Seriola lalandi), a commercially valuable, circumglobal, epipelagic fish species occurs in two genetically distinct Southern Hemisphere populations (South Pacific and southern Africa) with low levels of gene-flow between the regions. Two shallow oceanic seamounts exist in the ocean basins around southern Africa; Vema and Walters Shoal in the Atlantic and Indian oceans, respectively. We analysed rare samples from these remote locations and from the South African continental shelf to assess genetic structure and population connectivity in S. lalandi and investigated life-history traits by comparing diet, age, growth and maturation among the three sites. The results suggest that yellowtail from South Africa and the two seamounts are genetically and phenotypically distinct. Rather than mere feeding oases, we postulate that these seamounts represent islands of breeding populations with site-specific adaptations.Phenotypic plasticity in life-history traits in response to heterogeneous environments has been observed in a number of fishes. Conversely, genetic structure has recently been detected in even the most wide ranging pelagic teleost fish and shark species with massive dispersal potential, putting into question previous expectations of panmixia. Shallow oceanic seamounts are known aggregation sites for pelagic species, but their role in genetic structuring of widely distributed species remains poorly understood. The yellowtail kingfish ( Phenotypic plasticity might explain observed differences in growth, maturation and reproduction related traits, depending on natural and fishing mortality, access to resources and environmental variation, as there has been little evidence of genetic isolation of seamount associated pelagic fish populations2, yet, recent literature has detected genetic structure in wide ranging large pelagic fishes3, questioning the current understanding of seamounts as mere feeding oases and navigation aids.Seamounts have long been known as aggregation sites for large pelagic fishes such as tuna, billfishes and sharks5 with a pelagic propagule phase, which suggests the existence of hydrographic mechanisms for larval retention. Yet, both are also home to common, widely distributed organisms such as the yellowtail kingfish, Seriola lalandi Valenciennes, 1833 (Carangidae). Yellowtail are circumglobal predators occurring in temperate and subtropical waters. Yellowtail occur in a variety of epipelagic habitats from the surf zone to the open ocean and aggregate around isolated features such as seamounts and reef pinnacles8. An important fisheries and aquaculture species throughout its distribution, aquaculture production of yellowtail in some countries generally outweighs that of wild commercial harvesting by up to 75%9, but wild stocks continue to support fisheries such as in South Africa11.Two oceanic seamounts that extend into the photic zone are known to exist around southern African waters, Vema  in the southern Atlantic Ocean and Walters Shoal  in the Southern Indian Ocean  Vema and Walters Shoal function as aggregation and feeding points of a panmictic yellowtail population distributed around Southern Africa (Oasis hypothesis) or (2) that Vema and Walters Shoal host populations of yellowtail genetically and phenotypically differentiated from each other and from coastal populations (Island hypothesis).Genetic diversity was determined in a total of 351 individuals from three main sampling regions using genotype data generated from six microsatellite markers. Population differentiation was also successfully assessed using F-statistics, multivariate and clustering analyses.The software MICRO-CHECKER indicated that only two loci showed evidence of null alleles (Sdu29 and SduCA4j). These markers were included in subsequent analyses due to the limited number of microsatellite markers available in this study.Sdu10) to 26  to 15.640  between the two South African sampling sites North Blinder and Yzerfontein. Pairwise FST values based on only the four loci devoid of null alleles showed a similar overall pattern with significant values only between sampling sites from South Africa and the two seamounts; Vema and Walters Shoal . Most of the total genetic variance was found between individuals within populations (86.88%) with very little variance distributed among populations within groups (1.47%). Results of the DAPC based on the 95% confidence ellipses showed a strong separation between individuals from South Africa and those from the two seamounts, with overlap mostly between individuals of the two seamounts  populations, assuming no prior information on the number of sampling locations. Based on ΔK, three clusters were detected and membership assignment plots (K = 3) indicated a clear separation between the South African, Vema and Walters Shoal seamount populations Figure .All 54 (100%) stomach samples from Vema contained food. For the Walters Shoal specimens, only 28 (50%) of the 56 stomachs contained food whereas in the 62 South African samples, 47 (76%) contained food that could be partially identified.FO) with 81.5%, followed by fish with FO of 68.5%  had the highest FO in the Walters Shoal sample. Other crustacean species occurred in less than 15% of the stomachs at both seamount locations. The only crustacean group that made a significant contribution to the diet of yellowtail in South Africa were the crab megalopa larva (FO 19.35%).Bony fish dominated the stomachs of the South African yellowtail with an FO of 7.4% and > 2%, respectively.Yellowtail stomachs from Walters Shoal contained a lower variety of fish species with only three families identified, as compared to the Vema samples, which contained six different families and South Africa with four different families. No common fish species were found in yellowtail stomachs at the three locations. Cephalopods were found in 28.6% of the Walters Shoal stomach samples compared to 18.5% and 9.68% for the Vema and South African stomach samples, respectively. The cephalopods were difficult to identify even to family level, however, two families  from one order (Teuthida) were identified in the Vema diet while one family (Octopoteuthidae) and two orders  were found in the Walters Shoal sample. Molluscs did not feature in the Walters Shoal diet and were of negligible importance in the diet of the Vema and South African yellowtail with a K . The condition factor was highest for Vema followed by South Africa and Walters Shoal.Yellowtail sampled at Vema displayed a broad size range from 445 mm FL and a weight of 1.220 g to 1.005 mm FL and a weight of 12.900 g. The South African yellowtail ages ranged from 490 mm FL and a weight of 2.000 g to 916 mm FL and a weight 6.550 g. The fish sampled at Walters Shoal ranged from 521 mm FL and a weight of 1.772 g to 844 mm FL and a weight of 6.894 g. Length–weight relationships differed among the locations Fig. a, resultK Fig. b, as judF = 20.08, df = 222, p < 0.001) , 16.2% and 21.7% (South Africa), and 10.5% and 11.5% , respectively. Age readings ranged from 2 to 7 years for all subsamples combined, but only the year classes 3 and 4 had large enough sample sizes  for a meaningful comparison. The two-way ANOVA showed that there was a highly significant difference in length-at-age among the three locations , 25 females ranging from 452 to 1005 mm FL and the remainder were unidentifiable. The sex ratio of males to females was found to be 1:1.32. The number of mature females in the sample was 20 (80%), the smallest mature females were 452 mm FL in both stage 2 and 3 classes while the largest mature female was recorded at 1,005 mm FL (stage 3). The number of mature males in the sample was 18 (95%), the smallest mature male was recorded at 445 mm FL (stage 2) while the largest mature male was found to be 914 mm FL stage 3.In the 51 South African samples, the smallest mature male was 520 mm FL. All males above 820 mm FL were mature. The smallest mature female observed in this study was 520 mm FL. All females above 780 mm FL were mature. The sex ratio of males to females was found to be 1:1.15.p < 0.001), with length-at-50%-maturity (Lm50) estimated to occur significantly smaller at Vema with 395.1 mm FL (p < 0.001) and Walter Shoal with 473.2 mm FL (p < 0.01) than in South Africa with 551.2 mm FL  and the largest mature female was 815 mm FL, also stage 3. The number of mature males in the sample was 10 (45%). The smallest male was 530 mm FL (stage 2) and the largest male was 844 mm FL (stage 2). Analysis of deviance found that locality explained significant variation in female length-at-maturity (20) as opposed to the ‘Oasis’ hypothesis21.Despite the species’ movement behaviour and dispersal potential, our results revealed the following: (1) the existence of genetically and phenotypically distinct populations of yellowtail associated with the South African continental shelf and the two seamounts in the Atlantic and Indian oceans; and (2) supports the ‘Island’ hypothesis  and Bayesian clustering analysis supported the existence of three distinct genetic clusters corresponding to South Africa, Walters Shoal and Vema seamount.Overall estimates of genetic variability were significantly different between the three main sampling regions and significant pairwise F22. Retention and dispersal of sedentary species is largely a function of passive drift of eggs and larvae, limiting connectivity between seamounts24. On the other hand, mobile species such as yellowtail are theoretically able to move between seamounts as juveniles and during their reproductive phase25.Marine animal populations are linked to each other via dispersal of individuals as eggs, larvae, juveniles or adults, a process known as connectivity. The extent of dispersal and connectivity is determined by several factors, including oceanographic and hydrological features as well as the reproductive strategy of the species26 could result in dispersal over hundreds of kilometers along major ocean currents, resulting in high gene flow over a large distance. Walters Shoal is occasionally swept by mesoscale eddies originating from the East Madagascar Current, one of the Agulhas Current’s tributaries. The Agulhas Current flows in south-westerly direction along the edge of the continental shelf of the African continent. Near 36°S at the Agulhas Bank, it separates from the continental shelf and turns eastwards in an anticyclonic loop, named the Agulhas retroflection by Bang27. It then flows back into the South Indian Ocean as the Agulhas Return Current. Periodically the loop of the retroflection forms an eddy, which is ejected into the South Atlantic Ocean28. The Vema seamount is in the path of these Agulhas rings30. These oceanographic features provide a mechanism for passive transport of propagules and juveniles from Walters Shoal to Vema, bypassing the South African coastline. This would also explain some of the genetic admixture observed around Cape Point which is the closest 'exit point' for passively drifting objects before the uptake into the Benguela jet.A higher level of connectivity and thus gene flow was evident between the two seamounts. The passive dispersal of eggs and larvae (17 days of preflexion) and the movement of juveniles via flotsamPseudopentaceros wheeleri), the alfonsino (Beryx splendens), black oreo (Allocyttus niger), smooth oreo (Pseudocyttus maculates), orange roughy (Hoplostethus atlanticus) and offshore rockfish (Pontinus kuhlii)36. On the contrary, genetic heterogeneity or differentiation between seamount and continental populations has been identified in only a few species of fish, 37. Mitochondrial DNA and microsatellite analyses of black sea bream (Pagellus bogaraveo) populations between slopes of the Azores Islands in the North Atlantic revealed low to moderate (but significant) genetic differentiation with evidence for a recent bottleneck, perhaps due to the impacts of fishing38. In the patagonian toothfish (Dissostichus eleginoides), although the mitochondrial DNA marker 12S rDNA failed to reveal genetic structure39, differences in microsatellite frequencies were evident between seamount and non-seamount populations.Connectivity between seamounts is a key element affecting the degree of isolation or similarity of seamount populations. Studies on the genetic population connectivity among commercially important seamount and slope fish populations have however provided varied results. Most studies report genetic homogeneity between seamount and continental and oceanic island slopes including species such as the northern Pacific pelagic armourhead  have been reported to be the preferred food source around coastal South Africa in previous studies41 and studies suggest that small pelagic fish are of higher nutritional value than demersal fish and crustaceans42, the latter two being more prevalent in the diet of the seamount associated samples.The Benguela current system is one of the largest upwelling zones in the world with highly productive waters43. A trade-off between early maturation, with fewer numbers of offspring with shorter generation length, and later maturation, with increased viability and number of offspring and longer generation length, depending on mortality and access to resources can explain inter- and intra-species variation in these traits45.We found significant differences in growth rates, represented as length-at-age, as well as in the length-at-maturation among the three sampling localities. Life history theory assumes adaptive plasticity in age and size at maturity46. Delayed maturity is expected to reduce sub-adult mortality and promotes sustained higher growth performances, as no energy needs to be invested into gonad development. As female body mass and fecundity increase exponentially with size, the advantage of this strategy has been suggested to increase the total egg production capacity (fitness) per spawner47. The largest size at maturity found in Walters Shoal yellowtail is in line with the assumptions that maturation can be delayed when offspring viability needs to be enhanced. Walters Shoal, due to the high temperature and oligotrophy of the surrounding waters possibly represents an environment that requires more larval resilience. Vema yellowtail on the other hand matured early and were of the smallest size at age. As food restrictions are unlikely an issue at Vema, the observed pattern might be best explained by increased fishing mortality. Selection for early maturation as well as smaller size at age is thought to be common as fisheries often remove larger fish48. Vema has been under heavy fishing pressure since its discovery in 1957 and a population of the spiny lobster Jasus tristani had been fished to local extinction within less than ten years of its discovery50. Reported catch of yellowtail at Vema by tuna directed commercial fishing vessels is substantial  and IUU fishing is thought to be considerable. The fish sampled off South Africa have the largest length at age and intermediate size at 50% maturity, representing an intermediary position in the trade-off envelope with highest growth due to the most consistent food availability, combined with moderate fishing mortality.Life history theory predicts trade-offs among growth, maturation and survivalThis study represents one of only a few genetic studies to include samples from these remote seamounts, and is the first for an actively dispersing species. The findings suggest that seamounts can harbour unique populations of commercially fished species distinct from populations associated with continental shelf areas. Differences in life history patterns can largely be explained by phenotypic plasticity related to different environmental conditions and mortality rates, but in combination with the observed genetic differences our findings are in agreement with the ‘Island’ Hypothesis that yellowtail exist in genetically and phenotypically distinguishable populations. Our findings imply that these populations might need separate assessment, management and conservation.50. Walters Shoal  seamount is an isolated seamount found in the Indian Ocean on the Madagascar Ridge. Its location is approximately 1700 km east of South Africa and 850 km south of Madagascar  is situated in the South Atlantic Ocean approximately 950 km west of South Africa  land based observer programme (see fig. 1 in Swart18). Walters Shoal was sampled during a dedicated, month-long scientific expedition on the RV Algoa in May 2014 when fish were caught by hook and line. Only material from dead animals was used for further analyses. All collections were made under a “permit for the purposes of a scientific investigation or practical experiment in terms of section 79 of the Marine Living Resources Act 1998” issued by the then Department of Agriculture, Forestry and Fisheries, South Africa. All methods were carried out in accordance with relevant guidelines and regulations.Samples from Vema were obtained in May 2015 from a pelagic fishery operating occasionally around the seamount. South African yellowtail samples were collected by hook and line fishing during dedicated research outings during austral summer 2011 and 2012 between Dassen Island and Struis Bay. Further genetic samples were obtained from across the South African distributional range of The samples were frozen until processing in the laboratory. Upon defrosting all specimens were measured to the nearest mm fork length (FL) and total length (TL) and weighed to the nearest gram. Gonads were weighed to the nearest gram and sex and stage of maturation was determined. Stomachs were staged in terms of fullness and weighed to the nearest gram after which the stomach contents were removed and frozen. Both otoliths were removed from the fish, cleaned and kept dry for further processing. In addition, fin clips were taken and stored in 90% ethanol for subsequent genetic analyses. Information on sample sizes for individual analyses are included in the methodology sections.19). The South African sample sites were included in a previous study investigating the global and regional population structure of the species19.In this study, a total of 201 individuals from South Africa (SA) as well as samples from Vema Seamount (V = 95) and Walters Shoal (WS = 55) were included for microsatellite genotyping. The South African samples comprised Port Elizabeth (H) = 37, Struis Bay (G) = 55, Cape Point (E) = 50, Yzerfontein (C) = 29 and North Blinder (B) = 30)  protocolS. dumerili52 were successfully amplified by PCR53 and genotyped in the seven sampling populations . Fragment analysis was performed by capillary electrophoresis on the ABI 3100 automated sequencer. Alleles were scored using MICROSATELIGHT v154.Six microsatellite markers previously published for 55.MICRO-CHECKER v2.2.3 was used to test for the presence of null alleles, genotyping errors or other reasons for deviations from HWE, such as heterozygote deficiencyS. lalandi samples was characterised for the two seamounts and the five locations along the South African coastline19. Genetic diversity estimates of allele frequencies, observed- (Ho) and unbiased expected heterozygosity (He) were calculated with GENETIX v4.0356. Fstat v2.9.357 was used to determine the number of alleles per locus and allelic richness based on a minimum sample size of 29. The inbreeding coefficient, FIS was calculated by Fstat and deviations from Hardy–Weinberg equilibrium (HWE) were tested with ARLEQUIN.The population genetic variation for 58. This analysis simulates multiple populations that have diverged to predefined true levels of divergence (FST), and determines the power of a dataset with sample sizes, number of loci, and allele frequencies equal to the present study to differentiate populations. Simulations included true levels of divergence (FST) that ranged from 0.002 to 0.049, and significance for both Fisher’s exact test and χ2 tests from 1000 replicates. Three simulations were conducted with varying effective population size .The statistical power of the microsatellites to differentiate populations were evaluated and conducted in POWSIM v4.1ST60  and Nei’s (1978) genetic distances61 were calculated with GENEPOP v4.062 using 1000 permutations. In addition, pairwise G”ST63 and Jost DEST64 were calculated with GenAlEx 6.566 (999 permutations) between the three main sampling regions. To visualise genotypic partitioning, discriminant analysis of principal components (DAPC)67 was carried out using ADEGENET68 in R 3.0.269. To test for population differentiation, a hierarchical AMOVA was carried out in ARLEQUIN19.Pairwise F70. For the analysis on the entire dataset, number of clusters (K) was tested between 1 and 6. For each K, 10 runs were performed consisting of 10 million Markov chain Monte Carlo (MCMC) iterations with a burn-in of 100,000. K was calculated assuming admixture and correlated allele frequencies71. The statistic ΔK calculated by STRUCTURE HARVESTER v0.672 was used to estimate the true number of clusters73. For the selected K value, the individual membership coefficient was assessed to infer the probability of a certain individual belonging to a specific cluster. CLUMPP v1.1 was used to average the runs and produce the admixture (Q) matrix74. DISTRUCT v1.1 was implemented to visualise the membership coefficients of the individuals within each population75.Bayesian clustering analysis of populations was performed with STRUCTURE v2.1FO) for each prey item was calculated as the number of stomachs that particular prey item was found in divided by the total number of stomachs examined.Stomachs  were defrosted and the contents extracted by opening the stomach and squeezing out the contents. The numbers of stomachs with food present or absent were recorded to determine the coefficient of repletion (percentage of stomachs containing food). Upon visual inspection, the contents were separated into individual components where possible. For the identification of fish, cephalopods and mollusc prey items, the otoliths and beaks and shells were collected, respectively, after initial visual examination of partially digested specimens was deemed inconclusive. Only, crustaceans could be identified from largely complete exoskeletons. Where necessary, specimens were examined under a dissecting microscope and identified to the lowest possible taxon using identification guidebooks and experts. Due to the variation in stomach fullness and regurgitation rates among sites, comparisons involving prey weight such as the index of relative importance for specific prey species were not feasible. The frequency of occurrence (FL) and weighted to nearest gram. Morphometric relationships were determined by plotting the FL of the fish against their corresponding weights. The values for the growth parameters a and b were determined by log transformation and linear regression of the form:All fish sampled (345) were measured to the nearest mm fork length (K) was determined as follows76:The condition factor  was therefore only applied to specimens from the representatively sampled 500–700 mm size classes across the three locations.The majority of specimens (74%) fell within 500 and 700 mm size classes, which were representatively sampled for all three locations, whereas smaller fish and very large fish (> 800 mm) were underrepresented in samples from the two seamounts. For appropriate statistical comparison of differences in Age and Location.The right otolith of a pair was used for age determination. Whole otoliths set in black plastic trays were viewed and imaged under a Nikon dissecting microscope at 8–16 × magnification with reflected light. Images were taken and three readers examined the samples independently after which the data was collated and compared. The accepted age was the age for which two or three readers were in agreement. As the small sample sizes and the incomplete size spectrum of the samples precluded fitting a meaningful growth model, the sizes of yellowtail with 3 and 4 growth rings  were compared among locations by fitting a two-way ANOVA with the covariates 2-tests of changes in total deviation explained.Visual inspection of the gonads provided the sex and the stage of sexual maturity . The proportion of sexually mature males and females was determined by considering any individual at stage 2 or higher as mature. The statistical analysis) only included female data due to their higher relevance to spawning output of the populations. The maturity status of each female was transformed into a binary response variable by assigning 0 for immature and 1 for mature or maturing females. Due to the expected binominal distribution of the binary response, a GLM  with binomial distribution and a logit link function was fitted to test for statistical differences in length-at-maturity among the three locations, where location was treated as factor. Analogous to an ANOVA, analysis of deviance based on was conducted to test if the factor location explained significant variation in the data as judged by χSupplementary Information


Insertion/deletion polymorphisms (InDels), combined with all the desirable features of both short tandem repeat and single nucleotide polymorphism, have been used in archaeological and anthropological research, population genetics and forensic application.Thirty InDels in 530 individuals residing in the Tibetan‐Yi corridor  were genotyped using the Investigator DIPplex. Forensic parameters and allele frequency spectrum were calculated. Genetic relationships between the investigated populations and worldwide and nationwide populations were assessed based on both the allele frequency distribution and genotype data.The combined powers of exclusion were 0.9807 (Dujiangyan Tibetan), 0.9880 (Muli Tibetan), 0.9852 (Xichang Yi) and 0.9892 (Yanyuan Mosuo). The combined powers of discrimination were 0.999999999983 (Dujiangyan Tibetan), 0.999999999942 (Muli Tibetan), 0.999999999982 (Xichang Yi) and 0.999999999962 (Yanyuan Mosuo), respectively. The comprehensive population comparisons among worldwide and nationwide populations uniformly illustrated that the investigated populations have a genetically closer relationship with Tibeto‐Burman‐speaking populations and geographically adjacent populations.These 30 loci can be regarded as an efficient genetic tool in forensic individual identification and as a supplementary tool in paternity testing in Dujiangyan Tibetan, Muli Tibetan, Xichang Yi, and Yanyuan Mosuo. The genetic proximity between the four populations in the Tibetan‐Yi corridor and other populations is strongly correlated with the linguistic origin and geographical distance.  Genetic diversity and forensic characteristics were investigated in four Tibetan‐Burmese ethnic corridor populations. The 30 insertion/deletion polymorphism can be used for forensic personal identification in Dujiangyan Tibetans, Muli Tibetans, Xichang Yis, and Yanyuan Mosuos. The genetic proximity between the four populations and other populations is strongly correlated with linguistic origin and geographical distance. The Yi population lives primarily in rural areas of Sichuan, Yunnan, Guizhou and Guangxi, usually in mountainous regions. The Liangshan Yi autonomous prefecture is a typical one and there are 2 million Yi people in the region (https://en.wikipedia.org/wiki/Yi_people). The Mosuo is a small ethnic group living in Yunnan and Sichuan provinces with a population of approximately 40,000. The Mosuo speak Na, a Naish language, a member of the Sino‐Tibetan language family (https://en.wikipedia.org/wiki/Naish_languages). Owing to the affluent and diverse indigenous cultural characteristics, the Tibetan‐Burmese ethnic corridor has become a research hotspot among ethnologists, archeologists and anthropologists  and quantified using a Quantifiler Human DNA Quantification kit (Thermo Fisher Scientific) on an Applied Biosystems 7,500 Real‐time PCR System (Thermo Fisher Scientific) according to the manufacturer's instructions. The genomic DNA was then diluted to 2 ng/μl and stored at −20℃ until amplification.2.2The multiplex PCR amplification was performed on a ProFlex 96‐Well PCR System (Thermo Fisher Scientific) acting in accordance with the manufacturer's recommendations. PCR products were mixed with deionized Formamide and BTO 550, and then isolated and detected using the capillary electrophoresis on an Applied Biosystems 3,130 Genetic Analyzer (Thermo Fisher Scientific) with a POP‐4 polymer based on the manufacturer's instructions. Allele allocation was carried out using GeneMapper ID‐X software v 1.5 (Thermo Fisher Scientific) using the allelic ladder and the set of bins and panels provided by the manufacturer, and the DIPSorter freeware (Qiagen) was used to validate the genotyping results.2.3K = 2 to 7. The Structure results were visualized in CLUMPP version 1.1.222 , probability of exclusion (PE), probability of match (PM) and the polymorphism information content (PIC) were calculated using the online tool of STRAF , we further studied the allele frequency differences of 30 forensic markers among our four studied populations and 96 previously published worldwide populations. Figure 3.2We computed Nei genetic distance among 100 populations based on the allele frequency distribution to explore the population's genetic diversity Table . The smaGenetic distance is an effective measurement to reveal genetic divergences between populations. The heatmap based on different genetic distances similarly indicated that a large genetic distance exists among intercontinental populations, while a small genetic distance exists in populations residing in the same continent. The above‐mentioned results further validated the earlier reports that genetic difference exists in intercontinental populations and genetic affinity exists in within‐continental populations  share the common ancestry components with both Asians and populations out of Asia. From K = 3–4, 9,317 individuals subsequently segregate into four continental groups of African, Asian, European and North American. Multiple sources of ancestry in Turkic language populations are also uncovered, which may be a result of the trade routes of the Silk Road between West Eurasia and East Asia or the discrimination power of this panel. Although the 30 InDels can distinguish ancestries of admixed populations in some degree, more effective AIMs need to identify ancestry contributions of admixtures in the next studies. At K = 5, a Tibetan‐dominant ancestry component can be identified in the Tibeto‐Burman‐speaking populations and a Han Chinese‐dominant ancestry component can be observed in Tai‐Kadai, Hmong‐Mien and Sinitic‐speaking populations. As the values of K continue to increase, Tibetan, Muosuo, and Yi residing in the Tibetan‐Yi corridor begin to show different patterns of genetic affinity with adjacent populations. The results indicated that our investigated populations share more genetic components with other Tibeto‐Burman‐speaking populations than other adjoining Asians. This is probably explained by the earlier finding of huge population migration from the surrounding lowland onto the Qinghai‐Tibet Plateau through the Tibetan‐Yi corridor since the initial formation of Tibetans in Neolithic time  have genetically closer relationships with other Tibeto‐Burman‐speaking populations and geographically close populations. The population genetic comparisons also reveal that genetic proximity is strongly correlated with linguistic and geographical boundaries. Our population comparison analyses only reflect a limited aspect of the genetic relationships between Tibetan‐Burmese ethnic corridor populations and worldwide populations. Further studies based on the high‐density InDels needed to be conducted to increase our knowledge of the genetic relationships and evolutionary history of Tibetan‐Burmese ethnic corridor populations.4In summary, we characterized 30 InDels in four typical Tibetan‐Burmese ethnic corridor populations of Dujiangyan Tibetans, Muli Tibetans, Xichang Yis, and Yanyuan Mosuos. High CPD values observed in this study indicate that 30 InDels can be used in forensic personal identification, while the relatively low CPE values suggest that these loci can be used as supplementary genetic markers in paternity testing. We also observed 20 InDels  with high allele difference in different continental populations, which demonstrate that the above 20 markers can be used as candidates of AIMs for continental population history reconstruction and forensic bio‐geographical ancestry inference. The comprehensive population comparisons based on the Fst, three different genetic distances, PCA, MDS, N‐J tree and Structure uniformly illustrate that the four populations of the Tibetan‐Yi corridor have genetically closer relationships with other Tibeto‐Burman‐speaking populations and geographically close groups. Genetic affinity and continuity among them indicate that the Tibetan‐Yi corridor is characterized by long‐term population admixture and genetic uniformity with highland adaptive Tibetans. We also demonstrated that genetic proximity strongly correlated with linguistic and geographical boundaries. Additional studies based on high‐density InDels are to be conducted to increase our knowledge of the genetic relationships and evolutionary history of Tibetan‐Burmese ethnic corridor populations.The authors have declared that they have no conflict of interest.ZW and YH conceived and designed the study. GH, LH and XC collected the samples. SW, JL and ZY prepared the materials. XZ and GH performed the experiments. XZ, GH, MW, and FW collected and analyzed the data. XZ wrote the initial manuscript and ZW, GH and YH revised the manuscript. All authors reviewed the manuscript.Samples were collected with the approval of the Ethics Committee of Sichuan University, China. This study was approved by the Ethics Committee of Sichuan University, China. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


The New World screwworm (NWS) fly is a pest related to economic impacts in livestock breeding, mainly because females lay their eggs in wounds and natural cavities of warm-blooded vertebrates. A direct way to control this pest is the sterile insect technique (SIT), which is based on the release of sexually sterilized males to mate with wild females, resulting in the absence of new flies. The first step to the implementation of such program is to know how flies’ populations are structured, that is, if they can freely mate in the field or, otherwise, if they are impeded of mate due to spatial or temporal barriers. Here we investigated genomic polymorphisms to infer temporal structure in a population of the NWS fly from Uruguay in two consecutive years (approximately 18 generations). We found that this time was enough for this population to change its genetic composition, but it was not sufficient to prevent free mate among individuals from one year to the next. We also found signal that some of the polymorphisms are related to the response of the population to insecticide chemical control. This approach can be used in the future to estimate spatial barriers to geographically isolated populations.Cochliomyia hominivorax , is an economically important ectoparasite currently distributed in South America and in the Caribbean basin. The successful eradication of this species in USA, Mexico and continental Central America was achieved by a control program based on the sterile insect technique (SIT). In order to implement a genetic control strategy over the NWS fly’s current area of occurrence, first, it is necessary to understand the species dynamics and population structure. In order to address this objective, the spatial genetic structure of the NWS fly was previously reported in South America based on different genetic markers; however, to date, no study has investigated temporal changes in the genetic composition of its populations. In the current study, the temporal genetic structure of a NWS fly population from Uruguay was investigated through two consecutive samplings from the same locality over an interval of approximately 18 generations. The genetic structure was accessed with neutral and under selection SNPs obtained with genotyping-by-sequencing. The results gathered with these data were compared to estimates achieved with mitochondrial DNA sequences and eight microsatellite markers. Temporal changes in the genetic composition were revealed by all three molecular markers, which may be attributed to seasonal changes in the NWS fly’s southern distribution. SNPs were employed for the first time for estimating the genetic structure in a NWS fly population; these results provide new clues and perspectives on its population genetic structure. This approach could have significant implications for the planning and implementation of management programs.The New World screwworm (NWS) fly,  Population genomics is an area of considerable growth, accompanying the development of next-generation sequencing (NGS) technologies, with the potential to improve population studies . Many die), an important demographic parameter usually difficult to accurately estimate, can be more robustly assessed with temporal sampling and population genomic data [This approach, which combines the sampling of many individuals and the characterization of a huge number of markers, allows the development of temporal studies, where samples from the same population are taken at different times with the aim to observe temporal changes in allelic frequencies, offering insights about the species population dynamics and demography. Effective population size  , is an ectoparasite that causes myiasis in warm-blooded vertebrates, including species of economic importance, consequently leading to negative economic impacts in livestock breeding [The NWS fly, breeding . In thisbreeding . The spebreeding . This tebreeding .LcαE7 sequences). For this reason, the use of more informative molecular markers, such as SNPs obtained with the GBS methodology, could provide higher resolution in population genetic studies, allowing establishing boundaries  and management units.Several population genetic studies have been conducted in recent years in order to better understand the dynamics and distribution of the NWS fly (reviewed in ) with thIn the current study, we standardized the two-enzyme GBS protocol for the NWS fly and used SNPs markers to investigate temporal genetic changes in a Uruguayan NWS fly population at an ecological timescale. For a better understanding of the information provided by these markers we compared the obtained results with estimates based on mtDNA sequences and microsatellites for the same samples. Our study is the first to implement the GBS technique for this pest species, allowing its future implementation in spatial NWS fly population studies.NWS fly samples were collected from wounds and natural cavities of sheep and cattle from the locality of Cañas  in two distinct periods: February/March 2015 and March 2016, hereafter named as 2015-Sample and 2016-Sample, respectively. Genetic property was registered under SISGEN #A61EDD2. Larvae were collected from only one wound per animal to lower bias and were kept in absolute ethanol and freezer at −20 °C.GBS libraries demand high quality DNA and the best quality total genomic DNA was obtained by extracting one to three larvae per wound using the CTAB method , with anPstI–MspI . Barcoded adapters were ligated on the rare cut site  for each individual separately, and the common adapter was subsequently ligated on the common cut site  for all individuals, using T4 DNA ligase (NEB). The samples were separated by year and pooled in two sets, which were amplified by multiplexed PCRs using standard forward primer A and modified reverse primer C with the Q5®® high-fidelity DNA polymerase (NEB). PCR products were purified with the Agencourt Ampure XP beads , and the DNA amount was estimated with a NanoDrop UV spectrophotometer . Library quality was accessed with the evaluation of fragment sizes and the presence of adapter dimers in an Agilent BioAnalyzer 2100 at Life Sciences Core Facility (LaCTAD) from University of Campinas (UNICAMP). The two libraries were sequenced separately in an Illumina NextSeq 500 lane , using 150-bp single-end reads, at the IBTEC—UNESP .SNPs were obtained using the genotyping-by-sequencing (GBS) approach. Two separated libraries were constructed with the individuals from 2015-Sample and 2016-Sample, based on standard protocols ,5 with mprocess_radtags was used for raw data demultiplexing, removing low quality reads and with uncalled bases reads. Adaptors and restriction enzyme recognition sites were trimmed, and reads were filtered to consider only the initial 70 bp. We excluded 11 individuals after this process (five from 2015-Sample and six from 2016-Sample), due to the low number of reads . This cleaned dataset was the input of ustacks function to assemble de novo loci present in each individual and to SNPs calling . Then, the data from each individual from both samples were merged into a catalog of loci using the cstacks function , which contains all loci and alleles in the considered populations. Subsequently, each individual was matched against the catalog with sstacks. The function populations was executed for additional filtering considering (i) locus that must be present in both sampled years, (ii) only the first SNP of each locus, (iii) locus in at least 75% of individuals and (iv) a minimum minor allele frequency of 0.10.Stacks v1.48 de novo pipeline was used to demultiplex and perform SNP calling without a reference genome ,30. Firsp-value of 0.05. Loci putatively under selection were detected with Lositan [ST and expected heterozygosity (He) values from each locus and compares them with the neutral distribution to detect outliers  [ST, using 50,000 simulations, 99% confidence interval, infinite alleles mutation model and false discovery rate of 0.1%; (ii) the second run was conducted with the same parameters, except that only neutral loci from the first run were considered to recalculate the mean neutral FST; (iii) the third run considered all loci again and the neutral FST calculated in the second run. Outlier loci in this last run were inferred as under selection and consequently excluded from the dataset used in population genetic analyses. These outlier loci sequences detected by Lositan were blasted, mapped and annotated against the National Center for Biotechnology Information Search database (NCBI) using Blast2Go [Lastly, additional data filtering included pruning loci under Hardy–Weinberg disequilibrium and loci under selection from the two samples. Hardy–Weinberg tests were conducted in the web version of Genepop ,32 consi Lositan , which cressure) . The proressure) : (i) theBlast2Go  for geneBlast2Go  for furtThree mitochondrial DNA fragments were amplified according to the procedures described in : the B dindel) in the CR fragment was considered a single mutational step and recoded as a single position in the final alignment in Mega. Individual sequences of the three fragments were collapsed into haplotypes with Fabox . Sequences of the three fragments for each individual were concatenated in a unique haplotype for further analyses.CR, COI and COII sequences were independently aligned using ClustalX  and manuEight polymorphic microsatellite loci were amplified and genotyped according to the procedures previously described ,41. PCR Microsatellite alleles were genotyped by size using GeneMarker v2.4.2 . The preGenetic diversity and population structure analyses were conducted for each molecular marker in order to detect temporal changes in the NWS fly population from Cañas.o), expected heterozygosity (He) and Wright’s F-statistic (FIS) were estimated with the function populations available in the Stacks pipeline. Genetic differentiation between 2015-Sample and 2016-Sample was estimated with a nonhierarchical AMOVA with Arlequin v3.5 [find.clusters was used to determine genetic clusters, based on principal component analysis (PCA) retaining the maximum number of PCs and the Bayesian information criterion (BIC) to generate a graph of optimum K. This procedure produced a scatterplot and a smaller number of PCs were retained then, following recommendations in the package. Subsequently, the contributions of the alleles to the best clustering scenario was also computed, considering a threshold of 0.005.Filtered neutral SNPs were used to investigate nucleotide diversity. Observed heterozygosity , nucleotide diversity (π) and overall genetic structure  were estimated using Arlequin v3.5  with stao) and expected (He) heterozygosities with GenAlex v6.5 [IS with the web version of Genepop [The genetic variability of microsatellite loci was accessed through allelic richness (AR) with MSA v4.05 ; observelex v6.5 ; and est Genepop ,32. Each Genepop ,32, cons Genepop  and usin Genepop ,32. Gene Genepop , using 1 Genepop , using te) estimates were calculated using Jorde and Ryman method [For both SNPs and microsatellite data, two-sample effective population size (Nn method  in NeEstn method . This ann method ).Raggedness index (rg) was used to measure the adjustment to a population expansion model.The demographic history of the NWS fly samples was inferred from mitochondrial data with Tajima’s D  and Fu’sSequencing of both 2015 and 2016 GBS libraries yielded more than 135 Gb of raw data . After tMusca domestica (XP_011296241.1) and Stomoxys calcitrans (XP_013113360.1) . The locus 10,178 blasted with the highest similarity to Lucilia cuprina cadherin-related tumor suppressor (KNC26434.1) , which is known to be related to Bt-resistance in insects [Sarcophaga dux (QGJ03700.1) , Bactrocera oleae (XP_014086721.1) , Ceratitis capitata (XP_004523110.1) , Musca domestica (5ZWP_A)  and Lucilia cuprina (P42860.2) . Glutathione transferase is highly related to insecticide resistance in insects [SNP calling after successive filtering recovered 1137 loci in 52 individuals . From the 1137 loci 52 were in Hardy–Weinberg disequilibrium and 257 were detected as outliers . From these 257 outliers’ loci, only 50 were successfully annotated against NCBI database and were related to 171 Gene Ontologies (GOs); no information was found for the remaining 207 outlier loci. Most of the annotated sequences were associated with metabolic process (72.22%) when considering the category of biologic process, such as proteolysis (12%), regulation of transcription by RNA polymerase II (10%), positive regulation of gene expression (8%) and negative regulation of transcription (7%) . A small insects ,62. The ssor KNC234.1 (ideIS estimates (0.0981 for 2015-Sample and 0.0498 for 2016-Sample). Besides the 52 loci with deviations from Hardy–Weinberg equilibrium in both samples (previously excluded), 116 and 94 loci showed deviations in 2015- and 2016-Samples, respectively (data not shown).After all the filtering steps described above, the data matrix for downstream population analyses was represented by 828 neutral loci. Mean expected heterozygosities for both 2015- and 2016-Samples were similar, being somewhat lower in 2016-Sample (0.1317 for 2015-Sample and 0.1141 for 2016-Sample) and were slightly higher than the mean observed heterozygosities (0.1113 for 2015-Sample and 0.1044 for 2016-Sample), culminating in small positive FST = 0.01444, p < 0.05). Bayesian inferences of population structure based on neutral SNPs also indicated some population differentiation over time  and H2 (5/63) are shared between 2015- and 2016-Samples . The 201Haplotype network based on mtDNA concatenated sequences indicated that 2015- and 2016-Samples differ in haplotypes composition and frequency, although they share the two most frequent haplotypes . AdditioST = 0.27537, p < 0.001), which indicates that the NWS fly population from Cañas changed considerably from one year to another when the mitochondrial information is considered.AMOVA based on mtDNA data revealed genetic structure between 2015- and 2016-Samples (Fo ranging from 0.355 to 0.833 and He from 0.361 to 0.852 (p < 0.006) in the 2015-Sample. Otherwise, four out of eight loci significantly deviated from Hardy–Weinberg equilibrium in 2016-Sample after correction (p < 0.006), with CH15 and CH26 exhibiting heterozygote deficit  and CH05 and CH14 exhibiting excess of heterozygotes .Microsatellite loci showed a moderate to high degree of variability, with Hto 0.852 . No locup < 0.05 and 8 cases after Bonferroni correction (p < 0.006) . Bayesian inferences of population structure for microsatellite data also indicate population differentiation over time, revealing the presence of two genetic clusters (Tests for detecting genotypic linkage disequilibrium (LD) found significant association between pairs of loci only in the 2016-Sample, with 12 cases for < 0.006) . AMOVA fclusters D,E.e) based on SNPs and microsatellite data diverged about 3.5-fold: estimates based on SNPs indicated around 250 individuals, while those based on microsatellites indicated a Ne of approximately 70 individuals , indicating that we cannot reject the null hypothesis of population expansion.Neutrality tests based on mtDNA were statistically significant only in one case: Tajima’s D for 2016-Sample . The negC. hominivorax) using the two-enzyme based genotyping-by-sequencing (GBS) libraries for the first time. The GBS technique [e estimates based on SNP data  can be more accurate for our NWS fly sampling than microsatellite estimates . In spite of that, these results should be considered with caution, since accurate estimation of Ne and population demographic changes in a contemporary time scale (20 generations) is dependent on the number of samples and the targeted number of RADseq (or GBS) loci  [e estimates of approximately 70 and 250 individuals for microsatellite and SNP loci, respectively, is low compared to other insects. Estimates of Ne for Aedes aegypti across 17 geographic localities and time points resulted in values slightly different from the ones we found, considering the same method of Jorde and Ryman [e averaged 303.3, ranging from 25.0 to 1181.0, while for SNPs the average was 166.0, ranging from 22.9 to 549.2 [Single-nucleotide polymorphisms were hereby obtained for the NWS fly  , as welleclines) . Our Ne nd Ryman : for micto 549.2 .Wounded-host availability, extensive insecticide use by farmers, seasonality and climate are important factors affecting yearly population size of the NWS fly in its southernmost distribution. We consider two likely hypothesis for NWS fly populations dynamics in Uruguay: (i) during winter unfavorable conditions, characterized by cold temperatures and low precipitation, the species migrates to northern latitudes and recolonize the southern region when climate became favorable; alternatively (ii) during the winter the NWS fly populations are reduced, surviving in local refugees like riverine forests and when climate became favorable the populations expand. This cyclic phenomenon of population size changes based on annual seasons is well known for many insect species . HoweverThe three investigated molecular markers, SNPs, mtDNA and microsatellites, indicated temporal genetic structure between two consecutive years for the NWS fly populations from Cañas, Uruguay. However, SNP data variability showed no drastic change from one year to the next: a similar number of loci showed deviations from Hardy–Weinberg equilibrium and mean observed and expected heterozygosity were similar. Besides that, population structure analyses indicated differentiation between years. SNPs are bi-allelic and expected to be less variable than microsatellites . DespiteC. hominivorax [Bt-resistance in insects and, despite not being common for C. hominivorax control, it was demonstrated that Bacillus thuringiensis (Bt) isolates have applicability for Calliphoridae species control, including screwworms . Possibly, our NWS samples were exposed to Bt in the field, since it is widely used for controlling sympatric herbivorous species, both as transgenic part of the genome of host plants and as sprays [Spodoptera frugiperda  Brazilian populations [Although we have not found any important contribution of loci under selection for our NWS fly samples genetic structure, we were still able to annotate some of them as associated with insecticide resistance, which is of interest since it represents a challenge to species control. One of these genes is related to organophosphate metabolic process, considered the main insecticide resistance mechanism for inivorax ,73. Anots sprays ,75. We as sprays ,64. Annos sprays . Accordiulations . NeitherC. hominivorax samples demonstrate to be a potential powerful marker for both spatial and temporal population genomic studies for this species. Additionally, these markers allow the detection of loci putatively under selection that can be associated with insecticide resistance and other important functions for insect pest control.Despite limitations due to the low number of samples, SNPs obtained by GBS from


Bothrops insularis, the golden lancehead, which is endemic to Ilha da Queimada Grande, coast of São Paulo State, Brazil. This study aims to (a) characterize the genetic diversity of ex situ and in situ populations of B. insularis using heterologous microsatellites; (b) investigate genetic structure among and within these populations; and (c) provide data for the conservation program of the species. Twelve informative microsatellites obtained from three species of the B. neuwiedi group were used to access genetic diversity indexes of ex situ and in situ populations. Low‐to‐medium genetic diversity parameters were found. Both populations showed low—albeit significant—values of system of mating inbreeding coefficient, whereas only the in situ population showed a significant value of pedigree inbreeding coefficient. Significant values of genetic differentiation indexes suggest a small differentiation between the two populations. Discriminant analysis of principal components (DAPC) recovered five clusters. No geographic relationship was found in the island, suggesting the occurrence of gene flow. Also, our data allowed the establishment of six preferential breeding couples, aiming to minimize inbreeding and elucidate uncertain parental relationships in the captive population. In a conservation perspective, continuous monitoring of both populations is demanded: it involves the incorporation of new individuals from the island into the captive population to avoid inbreeding and to achieve the recommended allelic similarity between the two populations. At last, we recommend that the genetic data support researches as a base to maintain a viable and healthy captive population, highly genetically similar to the in situ one, which is crucial for considering a reintroduction process into the island.Information on demographic, genetic, and environmental parameters of wild and captive animal populations has proven to be crucial to conservation programs and strategies. Genetic approaches in conservation programs of Brazilian snakes remain scarce despite their importance for critically endangered species, such as  Bothrops insularis, an endemic critically endangered Brazilian snake. We recovered low  and medium (heterozygosity) genetic diversity indexes, significant inbreeding coefficient for the in situ population and a slight genetic structure within and between populations. Based on these results, we suggest some strategies that aim to reduce extinction risks, minimize genetic divergence, and genetic loss.This is the first report focusing on the conservation genetics of  GeneticCanis lupus)  located 33 km off the coast of São Paulo State (24°30′S and 43°42′O) that is part of a conservation unit classified as “Área de Relevante Interesse Ecológico (ARIE) das Ilhas da Queimada Pequena e Queimada Grande” , aiming to develop a healthy population which could be used for either scientific researches or future reintroduction  investigate the existence of genetic structures among and within populations; and (c) provide data for the conservation program of the species.In this context, knowing that the access of genetic data of wild and captive populations is crucial for conservation programs, our study aims to: (a) characterize the genetic diversity of ex situ and in situ populations of 22.1B. insularis  were used to amplify DNA sequences of B. insularis samples (Appendix 2 (50 mM), 0.282 µl dNTPs (5 mM), 0.4 µl forward primers with a M13 tail (1 mM), 0.4 µl reverse primers (10 mM), and 0.4 µl of a M13 primer , 0.075 µl Platinum Taq polymerase , and 7.5 µl DNA template at a concentration of 60 ng/µl. Reactions were performed on a LifeEco Bioer Thermocycler as described by Machado  for all tests described herein were adjusted with a sequential Bonferroni correction  and the number of private alleles (PA) were calculated using POPPR. Allelic richness was obtained using HIERFSTAT v.0.44‐22 package Goudet, . Additio2.6is) evaluates the mating system in the population, and the inbreeding coefficient of the genogram or per kinship (fis) assesses the individual probability of exhibiting an allele that is identical by descent (IBD) due to its parental relationships  using the FSTAT v.2.9.3.2 software  in each population using ADEGENET. Significance of each index was obtained using bootstrap, with 1,000 permutations.Since both biological meanings offer different insights for conservation, we investigated the inbreeding coefficient for the mating system  the standardized analogues Fst, and (c) Dest. Though none of these indexes are considered an ideal summary statistic, combining them may increase accuracy in attempts to elucidate demographic and genetic population structure  the classical Wright's F Winter, . AMOVA wGenetic structure within and among populations was analyzed with two approaches: (a) the Bayesian clustering method applied in STRUCTURE v.2.3.4  to address the best allele model, and initial λ value to be used in the analyses  from one to six, with a burn‐in of 500,000, followed by 1,000,000 Markov Chain Monte Carlo (MCMC) steps, repeated 30 times. The most probable number of genetic clusters was investigated through the comparisons of the: (a) medium α values obtained for the 30 runs of each K; (b) the symmetry distribution of each cluster in the plot generated for each K; (c) the Prob (LnǀK) Method; and (d) the Evanno Method  was estimated from one to ten using the lowest value of the Bayesian information criterion (BIC) retaining 30 principal components (PCs) using ADEGENET. We increased the numbers of possible K to 10 to improve the visualization of the plot obtained for BIC values. Subsequently, we conducted an initial DAPC analysis with parameters of 25 PCs, three discriminant analyses, and assuming K number of genetic clusters obtained in the step above. Once bias became possible as a result of the excess of PCs used, the correct number of PCs  was accessed, and the results were validated via the a‐scored method.For the DAPC analysis, the number of clusters , evincing that the populations did not show a congruent pattern in HWE departure. However, the only locus that showed HWE deviation in both the ex situ and in situ populations was Bmat_106; therefore, it was removed from subsequent analyses to avoid bias.After sequential Bonferroni correction, nine loci  indicate a significant deviation from the HWE , with the lowest heterozygosity indexes in the locus Bmat_080 in both populations.The final 11 microsatellites showed similar values of genetic diversity indexes between the populations .We obtained negative and significant average values of F3.3Under the models tested, the results obtained showed no evidence of a recent bottleneck in the in situ population after Bonferroni sequential correction . However, it is worth mentioning that under the IAM model, nine out of 11 loci showed heterozygosity excess.3.4st = 0.07/CI = 0.01 < x < 0.13, Gst' = 0.12/CI = 0.05 < x < 0.20, Dest = 0.06/CI = 0.02 < x < 0.10), which was corroborated by AMOVA results . However, some loci show moderate to high values of genetic differentiation indexes —it seems to be related to both the existence of private alleles (Table Levels of genetic differentiation between the populations are presented in Table es Table .3.5K) Method recovered one genetic cluster  equal to zero among this possible father and the three offspring  its diversification process and phylogenetic relationships within the B. jararaca group , possibly as a consequence of a fixation process of the allele “193”  also revealed the existence of clusters with no geographic relationship, suggesting family lineages within representatives as a possible explanation. The authors proposed that the mixture of these clusters in the space could indicate the existence of gene flow  the isolation of the ex situ population, and (b) existence of recognized families within the wild population due to the low vagility observed . Besides, the gene flow detected based on the pattern distribution of the family lineages suggests that the island might also be considered a single conservation unit.The genetic divergence between populations could be explained by the existence of private alleles, which, associated with the founder effect of the ex situ population and random sampling of the in situ population, could lead to the low but significant genetic differentiation observed. STRUCTURE results suggest the existence of one population, while DAPC recovered five distinctive genetic clusters with no geographical correlation to the sampling areas. A recent study on the Cascade red fox  screening the loss of genetic diversity in the ex situ populations; (b) incorporating new representatives from in situ population as a method to improve genetic diversity and maintain genetic homogeneity between populations; (c) following preferential breeding pairs to avoid inbreeding and to guarantee equal genetic contribution of all founders; and (d) analyze possible impacts of outbreeding if translocations and reintroductions are stablished as conservation purposes of the ex situ population. In sum, this study is a preliminary contribution to the conservation program of B. insularis and might be used as initial mark for comparative in future genetic assessments, as well as in studies with ecology and reproduction for this and related species.The authors have knowledge about the content within this manuscript and declare the absence of conflict interest.Igor Salles‐Oliveira: Conceptualization ; Data curation (lead); Formal analysis (lead); Investigation ; Methodology (lead); Validation (lead); Visualization ; Writing‐original draft (lead); Writing‐review & editing (lead). Taís Machado: Data curation (supporting); Formal analysis (supporting); Methodology (supporting); Writing‐original draft (supporting); Writing‐review & editing (supporting). Karina Banci: Resources (supporting); Writing‐original draft (supporting); Writing‐review & editing (supporting). Selma Maria Almeida‐Santos: Resources (supporting); Writing‐review & editing (supporting). Maria José de J. Silva: Conceptualization (lead); Data curation ; Formal analysis ; Funding acquisition (lead); Investigation (supporting); Methodology (supporting); Project administration (lead); Resources (lead); Supervision (lead); Validation (supporting); Visualization ; Writing‐original draft (supporting); Writing‐review & editing (supporting).The completion of this project and sample collection were performed under license and authorization of the Ethics Committee on the Use of Animals from Instituto Butantan .AppendixS1Click here for additional data file.AppendixS2Click here for additional data file.AppendixS3Click here for additional data file.AppendixS4Click here for additional data file.AppendixS5Click here for additional data file.AppendixS6Click here for additional data file.AppendixS7Click here for additional data file.


Oratosquilla oratoria. Herein, we applied nuclear microsatellite markers to examine patterns and causes of genetic differentiation in this morphotaxon. Population structure analyses revealed two genetically divergent and geographically structured clades in O. oratoria, one dominating the temperate zone of the Northwestern (NW) Pacific and the other occurring in the subtropical and tropical waters where are influenced by the Kuroshio Current. Two sympatric zones, one around the Changjiang Estuary in China coast and the other in the northern Japan Sea, were demonstrated to be hybrid zones where introgressive hybridization occurred asymmetrically. The interaction between historical climate shifts and contemporary factors  may contribute to the present-day genetic architecture in the Japanese mantis shrimp. Range shift induced by climate changes and oceanographic factors may promote hybridization and gene flow between the O. oratoria complex. Our results provide insights into the interacting mechanisms that give rise to diversification and speciation of coastal species in the NW Pacific.The interplay between historical and contemporary processes can produce complex patterns of genetic differentiation in the marine realm. Recent mitochondrial and nuclear sequence analyses revealed cryptic speciation in the Japanese mantis shrimp  Historic disturbances have been recognized to play a major role in affecting the distribution and abundance, and in shaping the population structure of marine organisms . Plio-PlFST), likely due to temporal variability, vertical behavior in the open-ocean migration as well as dynamic oceanographic features such as freshwater outflow, mesoscale eddies, upwelling systems and fronts  and nuclear ribosomal internal transcribed spacer (nrDNA ITS) sequences  depict hybridization in the sympatric areas by studying the ancestry and interspecific heterozygosity of hybrids. Based on these analyses, we further discuss the potential factors driving complex genetic patterns and fine-scale admixture in the Japanese mantis shrimp.With regards to population subdivision of O. oratoria individuals were collected from 14 localities along the coast of China and Japan during 2013 to 2015 , observed heterozygosity (Ho) and expected heterozygosity (HE) for each locus and population using the Excel Microsatellite Toolkit (AR), which is a standardized index of the mean number of alleles per locus irrespective of sample size, was calculated with Fstat v2.9.3  and geographic (kilometers) distances as implemented in ARLEQUIN v3.5 (DA distance (K) and the admixture proportion (Q) of the individuals to the inferred group based on their allele frequencies. The simulations were conducted assuming an admixture model with correlated allele frequencies. The simulated K value was tested from 1 to 14 with 10 iterations at each level of K to verify the consistency of the results. To ensure the discovery of additional cryptical genetic groups, we further preformed STRUCTURE analyses on the six temperate resident populations for cluster models of K = 1–6, and the eight populations in the subtropical and tropical region for cluster models of K = 1–8, respectively. All analyses were run with a burn-in period of 100,000 steps and 1000,000 MCMC steps after burn-in. The optimal value of K that best fits the data was estimated using Evanno’s ΔK method  was performed with ARLEQUIN v3.5   was alsoUIN v3.5  to estimadjusted .K = 2. Individuals with less than 50% posterior probability (q) of having pure ancestry from the assumed population were considered as hybrids or migrants , the USEPOPINFO model which uses sampling locations to detect migrants or hybrids was applied to test the hypothesis of mixed ancestry for individuals in AO, IS and SS during the past two generations  as implemented in STRUCTURE. Two main clusters were defined according to the results of STRUCTURE, DAPC and UPGMA dendrogram, with one containing individuals from populations in the temperate region and the other containing those collected from the subtropical and tropical region. We ran STRUCTURE for 1000,000 iterations after a burn-in of 100,000 iterations and fixed the number of populations to migrants .Q > 0.9 from the STRUCTURE results for either group were used as reference for parental populations in hybrid index calculations. Values of the hybrid index refer to the proportion of alleles derived from parental population 2 for each admixed individual. Values of the interspecific heterozygosity provide the proportion of the individual’s genome with alleles inherited from both parental populations  over samples ranged from 3.6 for c32844 to 9.6 for c45952, and the average number of alleles per population ranged from 5.2 (AO) to 7.1 (BH). Allelic richness (AR) varied between 5.035 (AO) to 6.847 (BH). The average of observed heterozygosity (HO) across loci per population varied from 0.436 (AI) to 0.613 (QHD), and the average of expected heterozygosity (HE) across loci varied from 0.596 (QD) to 0.721 (BH). The population with the highest value of polymorphism information content was the population BH (0.675), whereas that with the lowest value was the population DL (0.537). Significant departure from Hardy-Weinberg equilibrium was detected for 54 out of the 140 locus-by-population tests after Bonferroni correction. No linkage disequilibrium was detected for populations across loci after Bonferroni correction.Genetic polymorphism in terms of average number of alleles, observed and expected heterozygosities varied depending on the locus and population . The aveO. oratoria populations according to the distribution of mtCOI haplotypes and nrDNA ribotypes: temperate region (including the Bohai and Yellow Seas as well as the northern Japan Sea), and subtropical and tropical region (including the East and South China Seas as well as the southern Pacific coast of Japan). Pairwise FST values between populations of different regions were high (0.081–0.239) and all statistically significant after Bonferroni correction, whereas most of the pairwise FST values between populations within each region was relatively low  than that occurring among populations within regions   .K = 2, K = 5 resulted in the highest ΔK , this relationship was not consistent across the range of O. oratoria  had diverged during the mid-Pliocene , suggesting the presence of isolation by distance. However, no significant correlation between genetic and geographic distances was observed for populations from the subtropical and tropical region . This pattern is not unexpected considering the oceanographic current systems of this region. The NW Pacific is dominated by the Kuroshio Current  and Tsushima Warm Current (TSWC)  . At the n ranges .O. oratoria , where the majority of individuals in the hybrid zones were characterized with broad ancestry levels and low interspecific heterozygosity, indicative of recombinant hybrid generations. The results are coincident with our recent study that the recombinant nrDNA ITS sequence and intra-individual ITS polymorphism demonstrated the occurrence of hybridization in O. oratoria  may account for the patterns of genetic differentiation between and within O. oratoria cryptic species. Specifically, range fragment associated with glaciation allows accumulation of mutations and subsequently gives rise to allopatric differentiation of O. oratoria. The contemporary oceanographic barriers prevent gene flow between the southern and northern group in O. oratoria and facilitate the maintenance of the historical patterns. In addition, rang shifts induced by climate changes and oceanographic factors likely lead to contacts between the two O. oratoria cryptic species, thereby increasing the chance for hybridization. These results can provide insights into the mechanisms in determining the distribution and genetic structure of coastal species and the biogeography of the NW Pacific, which is important for understanding how marine species evolve in response to rapidly changing environments.Our microsatellite results add to the evidence that 10.7717/peerj.10270/supp-1Figure S1(A) all 14 populations, (B) the six temperate resident populations and (C) the eight populations from the subtropical and tropical waters.Click here for additional data file.10.7717/peerj.10270/supp-2Figure S2In the case of China coastal populations, the population DF from the sympatric zone was excluded from the analysis.Click here for additional data file.10.7717/peerj.10270/supp-3Figure S3r = 0.028, P = 0.403), but significant for pairs between populations from the temperate region , from the China coastal waters , and from the Japan coastal waters . Circle dots indicated pairwise comparisons between populations within regions.This relationship is also not significant when pairwise comparisons were conducted between populations from the subtropical and tropical region  of having pure ancestry from the assumed population are shown, which can be considered as migrants or hybrids descendants.Only individuals with less than 50% posterior probability (Click here for additional data file.10.7717/peerj.10270/supp-7File S1Click here for additional data file.


One such rare species is Physaria filiformis (Brassicaceae), a threatened, winter annual plant species. The species has a naturally fragmented distribution, occupying three different soil types spread across four disjunct geographical locations in Missouri and Arkansas. The goals of this study were to understand: (1) whether factors associated with fragmentation and small population size  have reduced levels of genetic diversity, (2) how genetic variation is structured and which factors have influenced genetic structure, and (3) how much extant genetic variation of P. filiformis is currently publicly protected and the implications for the development of conservation strategies to protect its genetic diversity. Using 16 microsatellite markers, we genotyped individuals from 20 populations of P. filiformis from across its geographical range and one population of Physaria gracilis for comparison and analyzed genetic diversity and structure. Populations of P. filiformis showed comparable levels of genetic diversity to its congener, except a single population in northwest Arkansas showed evidence of a genetic bottleneck and two populations in the Ouachita Mountains of Arkansas showed lower genetic variation, consistent with genetic drift. Populations showed isolation by distance, indicating that migration is geographically limited, and analyses of genetic structure grouped individuals into seven geographically structured genetic clusters, with geographic location/spatial separation showing a strong influence on genetic structure. At least one population is protected for all genetic clusters except one in north-central Arkansas, which should therefore be prioritized for protection. Populations in the Ouachita Mountains were genetically divergent from the rest of P. filiformis; future morphological analyses are needed to identify whether it merits recognition as a new, extremely rare species.Understanding genetic diversity and structure in a rare species is critical for prioritizing both  As natural habitats continue to be lost and degraded as the result of anthropogenic stressors, many plant species are becoming increasingly threatened ,2. For tEffective conservation requires knowledge of biodiversity, and population genetics is particularly useful for conservation because it can provide information about biodiversity at a variety of hierarchical levels. Estimates of genetic diversity within populations of a rare species can be compared to those of closely related species with similar life history characteristics to identify populations that have experienced losses in genetic diversity through processes associated with small population size and fragmentation, such as inbreeding, genetic drift, and genetic bottlenecks –11; suchPhysaria filiformis (Rollins) O’Kane & Al-Shehbaz , a threatened, herbaceous plant species with a winter-annual life history strategy [P. filiformis is endemic to Missouri (MO) and Arkansas (AR), U.S.A. and occupies sparsely vegetated, glade habitats with shallow soils and exposed bedrock [P. filiformis is fire maintained; when populations of P. filiformis are managed with controlled summer or early autumn burns, they often produce denser and larger flowering populations, although the response likely varies depending on the timing, frequency, and intensity of fire [P. filiformis are small in stature and are easily over-topped and outcompeted by other grass and forb species [P. filiformis has an annual lifecycle, populations demonstrate large year-to-year fluctuations in population size, associated not only with fire but also likely with a range of ecological and climatic conditions [In this study, we focused on understanding genetic diversity in strategy ,38. P. f bedrock –40. P. f of fire . Fire-su species . Becausenditions . In addinditions .P. filiformis was listed as Endangered under the U.S. Endangered Species Act (ESA) in 1987, at which time it was known from only nine sites in southwestern MO [P. filiformis was down-listed to threatened under the ESA in 2003 [stern MO ,43. Subs in 2003 . Current in 2003 . The fin in 2003 . The soi in 2003 . WhetherP. filiformis is now considered a potential candidate for de-listing. The criteria for de-listing outlined in the U.S. Fish and Wildlife Service’s (USFWS) recovery plan for Missouri bladderpod are the protection of 30 scattered, self-sustaining populations, 15 of which must be in public ownership [P. filiformis [P. filiformis have relatively high genetic diversity, consistent with its obligately outcrossing (self-incompatible) mating system [P. filiformis it is unknown whether current conservation efforts are adequately protecting genetic diversity in the species.Given the increase in the known number of populations, wnership . Howeverwnership , which hwnership . Also unliformis –49, theyg system ,47. HowePhysaria filiformis to inform conservation efforts. We developed 16 microsatellite markers for P. filiformis and used them to genotype populations sampled throughout the species’ range. We analyzed levels of genetic diversity in populations of P. filiformis, compared them to each other and to those of a close relative, Physaria gracilis (Hook.) O’Kane & Al-Shehbaz, to detect whether populations may have experienced losses in genetic diversity, and investigated genetic structure. The specific goals were to: (1) measure genetic diversity within populations and identify whether any factors associated with small population size, such as inbreeding, genetic drift or genetic bottlenecks, may have reduced levels of genetic diversity, (2) understand genetic structure and the factors affecting the structuring of genetic variation in P. filiformis, and (3) understand how much genetic variation of P. filiformis is currently publicly protected and develop in situ and ex situ conservation strategies to protect the full range of genetic diversity.The main goal of the study was to understand genetic diversity and structure in P. filiformis (Rollins) O’Kane & Al-Shehbaz is a winter annual plant in the Brassicaceae family that forms rosettes in the late fall or early winter, develops potentially numerous stems that bear flowers in the spring, and then completes its life cycle by June [P. filiformis are round capsules ~1/8 inch in diameter (hence the common name bladderpod) that contain four small seeds, which are gravity or water-dispersed [ by June ,40. Stem by June . The spe by June . Previou by June ). The frispersed ,39,40.P. filiformis from throughout its known geographic range. All necessary private landowner permission and collection permits  were obtained prior to collection. Whenever possible, we sampled 24 individuals per site, although fewer individuals were sampled in three sites with smaller population sizes  in Texas to understand how levels of genetic diversity in P. filiformis compared to a closely related congener. Although the phylogenetic relationship between P. gracilis and P. filiformis is unknown (no published phylogeny has included both species), we included P. gracilis because it is a morphologically well-differentiated congener and has the most geographically proximal distribution to P. filiformis. The final data set comprised a total of 480 individuals, with individuals sampled from 20 populations of P. filiformis and one population of P. gracilis. Leaf tissue was placed into silica gel for subsequent DNA extraction. Silica-dried leaf tissue was brought back to the lab, where DNA was extracted from each of the 480 individuals using a CTAB protocol [In 2015 and 2016, we sampled individuals from 20 populations of on sizes . When sa14 sites ; Fig 1. protocol .P. filiformis (from the AR01 and MO06 populations) was prepared for DNA sequencing using Nextera DNA sample prep kits and Nextera index kits (Illumina) and sequenced using 2×150 bp reads an Illumina MiSeq. The MiSeq run of the two samples resulted in 1,734,021 and 1,096,693 paired-end reads, which were deposited in GenBank (SRA bioproject number PRJNA675628). Reads were trimmed and assembled de novo into 601,797 contigs using the Medium sensitivity/fast setting using Geneious version 7.1.9 (Biomatters Inc.). Microsatellites were identified and polymerase chain reaction (PCR) primers were designed using MSATCOMMANDER [We used shotgun sequencing of genomic DNA to identify microsatellites following the protocol described in Swift et al. . BrieflyOMMANDER  and PRIMOMMANDER ,53. We aOMMANDER . MSATCOMP. filiformis listed above using protocols described in Swift et al. [2, 4.5 pmol each of the reverse primer and one of four fluorescently labeled M13 primers , 0.18 pmol of the M13-tagged forward primer, and 0.5 mM of each dNTP. PCR temperature cycling conditions were as follows: (I) 3 min at 94°C, (II) denaturation for 30 s at 94°C, (III) annealing for 30 s at 52°C, (IV) extension for 45 s at 72°C, (V) 35 repetitions of steps 2–4, and (VI) a final elongation at 72°C for 20 min. In this initial screening, 24 loci that amplified in both individuals of P. filiformis were selected for further testing in 8 individuals sampled from across the range of P. filiformis . We selected 16 polymorphic loci with consistent amplification and easily scoreable peaks . Private alleles were calculated using GenAlEx [F) within each population. We employed the Bayesian MCMC approach with 200,000 cycles, keeping every 200th update, and a burn-in period of 20,000 cycles to calculate the percent of null alleles and a revised F (FB) in each population. The TX01 population was missing genotypic information at two loci (PF84 and PF121), such that only 14 loci were analyzed for this population.We tested for linkage disequilibrium (LD) between pairs of loci in each population and for deviations from Hardy-Weinberg equilibrium (HWE) at each locus/population combination using Fisher’s exact tests in Genepop . Microsa GenAlEx . FSTAT v GenAlEx  was used GenAlEx . BecauseP. filiformis populations have undergone recent genetic bottlenecks, we used the program BOTTLENECK version 1.2.02, with the default parameters [To test whether any rameters . Becauserameters ,62, we trameters .FST for all possible pairs of populations, with 10,000 permutations to assess significance. Because FST values of highly variable markers, such as microsatellites, are constrained by population-level homozygosity levels and thus do not reach their maximum at 1 (see Hedrick 2005 for a more detailed explanation), we also calculated a standardized measure of pairwise population differentiation, G’ST [P. filiformis exhibited isolation by distance [To investigate patterns of genetic structure, we used Microsat Analyser to calculate pairwise on, G’ST  using thon, G’ST . We usedon, G’ST . Additiodistance . We calcdistance  genetic distance , and anadistance .P. filiformis without a priori grouping of individuals into populations, we analyzed the data using STRUCTURE version 2.3.4 [K) and assigned admixture proportions of each individual to these groupings. We used an admixture model, assumed correlated allele frequencies, used a flat prior of K, and did not incorporate population information into the analyses. We ran analyses following the recommendations of Gilbert et al. [K from 1 to 15, with a burn-in of 500,000 generations and a run length of 1,000,000 generations. We examined the groupings across all runs at each K to ensure that the results were consistent [K, we plotted the average natural logarithmic probability (-ln prob) of the data across multiple replicates of each K using structure harvester [K at which the (-ln prob) began to plateau. To determine priorities for ex situ conservation, we identified the populations within each STRUCTURE cluster with the greatest number of private alleles.To investigate patterns of genetic structure in on 2.3.4 . We varit et al. ; after pnsistent  using clnsistent ,73. To darvester , and selAnalysis of molecular variance (AMOVA) was conducted using Arlequin version 3.5.2.2  to underA) in each population ranged from 2.50–5.81  ranged from 1.87–3.23  averaged across all loci ranged from 0.31–0.51  ranged from 0.29–0.55 . The percentage of null alleles in each population averaged across all loci ranged from 0.01–0.16  were very close to zero, ranging from 0.02–0.09  showed significant evidence of a genetic bottleneck .P. filiformis. Pairwise FST analysis between all possible pairs of populations were significant for all but three population pairs after sequential Bonferroni correction  and those in all other locations  (P. gracilis (STRUCTURE was used to assess genetic structure without at K = 8 . Examinaclusters  by samplclusters . The genis (TX1) ; Table 1gracilis , consistgracilis .P. filiformis was found within populations (~50%). Less variation was partitioned among groups (~40%) and very little was found among populations within groups  in capturing among-population genetic variation. Regardless of how populations were grouped, the majority of the variation in n groups . When con groups ; the latslightly . Finallyslightly .P. filiformis were high. Levels of genetic diversity in P. filiformis were comparable or greater to those found in the congener P. gracilis. It is also worth noting that levels of genetic diversity in P. filiformis were similar to [P. filiformis had consistently high numbers of alleles, allelic richness, and observed and expected heterozygosity with few populations showing evidence of reductions in genetic diversity due to factors associated with small population size. Populations had inbreeding coefficients that were close to zero, consistent with the results of a previous pollination study [P. filiformis is self-incompatible and has a broad and generalist pollination system that is highly efficient at ensuring cross-pollination. Previous studies of P. filiformis also generally found high genetic diversity within populations and a lack of evidence for inbreeding [Overall, levels of genetic diversity in milar to  or greatmilar to –79 thoseon study  that conbreeding –49.P. filiformis, three populations, none of which have been the subject of a previous genetic analysis, showed evidence of reductions in genetic diversity from either a genetic bottleneck or drift. The lone population in NW-AR (AR06) showed significant evidence of a genetic bottleneck. Interestingly, the AR06 population was discovered recently and had not been managed, but the implementation of management practices such as land clearing and burning has recently resulted in an increase in population size , which may explain the observed bottleneck. In addition, the two OM-AR populations showed lower-than-average alleles per locus and allelic richness, which may be the result of random genetic drift eroding genetic diversity in these two populations. This is particularly notable because the OM-AR populations are very genetically divergent from the rest of P. filiformis and may represent a new, very rare species (see below); these results indicate that these populations may be in need of management to avoid further losses in genetic diversity.In contrast to the majority of populations of P. filiformis has a naturally fragmented distribution corresponding to the availability of glade habitats, which occur on rock outcrops that have a patchy distribution across the study area. Physaria filiformis is pollinated by a generalist cohort of insects and its seeds are gravity-dispersed [P. filiformis. For example, STRUCTURE divided individuals of P. filiformis into seven major clusters (with the eighth cluster comprised of P. gracilis), with each cluster encompassing populations occurring within a relatively small geographic area, particularly within SW-MO. The species also exhibited significant isolation by distance, indicating that migration is geographically limited [P. filiformis suggest that its insect pollinators and gravity seed dispersal are not only incapable of moving pollen or seeds across the distances that separate the four main population groups, but also between geographically proximal areas within SW-MO. Consistent with these results, a previous study on another glade-endemic plant in the region also showed very strong patterns of genetic structure [Genetic structure is created by limited migration among populations and genetic drift and different selection regimes acting across populations . Geneticispersed ,13, neit limited . The strtructure . AdditioFST and G’ST analyses also revealed that the OM-AR (AR01-AR03) populations were highly genetically divergent from all other populations of P. filiformis and are roughly as divergent from the rest of P. filiformis as the morphologically and ecologically distinct congener, P. gracilis. The STRUCTURE results and genetic distance tree also indicated that the OM-AR populations may be more closely related to P. gracilis than to P. filiformis, although additional phylogenetic analysis is needed to confirm this result. This was surprising, as the Ouachita populations are very morphologically similar to P. filiformis. Given the magnitude of the pairwise FST and G’ST values, it is highly unlikely that any recent gene flow has occurred between the OM-AR populations and the rest of P. filiformis. The OM-AR populations and the populations occupying the Ozark plateau are separated by the Boston Mountains and the Arkansas Valley, and these physical barriers appear to have prevented gene flow, as was found previously in other organismal groups distributed in both the Ouachitas and Ozark Plateau [Interestingly, the geographic distance separating the population groups may have resulted in cryptic speciation. The greatest among-population variation in AMOVA analyses was explained when populations were separated based on physiographic region, with the Ouachita populations separated from the remaining populations that occupy the Ozark Plateau . The gen Plateau .P. filiformis.Several factors suggest that the populations in the Ouachita Mountains may comprise a previously unrecognized, extremely rare, cryptic species. In addition to being geographically and genetically distinct, the populations in the Ouachita Mountains are ecologically divergent because they occupy sites with shale substrates, whereas the Ozark Plateau populations occur on calcareous substrates. Whether adaptation to substrate differences has contributed to the divergence among these groups is unknown, but merits future study. However, even though this putative new species can be differentiated based on substrate, geography, and genetics, we feel that it is important to be able to differentiate a species based solely on morphological observations; we are therefore currently conducting a morphological study to identify features that could be used to differentiate them from P. filiformis, because it would result in a decrease for P. filiformis in both the known number of populations and its geographic range size. Thus, determining the taxonomic status of this potential new species is critical for conservation of both it and P. filiformis. However, regardless of whether the Ouachita populations are recognized as a distinct species, they undoubtedly represent an Evolutionarily Significant Unit  and it P. filiformis is structured at an even finer geographic scale than represented by the four main geographic groups, particularly in SW-MO;, to protect as much genetic variation in the species as possible with limited resources, conservation efforts should therefore focus on conserving at least one population within each genetic group of P. filiformis. At least one population is publicly protected for six of the seven genetic clusters identified by STRUCTURE . However, even sites where plants were present invariably maintained smaller populations than those observed 5–10 years ago. It is possible that this reflects the normal year-to-year fluctuations in population size observed previously in P. filiformis and that environmental conditions will be favorable for P. filiformis in future years. However, it is also possible that this is indicative of an overall decline in the species in MO. To distinguish whether the species is declining or simply demonstrating normal population fluctuations, we recommend more frequent population monitoring to track population trends [P. filiformis, such as removing cedars and conducting late summer or early fall prescribed burns [Along with public protection of the NC-AR population group and additional seed banking of priority populations, we also recommend additional conservation actions for the species such as monitoring and land management. Through fieldwork to collect genetic samples, we found that n trends . The speed burns , would aS1 Fig(TIF)Click here for additional data file.S2 FigK = 8.Values begin to plateau at (DOCX)Click here for additional data file.S3 FigPopulations are separated by black lines. Each vertical line within a population represents an individual, the genetic clusters are represented by a unique color, and the proportion of membership of each individual in genetic clusters is indicated by the colors of each line. The four main geographic regions are indicated at the bottom of. OM-AR, Ouachita Mountains, Arkansas; NC-AR, north-central Arkansas; NW-AR, northwestern Arkansas; SW-MO, southwestern Missouri.(TIF)Click here for additional data file.S1 TableCollection information for each population sampled in the study, including the population identifier used in the study, county, geographic area, locality, latitude and longitude (with the number of coordinates truncated to protect the species), soil type, conservation status, and voucher information.(DOCX)Click here for additional data file.S2 TableIncludes primer sequences (forward and reverse), repeat motif, and the size range of alleles as determined in 8 individuals sampled from across the range of the species.(DOCX)Click here for additional data file.S3 TableP. filiformis and P. gracilis were conducted using five different models of evolution: The infinite alleles model (IAM), the stepwise mutation model (SMM), or the two-phase mutation Model (TPM) with 70, 80, and 90% of the loci assumed to be following the SMM. Values are the significance value of the one-tailed Wilcoxon signed rank test for heterozygote excess, with bold values indicating those that are significant (P<0.05).Analyses of (DOCX)Click here for additional data file.S1 FileData in single-row STRUCTURE format. The locus names are shown in the first row with all other rows showing data for each individual. The first column indicates the name of each individual with the names corresponding to the populations listed in (TXT)Click here for additional data file.