Cyprogenia. Cyprogenia stegaria is restricted to the Eastern Highlands and displays little genetic structuring within this region. However, two allopatric lineages of C. aberti in the Ozark and Ouachita highlands exhibit substantial levels (mean uncorrected FST = 0.368) of genetic differentiation and each warrants recognition as a distinct evolutionary lineage. Lineages of Cyprogenia in the Ouachita and Ozark highlands are further subdivided reflecting structuring at the level of river systems. Species tree inference and species delimitation in a Bayesian framework using single nucleotide polymorphisms (SNP) data supported results from phylogenetic analyses, and supports three species of Cyprogenia over the currently recognized two species. A comparison of SNPs generated from both destructively and non-destructively collected samples revealed no significant difference in the SNP error rate, quality and amount of ddRAD sequence reads, indicating that nondestructive or trace samples can be effectively utilized to generate SNP data for organisms for which destructive sampling is not permitted.Detailed information on species delineation and population genetic structure is a prerequisite for designing effective restoration and conservation strategies for imperiled organisms. Phylogenomic and population genomic analyses based on genome-wide double digest restriction-site associated DNA sequencing (ddRAD-Seq) data has identified three allopatric lineages in the North American freshwater mussel genus  However, uncertainty about the accurate delimitation of species and the identification of distinct populations within species remain major challenges to the conservation of imperiled species that are also poorly studied from a taxonomic perspective3.Precise information on the delineation of evolutionary lineages, their genetic composition and population sub-structure is imperative for guiding population restoration and the long-term preservation of biodiversity4. Estimates are that 65% of the North American mussel fauna are imperiled5, and additional extinctions (up to 50% of extant species) are projected over the next century6. Freshwater mussels are also important components of aquatic ecosystems and provide important ecosystem services in the systems they inhabit9. The life cycle of North American freshwater mussels includes a larval stage, called a glochidium, that must attach to a host fish to complete their development10. The larval dispersal distance, and therefore gene flow, of freshwater mussels is believed to depend largely on the dispersal capacity of their host fishes12. It has been shown that some freshwater mussel species utilize different host fishes in different river systems14, and research has shown that mussel larvae transform to juveniles at higher rates on sympatric populations of host-fish species15, indicating that the relationship between obligate parasitic mussel larvae and host fishes may be reinforced by co-evolutionary forces.Freshwater mussels , are one of the most endangered faunas in the worldCyprogenia inhabits high-gradient streams within the Mississippi faunal province which includes the Eastern, Ozark, and Ouachita highland regions of North America16. Currently, two species of Cyprogenia are recognized: the Fanshell Cyprogenia stegaria (Rafinesque 1820), which is listed as a federally endangered species (USFWS 1990) and is found east of the Mississippi River in tributaries of the Ohio River, and the Western Fanshell Cyprogenia aberti (Conrad 1850), which is found west of the Mississippi River in the Arkansas, White, Black, and Ouachita rivers18. Populations of C. aberti are declining and have been severely fragmented due to habitat loss and degradation by human disturbance19, and Harris et al.18 recommended that the status of C. aberti in Arkansas be changed from Threatened to Endangered. In many mussel species, the glochidia larvae are packaged, along with unfertilized eggs, into structures called conglutinates that resemble worms and facilitate host infection. The mature glochidia are almost completely transparent, and the color of the conglutinate lure results from the pigmentation of included unfertilized eggs14. In Cyprogenia species, the colors of conglutinates observed to date include brown, red, and white.The freshwater mussel genus Cyprogenia have provided conflicting answers regarding the validity of the two nominal species. Phylogenetic research based on mitochondrial DNA (mtDNA)21 indicated that the two species of Cyprogenia were not reciprocally monophyletic, and that the divergent mitochondrial clades recovered in analyses were associated with the color of the conglutinate lures used by mussels to attract and infest host fishes22. In contrast, population genetic research using microsatellite markers23 supported the recognition of both C. aberti and C. stegaria, as allopatric species inhabiting the Western and Eastern highland regions respectively. Additionally, Chong et al.23 further advanced the hypothesis originally proposed by Serb & Barnhart22 that the mitochondrial lineages of Cyprogenia were associated with conglutinate color and these divergent mtDNA lineages were maintained by negative frequency-dependent selection by host fish. Despite the above findings, the taxonomic status of these species while clearer was still not fully resolved, and C. aberti has been assessed as Data Deficient due to these ongoing taxonomic issues (http://www.iucnredlist.org/details/6182/0).Previous phylogenetic and population genetic research on 25. Application of population genomic tools, including reduced representation with restriction site associated markers allows for the harnessing of high-throughput next-generation sequencing to yield thousands of polymorphic genetic markers to address the conservation and management of this group. Genotyping-by-sequencing (GBS) or double digest restriction-site associated DNA sequencing (ddRAD) are reduced representation approaches which have generated highly informative single nucleotide polymorphisms (SNPs) for population genomic inference29 and phylogenomic research41 for numerous species across a broad range of taxa.Non-genomic markers are still the most commonly applied tool to address conservation genetic questions for freshwater mussels, however, the low information content and the potential non-neutrality of mtDNA markers, and costs and scoring issues associated with microsatellite markers have limited their usefulness. Recently, genomic data has been shown to be a valuable tool to address these challenges42. Despite these advances, perceptions about the necessity of high quality starting material for the development of genomic libraries may have precluded the application of these techniques for endangered taxa because collecting permit restrictions only permit non-destructive sampling, which can yield small quantities of genomic DNA, are otherwise contaminated with non-target species or otherwise degraded43.The availability of a large number of genome-wide SNP markers using the ddRAD-Seq approach has enabled high-density SNP discovery, genotyping, and genetic mapping at high resolution, and has provided solutions to unresolved questions concerning population genetic structure, species delineation, and to inform the management and conservation of a number of high-profile species of conservation concernCyprogenia, document the genetic structuring, and attempt to further elucidate the relationship between conglutinate color and mtDNA lineages. We also explore and discuss possible barriers inhibiting gene flow and genetic connectivity across the range of Cyprogenia, the timing of population changes, as well as forces driving genetic divergence among mussel populations within the highlands. Finally, we used this opportunity to examine the utility of reduced representation approaches when only non-destructive samples are available as source material for genomic library creation by comparing the quality of results obtained from samples collected using a non-destructive method (cytology brushes) with those collected from mantle tissue biopsies.Here, we employed genome-wide ddRAD-Seq data to address species delimitation in C. stegaria_Eastern, C. aberti_Ozark, and C. aberti_Ouachita, and Dromus dromas) were retained. Total of 7,243 SNPs appeared to have a single SNP per locus with the mean depth of coverage of 7.24 (SD = 5.19).STACKS parameter exploration identified a combination of assembly parameters  as the best performing parameters  as well as for ten rivers throughout the range of Cyprogenia. Across all three species, measures of genetic diversity were the highest in C. aberti_Ozark, followed, in decreasing order, by C. stegaria_Eastern, C. aberti_Ouachita and D. dromas  were significantly different  among the three highlands regions.A total of 7243 SNPs were used to estimate population genetic parameters for four pre-defined mussel groups (as Table . Among ras Table . Among hFST) among rivers within each highland region was lowest in C. aberti_Ozark (FST = 0.0733), followed by C. stegaria_Eastern (FST = 0.013) and C. aberti_Ouachita (FST = 0.143) (Table FST) among pairs of all ten rivers throughout the range of Cyprogenia showed similar patterns of differentiation for both raw and corrected data. Corrected FST values are lower than uncorrected values due to the use of averages to impute missing data . The lowest and nonsignificant FST (FST = 0.007) was observed between the Licking and the Green rivers, and the highest  was found between the Salt and the Saline rivers for the uncorrected data. Most pairwise comparisons were statistically significant after correction for multiple tests  and 999 permutations), which may largely reflect species divergence .Analysis of molecular variance (AMOVA) according to hierarchical groupings revealed a high degree of variance among the three highland regions, accounting for 21.6% of total variation , indicating no evidence for population subdivision based on this phenotype. This difference is smaller than the values among rivers  within highland regions.The estimated value for C. aberti were further divided into two separate groups, C. aberti_Ouachita and C. aberti_Ozark. Specimens of C. aberti_Ozark were grouped with those of D. dromas for PC1 vs. PC3, whereas C. aberti_Ozark was grouped with C. stegaria for PC2 vs. PC3.A PCA along PC1 vs. PC2 for all 156 specimens showed a clear segregation of samples into three clusters , and the Ozark Highlands (K = 3). Based on these results, at least five genetically distinct clusters are identifiable within C. aberti, that correspond to the 1. Ouachita/Caddo rivers AR, 2. Saline River, AR (Ouachita Highlands), 3. Black/Spring rivers, AR, 4. St. Francis River, MO, and 5. Fall/Spring rivers, KS (Ozark Highlands) , C. aberti  and the outgroup, D. dromas (Clade D). The trees also indicated at least 6 subclades within C. aberti, all with strong bootstrap and posterior supports , reflecting river systems  trees constructed using different filtering criteria produced nearly identical topologies, therefore for downstream analyses we used a data set in which SNPs were present in at least 60% of samples Fig. . This saC. aberti clades. These individuals are also detected as a distinct cluster in the STRUCTURE analysis. C. stegaria specimens from the Eastern Highland region did not show clear pattern of geographic clustering , and Clade C-2 . In the Ozark region, specimens from the St. Francis River, AR formed an internal clade within the Ozark clades. Notably, two individuals (Ozark 105 and Ozark 107), from the Spring and Fall rivers, in the Ozark region formed a clade Clade B-3) sister to all other  sister tC. aberti specimens with pre-defined conglutinate lure color displayed clear allopatric clades that corresponded to geographic regions (Ozark and Ouachita) and not with the color of the conglutinate lures  and Ouachita Highlands . Model 2 has the largest MLE value and received greater supported than the model (Model 1) that includes two Cyprogenia species, (current taxonomy model). Among models, the BF in support for Model 2 was decisive compared to other models (Table The species delimitation results for the six ls Table . The curNe) for each sampling site are summarized in Table Cyprogenia populations showed either model 2 (DEC) or model 4 (INCDEC), or both models as the best fit model, depending on estimation method (direct or logistic regression) (Table The past demographic changes of each of the sampling locations (five demographic models) were explored using Approximate Bayesian Computation (ABC) approach. The best-fit demographic model was selected by comparison of five models and the resultant estimations of current effective population sizes (n) Table , indicatn) Table .Table 5BGenetic samples collected using cytology brushes showed a lower level of sample retention, compared to those from tissue samples. Forty six out of 88 nondestructive samples (52.3%) passed the quality filtering steps of the STACKS pipeline, whereas 110 out of 126 destructive samples (87.3%) passed the steps, which is statistically significant at the threshold of 0.05 . However, after filtering, the amount (in estimates of mean ± SD) of ddRAD sequence reads  do not appear to differ between genetic samples using either method Fig. . There w44 and can inform management decisions to conserve species45. Detailed information on species delimitation, population genetic structure and genetic composition of populations at both regional and fine scales together will help establish the long-term management and design tailored conservation plans for freshwater mussel species that are among the most endangered faunas in North America. The present genome-wide SNP data provides improved resolution on species delimitation of Cyprogenia and also allowed detection of unrecognized population genetic structure at the regional scale.Understanding the population subdivision and genetic differentiation within a species can serve as a basis for defining units for conservation managementCyprogenia specimens were genetically more similar within regions than between different regions  and natural barriers can limit dispersal of host fishes within and between regions. These barriers may further reinforce genetic differences between host fish populations by inhibiting gene flow among Highlands, leading to population subdivision within and between highlands.Given the relationship between freshwater mussel larvae and host fishes, the reduced compatibility of freshwater mussels with allopatric populations of host fishes as compared to sympatric host fishes may serve to limit gene flow between mussel populations in different riversCyprogenia. Cyprogenia stegaria formed a distinct clade corresponding to the Eastern Highland Region, whereas C. aberti was divided into two major clades, which corresponded to the Ozark and Ouachita highlands of the Western Highland region  as a distinct entity occurring east of the Mississippi River and occupying tributaries of the Ohio River basin. Cyprogenia that are found west of the Mississippi River, within the range of Cyprogenia aberti  form two distinct lineages, one restricted to the Ozark region (White and St. Francis River systems) and the other in the Ouachita region . Both of these entities are distinct enough to warrant recognition as separate species, however our current data set included only a small number of samples from the Spring and Fall rivers in Kansas, and we feel that additional samples from this area are needed to confirm this pattern. These samples formed a sister clade to the remaining western C. aberti populations and were also a distinct group in the Bayesian clustering analysis.Recognition of the three evolutionary lineages within Unio aberti Conrad, 1850 and Unio popenoi Call, 1855, the affinities of the Fall River populations and any extant populations in the Verdigris River are critical to the assignment to the name Cyprogenia aberti . Because of the small number of samples from the Fall River and total lack of samples from the Verdigris River, additional taxonomic changes must await a more thorough geographic sampling of Cyprogenia.Since the Fall and Verdigris rivers are the type localities for the names Cyprogenia populations experienced a decline in size following the LGM . The inferred reduction in population size and concomitant loss of genetic diversity likely contributed to the genetic differentiation revealed between these two highland regions in this study.Our Approximate Bayesian Computation (ABC) approach investigating demographic changes indicates that all GM Table . AlthougCyprogenia should consider at least 5, and possibly 7 distinct evolutionarily lineages when developing management plans; 1. C. stegaria in the Licking, Salt, and Green rivers, KY), 2. C. “aberti” in the Black/Spring rivers, AR, 3. C. “aberti” in the St. Francis River, MO, 4. C. “aberti” in the Ouachita/Caddo rivers, AR, 5. C. “aberti” in the Saline River, AR. These recommendations are justified by the fact that these five lineages have distinct genetic compositions and high level of genetic differentiation from other lineages , and destructive (mantle biopsy) methods. Using cytology brushes (or swabs) is commonly used for extracting genomic DNA from endangered and threatened freshwater mussels. Despite the lower recovery rate of samples that passed the filtering steps of the STACKS pipeline in nondestructive genetic samples, our data showed that the quality and amount of ddRAD sequence reads were not significantly different between genetic samples using nondestructive and destructive methods. Our results therefore provide evidence that this nondestructive method can successfully be used for phylogenomic and population genomic studies using ddRAD-Seq analysis and can benefit conservation and management of endangered species such as freshwater mussels in North America.23. Cyprogenia aberti specimens were collected from rivers in Arkansas, Missouri, and Kansas, which occur within the Ouachita  and the Ozark  highland regions .Genomic DNA for this study was derived from DNA extractions of specimens prepared for previous studiesons Fig. . C. steg2 tissue and storing it in 95% ethanol . DNA was extracted from tissue samples using the DNeasy Blood & Tissue Kits . For population genomic analyses, all Cyprogenia specimens were grouped according to their sampling locations, i.e., river. In addition, 14 Dromus dromas specimens were collected from the Clinch River, TN for this study and were used as an outgroup for phylogenetic analysis and for genetic comparison at the genus level. A total of 214 samples (200 Cyprogenia sp. + 14 Dromus sp.) were used in this study. Of these, 24 samples were randomly chosen as replicates to explore STACKS60 parameters for de novo assembly and SNP discovery (see below).Samples were collected nondestructively using cytology brushes and stored in the buffer supplied with the Puregene Buccal Cell Kit  and DNA was extracted following the manufacturer’s instructions. Mantle biopsies were conducted by taking ~ 2 mm61. Briefly, a total of 200 ng of genomic DNA for each sample was normalized into 20 ng/μl, which was then digested using high fidelity restriction enzymes Pst1 and Msp1 (New England Biolabs). Resulting fragments were ligated to custom-made P1 containing sample-specific barcodes and forward primer annealing sites and P2 adapters with index and reverse primer annealing sites. Each individual was then PCR-amplified using Phusion Polymerase and following PCR conditions: 25 cycles at 98 °C for 20 s, 60 °C for 30 s and 72 °C for 40 s, with an initial denaturation step at 98 °C for 30 s and a final extension step at 72 °C for 12 min. PCR products were electrophoresed in 1.5% agarose gels and only samples with smeared PCR products were chosen for the next steps. Individually barcoded and indexed samples were then pooled into libraries and size-selected (325–500 bp) using a Blue Pippin (Sage Science) so that the remaining adapters and unbound primers/primer dimers can be removed. Each library was quantified and quality-checked using an Agilent BioAnalyzer 2100 (Agilent Technologies).DNA libraries were prepared with modifications of Peterson et al.A total of 238 samples (214 samples + 24 replicates) were divided into 5 lanes  and subjected to sequencing. Libraries were single-ended sequenced (100-bp) in separate flow cells on an Illumina HiSeq 3000 (Illumina Inc.) at the DNA Facility of Iowa State University. A simplified flowchart of our methods including ddRAD raw sequence reads, STACKS parameter selection, resultant datasets and data analyses is shown in Fig. 60 was used for demultiplexing, quality filtering, de novo locus assembly, and SNP discovery  were selected for phylogenetic and population genetic analyses. In this study, we applied the following STACKS population constraints to the 156 specimens. In STACKS, − p: refers to the minimum number of samples that a locus must be present in for inclusion of the locus in phylogenetic analyses: (1) for the phylogenetic study, only loci that were present in > 60% of all samples (i.e. 94 of 156) were retained, (2) for the population genetic study, only loci that were present in 50% of four species-groups , according to Chong et al.23, and in > 60% of all samples within each group (i.e. − r 0.6) were retained. Additionally, concatenated SNP data for all samples were also used for phylogenetic analysis of C. aberti specimens identified by conglutinate egg colors (14 red and 17 brown). Sample information for the 156 specimens used in this study is shown in Table The STACKS software pipeline, version 1.46ery see “”. We evaC. stegaria_Eastern, C. aberti_Ozark, C. aberti_Ouachita, and D. dromas) and present in over 60% of all samples within each group. In total, 14,890 RAD loci were obtained and 7243 SNPs (consisting of only the first SNP per ddRAD-Seq locus) were recovered and used for subsequent population genetic analyses.The STACKS populations program  was used to retain only those loci that were present in at least two of the four species groups . Significance level was adjusted for multiple tests using Bonferroni correction.Measures of genetic diversity for each of ten sampling locations (rivers) were calculated using GENODIVEFST’s calculated for both raw data and corrected data in which missing data were replaced with randomly drawn alleles based on the overall allele frequencies using GENODIVE63.To explore the effect of missing data in each locus, we compared pairwise 66 to assess the partitioning of genetic variation among highlands, populations within highlands, individuals within populations and within individuals. Two separate AMOVA analyses were conducted (three highland regions vs. two highland regions). The comparison of three highland regions compared the Ozark, Ouachita, and Eastern highlands, which mainly accounts for variance between Cyprogenia aberti and C. stegaria. Whereas the comparison of two highland regions (Ozark vs Ouachita highlands), account for variance within C. aberti. Analyses were carried out using an Infinite Allele Model (IAM) and 999 permutations to assess its significance, where missing data were replaced by randomly drawn alleles based on overall allele frequencies.GENODIVE was further used to conduct a hierarchical Analysis of Molecular Variance (AMOVA)Cyprogenia samples only using the adegenet package67 implemented in R68. A scatter diagram was plotted based on factor scores along the first two PC axes accounting for the most variation. We also conducted a Discriminant Analysis of Principal Components (DAPC) using an interactive web interface for DAPC distributed with the package [using R command: adegenetServer(what = "DAPC")]. DAPC aims to provide an efficient description of genetic clusters using a few synthetic variables that are constructed as linear combinations of the original variables  which have the largest between-group variance and the smallest within-group variance69. We used the default parameters except selecting the option of suggested number of PCA components.We examined the distributions of individual multilocus genotypes in multivariate space, to assess the geographic structuring among individuals. We carried out a principal component analysis (PCA) based on 7243 SNP loci across all samples and 70. The admixture ancestry model was used with correlated allele frequencies and no prior on population membership. The initial analysis of the complete dataset was conducted ten times for each K value (the number of inferred genetic clusters) from 1 to 10 with 200,000 Markov Chain Monte Carlo (MCMC) generations and a burn-in of 100,000. The optimal K value was inferred from ad hoc posterior probability models of [Pr(X|K)]70 and deltaK71 using STRUCTURE HARVESTER72. To assess the presence of substructure within major clusters, subsequent analyses were conducted within each of main clusters, using the same parameters as described above. A STRUCTURE bar plot to infer the genetic composition of individuals was visualized using DISTRUCT v.1.173.To infer the most likely number of genetic clusters, we applied a Bayesian clustering approach implemented in STRUCTURE 2.3.4D. dromas specimens as an outgroup, these four data sets of concatenated SNPs were used to investigate evolutionary relationships among the156 specimens. These four different filtering criteria respectively generated concatenated SNPs of 15,829, 9673, 4674, and 1283, each with parsimony-informative sites of 7440, 4395, 2014, and 511. Phylogenetic trees were constructed with Maximum likelihood (ML) analyses in IQtree 2.1.275 with 1000 ultrafast bootstrap replicates (− bb), 10,000 iterations (− nm) and Ascertainment bias correction (+ ASC) model. The TVM + F + ASC + G4 model of sequence evolution was selected as a best-fit model according to a Bayesian Information Criterion (BIC) model selection implemented in IQtree 2.1.2. ML trees constructed from different filtering criteria produced nearly identical topologies ; − p 78, − p 94, − p 109, and − p 125, which only retains loci present in at least 50%, 60%, 70% and 80% of the 156 samples respectively. Using the STACKS population program (–phylip_var) and the ies Fig. , therefo76 to construct phylogenetic trees using Bayesian inference (BI) with the GTR-I-GAMMA model , which includes a parameter for site heterogeneity. BI employed four simultaneous Monte Carlo Markov chains (one cold and three heated) with 1,000,000 generations and sampled every 500 generations. The first 25% of the data points were discarded as burn-in. We confirmed that both ML and Bayesian tree searches achieved convergence . The consensus trees from both ML and BI were illustrated using FigTree v 1.4.3 .We used MrBayes v 3.277 and MODEL_SELECTION implemented in BEAST version 278. Species Trees were inferred directly from SNP markers by bypassing gene trees in a full coalescent analysis, using a polynomial-time algorithm that computes the likelihood of a species tree directly from the markers under a finite-sites model of mutation effectively integrating over all possible gene trees77. Out of 148 samples for Cyprogenia, a total of 40 samples for ten sampling sites (four samples per each sampling site) were aligned and selected based on two sampling criteria, random sampling of individuals or by selecting the individual samples with the highest coverage  using a custom Python script. Coalescent-based species tree was inferred using XML file with following model parameters; with selecting “Cal mutation rates”, unselecting "Include non-polymorphic sites" and selecting "Use Log Likelihood Correction" in Model Parameter, and the chain length of 5,000,000 with storing every 1000 in MCMC, and the rest of parameters were set to default.Species tree inference and species delimitation in a Bayesian framework using SNP data were investigated using SNAPP79 to assess support for model rankings. For species delimitation using genetic data and coalescent methods, we designed six candidate species delimitation models; Model 1: current taxonomy (two Cyprogenia species), Model 2: split C. aberti based on highland regions into two species  and Ouachita ), Model 3: intermix half of two candidate C. aberti species from each other , Model 4: reassign C. aberti , Model 5: reassign C. aberti , Model 6: reassign C. aberti (Black from Ozark was moved to Ouachita). For current and alternative species delimitation models, we edited the XML file for marginal likelihood estimation, with the path sampling steps of 36, MCMC sample length of 100,000 for each path sampling step, alpha of 0.3, burnInPercentage = 10, and preBurnin of 10,000. Our path sampling parameters might be low, and a thorough analysis may require more path sampling steps and higher MCMC. However, given computational time and capacity, and similar results from various parameter sets applied, such parameter sets are reasonable.For species delimitation exploration, candidate species delimitation models were explored using marginal likelihood estimation (MLE) of each competing species delimitation model, ranking models by marginal likelihood, and using Bayes factors79. The BF scale is as follows: 0 < BF < 2 is not worth more than a bare mention, 2 < BF < 6 is positive evidence, 6 < BF < 10 is strong support, and BF > 10 is decisive. All Bayes factor (BF) calculations are made against the current taxonomy model (Model 1). Therefore, positive BF values indicate support for current taxonomy model, and negative BF values indicate support for alternative model.Bayes factor (BF) calculations based on marginal likelihood estimation (MLE) of each competing species delimitation model are made against the current taxonomy model (Model 1), subtracting the MLE values for two models, and then multiplying the difference by two . The strength of support from BF comparisons of competing models was evaluated using the framework of Kass and Rafteryhttp://tree.bio.ed.ac.uk/software/tracer). Posterior distribution of species trees was summarized to identify the topology with the best posterior support using TreeAnnotator implemented in BEAST version 278. The target tree file generated with Maximum clade credibility (MCC) and Median heights option in TreeAnnotator was displayed in FigTree.Trace files generated by SNAPP were inspected and convergence of chain by checking Effective Sample Size (ESS) for various variables was evaluated using the program Tracer (Cyprogenia sampling locations in the three highland regions using the Approximate Bayesian Computation (ABC) approach implemented in DIYABC 2.180. To evaluate the past demographic changes, we applied the methods of Cabrera and Palsbøll81. Five demographic models were evaluated. Model 1: CON (constant population size), Model 2: DEC (a single instantaneous decrease in population size), Model 3: INC (a single instantaneous increase in population size), Model 4: INCDEC (a single instantaneous increase followed by a single instantaneous decrease in population size), Model 5: DECINC (a single instantaneous decrease followed by a single instantaneous increase in population size). These five models were tested for each of sampling locations and the best fit model was selected by the model comparison implemented in the DIYABC 2.1. We followed the prior model parameterization designed by Cabrera and Palsbøll81 with the slight modification of generation time (T) and a uniform distribution with a range of 10–20,000 (N). Similar to Cabrera and Palsbøll81, the Last Glacial Maximum (LGM) of approximately 26,500–15,000 years ago was employed as the main cause for changes in population size of these species. In the case of the models DEC and INC, the change in population size occurred after the LGM , and in the case of the models INCDEC and DECINC, the changes in population size occurred just before and just after the LGM 81. We employed a generation time of 5 years for Cyprogenia, therefore a uniform prior distribution with a range of 100–2250 for T1 (after the LGM), and a range of 2250–4500 for T2 (before the LGM). These prior distributions are reasonable given the species’ presumed census size  and generation time (5–9 years per generation)82.We investigated the demographic changes for each of the To minimize effect of missing data in the analyses, we used only loci that were found in all populations using independent parameters of populations program in STACKS for each of three Highlands . A vcf dataset generated from the Stacks populations command was changed to .snp file using phyton script (vcf2diyabc.py) implemented in the DIYABC program and monomorphic loci were further removed from the dataset so that only polymorphic loci were employed in the ABC analyses. No mutation model parameterization was required for SNPs. Models for demographic changes and their prior distributions were provided in the “23, and a total of 31 C. aberti specimens with known conglutinate lure color (14 with red conglutinates and 17 with brown conglutinates) were selected to examine genetic relationships between specimens. Concatenated SNPs obtained from a moderate criterion (− p 94) described earlier were used for phylogenetic analyses. Maximum likelihood (ML) and Bayesian trees were constructed to examine phylogenetic relationships for total of 33 specimens, 31 C. aberti specimens, and two C. stegeria specimens used as outgroups. The TVM + I + G4 model of sequence evolution was selected according to a Bayesian Information Criterion (BIC) implemented in IQtree.The color of conglutinate lures (red and brown colored eggs) of included specimens were identified previouslyhttps://www.bioinformatics.babraham.ac.uk/projects/fastq_screen/). Genome sequences of a bivalve, Mytilus galloprovincialis, are available from GenBank (accession: LNJA000000000). Genome sequences of human (Homo sapiens), and yeast (Saccharomyces_cerevisiae) are available from ftp://ftp.ensembl.org/pub/current_fasta. Genome sequences of a bacterium (Escherichia coli) are available from GenBank (accession: U00096.3). PhiX sequences are available from Refseq accession NC_001422.1.To investigate the utility of non-destructive methods as source material for genomic library creation, we compared the results obtained from samples collected using cytology brushes and mantle-tissue biopsies. First, we compared the retention rate of samples from each method after the aforementioned filtering steps. Then, we compared the quality and amount of ddRAD sequence reads between genetic samples using both methods. In addition, we estimated and compared differences in SNP error rates between replicated samples from each sample type. Lastly, we mapped and compared ddRAD fastq sequences from all samples to four genome sequences  and PhiX sequences using FastQ_Screen (Supplementary Information.


Pocillopora verrucosa from Ludao, a small oceanic island off the eastern coast of Taiwan. We genotyped 75 P. verrucosa specimens from three sites  at three depth ranges , spanning shallow to upper mesophotic coral reefs, with eight microsatellite markers. F-statistics showed a moderate differentiation  between two adjacent locations (Dabaisha 23–30 and Dabaisha 38–45 m), but no differentiation elsewhere, suggesting high levels of connectivity among sites and depths. STRUCTURE analysis showed no genetic clustering among sites or depths, indicating that all Pocillopora individuals could be drawn from a single panmictic population. Simulations of recent migration assigned 30 individuals (40%) to a different location from where they were collected. Among them, 1/3 were assigned to deeper locations, 1/3 to shallower populations and 1/3 were assigned to the right depth but a different site. These results suggest high levels of vertical and horizontal connectivity, which could enhance the recovery of P. verrucosa following disturbances around Ludao, a feature that agrees with demographic studies portraying this species as an opportunistic scleractinian.Mesophotic habitats could be sheltered from natural and anthropogenic disturbances and act as reproductive refuges, providing propagules to replenish shallower populations. Molecular markers can be used as proxies evaluating the connectivity and inferring population structure and larval dispersal. This study characterizes population structure as well as horizontal and vertical genetic connectivity of the broadcasting coral  Mesophotic Coral Ecosystems (MCEs) are deeper sections of shallow tropical and subtropical coral reefs . GeneralIn most benthic marine species, recruitment and settlement of new individuals are achieved through pelagic larval movement . While lPocillopora verrucosa is one of the most dominant scleractinian hard corals, distributed from the near-surface to as deep as 55 m [Pocillopora species [Pocillopora verrucosa. The present study aims to make these assessments.In Taiwan, well-developed fringing reefs have been observed around Ludao, a small volcanic island 33 km off the coast of Taitung. Along the bathymetric gradient,  as 55 m . Despite as 55 m , sequenc as 55 m . Several species –54 and w species . HoweverPocillopora verrucosa specimens were collected at three sites around Ludao—Gongguan, , Guiwan, , and Dabaisha —using compressed air or Trimix diving, with a minimum distance of 3 m between samples to avoid collecting colonies that might be clones , Mid-depth , and Deep —were previously confirmed to be quencing . Nine loDNA extraction process followed De Palmas et al. . AdditioPocillopora species. Each PCR mixture contained 15 μL of Master Mix RED , 15 μL of ddH2O, 2 μL of each pair of primers (2.5 μM), and ~50 ng of DNA template. PCR conditions were as follows: 60 s of denaturation step at 94°C; 40 cycles of 60 s at 94°C, 60 s at the annealing temperature (Ta), and 60 s at 72°C; and an extension step at 72°C for 5 min. PCR amplifications were checked on a 2% agar gel to confirm amplification success rate and product size. Successful amplifications were diluted 22X before processing on a 5400 Fragment Analyzer™ Automated CE System  using a dsDNA 905 reagent kit (1 to 500 bp). Any markers displaying low PCR amplification success or non-specific PCR amplification  were removed from subsequent analyses.Microsatellite marker effectiveness was determined by amplifying eight samples haphazardly picked with 30 microsatellite markers, accounting for most microsatellites developed for P. verrucosa. Each PCR product was run three times on the fragment analyzer (as described above) to account for the accuracy of the fragment analyzer . The three repeated electropherograms were scored using Prosize 3.0  and allele repeat size was then estimated using GenAlEx 6.5 [Loci that passed the selection process were amplified to genotype the 85 AlEx 6.5  based onAlEx 6.5  was used6 steps in a Markov chain and 105 dememorization steps. Arlequin was also used to investigate linkage disequilibrium between all pairs of loci with 5 x 105 permutations. Null allele frequency was inferred using MicroChecker 2.2.3 [5 randomizations.Arlequin 3.5.2.2  was useder 2.2.3  with a 9ST comparison at all loci and their statistical significance with 5 x 105 permutations in Arlequin. Pairwise FST were also calculated using FreeNa software [ST values [ST were also computed at the site level (pooling 3 depth ranges at one site together) and at the depth level .Genetic differentiation among all locations was estimated using a pairwise Fsoftware  with theT values . To acco6 Markov Chain Monte Carlo simulations, a burn-in period of 105 and three iterations for each K from k = 1 to k = 12. The LOCPRIOR option was used (to account for the location characteristics) under the admixture model and assuming independent allele frequency. The web-based program STRUCTURE HARVESTER 0.6.94 [Genetic structuring was assessed using STRUCTURE 2.3.4  with 106R 0.6.94  was usedR 0.6.94 . A princR 0.6.94 .home/Lmax  using the frequencies-based method from Paetkau et al. [In order to detect individuals with a recent migration history, we use the “Detection of first-generation migrants” option from Geneclass 2  which asu et al.  and Montu et al. . To accoP. verrucosa specimens genotyped in this study, 75 produced satisfactory amplification and scoring results (≤ 4% of data missing) for eight microsatellite markers, Psp_16, Psp_23, Psp_29, Psp_32, Psp_35, Psp_39, Psp_41, and Psp_48 : ngguan M . The numngguan M . Twelve ngguan M , represeST = 0.106, p < 0.05, ST values were low (0.000 < FST < 0.058) and not significant. Pairwise FST computed at the site and depth levels were also low (0.000 < FST < 0.007) and non-significant  = - 1748.400, Prob (k = 1) = 0.688, ).Assignment tests yielded 40% of our specimens (30/75) as potential new migrants: 10 came from deeper locations, 10 came from shallower locations and the remainder relocated from similar depth but from a different site . This rePocillopora verrucosa colonies originating from three sites and three depth ranges were genotyped at eight microsatellite loci to evaluate genetic differentiation and genetic structure, and to estimate migration flux between sites and across depths. Overall, P. verrucosa exhibits a low level of genetic differentiation, with the exception of Dabaisha M and Dabaisha D, which showed a mild genetic differentiation despite being separated by a short horizontal distance. Pairwise FST values generally support high genetic connectivity between sites and across depths  act as an ecological refuge against disturbances for several other depth generalist species inhabiting this area and 2) benefit from the Kuroshio Current for dispersion of coral larvae and increase the overall connectivity along its path.s depths , and theion unit . We furtion unit , highligP. verrucosa from Ludao exhibited low genetic differentiation between sites located less than 6 km apart, and across depth zonations occurring at shorter horizontal distances. Only one pairwise comparison showed weak but significant differentiation (FST = 0.106), which corresponded to two adjacent locations at Dabaisha  are recruiting at both shallow and mesophotic depths [Fymbriaphyllia ancora: -45 m and Acropora spp.: -40 m, De Palmas S, personal observation). Despite the short horizontal distance separating Dabaisha M from Dabaisha D (< 100 m), both locations are well-known for their strong currents. The slight difference between these two adjacent locations could be due to diverging currents generating a physical barrier to P. verrucosa larval dispersal. Overall, low genetic differentiation and the absence of any genetic structures within and between sites further suggest that P. verrucosa specimens originated from a single population, and that this population was probably panmictic.In this study, c depths . In addiMontastraea cavernosa showed genetic differentiation with depth in Florida [Seriatopora hystrix from the Great Barrier Reef: the species displayed genetic differentiation with depth at Yonge Reef and Day Reef [Stephanocoenia intersepta [Pocillopora verrucosa is a broadcasting spawner, which spawns in the early morning [P. verrucosa to display positive photo-movements, i.e., phototaxis or photokinesis. They further suggested that P. verrucosa larvae could disperse further and benefit from stronger surface currents as they move upward into the water column. This larval behaviour could explain the absence of genetic structure observed in the present study. In general, the ability of mesophotic populations to reseed shallow water reefs should be regarded as species- and location-specific [Previous investigations have found contrasting patterns of connectivity between shallow and mesophotic populations, varying from open to closed populations, with connectivity patterns occurring over thousands of kilometers , 74–76.  Florida  and Litt Florida , but not Florida , 41. SimDay Reef , but notDay Reef . InteresDay Reef , low gentersepta . In additersepta –41, 43.  morning , 2–3 day morning . Mulla e morning  found laspecific . In Ludaspecific ), so thaP. verrucosa is one of the dominant species found across the depth gradient. Our data strongly suggest that larval movement creates connectivity between shallow and deep zonations. Our simulation test detected potential migrants between sites and across depths, which reinforces the notion that mesophotic populations can support themselves as well as adjacent populations. About 40% of our specimens were assigned to different populations from where they were collected  [P. verrucosa shows effective ecological connectivity around the island. Our study design includes adult colonies that are probably different ages, so the connectivity pattern is likely the result of multi-generational larval exchange, rather than associated with a specific P. verrucosa cohort. Population genetics comparing adult colonies to newly recruited P. verrucosa could tackle this issue and allow researchers to evaluate the current populations’ dynamics.Effective ecological connectivity between shallow and deep zonations is achieved under three prerequisite criteria: 1) suitable habitats supporting robust populations, 2) habitats that are physically connected via water or propagule movements, and 3) organisms with life-history traits that enable successful replenishment of both their own populations and those connected to them . In Ludaollected . In thisollected . Among tof loci) ; howeverPsp_29/Psp_39, and Psp_32/Psp_48. These markers were not reported to have linkage disequilibrium in Pocillopora populations from the Ryukyus for which they were developed [Psp_29, Psp_35, Psp_39, Psp_41, and Psp_48) were detected as null alleles, but no SAD effect was found at these loci, suggesting that the flanking regions of those microsatellite loci could have accumulated enough variation to prevent marker annealing during PCR. As a deviation from Hardy-Weinberg equilibrium results from a shortage in heterozygotes (or excess in homozygotes), we hypothesize that the high number of potential null alleles found in this study could influence both linkage disequilibrium and deviation from Hardy-Weinberg equilibrium. Null alleles can also influence population differentiation estimators [ST. Null alleles are common in a large variety of taxa and, interestingly, are often found in taxa with a large population [P. verrucosa is indeed locally abundant in Ludao, as it is one of the major components of the scleractinian community, residing from very shallow to at least 55 m deep [in fine the role of the mesophotic zone as an ecological refuge.Among all the combinations of sites, depths, and loci, 12 (out of 96) deviated from Hardy-Weinberg equilibrium . In addieveloped . Linkageeveloped . Howevereveloped . Microsaeveloped ; they coeveloped . Consequeveloped . In thistimators , so theypulation . P. verr5 m deep . PopulatPocillopora species , 3 (P. verrucosa), 4 (P. damicornis), 5 (P. acuta), and 8 (an undescribed Pocillopora species), while several other SSR markers could not . None of these previously developed markers produced satisfactory results in P. verrucosa from Ludao. For instance, PV3, PV5, PV6, Pd3-002, Pd2-003, and Pd13 had low amplification rates (<50%) and Pd3-010 did not amplify at all  showed no or poor amplification (<25%), and an additional marker (Psp_33) was found to have major additional products , and areandrina –87. Howea et al.  confirmey at all . Markerst Africa , and mosolynesia , 58, 89,olynesia . Among tolynesia  for popuproducts . We hypo regions , which m regions , to reveP. verrucosa populations around Ludao from 7 m to as deep as 45 m are close to panmictic. This species is more likely to recover from acute disturbances occurring in shallow waters (typhoons or warm water bleaching events) through the recruitment of larvae originating from adjacent shallow and deep habitats. We think that the reproductive status and reseeding capacity of mesophotic corals in this area require further investigations. Ludao, as an oceanic island could act as an important source of recruits along the Kuroshio Current for populations located on its path [Our results demonstrate that its path . Howeverits path . In factits path , and thiS1 TableData are reported in allele size in bp and missing data are coded 0. Mitochondrial Open Reading Frame (mtORF) haplotype and museum numbers are indicated for each specimen.(DOCX)Click here for additional data file.S1 File(DOCX)Click here for additional data file.


The genetic makeup of Indigenous populations inhabiting Mexico has been strongly influenced by geography and demographic history. Here, we perform a genome-wide analysis of 716 newly genotyped individuals from 60 of the 68 recognized ethnic groups in Mexico. We show that the genetic structure of these populations is strongly influenced by geography, and our demographic reconstructions suggest a decline in the population size of all tested populations in the last 15–30 generations. We find evidence that Aridoamerican and Mesoamerican populations diverged roughly 4–9.9 ka, around the time when sedentary farming started in Mesoamerica. Comparisons with ancient genomes indicate that the Upward Sun River 1 (USR1) individual is an outgroup to Mexican/South American Indigenous populations, whereas Anzick-1 was more closely related to Mesoamerican/South American populations than to those from Aridoamerica, showing an even more complex history of divergence than recognized so far. Indigenous populations, including in those in Mexico are underrepresented in genetic studies. Here, the authors perform a population genetics study of indigenous peoples in Mexico to explore demographic histories of the region in the context of geography and cultural influences. There has been great interest in understanding the genetic structure of Native American populations, partly because studying these populations has been helpful in elucidating aspects of the global dispersal of modern humans6. Today, the 68 recognized ethnic groups in Mexico are clustered into 11 linguistic families7, with unique customs and cultures. These populations can be divided into two main geographic/cultural areas: Mesoamerica and Aridoamerica. Mesoamerica comprised central and southern Mexico, and during the pre-Hispanic era was inhabited by sedentary agricultural societies favored by the great biodiversity of this region. Aridoamerica encompassed a semiarid area in northern Mexico that preserved nomadic forms of subsistence throughout the pre-Hispanic era.Mexico has long acted as a natural bridge for human migration from North America to Central and South America and vice versa. Together with historical events, these movements have been crucial in shaping the genetic makeup and structure of populations in the Americas8. Therefore, key questions about the genetics of Mexican Indigenous populations remain unsolved.Despite being one of the largest and most diverse groups in America, the Mexican Indigenous populations are still underrepresented in terms of the number of genotyped individuals and geographic regions sampled. To the best of our knowledge, few Mexican ethnic groups have been examined at the genome-wide level, yet a complex genetic structure has been observed in such groups11, which were merged with previously published data sets, yielding a total of 1086 Native Americans from Mexico, representing all linguistic families except Kickapoo (Algonquian language family)  cohort12, including Mexican Native American populations previously reported by Reich et al.8, Moreno-Estrada et al.2, and Silva-Zolezzi et al.12. The merged data set comprised 3490 individuals from 218 populations and 61,393 autosomal SNVs. Principal component analysis (PCA)13 indicated that Mexican Indigenous populations clustered with other Native American groups from North and South America  with 146 previously published populations worldwide15 in each data set, except for Reich et al.8 data set as detailed in the “Methods” section. Non-Native ancestry tracks were masked in the individuals from Indigenous populations, and the masking accuracy was assessed by running the admixture analyses again assuming K = 4 clusters  and Lacandon (Southeast)2, had the highest levels of genetic differentiation when compared with the other populations based on this statistic  tree based on the pairwise-ion Fig. . The NJ ion Fig. .14 using K = 2–16 clusters . On the other hand, two of the clusters were mainly observed in several ethnic groups inhabiting the Center and South (here referred to as multi-ethnics), principally in populations from the Oto-mangue linguistic family, and the other cluster in populations from the Southeast that are part of the Mayan linguistic family. We observed that the multi-ethnic and Mayan components had opposite gradients, where the Mayan component was the most prevalent in the Southeast and the multi-ethnic components were more prevalent in the Center and South of Mexico  across time based on two different methods. We included 48 ethnic groups from the masked data set, all of them with sample sizes of at least 10 individuals  tracks implemented in the IBDNe software19. We observed a decline in the Ne between 15 and 30 generations ago in all tested populations that overlaps with the beginning of the European colonization of the Americas, followed by an expansion , which is similar to previous findings21. However, here we documented a variation in the long-term Ne between ethnic groups . Other ethnic groups had a long-term Ne between 1825 and 3331 individuals . As expected, the Seri and Lacandon groups had the highest proportion of the genome in ROH compared to the other populations tested, suggesting that the high genetic differentiation observed in these populations is due to genetic drift as previously reported2  assuming a clean split between them. To scale T in years, we assumed 28 years per generation25. Seri and Lacandon populations have the highest T values compared to other populations   or large (>10 cM) shared IBD blocks revealed possible spatiotemporal interaction dynamic patterns among indigenous groups. Intermediate IBD block sizes are suggested to be dated to 500–1500 years ago (oldest), while large tracks are thought to be originated 0–500 years ago (youngest)Analysis of intermediate block sizes revealed that the Central East, South, and Southeast regions have older connections among them than do the northern regions 29 individuals with our data from the most representative sample of Indigenous peoples in the Mesoamerican and Aridoamerican regions of Mexico to date. First, we compared the ancient genomes with 59 worldwide populations and 325 individuals from our data set with at least 99% Native American ancestry . This analysis showed a high affinity of both ancient genomes with present-day Mexican Indigenous samples  using the Ancient Southern Ontario population Canada_Lucier31 and Athabaskan32 ancient genomes as representatives of NNA/ANC-B ancestry. We found these results to be consistent with D ~ 0, suggesting that the NNA/ANC-B populations are an outgroup for those from Aridoamerica and Mesoamerica from Mexico . We found that Mesoamerican populations share more alleles with Aridoamerica than Anzick-1 shares with Aridoamerica , where the ancient samples were either USR1 or Anzick-1, and the South American samples were either Karitiana or Aymara. We also included the Tzotzil population as an internal control. When USR1 was used to represent the ancient population, we did not observe any significant deviation from zero in any of the tests  when Karitiana and Tzotzil were used in the comparison, we observed a similar trend when Aymara was used in the test 29, and that a split ~15 ka years ago in the far North led to the northern (NNA/ANC-B) and southern (SNA/ANC-A) population branches in the Americas29. The SNA/ANC-A branch includes the ancestors of the Clovis child (Anzick-1) and all Indigenous peoples from Mexico to South America28. Nevertheless, the complex genetic structure observed in Mexican Native populations in this study, as well as the intricate series of migrations into South America identified in two other recent studies58, suggest that the history and ancestry of Indigenous populations in Mexico may be more complex than reported.On the other hand, studying ancient genomes has been helpful to elucidate ancient population relationships and migration patterns. In the last few decades, an increasing body of evidence has suggested that the peopling of the Americas brought at least four distinct streams of migration from Asia, beginning as early as ~23 ka ago29, and that the Mexican Indigenous populations experienced one of the following scenarios: (1) a split between Aridoamerica and Mesoamerica occurred prior to the split between Mesoamerica and Anzick-1 with posterior gene flow between the Aridoamerican and Mesoamerican populations, or (2) a split between Aridoamerica and Mesoamerica occurred after they split from the Anzick-1, with Aridoamerican populations carrying gene flow from a population that split above the population represented by Anzick-1 and below the NNA/ANC-B branch . From this cohort, a total of 716 unrelated individuals belonging to 71 Indigenous communities representing 60 ethnic groups from 10 linguistic families were selected for genome-wide genotyping based on the availability of samples  in Mexico City (protocol number 31/2011/I) with the support of the National Commission for the Development of Indigenous Communities  and with the agreement of the Indigenous leaders from each community. All participants provided written informed consent, and authorities or community leaders participated as translators when necessary.61. Each data set was processed individually, including per marker and per sample examinations. We removed SNVs with genotyping rates <98% and those with a minor allele frequency of 1%, and then removed mitochondrial and sex chromosome SNVs. Finally, we excluded individuals with missing rates >3% and with discordant gender information.To perform our estimations, we generated several data sets merging our genotype data with those previously published for several worldwide populations and modern and ancient Native American individuals as follows. For data generated using only an SNV array, we performed the data handling and quality control procedures in Plink v1.98, Moreno-Estrada et al.2, and Silva-Zolezzi et al.12. After QC in each data set, all data were merged using the mergeit function of the Eigensoft v5.0. Software13. After merging, SNVs with highly discordant allele frequencies (p < 0.001) between the same populations in different data sets or strand bias were excluded. A total of 3490 individuals from 219 populations and 61,393 autosomal SNVs were included. We used this data set to identify the proportion of admixture with other worldwide populations in the Indigenous samples from Mexico. We ran PCA13 analyses using default parameters and admixture analyses assuming K = 4 clusters, including cross-validation error estimation, a block relaxation algorithm as the optimization method, and 100 replicates. Finally, the run with the highest likelihood was selected.First, we merged our 716 Mexican Indigenous samples with worldwide populations reported by Reich et al.n = 716) and those previously reported by Reich et al.8 (n = 57), Moreno-Estrada et al.2 (n = 350), and Silva-Zolezzi et al.12 (n = 86), yielded a total of 1209 samples from 60 different Mexican ethnic groups  between the same populations in different data sets or strand bias were excluded. Finally, samples were merged and the same QC was repeated for the whole data set, including the removal of first- and second-degree relatives, yielding an intersect of 1086 individuals and 61,393 autosomal SNVs  identified in the admixture 2, both genotyped using the Affymetrix Genome-Wide Human SNP Array 6.0, yielding a total of 996 individuals from 60 populations and 504,581 autosomal SNVs 66. PCR duplicates were removed using picard-tools MarkDuplicates67, and local realignment was carried out following the GATK best practices guide69. Diploid genotypes were called using HaplotypeCaller from GATK v4.069.Fastq files from publicly available ancient genomes from the Americas and present-day South Americans were downloaded from their respective repositories69, and then converted to eigenstrat format using the converf program from Eigensoft v5.013. Finally, these data were merged with the array data from the 325 individuals from the MAIS cohort with a Native American ancestry >99  in R package APE v5.1. The consensus tree was generated under DendroPy v4.0.070 using the whole data NJ tree to maintain the topology, and each replicate was used to obtain the bootstrap values.The FST statistics and geographic distances between populations in R package adegenet v2.1.171.To test whether isolation by distance explained part of the genetic diversity observed in Native populations from Mexico, we performed a Mantel test for a matrix of pairwise-Ne across time from the LD analysis based on the method reported by McEvoy et al.16 in R package NeON17 with default parameters. To estimate more recent demographic history, we estimated Ne by identifying IBD tracks based on the pipeline reported by Browning et al.21. Briefly, the data sets were phased together using Beagle 5.172. IBD segments in the data from each selected population were detected by IBDseq19. We then used IBDNe18 for non-parametric estimation of the recent demographic history from IBD segments with the default parameters and a minimum IBD segment length of 2 centiMorgan (cM) to estimate the Ne across generations.The effective population sizes of Indigenous Mexican populations were estimated in a set of 48 selected populations from the second masked data set (Supplementary Table T) in generations in the 48 selected populations mentioned in the previous section .ROH were calculated by Plink v1.9 software26 was used to detect IBD segments between individuals with >99% Native American ancestry  with an individual selected at random from population B. Therefore, we divided the total number of individual pairs connected by more than 7 cM of IBD by the total number of possible individual pairs, for each pair of indigenous groups analyzed. Following the definition of the five geographical regions identified in the NJ tree and previously described in Contreras-Cubas et al9. IBD networks were generated in R with function ggnetworkmap from the GGally v2.1.2 library. Vertices are located on geographical sampling coordinates from each population. Connections between groups have a width proportional to the probability of any individual from population A sharing at least one IBD segment with any individual from population B. Five IBD networks were generated, where each figure focuses on the connections from populations within a given geographical region: North, Northwest, Central-east, South, and Southeast  or large (>10 cM) size, which have been reported to originate at different time points in the past. This analysis was made incorporating the populations at the regional level to display with a more detailed resolution, the difference in connectivity between different regions (Supplementary Fig. f-statistic framework to explore the relationship between USR1 and Anzick-1 ancient genomes and modern Mexican Native populations. The outgroup f3-statistic and D-statistic tests were computed in Admixtools v4.1 using the estimators described in Patterson et al.75, and standard errors were obtained using a block jackknife procedure over 5 Mb blocks in the genome.We used the 76 based on the Ancient and Modern Native American data sets. The tree was rooted using the Yoruba population as an outgroup, and standard errors were estimated using blocks of 50 SNVs.A maximum-likelihood tree was constructed using TreeMix v1.13Alternatively, we tested the presence of gene flow between Mexican Indigenous populations assuming 1–20 migration edges with TreeMix using the previously generated maximum-likelihood tree to maintain the topology of each tree and USR1 genome as an outgroup.Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1-8Reporting Summary


Calidris alpina schinzii) is a philopatric, long-distance migratory shorebird and shows reduced dispersal between isolated breeding patches. The endangered population of the Southern Dunlin breeding at the Baltic Sea has suffered from habitat deterioration and fragmentation of coastal meadows. We sampled DNA across the entire population and used 12 polymorphic microsatellite loci to examine whether the environmental changes have resulted in genetic structuring and loss of variation.Populations living in fragmented habitats may suffer from loss of genetic variation and reduced between-patch dispersal, which are processes that can result in genetic differentiation. This occurs frequently in species with reduced mobility, whereas genetic differentiation is less common among mobile species such as migratory birds. The high dispersal capacity in the latter species usually allows for gene flow even in fragmented landscapes. However, strongly philopatric behaviour can reinforce relative isolation and the degree of genetic differentiation. The Southern Dunlin (We found a pattern of isolation-by-distance across the whole Baltic population and genetic differentiation between local populations, even within the southern Baltic. Observed heterozygosity was lower than expected throughout the range and internal relatedness values were positive indicating inbreeding.Our results provide long-term, empirical evidence for the theoretically expected links between habitat fragmentation, population subdivision, and gene flow. They also demonstrate a rare case of genetic differentiation between populations of a long-distance migratory species. The Baltic Southern Dunlin differs from many related shorebird species that show near panmixia, reflecting its philopatric life history and the reduced connectivity of its breeding patches. The results have important implications as they suggest that reduced connectivity of breeding habitats can threaten even long-distance migrants if they show strong philopatry during breeding. The Baltic Southern Dunlin warrants urgent conservation efforts that increase functional connectivity and gene flow between breeding areas.The online version contains supplementary material available at 10.1186/s12862-021-01855-0. Habitat fragmentation leads to small local populations that are susceptible to stochasticity , 2. In sThe effects of a small population size may be reinforced by life-history characteristics such as poor dispersal ability or philopatry. Strong natal and adult philopatry suggests that there must be benefits from returning to the natal or previous breeding site . HoweverCalidris alpina schinzii) is a small, migratory shorebird species breeding exclusively in short-vegetated and wet grasslands [The Southern Dunlin .Habitat fragmentation can be expected to lead to genetic effects in the Southern Dunlin because both adults –25 and jschinzii populations in the southern Baltic and the alpina population in Lapland has never been studied. Therefore, we further (C) examine whether there is indication of gene flow between the nominate subspecies C. a. alpina and Baltic Southern Dunlin populations with special emphasis on the population at Bothnian Bay. Finally, we (D) discuss the evolutionary implications of our study and suggest relevant conservation measures.Building on extensive sampling from the entire Baltic Southern Dunlin population and genetic analyses based on polymorphic microsatellite markers, we (A) examine whether the populations at the Baltic are genetically differentiated, and (B) estimate levels of genetic variation of the Baltic populations in order to assess if the observed decline, increased isolation, and lack of connectivity of breeding sites have had an effect since previous studies. Importantly, the genetic connectedness of the Bothnian Bay population at the northernmost location of the Baltic Sea in Finland  to the sGenotyping error rate was low, with a mean of 1.6% when genotyped twice. When the ambiguous samples were again genotyped twice, the error rate dropped to 0.1% . In two populations, the presence of null alleles was suggested for loci CAS23 and Cme1  was found in the alpina subspecies. Observed heterozygosity was lower than expected  in every population . The mean internal relatedness (IR) values per population were highest in Pori (0.231), Denmark (0.133) and Estonia (0.093), and lowest in Bothnian Bay , corrected for the difference in sample size, was similar in all p = 0.021, number of individuals = 374). The intra-group (IG) class and the first distance class (mean distance 405 km) showed significantly positive kinship coefficients , whereas the third and fourth distance classes  were negative, with the last class significantly so (p = 0.028).We found that the kinship coefficient slowly decreased with increasing distance between individuals, indicating an isolation-by-distance pattern Fig.  with regalpina population formed their own cluster , with the remaining individuals mainly showing similarity with the eastern/southern Swedish cluster . The populations at Pori and Jurmo were similar to the Estonian population, with mixed ancestry consisting for the most part of clusters found in Bothnian Bay and eastern/southern Sweden. Estonian individuals also showed mixed ancestry, mostly with the western Swedish  and Bothnian Bay clusters . Individuals from eastern and southern Sweden clustered together , and both showed some ancestry with the western Swedish cluster. Western Sweden mostly formed its own cluster , but a few individuals had probabilities below 0.400 of belonging to that cluster, instead indicating partial membership with the eastern/southern Swedish cluster. The few individuals from Denmark were similar to the Pori and Jurmo populations, but with a higher proportion of their genotypes assigned to the alpina cluster (q = 0.217–0.603) with K = 4. However, they seemed to form a separate genetic constitution with higher K values . The Bothnian Bay population differed from all Swedish and the Danish population, the Danish population from western and southern Sweden, and alpina from every other population except Pori, Jurmo and Estonia, which did not differ significantly from any population. The largest pairwise differences arose in pairwise Dest comparisons: they were up to an order of magnitude higher than, but still concordant with the FST values , varying from − 0.007 to 0.334  that are furthest apart. The Estonian population, which is located somewhat in the middle between the Bothnian Bay and the southwestern populations, showed mixed ancestry in the Structure analysis, mostly with the Bothnian Bay and western Swedish populations. This indicates that the Estonian population receives immigrants from other parts of the range. DAPC suggested the best number of clusters would be the same four as suggested by the Structure analysis. However, the clustering was not as clearly in accordance with the geographic origins of the samples as in Structure. Based on the results of these two analyses, it seems that there is a gradual change in allele frequencies along the Baltic Sea from the southwestern areas through Estonia towards the Bothnian Bay, resembling the cline reported by Marthinsen et al. [The IBD pattern showed a decrease of genetic similarity with increasing geographic distance across the Baltic region. In general, individuals breeding up to 400 km away have significantly positive kinship coefficients , which is consistent with known long-distance movements between Pori and Bothnian Bay . Geneticn et al.  but on aImportantly, we also found differentiation  within a smaller scale of 200 to 500 km between the populations in the southern Baltic. This result fits earlier reports that show movements to be rare in these local populations , 24, 26.C. a. alpina individuals  and positive estimates of internal relatedness throughout the range, which is consistent with inbreeding. Indeed, previous work has documented severe inbreeding in the very small population in western Sweden [IS values of these populations were positive though not statistically significant , which is further supported by the mixed ancestry in Estonia and weak differentiation from the other populations. The Bothnian Bay population showed the lowest FIS and IR values and highest observed heterozygosity. These attributes are consistent with annually occurring immigration to the Bothnian Bay [We found lower levels of heterozygosity than expected .alpina were obtained from 26 breeding adults from Finnish Lapland, collected during 2010–2017. The total number of sampled Dunlins was thus 374.Samples (feathers) of the northern subspecies 2, 0.2 mM of dNTPs, 1 µl of 10 × PCR-Buffer and 0.l U of DNA-polymerase (Biotools). The amplification profile was 94 °C for 1 min followed by 35 cycles of 94 °C for 30 s, 52–58 °C for 45 s  using the Fragment Analysis Module v. 8.0.52. Due to possible differences between the allele sizes defined by the two sequencers, samples were calibrated by genotyping five Swedish individuals with both sequencers. Genotyping error rate was calculated by amplifying most individuals twice. If differences were found between the two runs, samples were genotyped twice more.DNA was extracted from blood and tissue samples either with the standard phenol–chloroform method , or usinIS (i.e. deviation from random mating). FSTAT v. 2.9.3.2. [IS and allelic richness. For the Structure run (see below), all females and the individuals whose sex remained undetermined were coded as both alleles missing regarding this locus.The microsatellite data were checked for potential genotyping errors  with MicroChecker v. 2.2.3.  for all 2.9.3.2.  was used2.9.3.2.  to calcu2.9.3.2.  for eachIsolation-by-distance within the Southern Dunlin was tested with program SpaGeDi v. 1.4. . The proST values were compared to permuted ones (pRST) using 10 000 random permutations. Observed values significantly larger than permuted ones indicate that stepwise mutations contribute to genetic structure, and R-statistics should be preferred over F-statistics [ST = 0.0300, pRST = 0.0261, p = 0.391), F-statistics were used in the analyses.An allele size permutation test  was perfatistics . As no sq-values) of each individual belonging to the estimated K genetic clusters.Genetic population structure was studied with program Structure v. 2.3.4. , which uadegenet [The genetic structure was further studied with the Discriminant Analysis of Principal Components (DAPC), a multivariate method for identifying genetically related individuals. DAPC was run with the package adegenet , 69 in Radegenet . CommandST values between the populations were calculated with program Arlequin [est, accounts for small sample size and can be a better estimator of population differentiation than FST [est values using the DEMEtics package [In addition, pairwise and overall FArlequin . SignifiArlequin , 72, butArlequin –75). FSTArlequin  and it cArlequin . A diffethan FST . Therefo package  in progr package . The numAdditional file 1: Table S1. Summary of microsatellites used in this study.Additional file 2: Figure S1. ΔK values from the Structure analysis. Table S2. Mean LnPs and standard deviations for different K-values from program Structure Harvester.Additional file 3: Table S3. Information on locations of sampled populations, sampling years and permits.


Anopheles nivipes in this area.Few studies have been conducted to investigate the distribution of mosquito vectors and the population structure of secondary vectors in the border region of Cambodia-Laos. The aim of this work was to study the mosquito diversity and molecular phylogeny of secondary vectors, i.e., An. nivipes DNA samples. Together with COII sequences deposited in GenBank, a total of 86 COII sequences were used for examining population variations, genetic differentiation, spatial population structure, population expansion, and gene flow patterns.1440 adult mosquitoes were trapped in the Cambodia-Laos border. mtDNA-COII were amplified and sequenced from 53 Culex  and Anopheles , and the major Anopheles species were An. nivipes  and Anopheles maculatus . Based on sequences analysis of COII, a high level of genetic differentiation was reported in two Northwest India  and Cambodia-Laos  populations , as well as lower gene flow  in the An. nivipes populations. Phylogenetic analysis and SAMOVA revealed a gene barrier restricting gene flow among three An. nivipes populations. Mantel test suggested a significant correlation between geography and gene distance in all An. nivipes populations . Neutrality test and Mismatch distribution revealed a recent population expansion of An. nivipes in the Cambodia-Laos population.The adult mosquitoes were classified into 5 genera and 27 species in this border region. The predominant genera were Anopheles nivipes was one of the major Anopheles species in the Cambodia-Laos border. Based on sequences analysis of COII, a genetic barrier between Cambodia-Laos and two Indian populations was found, and a recent population expanding or selecting of An. nivipes occurred in the Cambodia-Laos population, suggesting that COII might be an effective marker for describing the molecular phylogeny of An. nivipes. Further investigation and continuous surveillance of An. nivipes are warranted in this region.The online version contains supplementary material available at 10.1186/s12936-022-04121-w. Dengue fever and malaria continue to be critical parasitic diseases jeopardizing people’s lives in subtropical and tropical regions, primarily in the Greater Mekong Subregion  . Over thPlasmodium vivax and Plasmodium falciparum. However, P. vivax control is obviously less effective [P. vivax have major distributions crossing six northeastern Cambodia provinces , and particularly prevail in Stung Treng within the Cambodia-Laos border  [Though its incidence declines significantly, malaria continuously poses a major burden on public health in this nation, in which there is a co-occurrence of ffective . Cases ontified) .Anopheles minimus, Anopheles dirus complex, and Anopheles sinensis refer to the major vector, whereas the significance pertaining to the respective species in the malaria-transmitting process changes extensively with areas [Anopheles nivipes) in terms of Plasmodium infection [Anopheles nivipes was found to be Plasmodium positive according to a study in Bangladesh in 2012 [Plasmodium parasites in India and other countries [An. nivipes was a secondary vector, as well as Anopheles philippinensis [The transmission of malaria in the GMS is characterized by vector diversity and a high degree of spatial heterogeneity in the distribution pattern . Generalth areas . Some thnfection –12. Anop in 2012  and has ountries , 15. In pinensis  in transAn. nivipes in the Cambodia-Laos border. Data regarding genetic diversity and population structure can help develop available mosquito control plans [On the other hand, though mosquito population exhibiting various gene makeups is likely to be different in vector competence, rare data has been achieved regarding population genetics of ol plans , 17. Owiol plans , Mitochool plans .An. nivipes in the Cambodia-Laos border. Siem Pang County (Stung treng Province) was taken as the investigation site and mosquito surveillance was firstly performed. The cytochrome c oxidase subunit 2 gene (COII) of mtDNA from An. nivipes samples were subsequently sequenced. Furthermore, the gene variability and phylogenetic relationship of An. nivipes along the Cambodia-Laos border were investigated and compared with other samples from India, Thailand, and Myanmar for examining their gene relations. This work largely aimed at gaining more insights into the continuous surveillance of mosquito vectors, as well as molecular phylogeny and evolution of An. nivipes in the Cambodia-Laos border by using an effective genetic marker, mtDNA-COII.Given the existing drastic variations of malaria conditions in Cambodia, this work aimed to study the mosquito diversity and molecular phylogeny of secondary vectors i.e., Stung treng borders Laos in the north and is located on the east bank of the Mekong River. This endemic region takes up nearly 11,000 square kilometres and has about 42,000 residents. The average population density is 3.82 people per square kilometre. It has dense forests, diversified landforms, the temperature changes greatly and the annual rainfall is 1800 mm.An. nivipes based on COII was subsequently performed. Final species confirmation is required to have ≥ 98% sequence identity to the voucher specimens/sequences in the NCBI nt databases. To avoid the issue that COII alone did not produce significant results for the voucher sequence, the amplification and sequencing on ITS2 was also performed. As a result, ITS2 and COII database comparisons of each morphologically identified An. nivipes samples were paired to determine the species of An. nivipes  in the cattle/pig pens or human rooms from 8:00 pm to 08:00 am and continued for 1–5 nights, in 2018. Four CDC light traps were operating in two cattle/pig pens and two human rooms each night. All live adult mosquitoes were killed by freezing in the refrigerator and subsequently isolated and distinguished according to subgroup, species, and sex through dissecting microscope based on standards procedures in the field office of Siem Pang County . All mosAn. nivipes genomic DNA in individual mosquitoes was carried out following the producer’s manual . The amplification for approximately 620 bp of COII gene was carried out using primers, LYS-R (5′-ACTTGCTTTCAGTCATCTAATG-3′) and LEU-F (5′-TCTAATATGGCAGATTAGTGCA-3′). The overall PCR reaction volume reached 20 µl and the mixture of PCR reagent comprised 2 µl of DNA, 0.05 unit of Takara Taq , 0.3 µM of the respective primer, 0.2 mM of dNTPs, and 2.5 µl of 10× Buffer . Besides, 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. The PCR products were analysed by 1.5% agarose gel electrophoresis stained with GoldView , under UV transillumination. The sequencing reaction proceeded in both directions using an ABI Big Dye Terminator Kit  and was analysed through ABI Prism 3500xL Genetic Analysis Tool  in Shanghai (Sangon Biotech).The extraction of An. nivipes from Cambodia-Laos and neighbouring nations, multi-sequence alignment of COII sequence was firstly carried out after the retrieval of sequence deposited in GenBank by using the keywords “(species name) & COII” (http://www.ncbi.nlm.nih.gov/). The manual adjustment was conducted by using BioEdit V7.0.9 if required [Anopheles lindesayi (GenBank Accession No. AJ620898) and Anopheles claviger (GenBank accession nos. AY129232 and DQ229313) were used as outgroup taxa to the An. nivipes, following previous studies [In order to analyse the sequence variations and genetic relationship of required . Gaps werequired  based onrequired . The visrequired . Anophel studies .The average of nucleotide differences per site (K), nucleotide diversity (π), haplotype diversity (Hd), and haplotypes (H) was calculated by using DnaSP v.5.0 . For morFST for estimating population differentiation by complying with a difference in haplotype frequency, Nei’s Nm estimated gene flow conformed to GST [The calculation of pairwise d to GST , as welld to GST .FCT, FST, FSC), with the use of the AMOVA approach, to K group for maximizing group variations. SAMOVA estimate was calculated for K = 2–8, with 1000 simulated annealing steps from each of 100 sets of initial starting conditions. Furthermore, isolation by distance (IBD) was examined using a nonparametric Mantel with the web-based computer program IBDWS v.3.16 [Moreover, a spatial analysis of molecular variance (SAMOVA 2.0) was conducted for clustering COII sequences to homogeneous populations with genetic and geographical homogeneousness . SAMOVA S v.3.16 .An. nivipes population based on DnaSP v.5.0. The mismatch distribution (simulated in Arlequin v.3.5) was carried out for distinguishing smooth unimodal distribution from multimodal or ragged distribution [Using the statistics D  and Fu’sribution –38. For Anopheles, Culex, Aedes, Armigeres, Tripteroides, and 27 species. The predominant genera included Culex (9 species) and Anopheles (12 species), respectively taking up 60.07% (865/1440) and 31.25% (450/1440); Other genera including Aedes, Armigeres, and Tripteroides respectively accounted for 3.61% (52/1440), 2.71% (39/1440), and 0.07% (1/1440); In addition, undefined genera/species accounted for 2.29% (33/1400)  and 14.22% (64/450), respectively, while Anopheles peditaeniatus, Anopheles argyropus, Anopheles tessellatus, Anopheles barbirostris, Anopheles sandaicus, Anopheles vagus, Anopheles interruptus, An. dirus, Anopheles kochi, and An. sinensis accounted for merely 0.22% (1/450), 0.22% (1/450), 1.33% (6/450), 1.11% (5/450), 0.44% (2/450), 2.00% (9/450), 0.22% (1/450), 1.33% (6/450), 4.00% (18/450), and 1.33% (6/450) , Tripura , Cheema , Bathinda , Nagaland , Assam , Myanmar (n = 2), Thailand (n = 2), Mizoram  and Meghalaya  ; Group 3 with haplotype from Bathinda . The most common haplotypes referred to H1 (n = 19), H2 (n = 13), and H6 (n = 8), as only identified in 73.58% (39/53) of Siem Pang and 50% (1/2) of Thailand. H21 (n = 1), H22 (n = 1) was only identified in Cheema; H20 (n = 1) and H23 (n = 1) were only identified in Bathinda , among populations within groups (FSC), and within populations (FST) showed statistical significance (P < 0.05)  rather than within populations (4.56%) and among populations within groups (8.72%), suggesting that these populations could fall to several groups. The fixation index among groups (% rather 5) Table . AnophelFST based on sequences analysis of COII was between Cheema and Siem Pang . Estimates of gene flow (Nm) varied extensively in populations, ranging from 0.01112 to 8.44874. The minimal was between Cheema and Siem Pang (Nm = 0.01112). The maximal was between Assam and Myanmar (Nm = 8.44874)  with Fsc . In this case, two Punjab populations (Bathinda and Cheema) became separated and assigned to two single population groups, leaving other populations, which demonstrated that there is a significant genetic barrier to restrict gene flow from Punjab to other populations. As K value increased up to K = 8, Siem Pang and Thailand remained attributed to one group, whereas the other population created 8 distinct groups (Table P = 0.0070), suggesting that the genetic structure observed in An. nivipes population could be partially explained by distance isolation based on COII sequence analysis , suggesting a recent population expansion or selection  in Siem Pang County of Stung treng , r 11.55% , while iover 65% . In Thaiectively . AnophelCambodia . AccordiCambodia , An. nivountries –15. BesiAn. nivipes could fall to two Northwest India groups  and a Northeastern India/SEA group  of the mitochondrial genome (mtDNA), followed by the internal transcriptional spacer 2 (ITS2) of ribosomal DNA (rDNA). Since mtDNA is typically inherited maternally, any hybrid or offspring would only have the mtDNA from the maternal species . Furtherup Figs. . As reveup Figs. , and thepes Fig. . Unimodapes Fig. : Fig. S5on Table .An. nivipes based on ITS2 was also conducted to evaluate its effectiveness compared to mtDNA-COII ** indicated the genus or species composition of mosquitoes which were described in numbers.Additional file 3: Table S3. COII sequences of Anopheles nivipes were downloaded from the NCBI. * indicated the longitude and latitude coordinates to the geographical center of a certain province, due to the samples being initially collected from various sampling sites in a certain province. An. niv, Anopheles nivipes.Additional file 4: Figure S1. Map of the populations from different geographical regions. Populations of mitochondrial COII sequences: KH, Siem Pang County ; Tri, Tripura (India); Ch, Cheema ; Ba, Bathinda ; Nag, Nagaland (India); Ass, Assam (India); MM, Myanmar; TH, Thailand; Miz, Mizoram (India); Meg, Meghalaya (India). The map was prepared by using LocaSpace Viewer.Additional file 5: Figure S2. Distribution heatmap of haplotype based on COII. KH-St-Sp, Siem Pang County ; MM, Myanmar; TH, Thailand; IN-Pun-Ba, Bathinda ; IN-Pun-Ch, Cheema ; IN-Tri, Tripura (India); IN-Meg, Meghalaya (India); IN-Ass, Assam (India); IN-Nag, Nagaland (India); IN-Miz, Mizoram (India). The numbers of haplotypes are shown on the right side of the figure. The color scale ranges from blue to red, showing a range from minimum number (0) to maximum numbers (19) for each haplotype.Additional file 6: Figure S3. Neighbor-joining phylogenetic tree of An. nivipes haplotypes based on COII sequences from GenBank and original data in this study. Bootstrap values (1000 replicates) of Neighbor-Joining analyses are shown above/below the main lineages. Lineage designation is indicated on the right. Bars represent 2.0 substitutions per site based on COII. Different colors indicated different population groups of An. nivipes.Additional file 7: Table S4. Genetic differentiation and Gene flow among the Geographic Groups 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 indicate the significance (P < 0.05). inf, infinite. KH_St_Sp, Siem Pang County ; IN_Tri, Tripura (India); IN_Pun_Ch, Cheema ; IN_Pun_Ba, Bathinda ; IN_Nag, Nagaland (India); IN_Ass, Assam (India); MM, Myanmar; TH, Thailand; IN_Miz, Mizoram (India); IN-Meg, Meghalaya (India).Additional file 8: Figure S4. Cluster analysis based on COII sequences in Anopheles nivipes populations. UPGMA dendrogram based on Nei ‘s unbiased genetic distance between different populations of An. nivipes. Bars represent 0.7 substitutions per site based on COII. KS, Siem Pang County ; IN_Tri, Tripura (India); IN_Pun_Ch, Cheema ; IN_Pun_Ba, Bathinda ; IN_Nag, Nagaland (India); IN_Ass, Assam (India); MM, Myanmar; TH, Thailand; IN_Miz, Mizoram (India); IN_Meg, Meghalaya (India).Additional file 9: Figure S5. Mismatch distribution graphs for Siem Pang population. 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. (a) all populations-COII; (b) Siem Pang population-COII.Additional file 10. Supplementary data of molecular phylogeny data based on ITS2. This file contains Tables