Lancea tibetica. A total of 456 individuals from 31 populations were analyzed using eight pairs of microsatellite makers. The total number of alleles was 55 and the number per locus ranged from 3 to 11 with an average of 6.875. The polymorphism information content (PIC) values ranged from 0.2693 to 0.7761 with an average of 0.4378 indicating that the eight microsatellite makers were efficient for distinguishing genotypes. Furthermore, the observed heterozygosity (oH), the expected heterozygosity (eH), and the Shannon information index (I) were 0.5277, 0.4949, and 0.9394, respectively, which indicated a high level of genetic diversity. We detected high genetic differentiation among all sampling sites and restricted gene flow among populations. Bayesian-based cluster analysis (STRUCTURE), principal coordinates analysis (PCoA), and Neighbor-Joining (NJ) cluster analysis based on microsatellite markers grouped the populations into two clusters: the southern branch and the northern branch. The analysis also detected genetic barriers and restricted gene flow between the two groups separated by the Tanggula Mountains. This study indicates that the geographical isolation of the Tanggula Mountains restricted the genetic connection and the distinct niches on the two sides of the mountains increased the intraspecific divergence of the plants.The uplift of the Qinghai-Tibetan Plateau (QTP) had a profound impact on the plant speciation rate and genetic diversity. High genetic diversity ensures that species can survive and adapt in the face of geographical and environmental changes. The Tanggula Mountains, located in the central of the QTP, have unique geographical significance. The aim of this study was to investigate the effect of the Tanggula Mountains as a geographical barrier on plant genetic diversity and structure by using  High-mountain zones experience long-term uplift and climatic fluctuations, which result in a variety of evolutionary processes leading to high levels of taxon richness and rarity ,2. This Rhodiola alsia [Stuckenia filiformis [Complex and diverse genetic diversity patterns are evident in the QTP due to the long and complicated plateau uplift history and periodic glacial alternations, and the intrinsic characteristics of different species  ,18. In tla alsia , and Stuliformis . In thisliformis . For insliformis , which iliformis ,25,26. SLancea tibetica is a perennial herb belonging to the Mazaceae family [L. tibetica is widely used in the treatment of pneumonia, asthma, cough, carbuncles, colic, and even heart disease [e family  and is me family . As a tr disease , because disease ,32,33,34 disease .Lancea tibetica was selected as the ideal study species, not only because this species is a widely distributed species on the QTP, especially its distribution range across the Tanggula Mountains, but also because the seeds are small and wingless and their dispersal capacity is limited, a trait that enhances the degree of genetic differentiation by restricting gene flow. In order to test the hypotheses mentioned above, eight microsatellite markers were used to determine the genetic diversity and population structure patterns of 31 wild populations of L. tibetica and to elucidate if these genetic differentiations correlated with environment gradients. Such information contributes to the understanding of the distribution shift of alpine plants caused by geographical changes in the QTP.L. tibetica individuals from 31 wild populations were collected. Populations were separated from each other by at least 50 km, and the sampling was performed in Qinghai, Gansu, Sichuan, and Tibet  method . The qua2), 0.5 μL of each primer (5 pM), 0.2 μL of Taq DNA polymerase , and 0.5 μL of dNTP mix (10 mM), supplemented with ddH2O. The PCR reaction conditions included an initial denaturation (94 °C for 5 min), followed by quantification for 40 cycles , and a final extension (72 °C for 10 min). The amplified products were size separated by capillary electrophoresis on an Applied Biosystems Genetic Analyzer (ABI 3730) from Sangon Biotech Co., Ltd.  to obtain the size of microsatellite fragments.Eight pairs of microsatellite markers developed by Tian et al.  were usehttps://sites.ualberta.ca/~fyeh/popgene.html) was used to determine the genetic diversity parameters such as observed number of alleles (aN), effective number of alleles per locus (eN), observed heterozygosity (oH), Nei’s expected heterozygosity (eH), Shannon information index (I), fixation index (stF), and the inbreeding coefficient (isF) of eight microsatellite loci. The polymorphism information content (PIC) of the microsatellite marker was calculated using PowerMarker 3.25 . Additionally, Nei’s coefficient of genetic differentiation (STG) and Shannon’s coefficient of genetic differentiation (ST’G) was calculated using a Microsatellite Analyzer (MSA) 4.05 (http://i122server.vu-wien.ac.at/MSA). In the current study, we hypothesized that the population patterns conformed with the island model, and the gene flow (mN) can be calculated based on the following formula:POPGENE 1.32 (http://bioinformatics.org/populations/). A Neighbor-Joining (NJ) tree was constructed by MEGA 7.0 (https://www.megasoftware.net/) based on Nei’s genetic distance matrix. Meanwhile, the principal coordinate analysis (PCoA) of 31 populations was carried out using the Multi-Variate Statistical Package (MVSP) 3.13 .To further analyze the genetic relationship among 31 populations, Nei’s genetic distance matrix was calculated using POPULATIONS 1.2.28 (https://web.stanford.edu/group/pritchardlab/structure.html). The population structure was detected under both the admixture model and no admixture model at the same time, with a burn-in period set at 100,000 and Markov Chain Monte Carlo (MCMC) repetitions after burn-in set at 100,000. The optimal K value was determined based on the change in slope of the plot of Ln Pr(X|K) versus ΔK, which is generally the corresponding K value at the inflection point of the curve.We evaluated the genetic structure using Bayesian model-based clustering in STRUCTURE 2.3.4 (http://cmpg.unibe.ch/software/arlequin35/) after populations were grouped by STRUCTURE model-based and geographic regions of genetic diversity. Molecular variance was analyzed at two hierarchical divisions, within and among populations. Pairwise comparison of stF values between the populations was conducted in GENEPOP 4.0 (http://www.genepop.curtin.edu.au/). We investigated the historical barriers to gene flow among collection sites using Monmonier’s maximum difference algorithm in the software BARRIER 2.2 (http://ecoanthropologie.mnhn.fr/software/barrier.html).Analysis of molecular variance (AMOVA) was performed using ARLEQUIN 3.5 (http://cmpg.unibe.ch/software/arlequin35/).To investigate the correlation between genetic distance and geographical distance, a geographic distance matrix between 31 populations was calculated in Microsoft Excel. The obtained geographic distance was tested against genetic distance by the Mantel test in ARLEQUIN 3.5  to 11  alleles per locus with an average of 6.875. We identified 34 rare alleles (RA) with a frequency less than 0.5% in seven microsatellites (except LT7), ranging from one to nine per locus with an average of 4.25 and accounting for 61.8% of all the alleles. The Shannon information index (I) varied from 0.6333 (LT15) to 1.8425 (LT25) (mean = 0.9394). The observed heterozygosity (oH) ranged from 0.1752 (LT4) to 0.9978 (LT16) with an average of 0.5277. The Nei’s expected heterozygosity (eH) ranged from 0.2760 (LT4) to 0.8008 (LT25) with an average of 0.4949. The inbreeding coefficient at the population level (isF) ranged from −0.9328 (LT16) to 0.0771 (LT4) with an average of −0.2802, and isF >0 was observed only in LT4 and LT25. The proportion of differentiation among populations (stF) ranged from 0.0034 (LT16) to 0.4180 (LT25) with an average of 0.2215. In addition, the inbreeding coefficient at the total sample level (itF) ranged from −0.9262 (LT16) to 0.3850 (LT28) with an average of −0.0581. The polymorphic information content (PIC) for each marker was computed, and it varied from 0.0971 (LT18) to 0.7761 (LT25) with an average of 0.3727. The Nei’s genetic differentiation coefficient (STG) ranged from 0.0075 (LT16) to 0.3908 (LT28) with an average of 0.2075. Shannon’s coefficient of genetic differentiation (ST’G) was consistent with STG. The highest value was observed in LT28 (0.5328) and the lowest in LT16 (0.0157), with an average of 0.3406. Based on stF calculation, gene flow (mN) ranged from 0.3481 (LT28) to 72.932 (LT16) with an average of 0.8832.We employed eight microsatellite markers to genotype 456 individuals of aN) ranged from 1.75 in DL to 3.875 in JD, with an average of 2.65. Meanwhile, the effective number of alleles per locus (eN) varied from 1.4419 in GH to 2.4170 in LZ, with an average of 1.8798. The Shannon information index (I) varied from 0.3763 (GH) to 0.8751 (LZ) with a mean of 0.6536. The percentage of polymorphic loci (PPL) ranged from 62.5% to 100% with an average of 89.5%. The observed heterozygosity (oH) and the expected heterozygosity (eH) ranged from 0.4195 (GH) to 0.6562  and 0.2453 (GH) to 0.5091 (MLS), with an average of 0.5343 and 0.3954, respectively.Genetic diversity of 31 populations was evaluated based on the amplified polymorphic alleles . The obsX|K) value and ΔK value were plotted (X|K) and ΔK values had obvious inflection points with change in K value, and the best K value was 2. This supported the division of L. tibetica into two groups (K = 2). According to the recommended K values, STRUCTURE groupings are shown in Based on the microsatellite data, the STRUCTURE group calculation was performed, and the change in slope of Ln Pr.For studying the genetic structure of e matrix . The resK = 2) and NJ tree.To investigate the population structure, principal coordinate analysis (PCoA) was carried out and a scatter plot was generated based on the entire microsatellite dataset of 31 populations. The first and second principal coordinates explained 84.188% and 8.216% of the molecular variance. The PCoA results indicated the division of 31 populations into two distinct groups . LZ, AJLL. tibetica was within the populations (81.6839%) rather than between populations (18.3161%). AMOVA was also performed on the two groups obtained by the population structure analysis. The results revealed that 20.9305% of the genetic variance occurred between the two groups, while 9.0187% of the genetic variance occurred within groups among populations. Furthermore, 70.0508% of the genetic variance occurred within populations.In order to estimate the partitioning of genetic variation, an AMOVA based on microsatellite data was conducted . The anaGenetic barriers among 31 populations were predicted using Monmonier’s maximum difference algorithm on BARRIER 2.2. When the number of barriers was one, BX and AJL were separated from all other populations. When the number of barriers was two, YD, LZ, MLS, LNZ, and DX were further separated . This wastF and mN for 31 populations were also calculated (stF = 0.5222) and lowest gene flow (mN = 0.2287) between DL and AJL. Meanwhile, the lowest genetic differentiation (stF = 0.0011) and highest gene flow (mN = 227.0227) were between DX and LNZ.The pairwise lculated . The res2R = 0.6702, p = 0; 2R = 0.5324, p = 0.004; 2R = 0.2018, p = 0.012; L. tibetica populations projected onto a map using ArcGIS 10.2 and the results of genetic grouping are shown in We performed a Mantel test on all the populations that revealed a significant correlation between genetic distance and geographical distance . The high level of genetic diversity within the populations of L. tibetica is similar to other species that are distributed in the QTP  might also result in a high level of genetic diversity. isF and itF values indicated heterozygote excess in L. tibetica populations, suggesting a heterozygous advantage. This may be attributed to the fact that heterozygous genotypes grow faster and have lower mortality than homozygous genotypes, resulting in higher population heterozygosity [In the current study, a rich genetic variation was detected in  = 0.719 ; Armilla = 0.521 ; Sibirae = 0.834 ; Sibirae = 0.832 ; Stipa p = 0.585 ; Carex m = 0.579 ). The hiL. tibetica. Meanwhile, Bayesian clustering (STRUCTURE), NJ cluster analysis, PCoA and genetic barrier prediction analysis demonstrated that the Tanggula Mountains played a significant role in intraspecific divergence. All the populations can be divided into two main groups: the northern branch and the southern branch, roughly bounded by the line of the Tanggula Mountains. A similar pattern was also detected in previous studies of Rhodiola alsia [Stuckenia filiformis [stF and mN demonstrated that gene flow between populations on each side of the Tanggula Mountains is greater than across the Tanggula Mountains. Take MLS and JD for example, the two populations were located on different sides of the Tanggula Mountains, and the geographic distance was not very great, but a high genetic differentiation (STG = 0.2710) and low gene flow (mN = 0.6722) was detected. The gene flow between them is less than that between populations on the same side of the mountains. The genetic divergence pattern strongly suggests that the Tanggula Mountains might be a geographic barrier that restricts the gene flow between the populations on the different sides of the Tanggula Mountains.Mantel tests showed that isolation by distance had an important impact on the genetic divergence of la alsia  and Stucliformis  by usingL. tibetica in the Tanggula Mountains can mainly be attributed to the snow-cover on the ridges of the mountains. High altitude snow-covered ridges can directly obstruct the genetic connection and reduce the distance of transmission. The differences in the ecological niches on the two sides (north and south) also play an important role. In terms of geomorphology, the northern side is relatively flat with an intact plateau environment and the headward erosion of modern rivers is not obvious, while the topography of the southern side is relatively fragmented forming mountains and gorges, and the rivers are severely cut [The genetic divergence of rely cut . Meanwhirely cut ,52,53. Irely cut . Such diL. tibetica based on microsatellite markers. The study revealed the significant role of the Tanggula Mountains in determining the genetic diversity and population structure of L. tibetica. Geographical barriers restricted gene flow among different populations and resulted in intraspecific genetic divergence. Genetic structure and eco-geographical differentiation can maintain adaptation to continuous geographical and environmental change.This is the first study presenting the genetic diversity of


Paratya australiensis Kemp (Decapoda: Atyidae) is a widely distributed freshwater shrimp in eastern Australia. The species has been considered as an important stream organism for studying genetics, dispersal, biology, behaviour and evolution in atyids and is a major food source for stream dwelling fishes. Paratya australiensis is a cryptic species complex consisting of nine highly divergent mitochondrial DNA lineages. Previous studies in southeast Queensland showed that “lineage 4” favours upstream sites at higher altitudes, with cooler water temperatures. This study aims to identify putative selection and population structure between high elevation and low elevation populations of this lineage at relatively small spatial scales. Sample localities were selected from three streams: Booloumba Creek, Broken Bridge Creek and Obi Obi Creek in the Conondale Range, southeast Queensland. Six sample localities, consisting of 142 individuals in total were sequenced using double digest Restriction Site Associated DNA-sequencing (ddRAD-seq) technique. Among the 142 individuals, 131 individuals shared 213 loci. Outlier analysis on 213 loci showed that 27 loci were putatively under selection between high elevation and low elevation populations. Outlier analysis on individual streams was also done to test for parallel patterns of adaptation, but there was no evidence of a parallel pattern. Population structure was observed using both the 27 outliers and 186 neutral loci and revealed similar population structure in both cases. Therefore, we cannot differentiate between selection and drift here. The highest genetic differentiation was observed between high elevation and low elevation populations of Booloumba Creek, with small levels of differentiation in the other two streams. Genetic variation within and among populations arises as a result of a number of factors, namely-mutation, genetic drift, gene flow/dispersal and natural selection . WidesprPopulation genomic investigations are powerful approaches for disentangling stochastic and deterministic processes generating population genetic subdivision across space and habitat. When studying population genomics, it is important to consider both neutral loci and loci under selection in order to illustrate population demography clearly. Neutral loci will give information on population demography and evolutionary history whereas loci under selection can show evidence of adaptation . PopulatRestriction site Associated DNA-sequencing (RAD-seq) is a technique where thousands of genetic markers (SNP’s) can be identified across the genome . CompareParatya australiensis is a small bodied (2–4 cm long) freshwater shrimp. Additionally, there are some estuarine populations in southern Victoria. The shrimp has a filter feeding and scavenging habit and plays a key role in lotic community structure  and are sometimes present through to February. Late breeding and larval development is observed at the high-altitude sites, which suggests the importance of temperature to this species  contained lineage 4, while Branch Creek, which is at a lower elevation (∼400 m asl), contained lineage 6  but mtDNA COI data suggested that there was no contemporary dispersal between up and downstream populations. So, there is a need for a larger data set to observe population structure in the Conondale Range even at this small spatial scale.The concept of restricted dispersal of analysis . The stuanalysis . FurtherThe aims of the present study were to investigate population structure at a small spatial scale using genomic data and determine if loci putatively under selection between high elevation and low elevation populations indicate evidence of adaptation to altitude. We were interested to determine if the divergence between populations is influenced by adaptation and selection or genetic drift. Adaptation and selection can act on much shorter timescales than genetic drift to drive divergence of populations. Thus, loci under strong selection may reveal population genetic structure between populations from different ecological habitats neutrally evolving loci may not. This pattern would provide clear evidence that selection/adaptation is working to structure these populations. If the same pattern of population genetic structure is observed in both loci under selection and neutrally evolving loci, there is little evidence of selection acting on these populations. Drift and selection are not mutually exclusive processes, and divergence that may have been initially driven by selection can also result in the same pattern via genetic drift if dispersal is also limited in the system. For example, a chance dispersal event to a new habitat followed by local adaptation and complete isolation from the parental population will show evidence of selection early on in the divergence process and also show evidence of neutral genetic drift following an extended period of isolation. In order to achieve our goals sampling was undertaken from both high and low altitude populations from each of three creeks belonging to a stream network. Outlier analysis was undertaken to detect loci putatively under selection, and later both neutral and adaptative loci were used to examine population structure.The sampling sites were in the Conondale Range which extends from 26°55′S, 152°45′E northwest to 26°37′S, 152°30′E in south-east Queensland and is 100 km northwest of Brisbane. The Conondale Range occupies the area which separates two large river systems, the Brisbane River to the south and the Mary River to the north . As thisThree subcatchments Booloumba Creek , Broken Bridge Creek  and Obi Obi Creek   were selIndividuals from the Booloumba Creek High site are denoted as BOH, Booloumba Creek Low site as BOL, Broken Bridge High site are as BBH, Broken Bridge Low site as BBL and Obi Obi High as OBH and Obi Obi Low site as OBL. High altitude sites were at ∼550 m asl and low altitude sites were ∼100 m asl.Paratya australiensis were collected from 2014–2016. Shrimps were collected from small pools at each site using a seine net and a dip net. 25–35 individuals were collected from each site  of each individual using DNA-easy Blood and Tissue kits  following the manufacturer’s instructions. DNA was eluted to 50 µl final volume. 4 µl of RNase-A (10 mg/ml) was added to the extraction and incubated at room temperature for 10 min. DNA concentration was quantified using a Qubit 3.0 fluorometer from Thermo Fisher Scientific according to the kit instructions. Samples containing 1.5 ng of DNA were used for ddRAD library.A double digest RAD library (ddRAD) was prepared at the Australian Genomic Research Facility (AGRF), Melbourne, Australia. The restriction enzyme combination used for this library preparation were PstI/HypCH4IV. Five types of barcodes were used ranging from 4 bp to 8 bp in size. ddRAD libraries prepared from the 142 individuals from high elevation and low elevation populations of all 3 streams were sequenced using Illumina NextSeq500 platform of the Australian Genomic Research Facility (AGRF). Single end sequencing was done in High output mode for 150 cycles.de novo assembly. The Stacks script denovo_map.pl was run to assemble the reads into stacks, build a tag catalogue and match individual samples to the catalogue. For the denovo_map.pl, the assembly parameters set were: minimum coverage value of -m 3; minimum number of mismatches allowed between loci while processing a single individual was -M 2; minimum number of mismatches allowed between loci when building the catalogue was -n 1. Next, the Populations program was run in Stacks v.1.35 to output sample genotype data and SNPs into different common formats for population genetics programs. The Populations program runs using the processed outputs of the denovo_map. The following filtering parameters were used: loci present in all 6 populations -p 6, loci present in 75% of the individuals in each population (r = 0.75), a minimum depth of coverage for each individual of -m 10. A minor allele frequency of 5% (-min_maf 0.05) was also applied to exclude loci with minor allele frequency of less than 5%. A whitelist was made with loci containing 2 haplotypes and then a single SNP per locus was selected from those haplotypes. This produced a white list of a single SNP per locus.Preliminary analysis of the FASTQ reads was undertaken with the programme Stacks v.1.35 . All seqGenotypes were exported in different file formats including Structure, Fasta, vcf and Genepop format. vcf files generated from the Populations program were converted into the file formats necessary for downstream analyses using PGDspider v.2.0.5.0 .In the final output files, Broken Bridge High (BBH) had 17 individuals, Broken Bridge Low (BBL) had 29 individuals, Booloumba High (BOH) had 24, Booloumba Low (BOL) had 15, Obi Obi High (OBH) had 18 and Obi Obi Low (OBL) had 28 individuals. In total 131 individuals from 6 populations were used for subsequent analysis. 213 loci, each containing a single SNP in 131 individuals from 6 populations, were used for estimating genetic diversity, population structure and outlier analysis. Outlier analysis was also undertaken on each individual stream and compared to one another in order to identify any parallel pattern of adaptation.FIS)) were calculated for RAD data using the populations pipeline of Stacks v.1.35 , and Wright’s F statistics  were done with Exact tests for each locus using default settings in Arlequin v.3.5 . SequentTwo methods were employed here to identify population structure. Admixture analysis was used to detect genetic structure among all populations using the R package LEA . SimilarPopulation structure analysis was also done using individual-based Principal Component Analysis (PCA) implemented in the R package Adegenet . Here 21Due to the large divergence between the two populations from Booloumba Creek, 7 samples (3 samples from Booloumba Creek High site (BOH) and 4 samples from Booloumba creek low site (BOL) were sequenced for the mitochondrial COI gene. This was in order to confirm the mtDNA lineage to which they belonged. Details of the barcoding of Booloumba Creek individuals is described in FST outliers. An FDR of 0.001 was used. The logarithmic scale proposed by FST outlier analysis was also done using the program Arlequin v.3.5 . q value is used when multiple testing is done whereas for single testing the p value is used. The q value is the minimum FDR at which a locus becomes significant  method. After Principal Component 4 the scree plot reached a plateau so  q-value  was usednificant .P values. Furthermore, using the 213 loci, outlier analysis  was performed on Booloumba populations only (BOH and BOL) and the outliers obtained were matched against the outliers obtained from the overall analysis. The analysis with only Booloumba Creek sites was done to observe if the outliers obtained in the overall analysis were much influenced by Booloumba Creek populations or not. If the outliers obtained in the overall analysis all matched with the outlier analysis on Booloumba Creek populations only, it would indicate high influence of Booloumba Creek population on the overall analysis and vice versa if there was less match.Genotype frequency of the outliers was observed using Adegenet in R. In order to observe deviation of any outlier locus from HWE (Hardy Weinberg Equilibrium) the Pegas package  in R wasFST outlier test, the PCA-based outlier test and the Bayesian method were followed for each stream separately. Outliers detected in each stream population were listed in a tabular format and catalog ID of the outliers were compared to find common outliers across streams.In order to determine whether there were parallel patterns of adaptation between high elevation and low elevation populations each stream was analysed separately for evidence of outliers. The Furthermore, PCA was done based on the outliers produced in any of the 3 outlier detection methods using 27 loci  from all 6 populations. If there is a parallel pattern of adaptation across all three streams there should be one grouping of high elevation populations and another grouping for low elevation populations based on the outliers, due to similar pattern of divergence . The AdeAn additional PCA was done excluding all the outliers to observe the structuring of the populations and compare the results with the PCA including outliers.We obtained RAD data from 142 Individuals. Lowest number of demultiplexed reads  were observed in the Obi Obi Creek samples and highest number of reads  were observed in Broken Bridge samples. Retained reads ranged from 7,438 (Obi Obi) to 21,321,266 (Broken Bridge).Denovo assembly for 142 individuals produced 2,666,679 loci in the catalog and the depth of coverage ranged from 4.888x to 33.884x per individual. A white list was created with 207,619 loci which had only two haplotypes. Using these loci and selecting only a single SNP per locus generated 85,401 SNPs in 142 individuals.Individuals with very low depth of coverage (below 10x) were excluded from the popmap, which resulted in 11 individuals being removed leaving 131 individuals in total. The Populations program was run on 85,401 loci, using filters described in methods. This generated 213 SNP’s in 131 individuals, common across all 6 populations. Once filtering was complete, the number of loci sequenced per individual varied from 4 to 213, with a mean of 109 loci per individual and a total of 213 variable loci overall.When only variant (only polymorphic nucleotide) positions were considered, the average number of individuals sampled at a locus ranged from 14.63 (BOL) to 27.17 (BBL). Only the Booloumba High population (BOH) had private alleles . Expected heterozygosity was more or less similar across all populations, ranging from 0.2159 to 0.2781 with the lowest in OBH and the highest in BBH. Nucleotide diversity ranged from 0.2231 (OBH) to 0.2877 (BBH) . UpstreaFISvalues were higher in upstream sites  than in downstream sites . In Broken Bridge and Obi Obi Creeks, individuals from downstream populations had an excess of heterozygotes as indicated by the negative FIS values. Booloumba High and Low populations had similar FIS values  was highly significant. The FST value between BOH and BOL (FST = 0.44616) was much higher than BBH and BBL (FST = 0.04289) although both were significant. OBH and OBL were not statistically differentiated from each other compared to the other population pairs (Three loci deviated from Hardy Weinberg Equilibrium (HWE) but the deviation of these loci was not consistent across all populations. So, they were kept in the overall analysis. In the overall comparison, genetic differentiation  which accounted for 26.28% of the variance . Since BIn summary, all three methods suggested that the two Booloumba populations were the most different and different from each other. OBH was slightly differentiated from OBL, BBH and BBL by admixture analysis although this was less clear in the PCA.FST outlier test in Arlequin produced 27 outliers (12.67%) at the 0.1% level of significance and 186 loci (87.32%) were neutral  at FDR of 0.001 and was potentially under divergent selection with a posterior probability of 1 (PO).q value) threshold of 0.001  correlated with PC1, with a False Discovery Rate . Locus number 90 was identified in FST outlier and Bayesian methods .In total, 27 outliers were identified from any of the three methods . The Bayesian method did not detect any outliers for Broken Bridge Creek using prior odds of 100. In contrast, the PCA based method revealed 159 outliers (9.27%) out of 1716 loci and outliers were correlated with PC1 and PC2. 9 loci were common to both the FST outlier method and PCA based method.In Broken-Bridge Creek, of 1,716 loci, 14 loci were identified as possibly under divergent selection, falling above the 99% quantile in the FST outlier method in Arlequin. In the PCA based method among 704 loci, 16 loci were outliers (2.27%) correlated with Principal components 1 and 2. 6 loci were common to both the FST outlier and the PCA based methods. However, no outliers were detected using the Bayesian method.In Booloumba Creek, out of 704 loci, 22 were identified as possibly under divergent selection and there were no loci inferred to be under balancing selection with the FST outlier and the PCA based method.In Obi Obi Creek out of 499 loci, 4 loci (0.8%) were identified as outliers under divergent selection and there were no loci under balancing selection. The Bayesian method did not detect any outliers between high elevation and low elevation populations of Obi Obi Creek. The PCA based method detected 5 outliers that (1.00%) were related to PC1 and PC2. No outlier was common to the The outliers detected in each stream using the 3 different methods were matched  against each other to detect a possible parallel pattern of adaptation. There were no matches between any two streams.Possible parallel patterns of adaptation were examined based on the 27 outliers  identified from all three methods , but theMacrobrachium rosenbergii sequence database. However, with BLASTn (somewhat similar sequence) most loci showed a small (10–36%) sequence match (BLAST (NCBI) of the outlier loci (27 outliers) did not show any very similar sequence match (megablast) with the ce match .Individuals from BOH and BOL populations aligned with Reference lineage 4 sequence in the phylogenetic tree . Due to The overall aim of the study was to identify population structure between high and low elevation populations using a genomic approach. Furthermore, loci, putatively under selection were also determined to indicate any evidence of adaptation to altitude.Gambusia holbrooki  population had a number of private alleles, which reflects the isolation of the high elevation population from the low elevation population and suggests extremely limited dispersal. In contrast to many other studies, higher genetic diversity was observed in BOH than BOL. Lower diversity has been reported at high elevation sites for olbrooki  and alsoaliensis . The resFST value (0.446) between the high altitude and low altitude Booloumba populations was very large, indicating restricted gene flow and limited dispersal between them. In contrast, Broken Bridge (FST = 0.04289) and Obi Obi Creek (FST = 0.00737) showed comparatively more gene flow between high and low altitudes, as the genetic differentiation was low. The overall pattern of population structure is concordant with past findings based on a few allozyme loci, which revealed that among the 4 sub-catchments, Booloumba and Kilcoy sub catchments showed the highest genetic differentiation and Mary (including Broken Bridge Creek) and Stony sub-catchments showed least genetic differentiation  occur in the two BOH and BOL populations. However, the phylogenetic analysis based on a fragment of the mitochondrial COI gene showed that the individuals from high and low elevation populations were from the same lineage (4) .Mogurnda adspersa  in north Queensland, where high and low altitude populations show high genetic differentiation that was suggested to have resulted from different historical colonization events  while the low elevation populations may be adapted to the slightly warmer environment as was suggested by Outlier analysis identified putative loci under selection. Detailed analysis of the outliers showed that there was fixation of alternate alleles in high elevation and low elevation populations for a few loci, but only in Booloumba Creek. Also, some loci showed frequency differences, which could be expected if selection was influencing them but was not strong enough to counteract other processes, such as geneflow. Booloumba Creek populations showed the most differences between high elevation and low elevation but distinguishing the effect of isolation (lack of gene flow) from selection is difficult. High elevation and low elevation populations in Booloumba are very different and have probably been isolated for a considerable time. This conclusion is also supported by earlier allozyme studies  and our FST outlier method, which is high compared to the percentage of outliers expected (5–10%) suggested by Oncorhynchus mykiss gairdneri) and they found 7.89% loci under selection. They concluded that with outlier analysis it is possible to identify putative genomic regions under selection but without pre-information about the species genome, it is difficult to determine specific loci/genes causing adaptation to different environmental conditions (desert/montane). Adaptation studies in natural populations have always taken into consideration the environmental conditions in which the organism resides because genetic variance is strongly related to the environment in which it is measured. Comparing populations from different environments indicates strong interaction between the expression of genetic variance and environmental difference (Details on the outliers showed that there were a few loci that had deficiency/excess of heterozygote genotypes (than expected under HWE) in BBH, BBL, BOH and OBL populations. Few of these loci were common across these 4 populations. The percentage of outliers detected in this study was 12.67% according to the fference . As outlfference .Paratya australiensis, these are not necessarily candidate loci for adaptation. It was not possible to test this because there was no Paratya reference genome with which to compare the outliers. It is believed that outliers may lie in the genomic regions showing very high levels of genetic differentiation  Sequencing depth/coverage may not have been sufficient. This is also unlikely here, as 83% of individuals had a good depth of coverage (average 19.4x). In relation to our assumption at the beginning of the study, we are unable to distinguish between drift vs selection as we observed a similar pattern of genetic structure using outliers and neutral loci. So, in this scenario RAD-seq data could not differentiate the two processes and an additional RNA-seq analysis would be beneficial to describe the selection process better.There are other factors that could have influenced the results. (1) environmental conditions between populations were not massively different. The detection of outliers may require very strong environmental differences between populations. In our study, high elevation and low elevation populations in Booloumba Creek differed markedly in altitude while the other two creeks were less different. Hence, if these populations are separated for long periods of time, selection and drift will act on the population in a manner that makes it impossible to differentiate between them. Thus, loci under selection have the same population genetic structure as those evolving via drift (2) Low number of loci that matched across individuals. Most probably, RAD libraries did not have enough breadth of coverage which could be either due to large genome size or other technical issues. If a large number of loci (thousands) were matched evenly across individuals, we would have had a better chance of detecting potential outliers under selection. It is quite possible that the outliers identified in the present study are false positives, because in a fractal landscape like the stream environment, population genetic parameters are correlated with the number of demes. Hence, the variance of ositives . AccordiHeliotis rufescens) from three regions along the US west coast that differ in a number of environmental parameters . Population structuring with PCA using 21,579 SNPs showed no well-defined structure among the three regions  although 691 SNP showed significantly higher genetic differentiation than expected in an FST-outlier analysis  we could infer that the divergence was due to some degree of adaptation and there was adaptive divergence in Booloumba populations. However, there were no matching loci across any streams. According to Drosophila melanogaster. They did not find any candidate alleles that were fixed in different populations but they identified genomic regions showing similar genetic variation across independent replicate populations indicating a parallel pattern of selection.The number of outliers common to the Twenty-seven (27) outliers identified in the overall analysis were used to test parallel patterns based on PCA. In parallel patterns of local adaptation, populations inhabiting the same environmental conditions or from contrasting situations should show a similar pattern of groupings . Based oSimilar results were observed in two male color types of Lake Victoria Cichlid using 107 SNP markers . Also, aP. australiensis). In this study we evaluated evidence of local adaptation. We were interested to observe if divergence among populations is influenced more by selection or genetic drift in this part of the Conondale Range. Sites were selected based on altitudinal differences that differed in water temperature difference by 1−2 °C. We found similar genetic structure for both neutral and putative loci under selection inferring that there is no clear indication that the divergence is due to drift or selection. However, drift and selection are not mutually exclusive processes, so divergence could have been caused by selection initially but could show similar pattern to genetic drift if dispersal has been limited in the system. For the Booloumba Creek populations, there might have been chance dispersal to new habitat followed by local adaptation and complete isolation from the parental population, leading to further divergence due to genetic drift.In conclusion, this study used genomic approaches to observe population structure and local adaptation in the Australian glass shrimp (10.7717/peerj.8139/supp-1Figure S1Analysis done on 6 populations including 131 individuals.Click here for additional data file.10.7717/peerj.8139/supp-2Figure S2BOH population was separate based on PC1.Click here for additional data file.10.7717/peerj.8139/supp-3Figure S3Numbers on branches refers posteriors.Click here for additional data file.10.7717/peerj.8139/supp-4Table S1Click here for additional data file.10.7717/peerj.8139/supp-5Table S2Click here for additional data file.10.7717/peerj.8139/supp-6Table S3Black = common alleles, White = rare alleles and Grey = heterozygotes. “+” indicates presence of fewer heterozygotes than expected under HWE and “-” indicates more heterozygotes than expected under HWE.Click here for additional data file.10.7717/peerj.8139/supp-7Table S4Click here for additional data file.10.7717/peerj.8139/supp-8Data S1Click here for additional data file.


HO (0.335±0.13) and HE (0.352±0.15) in the North-west Highland and Central Highland populations, respectively. Overall, the highest HO (0.401±0.12) and HE (0.422±0.12) were found for Barki and Iranian goats, respectively. Barki goats had the highest average MAF, while Central Highland Cameroon goats had the lowest. Overall, Cameroon goats demonstrated high FIS. AMOVA revealed that 13.29% of the variation was explained by genetic differences between the six population groups. Low average FST (0.01) suggests intermixing among Cameroon goats. All measures indicated that Cameroon goats are closer to Moroccan goats than to other goat populations. PCA and STRUCTURE analyses poorly differentiated the Cameroon goats, as did genetic distance, Neighbor-Net network, and neighbor-joining tree analyses. The haplotype analysis of mtDNA showed the initial dispersion of goats to Cameroon and central Africa from north-east Africa following the Nile Delta. Whereas, the approximate Bayesian computation indicated Cameroon goats were separated from Moroccan goats after 506 generations in later times (~1518 YA), as supported by the phylogenetic net-work and admixture outputs. Overall, indigenous goats in Cameroon show weak phylogenetic structure, suggesting either extensive intermixing.Indigenous goats make significant contributions to Cameroon’s national and local economy, but little effort has been devoted to identifying the populations. Here, we assessed the genetic diversity and demographic dynamics of Cameroon goat populations using mitochondrial DNA (two populations) and autosomal markers (four populations) generated with the Caprine 50K SNP chip. To infer genetic relationships at continental and global level, genotype data on six goat populations from Ethiopia and one population each from Egypt, Morocco, Iran, and China were included in the analysis. The mtDNA analysis revealed 83 haplotypes, all belonging to haplogroup A, in Cameroon goats. Four haplotypes were shared between goats found in Cameroon, Mozambique, Namibia, Zimbabwe, Kenya, and Ethiopia. Analysis of autosomal SNPs in Cameroon goats revealed the lowest  Goats are an important economic resource in Africa. For instance, in Cameroon there are 6.2 million goats . They, tCapra aegagrus) were among the first species to be domesticated, about 10,500 years ago [Goats  are present in Africa , 22. HapIn a previous study in Cameroon, eight indigenous goat populations were characterized using SSR markers and, based on the results, these populations were regrouped to four , despiteSampling for this work was conducted in close collaboration with the Directorate of Veterinary Services under the supervision of the Ministry of Livestock in Cameroon. No animal sacrifice was required; hair samples were collected from 388 animals owned by smallholder farmers in rural areas of Cameroon .http://diagnostics1.com/MANUAL/General_Qiagen.pdf). Samples from the Djallonke (n = 48) and North-west Highland (n = 45) goat populations were used for mtDNA analysis. The PCR amplification steps, PCR reaction concentrations, and PCR programs, including the sequencing technology, were carried out as described in Tarekegn et al. [Genomic DNA was extracted from four indigenous goat populations in Cameroon  using the Qiagen DNA extraction protocol and DNeasy®Blood & Tissue Kit . In total, we generated 93 sequences and determined the number of haplotypes with DnaSP v5 10.01 [Hd), nucleotide diversity (π), and mean number of nucleotide differences between haplotypes and their standard deviations were determined for each goat population and across all populations, using ARLEQUINv3.5.2 [All chromatograms were visualized using CLC Workbench 7.0.4 (CLC Bio-Qiagen). The sequence fragments were edited manually using MEGA6  to correv5 10.01 . The levINv3.5.2 .d-loop haplogroups present in the dataset, the median-joining (MJ) network [www.fluxus-engineering.com). All mutations and character states were equally weighted. To visualize the variation and the gene flow in Cameroon goats in the context of the global caprine variation, 570 sequences from 29 countries representing the six globally defined mtDNA d-loop haplogroups [d-loop [C. hircus mitochondrial reference sequence (GenBank accession number: GU295658).To visualize the genetic relationship between individuals and populations, a phylogenetic tree was constructed using all the haplotypes with the Neighbor-Joining (NJ) algorithm implemented in MEGA6. The level of confidence associated with each bifurcation was evaluated with 1000 bootstrap replications. To complement the NJ tree, obtain further insights into the genetic relationships between the haplotypes, and determine the number of distinct mtDNA  network  was conslogroups  were ret [d-loop , which cSSD) from the simulated model of demographic or spatial expansion were tested with the Chi-square test of goodness of fit statistic and Harpending’s raggedness index “r” [FS [D [Population demographic history and dynamics were inferred from haplotype mismatch distribution patterns  for eachndex “r” , followi “r” [FS  and Taji” [FS [D . The sigAll the four Cameroon goat populations analyzed and the 10 reference populations were genotyped using the caprine 50K SNP BeadChip array  . AutosomHO), expected heterozygosity (HE), inbreeding coefficient (FIS), and deviation from Hardy-Weinberg equilibrium (HWE), was determined with ARLEQUIN v3.5.2. Analysis of molecular variance (AMOVA) was performed using the same software to apportion genetic variance within and between populations and among groups. The goat populations were considered as a group based on their country of origin. The distribution of MAF was assessed based on the following categories: fixed alleles (MAF = 0.00), rare alleles (0 < MAF < 0.05), intermediate alleles (0.05 ≤ MAF < 0.10), common alleles (0.10 ≤ MAF ≤ 0.5), and polymorphic SNPs (MAF > 0.01).Within-breed genetic diversity, represented as observed heterozygosity ((FST) [K) ranging between 2 and 15. For each K-value, we carried out five runs of 20,000 Markov chain Monte Carlo iterations after a burn-in of 10,000 iterations. The STRUCTURE output was further analyzed in STRUCTURE HARVESTER [K method [Population pairwise genetic differentiations (FST)  and Reyn(FST)  were est(FST)  and TASS(FST) , respect(FST) , 50. The(FST)  assumingARVESTER  and the K method  was usedd-loop region of mtDNA and autosomal SNP markers using Approximate Bayesian Computation (ABC) simulations [To infer the population history of Cameroon goat populations, we analysed HVI ulations  implemenulations . Both daThe reference tables were built by 100,000 and one million pods (pseudo-observed data sets) per scenario per set of priors for the autosomal markers and mtDNA, respectively using the default mutation settings. We compared the scenarios by estimating posterior probabilities with the logistic regression method using 0.1% (for mtDNA sequence) and 0.01%  of the simulated datasets . For thed-loop region were obtained. These sequences were deposited in GenBank (Accession No. MH621412-MH621504). From 1211 bp of the d-loop that was aligned against the caprine reference sequence , 78 variable sites were detected and these defined 83 haplotypes (π = 0.00895 ± 0.00050) than for North-west Highland goats (π = 0.00697 ± 0.00058).In all, 93 sequences of the mtDNA plotypes . The firplotypes . SimilarThe NJ tree constructed for Cameroon goats revealed one well-resolved cluster of haplotypes . The MJ FS and Tajima’s D, but the latter was not significant (P = 0.14200). These results provide evidence of spatial and/or demographic expansion by Cameroon goats.Mismatch distribution patterns were used to assess the demographic dynamics of the haplogroup revealed by the NJ tree and MJ network . For eacP<0.05) ranged from 1.70% (Iranian goat) to 17.33% (North-west Highland goats). The overall mean value of HO was 0.370 ± 0.140 (range 0.335 ± 0.13 (North-west Highland) to 0.401 ± 0.12 (Barki goat)). The overall mean value of HE was 0.383 ± 0.127  to 0.422 ± 0.12 (Iranian goats). The average inbreeding coefficient, FIS, was lowest (-0.02) for Chinese Cashmere goats and highest (0.08) for North-west Highland Cameroon goats. The Cameroon goats had an average FIS value of 0.05.The proportion of polymorphic SNPs ranged from 95% (North-west Highland) to 99.7% (Barki) . The proThe overall mean MAF was 0.288, and the lowest (0.258) and highest (0.320) values were observed in Central Highland and Barki goats, respectively. Comparatively low estimates of MAF were obtained for Cameroon goats, which was in agreement with the low estimates of heterozygosity. The distribution of MAF showed that percentage of fixed SNPs (MAF = 0.00) ranged from 0.00% in Barki to 4.07% in Djallonke, with an overall mean of 1.62% across populations . Rare alThe AMOVA results for the 13 populations grouped by country of origin  revealed that 13.29% (P<0.0001) of the total genetic variation was explained by genetic differences between groups and 81.67% of the variation occurred within individuals . SeparatFST value was 0.012. This value is lower than the average FST value of Ethiopian goats (0.032) included in this study. The pair-wise FST genetic distance ranged between 0.008  and 0.253  (FST = 0.176–0.253). Both FST and Reynolds’ genetic distances showed that Ethiopian goats were more differentiated from Barki goats than from Cashmere and Iranian goats. Cameroon goats were more differentiated from Barki and Cashmere goats than from Iranian goats.Among the Cameroon goats, the overall average ighland) . Barki gThe Neighbor-Net network showed that Cameroon goats were highly differentiated from the other goat populations, but were relatively close to Moroccan goats . CompareIn the PCA, principal components PC1 and PC2 accounted for 38.64% and 22.53% of the total variation, respectively . PCA sepK ≤ 15) are present in K = 6 was the optimal K value , Iranian, and Moroccan goats shared 45.43% to 76.16% similar genetic background at cluster5. This is in line with the results of NJ phylogenetic tree and Neighbor-Net network. However, there was only a very small proportion of common genetic background (6.4–10.8%) between Ethiopian and Cameroon goat populations.The results of the STRUCTURE analysis  to assess their maternal origin and historical demography. We observed 83 haplotypes defined by 78 variable sites. In Nigeria, 68 haplotypes generated from 68 polymorphic sites have been reported from 110 sequences in three goat populations  [In this study, we analyzed mtDNA ulation) . In Demoulation) . In eastulation) , 23. In ulation) . The difulation) . The relTo evaluate the genetic relationships between the 83 Cameroon goat haplotypes, we constructed an NJ tree and MJ network. The clustering pattern of the haplotypes revealed one well-resolved haplogroup lacking a phylogeographic structure Figs  and 3. FTo provide insights into the route of dispersal of goats towards Cameroon, we included an additional 570 HVI sequences from 29 countries downloaded from GenBank  and 182 In the present study, no shared haplotype was observed between Cameroon and north and west African goats, despite expected gene transfer from north Africa via the Niger valley and Atlantic coastline to Cameroon . This coOur mismatch distribution analysis revealed a unimodal pattern for each population and haplogroup A. A similar demographic pattern has been observed in Nigerian goats , 61, butIn the present study, the results for the Forest goat population from Cameroon showed that most (95.41%) of the markers for this population was monomorphic loci. This could be explained by small population size, limited gene flow to the forest environment, and/or the tsetse fly challenge for immigrant animals. The other possible reason could also be ascertainment bias , such thA low overall mean value of fixed SNPs (1.62%) was observed in this study . The oveFST genetic distance showed that Moroccan goat was closest to Ethiopian goat (FST = 0.176) was observed between Barki and Moroccan goats. This finding is strengthened by the absence of any signature of shared maternal haplotypes between these two populations. In line with this, the MJ network generated with modern maternal DNA showed that the Moroccan lineages were not derived from Egyptian lineages, as would otherwise be expected assuming a terrestrial route for dispersal of the species throughout northern Africa, rather via the Mediterranean routes [Based on AMOVA, the genetic variation (13.29%) observed among the goat populations in Cameroon, Ethiopia, Egypt, Morocco, Iran, and China  is similian goat . This min routes . Former n routes , 89 haven routes .K = 6  showed that Moroccan and Iranian goats shared 37.83% and 45.43% genetic background, respectively, with Ethiopian goats  found in hot and arid environments  and Cashmere goat (China) demonstrated the highest proportion of pure genetic background. The latter has undergone long-term artificial selection for production of Cashmere fiber  and coult1; Nm = 25) amongst the goat populations in Cameroon, Morocco and Ethiopia [t2; The posterior distribution analysis of ABC in the current study indicated that the second scenario in both data sets  best explains the goat population dynamics towards Cameroon. This indicates that the second route of dispersion of goats in Cameroon and west Africa could be from north Africa in later times (time of most common ancestor (TMCA) ~1518 YA; Ethiopia . In sameopia t2; –78 but lIn conclusion, this study provided insights about the population history and structure of Cameroon goats. The maternal DNA information revealed only haplogroup A detected in Cameroon. Population differentiation and admixture analyses showed that Cameroon goats appeared to have very low genetic differentiations and have two major genetic backgrounds, with different proportions, but that all three populations analyzed had been intermixed. The goats in Cameroon could have been dispersed from north and east Africa at different times. The haplotype analysis of mtDNA showed the initial dispersion of goats to Cameroon and central Africa from north-east Africa following the Nile Delta as supported by archaeological, social anthropology and modern human molecular (DNA) evidences. Whereas, the approximate Bayesian computation (ABC) indicated that Cameroon goats were separated from Moroccan goats after 506 generations in later times (~1518 years ago). However, including more mtDNA sequence data from north Africa may warrant to arrive firm conclusion. The differentiation of Barki goat from the other 12 goat populations analyzed shows that Barki goat could be representative of populations found in desert environments for the study of drought tolerance. Interestingly, the autosomal genomic DNA indicated a common origin for Moroccan, Ethiopian and Iranian goats, but this requires further study based on ancient and modern DNA.S1 Fig(TIFF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(XLS)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.S7 Table(DOCX)Click here for additional data file.S8 TableLogistic regression analysis of posterior probabilities  for the scenarios modelled in DIYABC based on: a) mtDNA simulated for 1,000,000 data set; b) Autosomal markers simulated for 100,000 data set.(DOCX)Click here for additional data file.


DNA sequencing allows efficient discovery of thousands of single nucleotide polymorphisms (SNPs) in nonmodel species. Population genetic theory predicts that this large number of independent markers should provide detailed insights into population structure, even when only a few individuals are sampled. Still, sampling design can have a strong impact on such inferences. Here, we use simulations and empirical SNP data to investigate the impacts of sampling design on estimating genetic differentiation among populations that represent three species of Galápagos giant tortoises (Chelonoidis spp.). Though microsatellite and mitochondrial DNA analyses have supported the distinctiveness of these species, a recent study called into question how well these markers matched with data from genomic SNPs, thereby questioning decades of studies in nonmodel organisms. Using >20,000 genomewide SNPs from 30 individuals from three Galápagos giant tortoise species, we find distinct structure that matches the relationships described by the traditional genetic markers. Furthermore, we confirm that accurate estimates of genetic differentiation in highly structured natural populations can be obtained using thousands of SNPs and 2–5 individuals, or hundreds of SNPs and 10 individuals, but only if the units of analysis are delineated in a way that is consistent with evolutionary history. We show that the lack of structure in the recent SNP‐based study was likely due to unnatural grouping of individuals and erroneous genotype filtering. Our study demonstrates that genomic data enable patterns of genetic differentiation among populations to be elucidated even with few samples per population, and underscores the importance of sampling design. These results have specific implications for studies of population structure in endangered species and subsequent management decisions.High‐throughput  Nevertheless, application of this information requires sound understanding of population genetics theory. 1The advent of high‐throughput DNA sequencing has enabled the characterization of the genomes of model and nonmodel organisms alike. Genomewide data can improve the precision and accuracy of estimates of population parameters, enhancing our understanding of present‐day structure, gene flow, and local adaptation  as a case study, given that a recent study has questioned the genomic distinctiveness of several species within this genus . They did not detect significant differentiation, as measured by FST, when comparing two groups . These two groups were constructed on what the authors identified as natural partitions, based on the observation that their samples fall into two different mtDNA clades  considered by Loire et al.  were selected for sequencing as part of a larger project on the phylogeography of Galápagos giant tortoises. These individuals were chosen as they displayed concordant and unambiguous genetic assignments between mitochondrial  and microsatellite (12 loci) ancestry based on a published database of 123 mitochondrial haplotypes  and ligated with Illumina‐specific adaptors representing up to 18 unique barcodes and two index codes. Ligated fragments of samples were pooled into 13 libraries and size‐selected to be ~310 bp (range 279–341 bp) with a BluePippin (Sage Science). Size‐selected libraries included 12–24 individuals and were paired‐end sequenced on 13 lanes of an Illumina HiSeq 2000 at the Yale Center for Genome Analysis.DNA was extracted from blood samples using a DNeasy Blood and Tissue kit (Qiagen) according to the manufacturer's instructions. We then prepared ddRAD libraries following Peterson et al. . For eac2.2Q < 20 with an ambiguous base (N) and discarded sequences containing more than four ambiguities. We used 85% clustering similarity as a threshold to align the reads into loci. We set additional filtering parameters to allow for a maximum number of SNPs to be called: retaining clusters with a minimum depth of sequence coverage (Mindepth) >5 and a locus coverage (MinCov) >10, a maximum proportion of individuals with shared heterozygote sites of 20% (MaxSH = p. 20), and a maximum number of SNP per locus of 15 (maxSNP = 15). For subsequent analyses, we filtered this dataset using vcftools .We used forward and reverse reads to generate a de novo assembly using the pyrad v.3.0.3 pipeline Eaton, . Reads w2.3F‐statistics  were calculated using the diveRsity package in R  were carried out in vcftools  on all 30 individuals, using the PLINK software  after demultiplexing and filtering reads for quality and ambiguous barcodes and ddRAD‐tags. de novo assembly of the data resulted in 48,004,056 ddRAD‐tags . From these, we called SNPs and obtained 973,321 variable sites. We then narrowed those loci down to only loci with genotypes called in every individual in our three species dataset, for a total of 23,057 SNPs. For the three species of interest, the number of loci retained within populations and between population pairs is presented in Table 3.2F‐statistics revealed values consistent with highly structured populations . Using the SNPs in common to each population pair  between PBL and CRU, 0.181 (95% CI: 0.175–0.187) between PBL and VA, and 0.233 (95% CI: 0.226–0.240) between CRU and VA , the majority of estimates were within 0.03 of the FST value calculated using the complete dataset of 10 samples per population  were included in the analysis, the FST estimates between 1000 pairs of these groups ranged from 0.045 to 0.136 . When only 1,000 SNP loci were used, as in Loire et al.   should still be detectable even with this grouping scheme.To test how pooling samples affected e et al.  samplinge et al. , the FSsee Fig. A,B. This3.6FST values should still be detectable using the grouping scheme adopted by Loire et al.  when comparing the three C. becki samples (PBL) to the combined two C. porteri and C. vandenburghi samples (CRU and VA). Notably, this FST value falls within our predicted range of FST estimates generated by subsampling the ddRAD‐seq data. Our FIT estimate for this dataset was −0.121 (95% CI: −0.129 to −0.113), with FIS estimated to be −0.185 (95% CI: −0.192 to 0.177).Given that our analyses of ddRAD‐seq data showed clear genetic structure among the populations from the three species, and our subsampling simulations Fig.  predictee et al. , we re‐ae et al.  from theC. becki, while the single samples from C. vandenburghi (VA) and C. porteri (CRU) are distinct from each other and from the C. becki samples , consistent with the findings of decades of research in this system , which was a focal metric used in the previous study  than between C. becki and C. porteri (PBL and CRU: FST = 0.169), and the greatest differentiation between C. porteri and C. vandenburghi (CRU and VA: FST = 0.233) , and another group of three individuals from the other species . The justification for this grouping was based on the closer phylogenetic relationship of mtDNA haplotypes from C. porteri and C. vandenburghi  were combined into one grouping, these FST estimates were still positive (mean FST = 0.075). We therefore would have expected Loire et al.  for all individuals included in this study and are awaiting review. The vcf file used in the analyses is available on the Dryad Digital Repository:  Click here for additional data file.


Pinctada margaritifera. The ongoing development of hatcheries for P. margaritifera in French Polynesia poses new challenges for the industry, particularly regarding the maintenance of genetic diversity in the hatchery stocks. This emphasizes the necessity to characterize the genetic diversity and differentiation within natural and exploited populations, to carefully select putative parental populations. The present study aimed at validating the phylogenetic status and investigating genetic attributes of populations from the only two non-exploited archipelagos of French Polynesia, the Marquesas archipelago, and the Australes archipelago, never analysed before. We found that individuals from both archipelagos belonged to P. margaritifera species. However, while the Australes population was genetically similar to non-exploited populations of the Tuamotu, the Marquesas populations were highly differentiated from the rest of the populations. This differentiation cannot not be only attributed to geographic distance and aquaculture status, but likely to hydrodynamic barriers allowing vicariant events to take place. Our results add up to other studies describing the Marquesas archipelago as a hotspot for biodiversity and differentiation, with some of the highest levels of endemism and vicariance found among marine species worldwide and provide precious information on available genetic resources for the implementation of P. margaritifera selective breeding and its genetic conservation in French Polynesia.The French Polynesian islands are internationally known for their black pearls, produced by culture of the black lipped pearl oyster  French Polynesia is internationally recognized for its black pearl industry and the aquaculture of the black-lip pearl oyster Pinctada margaritifera, and pearl farming sites are concentrated on 26 islands within three archipelagos: Tuamotu (80% of the production), Gambier (15%) and Society (5%).Located in the South Pacific Ocean, the French Polynesian islands consist of 118 atolls and high islands of volcanic origin distributed among five archipelagos: the Marquesas, Tuamotu, Society, Australes and Gambier archipelagos. These islands are separated by large bodies of open ocean (over 2000 meters of depth), and are considered as some of the remotest places on earth2. However, recent studies highlighting the interest of phenotypically selected hatchery produced oyster families for pearl quality and colour are now leading to the development of hatcheries of P. margaritifera in French Polynesia5. This poses new challenges for the industry, particularly regarding the maintenance of genetic diversity in the hatchery stocks. Indeed, many high fecundity marine organisms like oysters that underwent artificial breeding in the last few decades have shown a declining trend in genetic diversity within their broodstock7, as for hatchery produced P. margaritifera in Fiji8. This phenomenon is mostly explained by the high variance in reproductive success of oysters combined with high genetic load in hatchery-spawned pearl oysters, which can result in severe inbreeding depression and severely limit the long-term viability of selective breeding for a given species11. In addition, outbreeding depression can also act on the viability of a hatchery stock12. Outbreeding depression occur when the progeny from a cross between two genetically distant individuals exhibit lower fitness than its parents. It is thus extremely important to characterize the existing genetic diversity and levels of differentiation within natural and exploited populations, and carefully select putative parental populations in order to maintain similar levels of genetic diversity, effective population sizes, and fitness levels in hatchery families.The current exploited oyster stock originates entirely from natural spat collection through fixation of drifting larvae to a plastic substrate called a collectorP. margaritifera is concentrated in 26 islands belonging to three archipelagos: Tuamotu (80% of the production), Gambier (15%) and Society (5%). Previous genetic investigation of farmed and wild populations of P. margaritifera from the three exploited archipelagos revealed that translocations have impacted the genetic variability of the species: while natural spat collection promotes high levels of genetic diversity in the collected stock and thus the farmed populations, the translocation of this collected stock across different islands led to a genetic homogenization (reduction in the levels of genetic differentiation) of the farmed stocks across entire archipelagos7. Indeed, after 10 years of translocations, the genetic differentiation among exploited archipelagos and among populations within exploited archipelagos decreased from 0.032 to 0.006 and from 0.025 to 0.007 respectively, regrouping the three archipelagos together into one genetic unit14. In addition, farmed individuals reproduce with adjacent natural populations creating genetic leakage and homogenization of the natural populations within exploited lagoons as well7.In French Polynesia, aquaculture of 15. While this genetic peculiarity could be useful for the maintenance of the diversity of hatchery populations, their habitat specificities and their phenotypic characteristics put into question the affiliation of the Marquesas populations to the species margaritifera, and their possible use as a source population for hatchery stock production. While P. margaritifera individuals are usually found in calm oligotrophic lagoon waters, they were found in open sea coastal shelves in the Marquesas archipelago15. A recent exploration of two other islands (Ua Pou and Nuku Hiva islands) also reported the presence of individuals in small intertidal tide pools subjected to temperatures that could reach 34 °C during the day , a temperature that causes severe metabolic depression in individuals from the North Tuamotu16. In addition, the Marquesas’ P. margaritifera present a red/orange overtone of the shell and narrow spaced growth barbs, which diverges from the grey/black shell and wider spaced growth barbs phenotype of the rest of French Polynesia , and a second genetic clade grouping individuals from French Polynesia and Japan17. The polyphyletic status of P. margaritifera was actually previously suspected based on its wide geographic distribution and its intraspecific phenotypic polymorphism19, which further warrants our interrogation on the affiliation of the Marquesas samples. In addition, a recent investigation revealed that P. mazatlanica specimens, P. margaritifera’s sister species, has a basal position with regard to the Polynesian clade of P. margaritifera20. Early taxonomic classification of P. mazatlanica based on morphological characters had in fact classified it as a subspecies of P. margaritifera  showed high levels of differentiation from the rest of the French Polynesian populations, attributed the recurrent isolation and vicariance found for the marine fauna of the Marquesas Islands combined with the absence of local aquaculture development, preserving the original stock from contamination by translocationP. margaritifera samples obtained from two non-farmed French Polynesian archipelagos, the Marquesas and the Australes archipelagos, through a representative sampling of the different P. margaritifera habitats found in the Marquesas archipelago and the addition of samples from the Australes archipelago, never investigated before  assess levels of genetic diversity in the Marquesas and the Australes archipelagos, and (iii) assess levels of differentiation of the Australes and the Marquesas archipelago from other farmed and non-farmed atolls of the Tuamotu. Ultimately, we aim at providing estimates of genetic diversity and differentiation of populations from two pristine archipelagos, to assist in the current development of a hatchery production in French Polynesia.The present study aims at investigating the general patterns of genetic diversity and differentiation, as well as confirming/infirming the phylogenetic affiliation of ore Fig. . We willPinctada fucata, P. radiata and P. maxima were all separated from the rest of the samples with a Bayesian Posterior Probability (BPP) of 100%. The Mauritius P. margaritifera sample have a basal position with respect to all the other P. margaritifera individuals with a BPP of 100% . Bayesian phylogenetic analysis yielded the topology depicted in Supplementary Fig. ene Fig. . In addiP. margaritifera populations from French Polynesia15. All the populations show FIS values significantly different from 0, and deviation from HWE with excess of homozygotes . However, for the distance of 1000 km, pairwise comparison involving the Marquesas populations show high levels of FST, while pairwise comparisons involving the Australes population show FST levels similar to the intra-archipelagos one for the Marquesas and the Tuamotu populations (with less than 250 km separating the populations).Signal of isolation by distance is significant , with a positive correlation between geographical distance and genetic differentiation  and/or local selection, two processes that will be discussed below. Interestingly, genetic diversity indices of the Australe archipelago do not indicate a reduction in genetic diversity. The Australe population actually has a similar level of genetic diversity than that of the non-exploited Tuamotu populations, and shows the highest level of allelic richness, which reinforce the fact that the Marquesas populations are genetically peculiar at the scale of French Polynesia.Phylogenetic analysis of COI mitochondrial gene reveals that populations of the Marquesas archipelago and the Australes archipelago group with all the other 15 hypothesized that genetic differentiation of the Marquesas populations was correlated with a possible founder effect, followed by vicariance due to the presence of hydrodynamic barriers to connectivity. A founder effect and/or selection could also explain the unusual phenotype of the P. margaritifera individuals of the Marquesas archipelago. Indeed, the very common red/orange overtone of the outer shell of the Marquesas individuals  does also occur at low frequency in some populations of the Tuamotu22, and could have been overly represented in the founding population.The Marquesas archipelago is genetically highly differentiated from both the Tuamotu and the Australe archipelagos. On the other hand, the Australe archipelago resembles the non-exploited populations of the Tuamotu, especially Katiu. While one could argue that geographic distance plays a role in levels of differentiation, the positive signal of isolation by distance we observed in our data set is clearly driven by the presence of the Marquesas populations . This confirms that geographical distance per se is not the explanatory factor for the high levels of genetic differentiation of the Marquesas archipelago. There are thus two possible non-mutually exclusive scenarios that could explain the specificity of the Marquesas populations: selection, and a founding event. Lemer and PlanesP. margaritifera in the Marquesas archipelago . In these two extreme habitats, one might expect to find different selective pressures. However, despite the fact that the three populations of the Marquesas archipelago encompass both these extreme habitats, we did not find significant genetic differentiation among them. The lack of genetic structuring between the tide pool individuals and the open sea individuals seems to indicate no particular signs of selection for resistance to high temperature and hydrodynamic regimes. However, this result could also be due to the lack of power (through limited genome resolution) of our nine microsatellite markers to reveal a possible selection regime, likely acting on a small portion of the genome. Further analyses should be carried out with high density markers, such as SNPs, using both DNA sequencing and RNA Sequencing to target both expressed and non-expressed genomic regions (as performed and discussed in23).Selection can also act on (and/or accelerate) genetic and phenotypic differentiation of the Marquesas individuals, considering the very different habitats of 24. Many species are endemic to these islands, and populations of non-endemic species are often found to be highly genetically differentiated from populations elsewhere. The Marquesas Islands are located at 1300 km from Tahiti (Society archipelago) and at 4800 km from Mexico, Central America being the closest continental landmass. This geographical isolation and particular geomorphological and hydrodynamic characteristics make the Marquesas archipelago a rich, dynamic and highly productive system that allow species to thrive. Indeed, numerous studies have reported high levels of genetic differentiation of the Marquesas archipelago marine and terrestrial populations compared to the rest of French Polynesia (sometimes to the rest of the Pacific) as well as the occurrence of a particularly high number of endemic species29. This high rate of endemicity and genetic diversity is thought to be partly attributed to the role of the Marquesas archipelago as a refuge during the Last Glacial Maximum (LGM)30, while subsequent genetic divergence is often generally explained by vicariance27).Because of their geographic isolation, oceanic islands have been the centre of interest of many biological monitoring for conservation biology and evolutionP. margaritifera individuals from the Marquesas archipelagos are indeed genetically divergent from the rest of the French Polynesian populations, with a higher differentiation than could be expected just by geographical isolation. Vicariance of P. margaritifera individuals might have taken place, alongside selection, considering their presence in new types of habitats. Our results thus add up to previously cited studies describing the Marquesas archipelago as a hotspot for biodiversity and differentiation, with some of the highest levels of endemism and vicariance found among marine species worldwide.Our results indicate that Pinctada margaritifera is already living close to the limit of its physiological optimum in most atolls of French Polynesia31, and there is thus a growing interest to understand how the oyster can adapt to rising temperatures. The breeding of the Marquesas populations could thus assist in maintaining temperature-resistant stocks of oysters in the face of global warming. However, care will have to be taken considering their high genetic divergence to the Tuamotu populations, which could result in outbreeding depression in the hatchery stock. Experimental crosses will have to be performed to test for this hypothesis.The present study allowed a better understanding of the distribution and levels of genetic diversity and differentiation in two non-exploited archipelagos in French Polynesia. It highlights the peculiarity of the Marquesas populations, which were deemed of great interest for hatchery production because of their possible temperature-specific adaptations. Populations from the Australe archipelago were found to be genetically similar to the Tuamotu, and especially to the non-exploited atolls. This indicates that while the Tuamotu archipelago is mostly exploited and shows signs of genetic homogenization, non-exploited atolls might still carry a large amount of the original genetic diversity present in the Tuamotu before exploitation. Hence, these populations could represent the genetic baseline for the control of genetic diversity in hatchery populations.margaritifera were sampled   using a KingFisher Flex automat (Thermo) and MagAttract®96 kit magnetic beads (Qiagen) following manufacturer’s recommendations. Briefly, a volume of 500 µL of magnetic beads was added to tissue samples previously lysed for 3 hours at 56 °C in deepwell plates containing 180 µL lysis buffer and 20 µL proteinase K. The magnetic bead-sample solution mixes were homogenized by shaking for 30 s. The DNA-beads complexes were then magnetically collected by the KingFisher magnets and washed twice with supplied washing solutions. DNA was finally released from the magnetic beads by incubation in 200 µL of elution buffer. Eluted DNA was stocked at −20 °C until use.32. We randomly selected 10 individuals per population in NHV-D, NHV-S, UAP and RVV and sequenced the 40 corresponding sequences on an ABI 3730XL (Applied Biosystems) automatic sequencer. The new sequences generated in this study were archived and are available in Genbank .In order to test for the phylogenetic status of the Marquesas and Australes populations, we sequenced the mitochondrial gene Cytochrome Oxidase I (COI), with the following primers: LCX 5′-TCG TAT AGA GCT CCG TCG ACC TG-3′ and HCY 5′-TGG AAC AAA ACT GGATCG CC-3′). We used PCR conditions similar to those used inP. margaritifera samples from the Marquesas and the Australes archipelago, we completed our sequence set with Genbank archived COI sequences of the 13 P. margaritifera individuals listed in17, from French Polynesia and Mauritius. To root P. margaritifera’s sequences, we also collected the Genbank archived COI sequences of 21 individuals from other Pinctada species present in the Pacific Ocean: P. maxima, P. mazatlanica, P. fucata, and P. radiata17.To perform the phylogenetic analysis of the 33 to perform a global alignment of the 74 COI sequences using the MUSCLE algorithm with a maximum of 8 iterations. The global alignment obtained was curated manually to maximize positional homology. In order to obtain the optimal molecular model of evolution, we used the modeltest function in the R package phangorn34. The best fitting model was chosen according to the Akaike Information Criterion (AIC).We used Geneious V9.1.635. Four Metropolis-coupled Markov chain Monte Carlo (MCMC) analyses were run for one million generations and sampled every 100 generations, with a burn-in of 60000 generations. The GTR + G + I model of evolution selected by the Aikake Information Criterion in Modeltest. Trees were summarized through MrBayes and robustness was evaluated using Bayesian posterior probabilities (BPPs). The nexus file used in MrBayes is available in the Dryad repository (XXX). We used FigTree (http://tree.bio.ed.ac.uk/software/figtree/) to visualize the annotated tree. Finally, we built a haplotype network with the software PopART36, using the TCS haplotype builder algorithm.Phylogenetic reconstruction was performed using a Bayesian approach as implemented in MrBayes v3.2.5Pinctada margaritifera samples were genotyped using a panel of nine microsatellite markers previously developed37. PCR reactions were performed using the Qiagen Multiplex PCR Kit (QIAGEN) in a 10 µL final volume containing 3 µL of genomic DNA diluted at 10 ng/µL, 5 µL Qiagen’s multiplex master mix, 1 µL Qiagen’s Q-solution, and 1 µL of an end-labelled primer mix. The nine selected microsatellite markers were amplified in 3 PCR multiplexes as detailed in Table For the diversity and hybridization status of the populations, nuclear DNA is preferred because of its biparental transmission and faster rate of evolution. 38.Microsatellite alleles were visualized and scored using the GeneMapper v4.1 software system . Microsatellites were checked for repeat pattern accuracy and presence of null alleles in MICRO-CHECKERIS, and tested for HWE deviations with the diveRsity package39. FIS significance was tested by calculating the 95% confidence intervals with 10000 bootstraps. We also used the diveRsity package to estimate genetic differentiation by calculating FST and Jost’s D for each pairwise population, and significance was tested with 10000 bootstraps. FST usually defines differentiation as a process mainly influenced by allele fixation through drift, while minimizing the role of mutation. Jost’s D on the other hand, gives equal weight to mutation and drift as potential processes leading to speciation. We used the software FreeNA40 to estimate the frequency of null alleles for each population at each locus.To investigate the levels of genetic diversity within our samples, we calculated for each population and each locus the allelic richness, the observed, expected and corrected heterozygosity, F41, keeping 50 PC axes and using the ward method of clustering. The best number of clusters K was defined as the one minimizing the Bayesian Information Criterion (BIC). We then used a discriminant analysis of principal components (DAPC), a multivariate analysis that tries to maximize the variance among predefined groups of individuals . To find the optimum number of PC axes to use without overfitting the data, we performed a cross validation using the xvalDapc function from adegenet (which defined 26 as the number of PC axes Achieving Lowest MSE). We then performed a DAPC with adegenet, followed by an individual-level assignment estimation for each population. To identify which alleles are driving the differentiation among populations, contribution of each allele to the global variance was visualized by extracting their loadings on the first discriminant axis.To test for the presence of genetic structuring, we first used a non-supervised hierarchical clustering technique implemented in the find.clusters function in the adegenet packagehttp://boulter.com/gps/distance/). Indeed, because we are working on small oceanic islands, the distance “at sea” between each pair is similar to that of a straight line between two GPS points. We used transformed FST estimates (FST/(1-FST)) as the measure of genetic distance between populations, and then correlated the genetic and geographic distance matrix using the function mantel.randtest in the ade4 R package42. Results of IBD were visualized in a scatterplot with a fitted regression line, obtained with the function lm of the stats R package.Isolation by distance was tested using a matrix of geographic distance (in kilometres) obtained using a derivative of the great circle calculator, for latitude/longitude distance calculation . This method supposedly makes a better estimate of true relatedness in natural populations . Normality of the inbreeding coefficient and the relatedness indices was controlled with the Shapiro-Wilk test of normality through the shapiro.test function of the stats R base package. To examine whether any significant difference in the relatedness index among populations was correlated to a significant difference in levels of inbreeding among populations, we applied the Kruskal-Wallis test for multiple non-normal samples using the kruskal.test function of the stats R base package on both indices. For any significant difference among populations, the Wilcoxon rank sum test was used to identify the populations that were significantly different, and P-values were adjusted using the Bonferroni correction for multiple comparisons (function pairwise.wilcox.test in stats). Finally, we estimated NE for each population using the program NEESTIMATOR45, under random mating, using the bias-corrected version of the linkage disequilibrium method described in46, and considering only alleles with a Minor Allele Frequency (MAF) ≥ 0.05.We used the coancestry function of the related packageSupplementary Figures S1, S2 and Table S1


Mallotus oblongifolius, an evergreen shrub endemic to Hainan Island, China, is important both medicinally and economically. Due to its special medicinal significance and the continuing rise of market demand, its populations in the wild have been subject to long-term illegal and unrestrained collection. Hence, an evaluation of genetic variability is essential for the conservation and genetic reserve development of this species.M. oblongifolius growing in different eco-geographical regions of Hainan Island, China.Sequence-related amplified polymorphism (SRAP) and inter-simple sequence repeat (ISSR) markers were employed to assess the genetic diversity and genetic structure of 20 natural populations of h = 0.336, I = 0.5057, SRAP markers; h = 0.3068, I = 0.4657, ISSR markers) and weak genetic differentiation  with the same gene flow  among the M. oblongifolius populations. The Mantel Test showed that the distribution of genetic variation among populations could not be explained by the pronounced geographical distances . All results of the Unweighted Pair Group Method with Arithmetic Mean (UPGMA), Neighbor-joining (NJ), Principal Coordinate Analysis (PCoA) and Bayesian analyses supported a habitat-specific genetic clustering model for M. oblongifolius, indicating a local adaptive divergence for the studied populations.We revealed a considerable genetic diversity (M. oblongifolius populations weakened the natural gene flow and promoted an adaptation to special habitats, which was the main reason for local adaptive divergence among M. oblongifolius.We suggested that the habitat fragmentation and specificity for  Mallotus oblongifolius is a tropical plant belonging to Euphorbiaceae. This plant, native to regions of Hainan Island in China, is economically important and medicinally relevant. Mallotus oblongifolius is an evergreen shrub or small tree (2–10 m), mostly dioecious and rarely monoecious  must be based on scientific practices  to assess the level of genetic diversity and genetic differentiation across M. oblongifolius populations; (ii) to characterize the genetic structure within the populations and (iii) to assess the correlation between the two markers. These results could benefit M. oblongifolius germplasm collection, conservation and future breeding.In this study, we used SRAP and ISSR markers to assess the genetic diversity and genetic structure of 20 natural populations of M. oblongifolius were collected from 20 naturally occurring populations on Hainan Island, China from January 2016 to May 2017 , the same weight bands were marked as a line. The bands that were clearly visible and repeatable on the electrophoresis map were marked as “1” and the absence of a band at the same site was marked as “0”. A binary data matrix was compiled for each primer set. Both the total number of bands amplified by each primer and the number of polymorphic bands were calculated. Most of the following analyses were carried out based on adjoined matrix data.M. oblongifolius populations was evaluated by calculating the number of alleles (na), effective number of alleles (ne), Nei’s gene diversity (h), Shannon’s information index (I) and percentage of polymorphism bands (PPB). These parameters  were calculated using POPGENE (version 1.32) software . The most likely number of clusters (K) was estimated using STRUCTURE HARVESTER  were polymorphic   . Thus, Sp = 80.85%, h = 0.2722, I = 0.4087), followed by populations TX and SJ, and the lowest genetic diversity was found in population PJL. According to the SRAP markers, population TX had the highest genetic diversity , followed by populations ZJC and SJ, and the lowest genetic diversity was found in population PJL. The genetic diversity demonstrated by the SRAP marker was similar to that revealed by the ISSR marker. At the species level, h, I and P, meaning the genetic diversity, were significantly higher than at the population level. Each index exhibited a small change among populations, and there is a similar level of genetic diversity within each population.POPGENE 1.32 was used to analyze genetic diversity and genetic differentiation from both SRAP and ISSR data  and 3. FM. oblongifolius were calculated, as well as the genetic differentiation coefficients among different populations (Gst) , rather than within. Both SRAP and ISSR markers indicated that most genetic variation in M. obongifolius occurs within populations. The gene flow number (Nm) of the populations of M. oblongifolius was 1.346 (ISSR) and 1.3092 (SRAP), which indicates that the gene exchange was high between populations. Such gene flow could reduce genetic differentiation among populations that would otherwise be caused by genetic drift.Based on SRAP and ISSR data, the total genetic diversity (Ht) and mean within-population genetic diversity (Hs) of Gst) see . Nearly M. oblongifolius varied from 0.0252 to 0.2453, with an average of 0.1175. The largest GD was found in populations PJL and DPC and the smallest in populations DPC and XP, which were 0.0252 and 0.2453, respectively. Based on the results of ISSR, the GD of the 20 populations of M. oblongifolius varied from 0.0169 to 0.2213, with an average of 0.8911. The largest GD was found in populations PJL and ZCED, and the smallest in populations GX and SSC, which were 0.0.2213 and 0.0169, respectively. Genetic relationships among the populations were constructed by UPGMA cluster analysis based on a GD matrix using the SRAP and ISSR markers. The results show that the two markers completely divide the 20 populations of M. oblongifolius and that the clustering results had certain similarities but were not identical. The clustering of M. oblongifolius is not significantly related to its geographical origin. For example, the population GMS and population GPS with the closest geographic sources are clustered in large branches, but they are not clustered together in small branches. However, the dendrogram shows a spatial pattern of GD among the populations aligning with their habitat status.Both the GD and the GS among populations were calculated with POPGENE (version 1.32) using SRAP and ISSR data see  and S4. A dendrogram  that groM. oblongifolius were revealed by UPGMA and NJ-based GD using combined SRAP and ISSR data  was used to calculate the GD and the GS among populations using combined SRAP and ISSR data see . The relSSR data  and 5. BM. oblongifolius further, we performed a calculation based on a Bayesian model on the combined SRAP and ISSR data in STRUCTURE program version 2.3.4. Results showed a spatial pattern of GDs among the populations in accordance with their habitat status, which was similar to the result of UPGMA and NJ analyses. The results showed that when K = 2 .Geographic distances of the 10 studied populations ranged from 0.456 to 276.232 km, with a mean of 81.63 km . A Manteh = 0.3068 and I = 0.4657). However, SRAP markers produced a lower PPB (83.24%) than ISSR markers (87.94%). Sequence-related amplified polymorphism markers amplify the ORF of plant genome, whereas ISSR markers amplify the repetitive regions. Thus, differences exist in the polymorphism of the two amplification targets .From the perspective of modern genetics, species with higher genetic diversity will have a wider natural distribution and stronger environmental adaptability, survivability and evolutionary potential. Genetic variation is a necessary condition for a species to adapt to environmental changes . Therefo targets . Our resJatropha curcas L. (85.19% for ISSR) (Ricinus communis L. (68.1% for ISSR)  is higher than the average genetic diversity of multiple plant populations detected based on dominant markers such as RAPD, RFLP and ISSR (h = 0.22) in the review of When compared with other species belonging to the Euphorbiaceae family such as or ISSR)  and Ricior ISSR) , our stuM. oblongifolius, which were equivalent to or less than the mean values in the M. oblongifolius.Genetic differentiation is an important index of genetic structure. M. oblongifolius occurred among populations  with weak genetic differentiation among populations. By reviewing Gst values of plant species (based on RAPD data), M. oblongifolius was the outbreeding or the mixing of mating taxa with dominated outbreeding, which may have an influence on extant among-population genetic structure of M. oblongifolius.M. oblongifolius populations were 1.3092 (SRAP) and 1.346 (ISSR), which indicates that the gene exchange was high between populations. Such an exchange could prevent genetic differentiation caused by genetic drift between populations, and may play important roles in the genetic structure of M. oblongifolius populations.Gene flow is the movement of genes within and between populations. The intensity of gene flow has an important impact on population differentiation . It is gr = 0.01255, p = 0.5538). The populations in this study were clustered according to the degree of similarity of their habitats and had nothing to do with their geographical location, and the populations with similar habitats first clustered together. The pattern of genetic subdivision can be clearly demonstrated in the UPGMA and NJ cluster analysis . The results of PCoA and Bayesian analysis also support this habitat-specific genetic clustering model (see If gene flow by mating system and seed dispersal are the main causes of population variation, the closer the geographical distance between two populations is, the smaller the genetic differentiation should be. However, the result of the Mantel test suggested that the distribution of genetic diversity among populations might not be explained by the pronounced geographical distances (ysis see  and 5, iodel see  and 7.M. oblongifolius have long been subjected to illegal and unrestrained collection, and the number and size of populations have dropped dramatically. This has led to habitat fragmentation and specificity, which weaken the natural gene flow between the populations and promote the adaptation to special habitats to maintain high genetic diversity.Local adaptive divergence will occur when the gene flow is weakened, and populations in different habitats experience divergent patterns of selection . Due to M. oblongifolius, which will in turn enable better collection and management practices to preserve its diversity.Our evaluation of genetic diversity is an essential step toward the germplasm characterization of 10.7717/peerj.7173/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.7173/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.7173/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj.7173/supp-4Supplemental Information 4Click here for additional data file.10.7717/peerj.7173/supp-5Supplemental Information 5Click here for additional data file.10.7717/peerj.7173/supp-6Supplemental Information 6Click here for additional data file.