number of private alleles.The high genetic diversity observed in the evaluated populations of C. prunifera exploitation is an important source of employment and income for local communities in the semi-arid region of Brazil. In this context, the rational management of palm tree products should be a principal strategy in the efforts to conserve the natural habitats of the species. Another strategy aimed at conservation and sustainable use would be the development of a community and family forest management (CFFM) plan [FM) plan , which cFM) plan .In addition, practical measures aimed at successful plant regeneration, such as the pause of extractive activity during reproductive periods and the introduction of rotation cycles for leaf harvesting in the explored areas, need to be implemented for the sustainable management of carnaúba. However, the current social and economic conditions of workers employed in the activity of extraction and production of carnaúba wax must be considered. Workers in poorer areas need to be provided additional support, including investments, to maintain the balance between socioeconomic demand and conservation, which would pave the way to a more sustainable supply of resources while reducing the pressure of uncontrolled harvesting.C. prunifera populations  throughout their distribution would be fundamental for the successful implementation of species conservation management.A third approach would be to preserve populations and divergent genetic groups identified in this study throughout their geographic distribution range through effective long-term genetic and ecological monitoring, stimulating the development of ecological corridors between fragments and natural forests, and avoiding the reduction of genetic variability. In addition, interdisciplinary programs that study different aspects of S1 FigCopernicia prunifera).GO Annotations are summarized into three main categories: cellular location, biological process and molecular function for carnaúba ((DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 TableHE = expected heterozygosity of the locus; FST = genetic divergence among groups of accessions estimated based on the locus; e-value = number of hits expected by chance (E = x 10); Sim (%) = BLASTX percentage of similarity between SNP tags and annotated proteins.(DOCX)Click here for additional data file.


Primula elatior. Southern populations displayed a sharp genetic turnover and a considerable amount of local adaptation under diversifying selection was discovered. However, most of the outlier loci could not be linked to climate variables (71%) and were likely related to other local adaptation drivers, such as photoperiodism. Furthermore, specific adaptations to climate extremes, such as drought stress, could not be detected. This is in line with the typical occurrence of forest herbs in buffered climatic conditions, which can be expected to reduce selection pressures imposed by climate. Finally, populations in the south of the distribution area had increased sensitivity to climate change due to a reduced adaptive capacity and a moderate genetic offset, while central European populations were sensitive due to a high genetic offset. We conclude that assisted migration from southern source populations could bear significant risk due to nonclimatic maladaptation and a low adaptive capacity. Regional admixture and restoration of ecological connectivity to increase the adaptive capacity, and assisted range expansion to suitable habitat in the north might be more appropriate mitigation strategies.Climate change is increasingly impacting temperate forest ecosystems and many forest herbs might be unable to track the changing climate due to dispersal limitation. Forest herbs with a low adaptive capacity may therefore benefit from conservation strategies that mitigate dispersal limitation and evolutionary constraints, such as assisted migration. However, assisted migration strategies rarely consider evolutionary constraints of potential source populations that may jeopardize their success. In cases where climate adaptation is overshadowed by competing evolutionary processes, assisted migration is unlikely to support adaptation to future climates. Using a combination of population and landscape genomic analyses, we disentangled local adaptation drivers and quantified the adaptability and vulnerability to climate change of the self‐incompatible deciduous forest herb  Furthermore, genotypes that were not observed in at least 10% of samples (i.e. at least 29 samples) were removed from the dataset. We selected SNPs with less than 50% missing data within each of at least 11 populations. We made five subsets to maximize the number of SNPs, depending on the amount of populations in which a SNP with less than 50% missing data occurred: all 29 populations (3773 SNPs with 3.9% missing data), 25–28 populations (2638 SNPs with on average 21.7% missing data), 20–24 populations (2760 SNPs with 30.5% missing data), 16–19 populations (1005 SNPs with 34.7% missing data) and 11–15 populations (973 SNPs with 42.0% missing data). This selection procedure resulted in a total of 11,149 SNPs with 20.8% missing data for downstream analysis.To quantify potential library effects, which may result from sequencing errors in techniques with limited sequencing depth . The specific SNPs in these populations were then imputed based on a mean of three distinct regions . Specifically, the VCF data format was transformed to the genlight data format  and the imputation was based on the rounded population mean. Some SNPs still contained missing data because they had a high missing data percentage on a population level but a low missing data percentage over the whole range  tested ranged from 1 to 25 for the first subset and 1 to 15 for subset 2–4. The optimal K was selected based on the minimum cross‐entropy of each run. Each run was executed with 10 replicates per K, 200,000 iterations and a regularization parameter of 10. Statistical estimates of ancestry proportions were determined based on the sNMF models and depicted graphically.Individual ancestry coefficients were estimated based on sparse non‐negative matrix factorization (sNMF) algorithms , were used to determine outliers related to climate , were taken into account using a binary coding scheme following the recommendations of Bauman, Drouet, Dray, and Vleminckx  resulted in seven significant MEMs for the SNPs in subset 1 . The third  and second  MEM were highly correlated to temperature (r = −0.73) and precipitation  using the R package ‘Vegan v.2.5‐7’ , using r > 0.6) and were therefore evaluated separately with precipitation seasonality as second climate variable. Resulting models were highly similar due to the high correlation of temperature variables and only the model with the highest explanatory power (R2 adj.), which contained max temperature of warmest month and precipitation seasonality as fixed variables (Appendix R2 adj. Was 8.9 ± 2.7 for bio 5 compared to 8.5 ± 2.8 for bio 1 and 8.8 ± 2.6 for bio 10). Potential confounding effects between climate and geography were further evaluated with variation partitioning. The FDRs were determined based on q values and ranged from 0.1% to 50%, with a precision of 0.1%. Only outliers under diversifying selection, as detected by Bayescan, were evaluated further.To select relevant climate variables we used forward and backward selection of partial RDA (pRDA) models  under diversifying selection with <5% FDR. We considered significant GO terms of biological processes with a 2.5P. elatior across the latitudinal sampling gradient, we modelled the expected heterozygosity (He) against latitude (LAEA 89 as coordinate system), a quadratic latitude term to model nonlinear effects, outlier as a binary factor , and interactions between latitude and the outlier term (He ~ Latitude × outlier + Latitude2 × outlier), using a general linear model. Residuals were normally distributed and no visual heteroscedasticity was detected. Least‐square‐means and pairwise comparisons of outlier genetic diversity with neutral genetic diversity were evaluated with the ‘emmeans v.1.6.3’ package in R . Residuals were normally distributed and no visual heteroscedacity was detected.The models were constructed with the Hellinger‐transformed minor allele frequencies of partial RDA outliers  and neutral SNPs as response and the partial RDA selected climate and geographic MEMs as explanatory variables. Bioclimatic projections according to three greenhouse gas scenarios, namely RCP 2.6, RCP 4.5 and RCP 8.5 in 2050 and 2070, were used to predict future predicted turnover. The RCP variables were based on averages from 11 general circulation models . Residuals were normally distributed and no visual heteroscedacticity was detected.This formula gives a similar weight to both the expected heterozygosity and genetic offset as the range of both metrics are highly similar (0–0.3). The relation of climate sensitivity to latitude was modelled with a linear model and a quadratic term  under diversifying selection, while 163 outliers (2.1%) were detected with a FDR below 1%, and 107 outliers (1.4%) with a FDR below 0.1%. A large amount of outliers were thus featured by a decisive signature of diversifying selection . SNPs that did not occur in all populations subset 2–4) were aggregated in 14 distinct clusters but did not yield additional insights. Southern populations had a low amount of mixture but with three abrupt cluster transitions between populations  had strong regional eigenvector value turnover in south Europe and central Europe  was mostly related to regional eigenvector value turnover in north Germany, and the final included eigenvector  was related to regional eigenvector value turnover in south‐central Europe , which mainly represents the maximum temperature of the warmest month . The second RDA axis  was mostly related to precipitation seasonality  with the most northern populations and the most southern populations featured by strong precipitation seasonality, and some central European populations by weak precipitation seasonality  with a FDR below 5%, which were thus related to climatic clines, and 53 outliers  and PCAdapt  but not by the partial RDA . This could suggest a role for environmental variables other than climate (e.g. photoperiod) as an important driver of selection in P. elatior. The relation between significant photosynthetic outlier SNPs and spring trimester direct normal irradiation  of SNP frequencies under diversifying selection driven by climatic clines as predicted by our general linear model  was significantly lower  than the neutral genetic diversity , particularly towards the south of the distribution  and PCAdapt outliers .The genetic diversity (R2 = 80.5%), and was predicted to be significantly higher in 2050 under RCP 8.5  compared to RCP 4.5  and RCP 2.6 . Furthermore, the genetic offset was predicted to significantly increase in 2070 compared to 2050 .Our gradient forest algorithm predicted strong genetic turnover of climate related SNP outliers around the 22°C maximum temperature of the warmest month threshold . The sensitivity to climate change was predicted to be significantly higher in 2050 under RCP 8.5  compared to RCP 4.5  and RCP 2.6 . Furthermore, the sensitivity to climate change was predicted to significantly increase in 2070 compared to 2050 .The climate outliers of the eight most southern populations had a relatively low genetic offset Figure , but als4Primula elatior populations. Furthermore, strong genetic differentiation in the south of the distribution range was detected. Gene–environment association analyses uncovered local adaptations to clines of maximum temperatures and to a lesser extent the precipitation seasonality. Most of the outliers detected by population genomic analyses were, however, not related to climate. Biological functions related to climate outliers were generic metabolism functions and no adaptations to drought were detected. Based on current gene–environment associations and predictive machine‐learning‐based modelling, we identified a high sensitivity of P. elatior to climate change in the south and centre of the distribution range, relative to more northern populations. However, because detected preadapted alleles to climate change were limited and because target populations in the north of the range were characterized by relatively high adaptive potential, our findings suggest that P. elatior would not benefit from south‐to‐north translocation. Moreover, the important contribution of nonclimatic signatures of local adaptation (e.g. photoperiod), indicates a high risk of maladaptation when southern and central populations would be translocated to northern locations. Our integrated analyses provide a first insight on the potential evolutionary constraints that could hamper adaptation to climate change in forest herbs and could limit the potential of assisted migration as a mitigation strategy.Using population genomic analyses, we detected ample genetic outliers related to local adaptations along a latitudinal gradient of 4.1Primula elatior uncovered considerable diversifying selection (3.4–5.8%) over the entire latitudinal range. The population structure analysis (sNMF) indicated major clustering with sharp genetic turnover between southern regions. On the other hand, central and north European populations had a large degree of mixture, which is consistent with the idea of postglacial colonization of northern Europe from multiple southern refugia , most of the adaptive outliers under diversifying selection could not be linked to climate (71%), indicating that environmental factors other than climate cause stronger signatures of adaptive divergence along the latitudinal gradient. Interestingly, we detected multiple significant photosynthetic adaptations (5) unrelated to climate. As we discovered correlations between some outlier SNP frequencies and spring trimester direct normal irradiation, it is likely that photosynthetic adaptations are related to solar irradiation. Photosynthetic acclimation is particularly important in perennial, long‐lived species that experience a high seasonality during their lifespan (Yamori et al., 4.2Primula elatior is expected to lose over half of the total distribution area by 2050 due to climate change and dispersal limitation (Van Daele et al., P. elatior to adapt to the changing climate will depend on the genetic offset of climate outliers and the adaptive genetic diversity, together shaping its climate sensitivity. We found that the cumulative importance of climate outliers sharply increased between a max. Temperature between 22°C and 23°C, which corresponds to a sharp increase of the adaptive genetic turnover in Central France. The sharp increase in adaptive genetic turnover is likely related to a combination of climate selection pressures (Appendix Forest herbs lacking adaptations for long‐distance dispersal are estimated to lose genetic diversity at a faster rate under an increasingly changing climate (Alsos et al., Appendix  and low Appendix . When enAppendix . HoweverAppendix . OverallAppendix . FurtherAppendix , to imprAppendix . After cAppendix , experieAppendix , and areAppendix , it is lThe authors declare no conflict of interest.Appendix S1–S4Click here for additional data file.


In a previous study, 30 SINE-RIPs were applied for population genetic analysis in 7 Chinese miniature pig populations and approved effectively in the genetic distances and breed-relationships between these populations. There are abundant indigenous pigs famous across the world for their prolificacy in the Jiangsu Province of eastern China, such as Meishan, Erhualian. Since pork production relies on limited commercial breeds such as Landrace, Large White, and Duroc pigs, characterized by maximizing productivity in intensive production systems, these indigenous pigs are nowadays decrease sharply. The genetic characterizations of these resources are essential requirements for the development of conservation, selection, and sustainable utilizations. Therefore, SINE-RIPs were selected to evaluate the genetic variation and population structure of Jiangsu pig populations and the results may assist with the conservation and utilization of these native pig populations.FIS values. Based on the neighbor-joining tree, all of the Jiangsu native pig populations formed one branch, while the three external pig breeds formed the other branches, with the two crossbreeds containing more than 50% external pig ancestry. The Huai pigs were independent of the other Jiangsu native pigs but shared a common ancestor with Sujiang and Mi. The results provide a new perspective on the population structure of these native pig breeds and will assist with the conservation and utilization of Chinese native pigs.Short interspersed nuclear elements (SINEs), one type of retrotransposon, are considered to be ideal molecular markers due to their wide distribution in the genome, high copy number, and high polymorphism. Preliminary studies have identified more than 35,000 SINE-retrotransposon insertion polymorphisms (RIPs) in the pig genome. In this study, 18 SINE-RIPs were used to evaluate the genetic variation and population structure of seven native pig populations and two crossbreeds in the Jiangsu Province of China. Two commercial pig breeds (Duroc and Large White) and one Italian native breed (Sicilian Black pig) were selected as the control. The results showed that all 18 SINE-RIPs were polymorphic among these pigs. The Jiangsu native pig populations  were shown to be more polymorphic than the crossbreeds (Sushan and Sujiang) and external breeds  based on the expected heterozygosity and polymorphic information content values. Some native pigs, including Small Meishan, Mi, Middle Meishan, and Erhualian, had a higher degree of inbreeding according to the  There are abundant indigenous pigs in the Jiangsu Province of eastern China and they are famous across the world for their prolificacy, high-quality pork, and moderate temper . The mosDue to their high prolificacy, high-quality pork, and moderate temper, researchers have focused on the genetic evaluation, population structure, and conservation of some of the breeds around Taihu Lake. Chang et al., 1999) used the randomly amplified polymorphic DNA (RAPD) technique with 13 decamer primers and found that pigs around Taihu Lake exhibited a relatively low level of genetic variation and poor genetics . Fan et 999 used Retrotransposons are one class of transposable elements (TE) that can move freely in the genome via “copy-and-paste” mechanisms and induce allelic diversity ,10. One-Preliminary studies have determined that SINEs occupy 11.05% of the pig genome . Using REar or blood samples were collected and used as biological material for DNA extraction. The samples included seven native populations  and two crossbreeds (Sujiang and Sushan) in the Jiangsu Province of China. These pigs were kept in indigenous conservation breeding farms. Two commercial breeds (Large White and Duroc) and one European native breed (Sicilian black pig)  were useGenomic DNA was extracted from the ear or blood samples of each pig using the TIANamp Genomic DNA Extraction Kit . The concentration and quality of the DNA were determined using ultraviolet spectrophotometry . For amplification, 18 SINE-RIP markers were selected from a previous study  accordinThe DNA samples were amplified using the Amp9600 Thermal Cycler . The polymerase chain reaction (PCR) mixture contained 40 ng genomic DNA, 10 ul 2 × Taq Master Mix buffer , and 10 pmol of each primer. The PCRs were performed using the following conditions: an initial denaturation step at 94 °C for 5 min, followed by 30 cycles of denaturation at 94 °C for the 30 s, annealing at 55–60 °C for 30 s, and extension at 72 °C for 1 min. A final extension of 10 min at 72 °C was also included. The PCR products were loaded onto a 1.0% agarose gel in 1 × TAE buffer and visualized using a UV fluorescence system .Ho), expected heterozygosity (He), fixation indices , and Hardy-Weinberg equilibrium test [n is the number of alleles, pi is the frequency of the ith allele in the population, and pj is the frequency of the jth allele in the population [http://taylor0.biology.ucla.edu/struct_harvest/, accessed on 4 March 2021; [The POPGEN1.32 computer package was used to calculate the allele frequencies, number of alleles per locus (Na), Ne, observed heterozygosity . Then, ch 2021; ,32.FIS, and FST are listed in All 18 loci used in this study were polymorphic. Considering that the crossbreed Sujiang consists of 62.5% Duroc and 50% of the Sushan breed is Large White, the Duroc and Large White commercial pig breeds were designated as the control. Additionally, Sicilian Black pigs from Italy were used as a control as an external native pig breed. All of the loci had two alleles and the clear amplified bands are shown in He of the 12 populations ranged from 0.0172 to 0.3606, the Ho ranged from 0.0208 to 0.3854, and the average values were 0.2183 and 0.2203, respectively  to 0.1108 (Mi). The FIS values of Erhualian, Middle Meishan, Mi, Sujiang, and Small Meishan were positive, indicating that these five populations show a degree of inbreeding.The ectively . The aveFST between each pig population is shown in FST ranged from 0.0873 to 0.3069. The lowest FST (0.0873) was recorded between Shawutou and Middle Meishan, and the highest FST (0.3069) was recorded between Huai and Small Meishan. Six populations around Taihu Lake  showed relatively low FST values, indicating that they have a similar genetic background. The FST between the three external breeds  and Jiangsu native pig populations ranged from 0.3250 to 0.5835. The two crossbreeds (Sujiang and Sushan) and external pig breeds had a relatively lower FST (ranging from 0.0142 to 0.1652) in comparison with a higher FST between the crossbreeds and the Jiangsu native pig populations (ranging from 0.2051 to 0.4691). This may be due to the fact that these two crossbreeds consist of more than 50% external pig breed parentage.The heatmap of the Based on Nei’s genetic distance , betweenAs shown in UPGMA tree based on Nei’s distance A, these The population structure of the seven Jiangsu native pig populations, two Jiangsu crossbreeds, and three external breeds was quantified using Structure B. When KThe principal components classified the Jiangsu native pig populations and the other five breeds into two different groups . In addiHe values were used to evaluate genetic diversity and were recorded as 0.378, 0.375, 0.382, 0.382, and 0.371 using SNPs, and 0.621, 0.603, 0.429, 0.623, and 0.605 using microsatellites for the Erhualian, Middle Meishan, Small Meishan, Mi, and Shawutou breeds, respectively. In this study, the He values were 0.3195, 0.3178, 0.2567, 0.3042, and 0.3606 for these five pig populations. The values that were determined using SINE-RIPs were far lower than those that were determined using microsatellites and just lower than those that were calculated using SNPs. Presumably, this is due to the fact that SINE-RIPs are biallelic markers with long fragment insertion mutations, while microsatellites are multiple markers and SNPs are single nucleotide variations. Previous studies have identified that the Chinese native pig populations showed richer genetic diversity than external pig breeds [He and a higher FIS value; which means that this population showed evidence of low genetic variation. In the mid-1980s, a scheme to create an inbreeding line of Small Meishan was developed in order to increase the population size [In this study, 18 SINE-RIPs were used to evaluate the population structure of the Jiangsu pig populations. All of the 18 SINE-RIPs were identified as polymorphic using PCR. Several studies have focused on the genetic variation and population structure of some of the Jiangsu native breeds using SNPs . The He g breeds , which aHe and PIC of all of the external pig breeds were lower than those of the Jiangsu native pig breeds. It is possible that the commercial breeds have undergone more intensive selection than the Chinese native breeds which have been conserved. While the herd sizes of the Sicilian Black pigs might be small, Sujiang and Sushan  also underwent intensive selection during their breeding which may explain why their genetic diversity decreased.The The Chinese native pig and European pig originated from different original wild boars around 8000–10,000 years ago ,34. In bThe greatest span from north to south in the Jiangsu Province is about five hundred kilometers. The Huai pig is an indigenous breed in the northern area, while the other populations occur in the south of Jiangsu Province. The NJ trees and PCA plots showed that the two branches split according to geographical distribution and Huai is independent of the other Jiangsu pig populations. According to the history of the formulation of these pig breeds, Erhualian is the offspring of Mi and Dahualina (extinct) in Changzhou . Using SThe STRUCTURE analysis revealed that Erhualian, Fengjing, Small Meishan, Middle Meishan, Mi, and Shawutou around Taihu Lake shared a common ancestry K = 2–3) and Huai, Sujiang, and Mi shared a common ancestry (K = 4–6). The Huai pig, even recorded in the Compendium of Materia Medica, is an ancient native pig breed in the north of Jiangsu Province which is part of the Huaibei Plain. As a result of the war and commercial exchanges, the local people emigrated to the south of Jiangsu province, bringing with the Huai pigs on several occasions. Some arrived at the Jintan County and Mi pigs were gradually formed when Huai pig crossed with local pigs . Further and HuaiIn this study, 18 SINE-RIPs were selected to evaluate the genetic variation and population structure of Jiangsu pig populations in China. The results confirm that they provide an effective, reliable method for evaluating the genetic variation and distance of Jiangsu native and crossed pig populations. The results also will assist with the conservation and utilization of these native pig populations. Compared with the genetic information provided by microsatellites and SNPs using partial loci or whole genomic sequencing data, the SINE-RIPs could provide a simple and low-cost method to evaluate the population structure of different breeds and population.He and Ho, the Jiangsu native pig breed populations showed more genetic variation than the crossbreeds and external pig breeds. The FIS values of the Erhualian, Middle Meishan, Mi, and Small Meishan were positive, indicating that these four populations have a degree of inbreeding. In both the NJ tree and PCA, the external pigs and Jiangsu native pigs clustered separately. Erhualian, Fengjing, Small Meishan, Middle Meishan, Mi, and Shawutou around Taihu Lake shared a common ancestry, and Huai, Sujiang, and Mi also shared a common ancestry. Therefore, the 18 SINE-RIPs provide an effective, reliable, and simple method for evaluating the genetic variation and population structure of Jiangsu native and crossed pig populations and the results will assist with the conservation and utilization of this native pig population.In summary, 18 SINE-RIPs were used for genetic evaluation in 7 Jiangsu native pig populations, two crossbreeds, and three external pig breeds in this study. According to the values of


R. pseudochrysanthum complex. No distinctive species relationships were found using genetic clustering analyses, neighbor-joining tree, and neighbor-net tree based on the total AFLP variation, which is suggestive of the incomplete lineage sorting of ancestral variation. Nonetheless, strong isolation-by-environment and adaptive divergence were revealed, despite the significant isolation-by-distance. Annual mean temperature, elevation, normalized difference vegetation index, and annual total potential evapotranspiration were found to be the most important environmental variables explaining outlier genetic and epigenetic variations. Our results suggest that the four closely related species of the R. pseudochrysanthum complex share the polymorphism of their ancestor, but reproductive isolation due to ecological speciation can occur if local environmental divergence persists over time.The testing association of environmental variables with genetic and epigenetic variation could be crucial to deciphering the effects of environmental factors playing roles as selective drivers in ecological speciation. Although ecological speciation may occur in closely related species, species boundaries may not be established over a short evolutionary timescale. Here, we investigated the genetic and epigenetic variations using amplified fragment length polymorphism (AFLP) and methylation-sensitive amplification polymorphism (MSAP), respectively, and tested their associations with environmental variables in populations of four closely related species in the  Divergence between species and between populations in close association with environments is an important aspect of research in evolutionary biology . Local eNatural selection driven by environmental factors can act on both genetic and epigenetic variations, leading to ecological adaptation and speciation ,14,15,16Rhododendron pseudochrysanthum complex comprises four closely related species, including R. hyperythrum, R. morii, R. pseudochrysanthum, and R. rubropunctatum, that belong to the subgenus Hymenanthes [R. pseudochrysanthum grows on the periphery of cold adapted coniferous forests. R. morii inhabits the periphery of warm-temperate evergreen broadleaved forests at elevations around 2400–3000 m in central and southern Taiwan. Populations of these two species overlap at elevations of approximately 3000 m in the mountains of Hohuanshan and Tahsueshan in central Taiwan [R. hyperythrum is found dominating the alpine tundra at an elevation of around 3500 m on the peak of Nanhutashan in central Taiwan. R. rubropunctatum is distributed in the northern subtropical evergreen broadleaved forests at elevations around 600–1200 m [The enanthes . At elevl Taiwan . R. hype0–1200 m .R. pseudochrysanthum complex based on chloroplast DNA (cpDNA) sequences [R. pseudochrysanthum complex are the outcome of the past upward migration of R. hyperythrum, R. morii, and R. pseudochrysanthum populations to high elevations and the restriction of R. rubropunctatum populations in northern lower elevations. Only one out of the 26 EST-SSR loci assessed was found to be an environmentally-dependent selective outlier in global comparison and was also in pair population comparisons involving the low-elevation R. rubropunctatum populations [Close phylogenetic relationships were observed between the species of the equences . Populatequences . Effectiequences , based oequences ,6,7. Theulations . Althougulations ,4,5,13, ulations .R. pseudochrysanthum complex, due to its demographic history of glacial expansion and postglacial upward shifting, using a large number of molecular markers. Here, 171 and 132 samples from nine and six populations of the R. pseudochrysanthum complex were surveyed for AFLP and MSAP variation, respectively. The genetic and epigenetic variations surveyed were used to test the hypothesis of the early stage ecological speciation in the R. pseudochrysanthum complex.Tree species may diverge ecologically and lead to reproductive isolation because of divergent environments ,2,3. SelR. pseudochrysanthum complex? (2) Are AFLP and MSAP FST outliers; detected by genome scans methods; associated strongly with environmental variables? (3) Does IBE play a stronger role than IBD in explaining outlier genetic and epigenetic variations? And (4) What are the most important environmental variables contributing to outlier genetic and epigenetic variations? Answering these questions will provide information to investigate the probable evolutionary process shaping the patterns of genetic diversity and phylogenetic relationships and a probability of ecological speciation of four closely related species in the R. pseudochrysanthum complex.The AFLP and MSAP variations surveyed were used in a combination of phylogenetic; genetic clustering; genetic differentiation; genome scans; and multivariate analytic techniques to answer four specific questions: (1) Is there species integrity in the four closely related species of the R. pseudochrysanthum complex (uHE) was 0.2211 and ranged from 0.2038 (population PTHS) to 0.2439 (population RTGK). The analysis with the linear mixed effect model (LMM) showed no overall uHE significant difference when compared between species . Only the population pair between PLLS and PTHS had a significant uHE difference after Tukey’s p value adjustment . The test for multilocus linkage disequilibrium revealed a significant departure from random associations in both the index of association (IA) and the modified index of association (rD) between AFLP loci . These environmental variables include: bioclimatic ; topographic ; and ecological  variables. When compared between sample sites of different species, either significant or non-significant environmental differences can be found , based on the 11 retained environmental variables  . A significantly moderate level of differentiation between populations of the R. pseudochrysanthum complex was found . Moderate levels of genetic differentiation (FST) were also found to be significant for all pairwise population comparisons  tree, generated based on the total AFLP data, with mostly low bootstrap values, revealed no distinctive relationships of individuals between populations and between species  revealed shallow, but significant species differentiation  [ΦCT = 0.2283 (p = 0.011), ΦSC = 0.3099 (p = 0.001), ΦST = 0.4675 (p = 0.001) (FST = 0.2929 (p < 0.001), based on the variation of the 16 outlier AFLP loci.Out of 384 AFLP, 580 MSAP-m (methylated), and 274 MSAP-u (unmethylated) loci , 16  . High le= 0.001) , and amoFST outliers and associated with environmental variables when compared between low-elevation trailing-edge R. rubropunctatum populations with high-elevation leading-edge populations of R. hyperythrum, R. morii, and R. pseudochrysanthum  and populations MHHS and MTHS of R. morii   with both R. rubropunctatum populations . AP06_2592 was found to be the selective outlier when comparing R. pseudochrysanthum populations PHHS and PLLS  to the R. rubropunctatum RTGK population . AC03_1652 was strongly correlated with BIO1 and PET. AP04_3517 was significantly correlated with BIO1, NDVI, and PET. AP06_2592 was found to be strongly correlated with BIO1, BIO2, elevation, and CLO. The probability estimates of these three AFLP outlier loci against the associated environmental gradients were depicted in FST outliers by either DFDIST or BAYESCAN in pair population comparisons  . AP04_35parisons .FST outlier by either BAYESCAN or DFDIST when comparing low-elevation trailing-edge populations to high-elevation leading-edge populations  was found in contrast to high-elevation leading-edge populations with lower allele frequencies, no MSAP locus was detected as ulations .R2 = 0.1576), elevation (adjusted R2 = 0.1394), and PET (adjusted R2 = 0.1150), respectively, explaining the outlier AFLP variation (R2 = 0.1925), elevation (adjusted R2 = 0.1272), and PET (adjusted R2 = 0.1664), respectively, explaining the outlier MSAP-m variation. BIO1 (adjusted R2 = 0.3446), elevation (adjusted R2 = 0.1772), and NDVI (adjusted R2 = 0.3518) were the most important environmental variables in the three environmental categories, respectively, explaining the outlier MSAP-u variation.Eleven environmental variables, including three bioclimatic , three topographic , and five ecological  variables , were usariation . The mosr = 0.2858, p = 0.001; MSAP-m: Mantel r = 0.1551, p = 0.001; MSAP-u: Mantel r = 0.0825, p = 0.002).The retained 11 and 9 environmental variables were used in testing for IBD and IBE, respectively, based on the total variation. Significant relationships between environmental and geographic distances were found using a Mantel test and multiple matrix randomization regression (MMRR)  Table 4Table 4. βE= 0.3074, p = 0.001 vs. βD = 0.2488, p = 0.001; MSAP-m: βE = 0.3412, p = 0.001 vs. βD = −0.0175, p = 0.5755; MSAP-u: βE = 0.3431, p = 0.001 vs. βD = −0.1351, p = 0.001; MMRR implements a combined model of geographic and environmental distances, which revealed patterns of significant IBE based on the total data. MMRR showed that environmental and geographic factors together explained 19.02% AFLP, 0.46% MSAP-m, and 5.09% MSAP-u variation of the total data; and 28.97% AFLP, 10.74% MSAP-m, and 4.93% MSAP-u variation of the outlier data. Additionally, MMRR revealed genetic and epigenetic adaptive divergences strongly correlated with environments based on the outlier datasets  with Euclidean distances between the sample sites, calculated based on geographic coordinates, which were were found using Spearman’s rank correlation test , which are distributed in Hohuanshan  .Three outlier loci  were found by pairwise comparisons between the leading- and trailing-edge populations, with high or low allele frequencies, indicating an adaptation associated with local environments. Increases in the levels of genetic divergence and the rate of speciation are, among others, found to be closely associated with temperature ,61. TempR2 value , explainpiration  and was cia faba . Genetic species  and betwstifolia  were foustifolia ,70. NDVIerioides . The meaerioides . The hab complex . The R. 04_3517)  with a v04_3517) . Howeverr = −0.973), and those with a lower correlation coefficient, such as PET (r = −0.668) and NDVI (r = −0.420) in this study [R. pseudochrysanthum complex. The elevational difference in meters is not a factor driving population divergence and may not be a useful predictor for distribution modeling [Elevation is the most important topographic factor explaining outlier genetic and epigenetic variations . Environmodeling . Howevermodeling  and altimodeling ,24,76,77modeling .R. pseudochrysanthum complex (EcoRI (20 UµL−1) and mixed with 1 µL MseI (10 UµL−1), 1.5 µL ddH2O, and 1 µL CutSmart buffer , was incubated at 37 °C for 1.5 h. The reaction was terminated at 65 °C for 15 min. The digested DNA products were ligated with 1 µL (5 µM) of the EcoRI adaptor and 1 µL (50 µM) of the MseI adaptor using 1 µL (5 UµL−1) T4 DNA ligase , 3 µL ddH2O, and 4 µL 5X ligation buffer  in a 10 µL ligation reaction mixture at 22 °C for 1 h.We collected fresh leaf samples of 171 individuals from nine populations of the  complex  and used complex . We surv complex . Due to 2O) as a template in a 20 µL volume containing 8.6 µL ddH2O, 2 µL 10X PCR buffer , 1.6 µL EcoRI , 1.6 µL MseI  primers, 1.6 µL dNTPs (2.5 mM), 0.4 µL MgCl2 (0.15 mM), and 0.2 µL Taq DNA polymerase . The pre-selective amplification was performed with an initial holding at 72 °C for 2 min and pre-denaturation at 94 °C for 3 min, followed by 25 cycles of 30 s at 94 °C, 30 s at 56 °C, and 1 min at 72 °C, with a final 5 min holding at 72 °C. Eight EcoRI-MseI (E00 and M00) selective primer combinations with additional bases added at the ends were used for AFLP selective amplification (EcoRI selective primer was labeled with fluorescent dye (6-carboxyfluorescein or hexachloro-fluorescein) and amplification was performed in a 20 µL volume containing 11.3 µL ddH2O, 2 µL 10X PCR buffer (Zymeset), 2 µL EcoRI (20 µM), 2 µL MseI (20 µM) primers, 1.6 µL dNTPs (2.5 mM), 0.1 µL Taq DNA polymerase , and 1 µL diluted pre-selective amplified product (1:19 dilution with ddH2O). We performed selective amplification with an initial holding at 94°C for 3 min, followed by 13 cycles of 30 s at 94 °C, 30 s at 65–56 °C (decreasing the temperature by 0.7 °C each cycle), 1 min at 72 °C, then 23 cycles of 30 s at 94 °C, 30 s at 56 °C, and 1 min at 72 °C, with a final 5 min holding at 72 °C. In MSAP, the AFLP protocol was adapted by replacing restriction enzyme MseI with the methylation-sensitive enzymes HpaII and MspI in two separate experiments. Ten MSAP selective primer combinations were used with additional nucleotides at the ends of the E00 and HM00  (Pre-selective amplification was performed using 4 µL diluted digested samples (1:9 dilution with ddHfication . EcoRI sTCGG-3′) .MSAP-calc R script [PCR amplification products were electrophoresed on an ABI 3730XL DNA analyzer and scored with Peak Scanner v.1.0 . We scored AFLP and MSAP fragments using a fluorescent threshold set at 150 units in the range of 100–500 bp. The “mixed scoring 1” of the R script  in the RR script  was usedR script . Error rR script .uHE [uHE per locus was estimated using ARLEQUIN v.6.0 [IA [rD [ia function of R poppr package [uHE per locus among species and among populations using the lmer function of R lme4 package [Anova function of R car package based on type II Wald χ2 statistic [lsmeans function of R emmeans package [The proportion of polymorphic loci and uHE ,85 were uHE . uHE perIN v.6.0 . To test.6.0 [IA  and rD [ [IA [rD , were es package  with 999 package , and sigtatistic . Tukey’s package .vifcor function of R package usdm [adonis function of R package vegan [pairwise.perm.manova function of R package RVAideMemoire [A Pearson’s correlation coefficient threshold of |0.8| between environmental variables  was usedage usdm . VIF valge vegan , and paieMemoire  with 999K = 1–9 based on least-squares optimization using the snmf function of R LEA package [find.clusters and dapc functions of R adegenet package [nei.dist functions of R poppr package and the nj function of R package ape [neighborNet function of R package phangorn [aboot function of R package poppr for both NJ and NN trees.Genetic homogeneous groups of individuals were assessed using sNMF algorithm  and DAPC package . The fin package  were use package  using thkage ape . The NN phangorn . The booFST outliers  and a 1% FDR. Samβada was used to evaluate the associations of allele frequencies with the values of environmental variables. Both Wald and G scores with a 1% FDR for p value adjustment were used in assessing the fit of model with environmental variables against null model without environmental variables.LFMM  and Samβbrm function of R brms package [t distribution with mean zero and seven degrees of freedom were used as the weakly informative priors, and the scale of the prior distribution was 2.5 for intercept and predictors using the set_prior function. Credible intervals (95%) were determined using the posterior_summary function.Loci found to be associated strongly with environmental variables assessed using Samβada and LFMM were further tested with a Bayesian logistic regression analysis implemented in the  package ,39  using the mantel.partial function of R vegan package. MMRR was performed using the MMRR function [βE) and IBD (βD) were obtained and significance was determined after 999 permutations. Models for redundancy analysis generated using the rda function of R vegan package were used in the forward selection [forward.sel function of R adespatial package [The correlations of the total and outlier Euclidean distance matrices with the Euclidean distance matrix of environmental variables were analyzed in a Mantel test using the function  of R. Reelection  to test  package .intermingling of the genealogical relationships, revealed particularly in the NJ and NN trees, among individuals of the four closely related species in the R. pseudochrysanthum complex. A single ancestral phyletic line may diverge into a series of lineages, albeit with a shallow split in individual-based phylogenetic NJ and NN trees, adapting to rather different habitats. Our sampling of populations of the R. pseudochrysanthum complex, distributed at elevations below 1000 m and above 2000 m, and up to 3500 m, spanning a wide range of annual mean temperatures (5.1–18.9 °C), NDVI indexes (0.588–0.837), and PET indexes (953.2–1227.5), contributed to outlier genetic and epigenetic variations. The relatively stronger strength of IBE than IBD suggests spatial genetic and epigenetic structures driven by environmental conditions, and a strong IBE might play critical roles in causing the reproductive isolation and ecological speciation of closely related species in the R. pseudochrysanthum complex. However, locally adapted ecological lineages may risk extinction when encountering other environmental stressors during the course of migration. R. hyperythrum that grows in alpine tundra at high elevation may be vulnerable due to the detrimental effect of high-elevation environments on the growth of this species. Additionally, the introgression of adaptive genetic and epigenetic alleles and/or their combinations [Understanding the roles that geography and environment play in speciation is an important issue in evolutionary biology ,28,60,61inations ,104, harinations ,106.


Anopheles sinensis remains elusive in Laos, which makes it imperative to evaluate the true nature of chemical insecticide resistance-associated genetic mutations in An. sinensis in Laos.Vector control is still a pivotal method for preventing malaria, and its potency is weakened by the increasing resistance of vectors to chemical insecticides. As the most abundant and vital malaria vector in Southeast Asia, the chemical insecticide resistance status in An. sinensis were collected from three border regions in Laos. DNA was extracted from individual mosquitoes. PCR amplification and DNA sequencing of a fragment containing codon 1014 of the voltage-gated sodium channel (vgsc) gene were completed to study the kdr allele frequency distribution, kdr intron polymorphism, population genetic diversity, and the evolutionary status of the kdr codon. The mitochondrial cytochrome c oxidase subunit II gene (COII) was amplified and sequenced to examine population variations, genetic differentiation, spatial population structure, population expansion, and gene flow patterns.Adult kdr haplotypes of the vgsc gene were detected in this study, and eight of them, namely 1014L1, 1014L2, 1014L4, 1014L7, 1014L9, 1014L10, 1014L11, and 1014L21, were discovered in the China–Laos border (northern Laos), while 1014L3 was only detected in the Thailand–Laos border (northwestern Laos) and Cambodia–Laos border (southern Laos). The newly identified haplotype, 1014L21, was uniquely distributed in the China–Laos border and was not identified in other countries. Based on sequence analysis of the mitochondrial COII genes, significant genetic differentiation and limited gene flow were detected between the China–Laos and Cambodia–Laos An. sinensis populations, which suggested that those two regions were genetically isolated. The distinct distribution of the kdr haplotype frequencies is probably the result of geographical isolation in mosquito populations.Nine wild kdr mutations in the vgsc gene was probably due to genetic isolation and the absence of intense selection pressure in the three border regions of Laos. This study reveals that pyrethroid-based chemical insecticides are still appropriate for battling An. sinensis in parts of Laos, and routine monitoring of chemical insecticide resistance should be continuously implemented and focused on more restricted areas as part of chemical insecticide resistance management.Lack of The online version contains supplementary material available at 10.1186/s13071-022-05366-9. Plasmodium vivax reports in Lao PDR (nearly 50% of Plasmodium species from indigenous cases in 2018), although a large proportion of the reported cases of infection have long been related to Plasmodium falciparum  product of the kdr gene) was amplified using primers KDR-F (5′-TGCCACTCCGTGTGTTTAGA-3′) and KDR-R (5′-GAGCGATGATGATCCGAAAT-3′) [COII, the cycling parameter included 95 °C, 5 min; 95 °C/1 min, 51 °C/1 min, 72 °C/2 min for 35 cycles; with a final extension of 72 °C for 10 min. For amplification of KDR, the cycling parameter included 95 °C, 5 min; 94 °C/30 s, 55 °C/30 s, 72 °C/30 s for 35 cycles; with a final extension of 72 °C for 10 min. The PCR mixture (25 μl) consisted of 12.5 μl 2 × Taq PCR Mix , 5 μl of template DNA (< 1 μg), 1 μl of 10 μM each primer, and 5.5 μl water. The PCR products were analyzed by 1.5% agarose gel electrophoresis stained with GoldView  under ultraviolet (UV) transillumination. PCR products were purified by an agarose gel DNA recovery kit  before sequencing. The sequencing reaction was performed in both directions using an ABI Big Dye Terminator Kit v.3.1  and was analyzed using the ABI Prism 3500xL Genetic Analysis Tool  in Shanghai (Sangon Biotech).A total of 134 morphologically identified AATG-3′) ; a fragmAAAT-3′) . For ampCOII/kdr” for searching COII or kdr sequences of An. sinensis deposited in GenBank. Additional file COII, while kdr sequences are listed in Table COII and kdr sequence data set was combined with our original data and records retrieved from GenBank. A multiple sequence alignment was conducted in MEGA-X [The researchers used the keywords “(species name) & n MEGA-X , and then MEGA-X . Gaps wen MEGA-X  based onn MEGA-X . The visn MEGA-X .Table 1kCOII and kdr sequences. To compare the COII and/or kdr haplotypes between Laos and other geographical regions, existing data in GenBank from other countries were also analyzed. The parsimony framework was applied using Network 4.0 [D [Fs [An. sinensis population.DnaSP v.5.0  was usedwork 4.0  on both k 4.0 [D  and Fu’s0 [D [Fs  were cal0 [D [Fs  to test COII sequences, Arlequin v.3.5 [FST for estimating population differentiation by complying with a difference in haplotype frequency, Nei’s Nm estimated gene flow conformed to GST [Based on in v.3.5  was usedd to GST , and anad to GST . For disd to GST –50.An. sinensis mosquitoes were used for sequence polymorphism analysis. This sequence covered partial exon 19 (contains the codon 1014), intron 19, and partial exon 20 of the An. sinensis vgsc gene (KE525266.1). In general, 17 nucleotide polymorphic sites (PSs) were identified. The first to fifth PSs were located on exon 19, the sixth to 15th PSs on intron 19, and the 16th and 17th PSs on exon 20. The polymorphisms in the fourth and fifth PSs resulted in amino acid substitutions (L/F/S) at codon 1014, and the nucleotide variations in the first, second, third, 16th, and 17th PSs represented synonymous mutations ; other mutations such as TTT (1014F), TCG (1014S), and TGT (1014C) were not found. Accordingly, nine wild haplotypes, namely 1014L1, 1014L2, 1014L3, 1014L4, 1014L7, 1014L9, 1014L10, 1014L11, and 1014L21, respectively, were identified  based on the kdr intron of An. sinensis, 89 kdr intron sequences in this study and 65 kdr intron sequences retrieved from the GenBank database  and nucleotide diversity (Pi) were 0.788 and 0.02190, respectively. Compared to the three An. sinensis populations in Laos, high haplotype diversity and nucleotide diversity were found in six populations from China. Moreover, significant departures from neutrality were detected by Fu’s Fs test in all the populations , CN-GX  population , and the CN-HaN  population , whereas they were not detected in all the Laos populations by Fu’s Fs or Tajima’s D test. Additionally, slightly insignificant departures from neutrality were detected by Fu’s Fs test in the CN-AH  population  and CN-YN  population   accounts for the observed pattern.To analyze the genetic diversity indices and neutrality tests  might change the susceptible 1014L2 to the resistant 1014S2. Haplotypes 1014F1, F2, F3, F5, F7, and F9 were possibly derived from 1014L1, 1014L3, 1014L3, 1014L7, 1014L2, and 1014L11, respectively, while 1014F6 and F8 derived from the ancestor 1014L1 with two mutational steps, i.e., the additional mutations in exon 19 (T140A) or intron 19 (T229A) of 1014F1. Likewise, 1014F4 was derived from the ancestor 1014L11 with two mutational steps, i.e., the additional mutations in exon 19 (T140A) of 1014F9. Haplotypes 1014S1, S2, S3, S4, S5, and S6 were perhaps the results of an independent mutational step from six different wild haplotypes 1014L5, 1014L2, 1014L3, 1014L4, 1014L1, and 1014L6, respectively. Additionally, 1014W1 was derived from the ancestor 1014L1, while 1014C1 and 1014C2 were derived from 1014F1 with two or more mutational steps , number of haplotypes, and nucleotide diversity (Pi) were 0.799, 22, and 0.00351, respectively. Significant departures from neutrality were both detected by Fu’s Fs test  and Tajima’s D test  in all the populations. In contrary to the LXP and LCP populations, high haplotype diversity (0.783), number of haplotypes (20), and nucleotide diversity (0.00282) were found in the LPY population. Additionally, significant departures from neutrality were both detected by Fu’s Fs test  and Tajima’s D test  in the LPY population, whereas not detected in the LXP and LCP population.Eighty-nine sequences for COII sequences denoted the distribution pattern exhibited by 22 haplotypes in An. sinensis populations. The An. sinensis populations fell into two main clusters. Cluster 1 consisted of all the haplotypes except for one haplotype (H22) from LCP; cluster 2 consisted of only one haplotype (H22). Within cluster 1, two sub-clusters were also found; sub-cluster 1 consisted of all the haplotypes in LPY and LXP, while sub-cluster 2 consisted of only one haplotype (H21) in LCP , H4 (n = 37), and H6 (n = 7), as only identified in 67.47% (56/83) of LPY and 50% (1/2) of LXP. H21 (n = 3) and H22 (n = 1) were only identified in LCP , LCP (n = 4), LXP (n = 2), JP , KR , TH , and CN , and 46 haplotypes were found in the median-joining network. The An. sinensis populations fell into two main clusters. Cluster 1 consisted of all the haplotypes except for one haplotype (H27) from LCP; cluster 2 consisted of only one haplotype (H27). Within cluster 1, three sub-clusters were also found: sub-cluster 1 consisted of haplotype (H26) from LCP; sub-cluster 2 consisted of five haplotypes  from China; sub-cluster 3 consisted of other haplotypes  rather than within populations (38.93%) and among populations within groups (2.64%), suggesting that these populations could fall into several groups. However, no statistical significance was found in evaluating the fixation index among groups (FCT), among populations within groups (FSC). In contrast, the fixation index within populations (FST) showed statistical significance (P < 0.05) . while no significant genetic differentiation was found between LPY and LXP populations  or between LXP and LCP populations . Furthermore, the minimal estimate of gene flow (Nm) was between LCP and LPY (Nm = 0.31093). In contrast, high gene flows were found between LPY and LXP populations (Nm = 5.10835), as well as between LXP and LCP populations (Nm = 4.72727) , suggesting the genetic structure observed in An. sinensis populations could be partially explained by distance isolation based on COII sequence analysis (LCP and LPY populations) , suggesting a recent population expansion or selection (Table S3b). Furthermore, the observed smooth and unimodal mismatch distribution in the LPY population suggested a sudden population expansion, conforming to the mismatch distribution derived under the model of sudden expansion   Fig. , Table 1An. sinensis, the L1014F/L1014S/L1014C/L1014W/N1013S kdr mutations were identified in China [kdr mutations of An. sinensis in Laos. According to Verhaeghen’s study, kdr of An. sinensis (L1014S) was only found in southern Vietnam and in Cambodia near the Vietnamese border, but not in Laos [kdr haplotypes and the presence of region-specific haplotypes  and Anhui Province , while 1014W1 was only identified in Henan Province . The distinct distribution patterns of the 1014F/W allele are probably a result of independent mutation events in diverse geographical regions  could reflect the impact of intense selective pressure by the increased utilization of chemical insecticide on the kdr locus and the flanking regions of kdr, such as the kdr intron. In contrast, neutrality tests failed to provide evidence of selection at the kdr intron in Laos. The lack of strong selection pressure might partially elucidate the absence of chemical insecticide-resistant alleles in Laos  program, ITNs and LLINs were introduced in 2000 . Since t in Laos , 57, indre Table .An. sinensis in Laos, and LLINs or IRS using pyrethroids remain valid for protecting people from indoor Anopheles bites and ought to be used extensively, particularly in the southern part of the country where malaria is endemic. A potent management program of chemical insecticide resistance should be executed to maintain the susceptibility of vgsc alleles. Furthermore, the application of pyrethroids should not be the only measure for malaria vector control and should be used in combination with insecticides with alternative modes of action or in a rotational manner [Anopheles mosquito populations should be implemented focusing on more restricted areas.Based on the findings in this study, we suggest that pyrethroids are still appropriate for controlling l manner . As the l manner . On the kdr, with a neutral marker, COII, and demonstrates the combined impact of demographic and selection factors on the An. sinensis population structure. The strong genetic differentiation and limited gene flow between the China–Laos (northern Laos) and Cambodia–Laos (southern Laos) An. sinensis populations based on COII analysis suggest that these two regions are genetically isolated. Only nine wild haplotypes of kdr, TTG (1014L) were observed in An. sinensis populations, as well as distinct distribution of kdr wild haplotypes among different geological regions in Laos.The present study compares the genetic patterns revealed by a functional gene, kdr mutations in the vgsc gene in the three border regions of Laos is likely a consequence of genetic isolation and the absence of intense selection pressure. The present study suggests that pyrethroids remain suitable for use against An. sinensis in Laos, but routine monitoring of chemical insecticide resistance should be continued and focused on more restricted areas in this country as part of chemical insecticide resistance management.Lack of Additional file 1: Table S1. Full list of 89 An. sinensis specimens collected in China–Laos, Thailand–Laos, and Cambodia–Laos borders. N/A, not identified. LXP, Pak lay County of Xayabuli Province (Thailand–Laos border); LPY, Yot Ou County of Phongsaly Province (China–Laos border); LCP, Pathoomphone County of Champasak Province (Cambodia–Laos border).Additional file 2: Table S2.COII sequences of An. sinensis were downloaded from the NCBI. KR-YC, Yeoncheon (South Korea); KR-IC, Incheon (South Korea); KR-GR, Guryongpo (South Korea); CN-YN, Yunnan (China); Yunnan; CN-CQ, Chongqing (China); CN-AH, Anhui (China); TH-CM, Chiang Mai (Thailand); JP-NS, Nagasaki (Japan). N/A, no data.Additional file 3: Table S3. a Genetic diversity indices and neutrality tests (Fu’s Fs and Tajima’s D) based on the kdr intron of An. sinensis. b Genetic diversity indices and neutrality tests (Fu’s Fs and Tajima’s D) based on the COII gene of An. sinensis.nd not determined; #P < 0.10; *P < 0.05; **P < 0.02; ***P < 0.001. n number of sequences; s number of polymorphic sites; pi nucleotide diversity; h number of haplotypes; Hd haplotype diversity; LPY Yot Ou County ; LXP Pak lay County (Xayabuli Province); LCP Pathoomphone County (Champasak Province); CN-AH Anhui Province (China); CN-GX Guangxi Province (China); CN-YN Yunnan Province (China); CN-HaN Hainan Province (China); CN-HeN Henan Province (China); CN-ZJ Zhejiang Province (China).Additional file 4: Figure S1. Phylogenetic analysis based on the COII sequences in An. sinensis populations from Laos and other countries. a Phylogenetic network of 46 mitochondrial haplotypes of the COII gene in An. sinensis. Localities are indicated by different colors (bottom right). The size of each circle is proportional to its corresponding frequencies. b UPGMA dendrogram based on Nei’s unbiased genetic distance between the 46 haplotypes of An. sinensis. Yellow, green, and blue circles/rectangles represent haplotypes found in this study from LPY , LCP (Champasak Province: Pathoomphone County), and LXP (Xayabuli Province: Pak lay County) populations, respectively. Black, white, red, and sky blue circles/rectangles represent haplotypes found in NCBI data from Korea, Japan, China, and Thailand (Tables S2), respectively.Additional file 5: Table S4. Analysis of molecular variance (AMOVA) of 10 An. sinensis populations based on COII. FCT, fixation index among groups; FSC, among populations within groups; FST, within populations.Additional file 6: Table S5. Genetic differentiation and gene flow among the geographical groups of An. sinensis based on COII. The pairwise FST values and Nm values based on the COII are shown below and above the diagonal, respectively. Characters in bold indicated the significance (P < 0.05). Inf, infinite. LPY, Yot Ou County, Phongsaly Province; LXP, Pak lay County, Xayabuli Province; LCP, Pathoomphone County, Champasak Province.Additional file 7: Figure S2. a Isolation by distance; the relationship between geographical and genetic distances based on COII sequences in An. sinensis populations. Isolation by distance (IBD) was examined using a nonparametric Mantel test with the web-based computer program IBDWS v.3.16. b–d Mismatch distribution graphs for An. sinensis population based on COII sequences. The x- and y-axis show the number of pairwise differences and the frequency of the pairwise comparisons, respectively. The observed frequencies are represented by a dotted line. The frequency expected under the hypothesis of the constant population model is depicted by a solid line. LPY, Yot Ou County, Phongsaly Province; LXP, Pak lay County, Xayabuli Province; LCP, Pathoomphone County, Champasak Province.


Bellamya aeruginosa across three river systems in China was conducted by specific-locus amplified fragment sequencing (SLAF-seq). A clear division was found among populations from the Yellow River basin and the Pearl River basin, as well as population YC from the Yangtze River basin using the SNPs data. However, there existed no distinct population structure using the mitochondrial DNA. Anthropogenic translocation from the Yangtze River to the Pearl River basin and the passive dispersion from the Yangtze River basin to the Yellow River basin by flooding have weakened the phylogeographic pattern of B. aeruginosa. These results provide useful guidance for the effective selective breeding of Bellamya, which is very important for the development of the industry of LZRSRN.The population genomic study of seven populations of Bellamya is an economically and ecologically important freshwater snail for fish, birds and even humans. Population genetic structures of the Bellamya species, however, were unknown in previous studies. Population genomics approaches with tens to hundreds of thousands of single nucleotide polymorphisms (SNPs) make it possible to detect previously unidentified structures. The population genomic study of seven populations of B. aeruginosa across three river systems  in China was conducted by SLAF-seq. SLAF-seq obtained a total of 4737 polymorphisms SLAF-tags and 25,999 high-consistency genome-wide SNPs. The population genetic structure showed a clear division among populations from the Yellow River basin (YH and WL) and the Pearl River basin (QSH and LB), as well as population YC from the Yangtze River basin using the SNPs data. However, there existed no distinct population structure using the mitochondrial DNA (mtDNA). The anthropogenic translocation from the Yangtze River basin to the Pearl River basin and the passive dispersion from the Yangtze River basin to the Yellow River basin by flooding have weakened the phylogeographic pattern of B. aeruginosa. The divergence of B. aeruginosa in the three river systems suggests that the anthropogenic dispersal for aquaculture and breeding requires serious consideration of the population structure for the preservation of genetic diversity and effective utilization of germplasm resources.Clarifying the genetic structure can facilitate the understanding of a species evolution history. It is crucial for the management of germplasm resources and providing useful guidance for effective selective breeding.  The process of population isolation and differentiation is the core of micro-evolution, which has long fascinated evolutionary biology ,2,3. TheBellamya has attracted considerable attention from ecological physiologists and evolutionary biologists. B. aeruginosa can significantly change the water’s physicochemical properties and the phytoplankton community  s sBellamyThessaly . There eBellamya ,23. Furtbspecies ,25. The adiation . HoweverHigh-throughput Illumina sequencing through the Illumina HiSeq platform has revolutionized molecular ecology . For IllUsing the high-throughput Illumina sequencing of restriction-site associated libraries to generate genome-wide genotypic data and haplotype-tagging SNPs has been particularly valuable for ecological, evolutionary, phylogeographic and genetic mapping studies. They are based on analyzing tens to hundreds of thousands of SNPs in hundreds of barcoded samples at the same time . These dB. aeruginosa is widely distributed in the Yangtze River basin, Yellow River basin and Pearl River basin. The evolution history of the three drainage systems would play a very different role in the diversification of B. aeruginosa. However, previous studies using mtDNA and SSR markers showed that there is nonsignificant genetic differentiation among populations in the three river systems and a weak phylogeographical structure in China [B. aeruginosa distributed over a long distance across the three river systems in China. In order to clarify the population genetic structure of B. aeruginosa, we used a population-genomic approach employing SLAF-seq for genotyping thousands of single nucleotide polymorphisms (SNPs) and compared these results with the methods using mitochondrial cytochrome oxidase I (COI). SLAF-seq has been widely used as an efficient large-scale genotyping method to identify single nucleotide polymorphisms (SNPs) at low cost due to its reduced representation of deep sequencing without sacrificing the genotyping accuracy [in China ,6,17,22.accuracy .B. aeruginosa was collected from different river systems, including the Pearl River basin (two populations), the Yangtze River basin (three populations) and the Yellow River basin (two populations) (lations) . A totalB. aeruginosa individuals representing 7 populations  were used in the SLAF-seq. The SLAF library was constructed according to previous studies [Bellamya, the restriction enzyme combinations were selected in silico based on the related species Oyster (Ostrea gigas thunberg) reference genome (557.72 Mb) to optimize the SLAF-seq yields and efficiency. The purified genomic DNA was digested into fragments of 314–364 bp using two restriction enzymes . The accuracy of the restriction enzyme digestion protocol was tested according to the control genome (Oryza sativaindica genome). Subsequently, SLAF library was constructed with the selected DNA fragments followed by fragment end reparation, index paired-end adapters’ ligation and adapter-modified ends, PCR amplification, and target fragment selection. High-throughput sequencing was performed with the 100 bp paired-end method on the Illumina HiSeqTM 2500  at the Biomarker Technologies Corporation in Beijing. The ratio of high-quality reads with quality scores greater than Q30 and guanine-cytosine (GC) content in raw reads was estimated for quality control. We also used the Seqtk  to filter the raw reads with low-quality bases or adaptor/primer contamination. Next, we mapped the high-quality paired end reads onto the reference genome (O. gigas thunberg) using the SOAP2, according to Li et al. (2009) [All of the 68  studies ,45,46. I. (2009) . Subsequ. (2009) . The low. (2009) . The finK = 1 to K = 10 [K, we conducted a 10-fold cross-validation and selected the estimate with the lowest error, and the output was visualized in the R package pophelper. The CLUSTER [FST) of B. aeruginosa, and 10,000 permutations were used to test the significance .We generated genomic phylogeny using an unrooted neighbor-joining (NJ) tree with 100 bootstraps in SNPhylo , which ho K = 10 . To dete CLUSTER  was used CLUSTER . ARLEQUI CLUSTER  was usedCOI sequences were amplified using the universal primers for invertebrates described by Folmer et al. (1994) [COI sequences were deposited in GenBank (OP889150–OP889217).The . (1994) . Next, tCOI sequences. ML tree was implemented in RAxML v8.2.4 [The resulting 68 sequences were aligned by using MAFFT implemented in PhyloSuite v1.2.2 . The maxL v8.2.4  with 100L v8.2.4  using thp-value < 0.05) for the variance components was computed via 1000 permutations. The hierarchy for this analysis was selected based on the population of the river systems: (i) QSH and LB; (ii) YW, LZH, and YC; (iii) YH and WL. Furthermore, we analyzed another hierarchical population structure, where individuals were grouped into three clusters in ADMIXTURE. Pairwise ΦST values were calculated to assess population differentiation in ARLEQUIN v3.5.2.2.An analysis of molecular variance (AMOVA) was conducted in ARLEQUIN v3.5.2.2 to quantify the genetic variation of the populations at three different hierarchical levels: among different groups according to three river systems, among populations within different groups, and within populations. The significance level  of all 68 accessions was 90.37%, and the average GC content was 38.86% , but most ΦST were low and not statistically significant (p > 0.05). Furthermore, pairwise FST and ΦST values between YC and other populations were much higher (p = 0.11), while 61.16% (p < 0.00) of genetic variation was explained within population variation (p > 0.05) of the total genetic variance, and most genetic variation was found within the population , amphibians (Andrias sligoi), and aquatic insects (Microvelia horvathi) [Bellamya in China were much later than the formation of the Nanling Mountains and the watershed between the Yangtze River basin and the Pearl River basin [B. aeruginosa. The passive transport of waterfowl across such a huge geographical barrier seems impossible, as the retention time of intact invertebrate propagules in the intestine of a bird is limited [Bellamya in LZRSRN had been mainly captured from the lakes distributed in the Yangtze River basin [Bellamya from the Yangtze River basin to the Pearl River basin might have weakened the population’s genetic structure.In previous studies, the mitochondrial sequencing and microsatellite data, which were neutral molecular markers and widely used in population genetics , howeverifestyle ,22,23. Tifestyle ,63,64,65in Range  between orvathi) ,68,69, worvathi) , the orier basin . The Naner basin , such asB. aeruginosa populations from the middle and lower Yangtze River to expand to nearby rivers easily by flooding in summer [H. leucisculus among the middle and lower Yangtze River, Huaihe River and the Yellow River basin was also mainly attributed to the flooding [The admixture between the Yangtze River basin and the Yellow River basin could be attributed to their spatial proximity, which enables n summer . There eflooding .B. aeruginosa received maximal support in the SLAF-seq tree  in a previous study [Bellamya, coinciding with other recent studies using reduced representation genomic data and population genomic approaches to resolve phylogenetic relationships and population structure for closely related species and non-model species [Compared with SLAF-seq, the mtDNA  BI tree b. The SL BI tree a with maus study  could be species ,42,44,71B. aeruginosa across three river systems in China. Compared with the mtDNA COI data, 25,999 SNPs revealed that the seven B. aeruginosa populations could be divided into three groups with distinct phylogeographic patterns, indicating the power of the SLAF-seq in population genomics for snail. PCA and admixture structure showed that B. aeruginosa in the Yellow River basin and Pearl River basin were clearly distinguished, but B. aeruginosa in the Yangtze River basin was mixed with the other two river systems. Pairwise FST using SNPs among populations was much higher and statistically significant, suggesting a high population differentiation, while most pair ΦST values were lower, except between population YC and other populations. The result of the phylogenetic analyses almost coincided with PCA and admixture structure results, where population QSH and LB from the Pearl River basin clustered together with some individuals from YW, which belongs to the Yangtze River basin; population YH and WL from the Yellow River basin clustered together with LZH and several individuals from YW; the remaining population YC from the Yangtze River basin clustered as a single clade. The previously unidentified population structure of B. aeruginosa was almost resolved by SLAF-seq, providing useful guidance for effective selective breeding and further conservation strategies for Bellamya germplasm resources, as the LZRSRN is making robust progress.In the present study, 25,999 SNPs and 4737 polymorphisms SLAF-tags were identified using SLAF-seq to investigate the population structure of