Brasenia schreberi J.F. Gmelin (Cabombaceae), an aquatic herb that occurs in fragmented locations in China, is rare and endangered. Understanding its genetic diversity and structure is crucial for its conservation and management. In this study, 12 microsatellite markers were used to estimate the genetic diversity and variation in 21 populations of B. schreberi in China. A total of 61 alleles were found; assessment of allelic richness (Ar = 1.92) and observed and expected heterozygosity  suggest lower genetic diversity compared to some endangered species, and higher variation was observed within populations (58.68%) rather than among populations (41.32%). No significant correlation between geographical and genetic distance among populations was detected , which may have likely resulted from barriers to gene flow (Nm = 0.361) that were produced by habitat fragmentation. However, Bayesian and neighbor-joining cluster analyses suggest a population genetic structure consisting of two clusters (I and II) or four subclusters . The genetic structure and distribution of B. schreberi in China may have involved glacial refugia that underwent range expansions, introgression, and habitat fragmentation. The findings of the present study emphasize the importance for both in situ and ex situ conservation efforts. Brasenia schreberi J.F. Gmelin, a basal angiosperm that belongs to family Cabombaceae , is a perennial aquatic plant. Similar to most aquatic plants, B. schreberi can reproduce sexually by outcrossing or asexually through rhizomes and winter buds  how the extent of genetic diversity is apportioned within and among populations of B. schreberi, and (2) the genetic structure and its association with geographical distribution. The findings of the present study may be utilized in conservation efforts on this species.Compared to other molecular markers, simple sequence repeat (SSR) markers have numerous merits, including co-dominance, high reproducibility, a relatively high level of polymorphisms, and are plentiful in the genome . SSR mar emersum , Zosteraa marina , Sagittaa natans , Zizaniaatifolia , 2017, N peltata , Isoetespsophila , Ruppia cirrhosa , Nuphar submersa , and Ottcuminata . Here, wB. schreberi in China were collected , one mM of each forward primer labeled with a fluorescent chemical and an unlabeled reverse primer, one U of Taq polymerase , and 10–30 ng of genomic DNA as template. PCR amplification was performed with a PTC-100 thermocycler  using the following program profile: 95 °C for 3 min; followed by 30 cycles of 95 °C for 30 s, annealing at 55–60 °C for 30 s (depending on primer), and 72 °C for 40 s; and a final extension at 72 °C for 10 min. The PCR products were separated via electrophoresis on 1.0% (w/v) agarose gels, stained with ethidium bromide, and observed under UV light. Then, the multiplex amplified PCR products were sequenced on an ABI prism 3730xl and sized using an internal DNA standard . The SSR fragments were visualized with GENEMAPPER v.4.0 .Total genomic DNA was extracted from silica-dried leaves using the modified cetyltrimethylammonium bromide method described by The loci were tested for Hardy–Weinberg equilibrium (HWE) and linkage disequilibrium using GENEPOP ver. 4.0 . DeviatiNa), the effective number of alleles (Ne), observed (Ho) and expected (He) heterozygosity, Shannon’s information index (I), F-statistics , and gene flow (Nm) were computed for each locus using POPGENE v.1.31 , NE, the number of private allele (AP), HO, HE, I and, the pairwise FST between each pair of populations were estimated using GENALEX ver. 6.0 (Ar) and inbreeding coefficients (FIS) were calculated using the diveRsity (Nm) among populations was calculated using the expression Nm = (1 − FST)/4FST (FCT), among populations within groups (FSC), and among individuals within populations (FST); these values, and the significance of each value, were tested using Arlequin ver. 3.5 /4FST . Analysiver. 3.5 .DA; DA and a pairwise matrix of geographic distance. Isolation by Distance Web Service ver. 3.23 (The neighbor-joining (NJ) tree was constructed using the software TreeFit  based oner. 3.23  was usedThe BOTTLENECK program  was usedK) was tested from K = 2 to 20 clusters based on assuming admixture, correlated allele frequencies. The analysis was performed for 10 iterations, with a burn-in of 80,000 replications, followed by 800,000 Markov Chain Monte Carlo replications. The most likely number of clusters was verified based on the ΔK method  and PIC values  were detected at all loci. The average FIS across all loci was 0.341. Nm per locus varied between 0.102 in locus BS09 and 1.393 in locus BS18 . A total of 61 alleles were detected across 12 SSR loci in 376 individuals from 21 populations . The numcus BS18 .Ar ranged from 1.49 to 2.58. HO and HE ranged from 0.093 ± 0.083 to 0.354 ± 0.137 and from 0.139 ± 0.056 to 0.398 ± 0.051, respectively. I ranged from 0.220 ± 0.081 to 0.660 ± 0.091. FIS ranged from −0.349 to 0.666.The average number of alleles per population ranged from 1.5 ± 0.151 (QYSYH) to 2.667 ± 0.284 (YTLHS). Six private alleles, distinctive to a specific population, were observed in population YTLHS, three were recorded in population LBMH, and two were recorded in population GDSYD, whereas other populations, including SZDSZ, SZLTS, YNTC, MSGH, CLHL, and TWYL, had a single private allele each . Ar rangThe Wilcoxon signed-rank tests revealed significant bottlenecks in 11 populations based on the IAM, SMM, and TPM assumptions, and normal models of distribution (L-shaped) for allelic frequencies were exhibited in other populations .K values were calculated to assess the optimal number of genetic clusters (K) in the population structure. Analyses using STRUCTURE gave the highest value for two distinct genetic clusters (K = 2) and a second possibility was K = 4; both of these displayed biological significance , Zhejiang Province , Jiangsu Province , Jiangxi Province (YTLHS), and Taiwan (TWYL). At K = 4, each cluster defined above were further split into two subclusters. The rest of the populations were from cluster II, which were also separated into two subclusters . Further population structure analysis indicated differentiation between two clusters (I and II) or among four subclusters .The Δificance . We analDA distances  was observed among populations.Analysis of molecular variation indicated substantial genetic variations among groups, populations, and individuals . Of the r = 0.0694; P = 0.7985), suggesting that genetic differentiation in populations might not be the result of isolation by geographic distance  compared with other aquatic plants but a remarkable genetic structure among populations. For example, using 10 SSR markers, HE = 0.610, range 0.572–0.636) across seven studied populations of Zizania latifolia from central China; HE = 0.351, range 0.125–0.578) in 183 individuals of O. acuminata sampled throughout the species distribution. Two genetic clusters were revealed in B. schreberi in China by both Bayesian clustering and NJ cluster analyses, but with low bootstrap supporting values in NJ dendrogram, which were probably as a result of low genetic variation existed among populations. Within each genetic cluster, two subclusters were also detected, and the major genetic subclusters generally coincided with geographical regions. Several factors such as long-term survival in glacial refugia with some population expansion or long-distance dispersal, introgression, and population fragmentation may have contributed to the population genetics structure of B. schreberi in China.Analyses of polymorphisms of 12 SSR loci in 376 individuals from 21 populations of FST = 0.413, P < 0.001) of B. schreberi in China. Furthermore, Mantel testing did not detect any significant isolation by distance , thus suggesting that geographic distance might not be the main factor causing genetic differentiation among B. schreberi populations. The significant genetic differentiation found in B. schreberi populations can probably be explained by its long evolutionary history in glacial refugia. The current populations of B. schreberi in China are mostly located in biodiversity hotspots and centers of endemism for plants in China; for example, populations YNGLG and YNTC, LBMH and MSGH, GDSYD, CLHL, YTLHS, XGLHT, and NXQZS are distributed in the eastern Himalayan region, the Hengduan Mountain region, and the Nanling region, respectively, three of the world’s biodiversity hotspots , suggesting that long-term isolation of F. engleriana populations among multiple refugia during the Pleistocene climatic changes might be the main driving factor for its population divergence. Castanopsis eyrei, a widely distributed tree of subtropical evergreen broad-leaved forests in China. In their study, both nuclear microsatellite and cpDNA data indicated high levels of population differentiation . They observed that the patterns of genetic differentiation between the extant populations of Castanopsis eyrei may have been affected by topographical differences between the mountainous western and lowland eastern refugia.Analysis of molecular variation indicated substantial among-population differentiation , whereas the rest of the populations in this cluster are located in Southwest and Central China. Specifically, populations NXQZS and XGLHT from the Southeast China region were subclustered with the populations from the Southwest and Central China regions . Population TWYL from Taiwan Island was included in cluster I despite being geographically separated from the other populations in the Nanling region . However, most of the subclusters coincided with specific geographical regions. For example, populations QYBSZ, QYSYH, SCHSY, and NDXFS from Zhejiang and Fujian provinces, with their short geographic distances, formed a subcluster. Similar geographical groups were found in populations HZTJH, HZXH, SZLTS, and SZDSZ from Zhejiang and Jiangsu provinces, and populations MSGH, GDSYD, CLHL, and YTLHS from Hunan, Guangdong, and Jiangxi provinces. The admixture pattern of populations of the two main genetic clusters of B. schreberi revealed in this study may be the result of long-distance dispersals. However, considering the highly fragmented modern populations of B. schreberi in China, we would not propose contemporary long-distance migrations of this species; instead, we suggest that the long-distance dispersal events are likely to represent historical migration events such as the Pleistocene interglacial/postglacial expansions from different refugia.STRUCTURE and NJ tree analyses indicated that Sagittaria trifolia  and subcluster II-2 . A high number of individuals from these two subclusters and from populations TWYL and CLHL contained admixed genotypes, with their Q values supporting membership in both clusters. The populations from the subcluster I-2 and subcluster II-1 locations and the populations TWYL and CLHL might be the result of northward or eastward expansions of the southern, western, or central China populations. Similar dispersals such as the northward expansion from the Nanling Mountains along the Luoxiao Mountains to Dabie Mountain and the north-eastward expansion from the Nanling Mountains to the mountains of eastern Zhejiang and Fujian provinces along the coastline are evidenced by the extensive distribution of woody deciduous tree, Sargentodoxa cuneata  region and 20 chloroplast non-coding regions in six individuals sampled from different populations in China  can be used to construct a more extensive phylogeographic history of B. schreberi in China, which could lead to more robust conservation strategies.Although great efforts have been made to reveal the population genetic structure of in China . Thus, t10.7717/peerj.5296/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.5296/supp-2Supplemental Information 2P < 0.1/ 0.05/ 0.001).*/ **/ *** Significant difference (Click here for additional data file.10.7717/peerj.5296/supp-3Supplemental Information 3The sequences of six tested individuals for each DNA region were listed below the figure.Click here for additional data file.


Micropterus salmoides has shown its extremely economic, ecological, and aquacultural significances throughout the North American and Asian continents, systematic evaluation of genetic variation and structure of wild and cultured populations of the species is yet to be documented. In this study, we investigated the genetic structure of M. salmoides from 20 wild populations and five cultured stocks across the United States and China using eight microsatellite loci, which are standard genetic markers for population genetic analysis. Our major findings are as follows: (1) the result of Fst showed largemouth bass had high genetic differentiation, and the gene flow indicated the genetic exchange among wild populations is difficult; (2) AMOVA showed that 14.05% of the variation was among populations, and 85.95% of the variation was within populations; (3) The majority of largemouth bass populations had a significant heterozygosity excess, which is likely to indicate a previous population bottleneck; (4) Allelic richness was lower among cultured populations than among wild populations; (5) Effective population size in hatcheries could promote high levels of genetic variation among individuals and minimize loss of genetic diversity; China’s largemouth bass originated from northern largemouth bass of USA. The information provides valuable basis for development of appropriate conservation policies for fisheries and aquaculture genetic breeding programs in largemouth bass.Although largemouth bass  Micropterus salmoides (Lacépède) is native to South-eastern United States3. As one of the top predatory species, it has shown its extremely economic and ecological significances throughout the North American Continent8. This freshwater bass had gained the loyalty of the estimated 50 million U.S. anglers at the beginning of this century9; thus, fingerlings and juveniles of this species are widely cultivated in public and private hatcheries to supply recreational fishing waters10. It has also been regarded as one of the dominated non-salmonid freshwater food fishes11.Largemouth bass 14; and two subspecies of this fish species, i.e. the Florida largemouth bass M. s. floridanus and the northern largemouth bass M. s. salmoides, occupied their native habitats from peninsular “Florida to throughout northeastern Mexico”, southeastern Canada, and the U.S. corridor in between15. The further divergence of the latter subspecies at northern and southern latitudes has been identified through genetic analysis19. Growth differences and environmental adaptability of these populations has been focused on by many researchers20; and the perceived superior growth characteristics of M. s. floridanus resulted in its fast and wide introduction to far north of its native range almost all over the United States22.It is universally acknowledged that the divergence of lineages within largemouth bass occurred due to geological changes during some geologic time26, Asia29, and Africa30 since the 19th century and gained access to a large area of habitats outside its native range; and it is playing greater role in freshwater fisheries and ecological systems of these regions.Largemouth bass with uncertain lineages have also been introduced to many countries in Europe16. Studies on four cultured populations of largemouth bass in Guangdong, China indicated that the stocks are northern largemouth bass depleted in genetic variation28. Actually, short-term allelic changes like this had been a concern for some populations within United States decades ago33. Recently, attention has been paid to allele persistence of largemouth bass. Johnson and Fulton finished comparison of the allele frequencies of three discriminate allozyme loci between Florida and northern largemouth bass and inferred that genes persisted through many generations20. What puzzles ecologists and culturists who are working with largemouth bass is that they are not sure about the taxonomy and genetic background when they are confronted with either a wild or hatchery produced largemouth bass population.A trio of factors, including arbitrary introduction, intraspecific hybrid, and environmental stress, makes it difficult to detect the impacts of human activity, especially aquaculture and recreational fishing, on the genetic diversity of largemouth bass. Philipp revealed the genetic distinctions among some largemouth bass groups were continually being diluted by introgression34. Although single nucleotide polymorphisms (SNPs) are becoming new tools in genetics and genomics studies, comparative analyses of SNP and microsatellite marker data for population genetic analysis in Diabrotica virgifera virgifera resulted in similar conclusions with respect to population structure35. Therefore, microsatellite should still be an invaluable tool for various population genetics studies36.Microsatellite loci are standard genetic markers for population genetic analysis, and have many advantages, including codominant alleles, extensive genome coverage (present in both coding and noncoding regions), high allelic diversity at the loci, and the ability to determine genotypes from small samples of tissue using polymerase chain reaction (PCR)37 examined microsatellite DNA variation at 11 loci in populations of Florida largemouth bass M. s. floridanus and northern largemouth bass M. s. salmoides from northern and southern latitudes in North America and found despite the systematic introduction of Florida largemouth bass into Texas, genetic integrity of a number of populations had not been or were only minimally affected. But they suggested that a full assessment of the variation throughout the range of each subspecies should be conducted to identify minimally impacted environments and quantify the levels of gene flow from impacted to intact water bodies to continue to restart the necessary work performed by Philipp16 over 20 years ago. Lutz-Carrillo subsequently developed 52 novel microsatellite markers from M. s. floridanus, for use in population genetic studies and expected them to be helpful for the resolution of hatchery-released individuals in the wild, introgression among Micropterus species, and gene flow following stocking and restoration efforts38.Lutz-CarrilloIn this study, we investigated the population genetic structure of hatchery stocks and wild populations in the largemouth bass based on eight polymorphic microsatellite loci. Samples encompassed both populations from wild habitats and hatcheries or domestic fish ponds. Our objectives were as follows: (1) to determine genetic diversity of wild largemouth bass populations in North America; (2) to evaluate genetic variation in hatchery largemouth bass and identify differential strain contribution affecting the genetic variability (3) to provide valuable information for the fishery management and conservation implication of largemouth bass.Across all 25 populations sampled, the w96 locus had the highest number of alleles 18.8) while the mdo6 locus had the least (6.6). The total number of alleles at each locus ranged from 3 to 53. The average number of alleles across all loci was the highest (20.8) in the RBRMS and lowest (7.3) in the SJFL. No significant genetic associations among loci were observed. The value of observed heterozygosities per population was detected from 0.0213 to 1. The expected heterozygosities were from 0.1158 to 0.9905 .Fst was 0.19 (among 25 populations), showing that largemouth bass had high genetic differentiation, and the gene flow indicated the genetic exchange among wild populations is difficult  and LKTX (P = 0.008) suggested that there had been a recent population bottleneck.The majority of largemouth bass populations had a significant heterozygosity excess, which is likely to indicate a previous population bottleneck. However, under the SMM and TPM models, there is no bottleneck signature; under the IAM model, PLWI (39 was lower than we observed (18.1), which may have been due to the geographic we sampled, the number of populations we sampled, or the loci we selected. Some loci failed to be amplified in cultured populations, which might be due to high sensitivity of these loci under environmental pressure and selective breeding. The highest values of the mean number of alleles and private alleles were detected in the RBRMS population. In the past, hatchery procedures often had overlooked genetic differences between populations, as well as the consequences of reducing heterogeneity through inbreeding16. In the present study, the value of observed heterozygosities per population was detected from 0.0213 to 1. The expected heterozygosities were from 0.1158 to 0.9905. Thus, the populations we collected can provide a wide range of selection to avoid reducing heterogeneity resulting from inbreeding. The inspection of genetic information provides fishery managers with a means for understanding and predicting stocking population variation for regulations and other management programs in largemouth bass fisheries.This study is the first to use microsatellite DNA markers to examine the genetic variation within and among wild and hatchery populations of largemouth bass across America and China. Other freshwater fish species using microsatellites revealed thenumber of alleles per locus (5.4)40. Genetic diversity values are a better indicator of the genetic polymorphism within a population, because estimates of the mean number of alleles are influenced by sample size. In the present study, the majority of largemouth bass populations had a significant heterozygosity excess, which is likely to indicate a previous population bottleneck. However, comparing Southern and Northern populations of largemouth bass, the South Carolina populations showed higher values of gene diversity and allele richness. The populations have maintained their genetic integrity and been only minimally affected. Nevertheless, the high genetic diversity of invasive species may be caused by hybridization and variation. In this study, the results showed that the largemouth bass had high genetic differentiation. In addition, according to AMOVA analysis, overall genetic differentiation among largemouth bass from the 25 populations was significant (P < 0.05), suggesting significant genetic differentiation among localities or populations. However, overall, the genetic variations within populations are higher than those among populations. In the natural environment, the long-term success of stocked largemouth bass might be the greatest for cohorts with the highest levels of genetic variability16. Microsatellite allelic frequencies and Fst analyses showed that all 25 populations were genetically unique. These genetic differences have resulted from long-term exposure to natural selective pressures and provide thespecies with enough variation to deal with the Barge range of different environmental conditions in which it exists41.Genetic diversity is increasingly declining as a result of various factors, including direct human impact and structural alteration of ecosystems resultingfrom changes in human life styles, bottleneck experienced, inbreeding, etcM. s. floridanus and the northern largemouth bass M. s. salmoides. They occupy their native habitats from peninsular Florida to throughout northeastern Mexico, southeastern Canada, and the U.S. corridor in between15. The genetic difference is particularly pronounced for the two recognized subspecies, but pertains to populations within each subspecies as well. The UPGMA tree built from the matrix of pairwise allele-sharing distance among 25 populations across America and the assignment tests revealed the multilocus microsatellite genotypes to discriminate populations of largemouth bass. The phylogeny based on eight microsatellites revealed a clear distinction between northern and southern populations. The China population and Minneapolis populations were clustered in a group, indicating that China’s largemouth bass originated fromnorthern largemouth bass, since China introduced largemouth bassonly one time from California, USA before our sampling time.The largemouth bass has two subspecies, the Florida largemouth bass In conclusion, there is relatively high genetic diversity of wild largemouth bass populations in North America, and significant genetic differentiation among localities or populations in the largemouth bass, but obviously lower genetic diversity in hatchery stocks. The study suggested that China’s largemouth bass originated from northern largemouth bass of USA. This study provided valuable information for the fishery management and conservation implication of largemouth bass, and indicated that the differentiation in largemouth bass was caused by the founder effect. Besides the founder effect, geographicaldistance might also be responsible for the significant genetic differentiation among wild populations. These analyses clearly reveal substantial genetic differences among populations in the United States. This study demonstrates the need for incorporating genetic information and principles into current and future fisheries management programs. However, the effectiveness of the stocking populations should be evaluated and the long-term effects on the genetic composition of specified largemouth bass populations should be monitored.A total of 1,045 individuals were collected by trapping and/or electro-fishing from 25 different localities and coordinated by geographic sampling coordinates throughout North America and China Fig. . Twenty Fin clips were the sources of nuclear DNA. They were collected from the freshly caught fish and immediately preserved in 95% ethanol.37. Amplification of microsatellite loci were performed using eight primers selected from those isolated and characterized by Lutz-Carrillo , 1.5 pmol of both non-tailed and labelled primers, and 0.1 pmol of the tailed primer, 25 ng DNA, in the presence of 100 μm spermidine. Amplification was be conducted in PTC-100 thermal cyclers (MJ Research) using an initial denaturation at 94 °C for 2 min, followed by 35 cycles of 30 s denaturation at 94 °C, 30 s annealing at a locus-specific temperature  and linkage disequilibrium (LD) were being tested for all locus-population and locus-locus combinations using ARLEQUIN version 2.0Analyses of molecular variance (AMOVA) were conducted with Arlequin. One AMOVA was performed under the null hypothesis of no genetic structure. AMOVAS will examine population structure after combining populations in various ways to test for geographical structure.45. A UPGMAtree was constructed using the programs NEIGHBOR and CONSENSE in PHYLIP version 3.6 in terms of Felsenstein46. Genetic structure among individuals was assessed using the model-based Bayesian clustering method according to Pritchard47 and Lutz-Carrillo37. Assignment tests were performed with geneclass version 2.048. The program STRUCTURE was used to identify genetic clusters without using any prior information of the sampling location of the individuals47. Two separate analyses were carried out in STRCTURE, both times with allele frequencies as correlated and the admixture model. The first analysis was used to infer the most likely range for K. Here, we used a burn-in of 10000 and a MCMC length of 5000 iterations and the simulated number of populations from K = 1–25. The upper limit of 25 was chosen as this corresponds to the number of sampled populations. Twelve independent simulations were performed of each K to check for consistency across runs. The preliminary results were assessed using the Evanno method where the most likely K was determined by the distribution of △K.Genetic structure among populations was analyzed based on a chord distance according to Cavalli-Sforza and Edwards49. All these tests were adjusted for multiple simultaneous comparisons using a sequential Bonferroni correction. Means are reported as ±SE. We used bottleneck version 1.2.0250 to test for evidence consistent with recent population bottlenecks or expansions by recognizing significant heterozygosity excess or deficiency for each population using a Wilcoxon sign–rank test. We conducted the tests using 1,000 iterations using the SMM of microsatellite evolution, as well as a two-phased mutation model (TPM). Allele frequency data was tested for evidence of a “heterozygosity excess” (HE) using the program bottleneck50. Three statistical tests  were conducted in order to determine whether there is significant HE, which may indicate that a recent bottleneck has occurred.Allelic richness was calculated with FSTAT version 2.9.3.2This study and all experimental procedures involving animals were performed according to the protocol approved by the Ohio State University Institutional Animal Care and Use Committee. Permissions for field studies were granted by: Ohio Department of Natural Resources, South Carolina Department of Natural Resources, Texas Parks & Wildlife Department, Minnesota Department of Natural Resources, Mississippi Wildlife Fisheries & Parks, Florida Blackwater Fisheries Research and Development Center, Wisconsin Genoa Federal Fish Hatchery (Supplementary Table Supplementary Table S1


In the combined 16S rDNA and CO I gene sequences, 54 variable sites and 44 haplotypes were observed among 219 individuals belonging to nine D. galeata populations. Average haplotype diversity and nucleotide diversity were, respectively, 0.72% and 0.56%. The F‐statistics (FST) value of the D. galeata populations was 0.149. According to the results of the neutral test, D. galeata in the middle and lower reaches of the Yangtze River had experienced a bottleneck effect in the history. Molecular variance analysis indicated that the genetic differentiation of the D. galeata populations mainly occurred within populations (85.09%). Greater genetic differentiations of D. galeata among individuals within populations appeared in the populations from the Huaihe River basin, whereas smaller genetic differentiations occurred in the populations from the middle reaches of the Yangtze River. Strong gene flows were all observed between Group I (four populations from the middle reaches of the Yangtze River) and Group ΙΙ (three populations from the middle and lower reaches of the Yangtze River), and Group ΙΙΙ (two populations from the Huaihe River basin). The effective migration rates (M) were 851.49 from Group I to Group ΙΙ and 685.96 from Group I to Group ΙΙΙ, respectively. However, no significant relationship was observed between the genetic differentiation and geographical distance of the nine populations . Results suggested that the genetic differentiation of D. galeata in the water bodies in the middle and lower reaches of the Yangtze River resulted mainly from geographical isolation.Mitochondrial  Daphnia galeata among individuals within populations appeared in the populations from the Huaihe River basin whereas smaller genetic differentiations occurred in the populations from the middle reaches of the Yangtze River. Strong gene flows were all observed between Group I (four populations from the middle reaches of the Yangtze River) and Group II (three populations from the middle and lower reaches of the Yangtze River), and Group III (two populations from the Huaihe River basin). The effective migration rates (M) were 851.49 from Group I to Group II and 685.96 from Group I to Group III, respectively. However, no significant relationship was observed between the genetic differentiation and geographical distance of the nine populations . Results suggested that the genetic differentiation of D. galeata in the water bodies in the middle and lower reaches of the Yangtze River resulted mainly from geographical isolation.In this study, greater genetic differentiations of  Hebert, Witt, and Adamowicz , with the illumination of 12 hr light: 12 hr dark at (25 ± 1)°C, and 4–6 mature females per individual were selected for DNA extraction. The culture medium was changed every day, and D. galeata were fed with Scenedesmus obliquus of 2 × 105 cells/ml. The culture medium was aerated tap water over 48 hr, filtered by boiling and cooling for reserve.a Figure  was colla Figure . The ideg and Du  and Benzg and Du . The col2.2D. galeata was extracted by using TIANamp Micro DNA Kit (Tiangen). Each D. galeata body was crushed with a sterile 10 μl tip before extraction because the chitin carapace of D. galeata hindered the digestion of internal organs by proteinase K. The concentration of DNA extraction was measured by a spectrophotometer (Biofuture) and then stored at −20°C for subsequent analyses.The genomic DNA of 16S rDNA was amplified with the L2510 5′ to 3′ (CGCCTGTTTAACAAAAACAT) and H3059 5′ to 3′ (CCGGTCTGAACTCAGATCATGT)  and HCO2198 5′ to 3′ (TAAACTTCAGGGTGAC CAAAAAATCA) , 1.0 μl of each upper and lower primers (10 mM) (Shanghai Shenggong), 0.25 μl of DNA polymerase TaKaRa‐LA‐Taq (5 U/μl) (Clontech), and 14.75 μl of double‐distilled H2O. The conditions of the 16S rDNA amplification included an initial denaturing step of 3 min at 95°C, 35 cycles of 45 s at 95°C, 45 s at 50°C, 55 s at 72°C, and a final extension of 72°C for 10 min. The conditions of the CO I gene amplification included an initial denaturing step of 1 min at 95°C, 35 cycles of 40 s at 95°C, 40 s at 45°C, 1 min at 72°C, and a final extension of 72°C for 10 min. The amplified products were detected by 1% agarose gel electrophoresis, and the PCR products with clear target and brightness were sequenced by GenScript.PCR reaction (25 μl) contained 1.0 μl of genomic DNA (approximately 100 ng/μl), 2.5 μl of 10× LA‐Taq Buffer II, 4.0 μl of dNTPs (2.5 mM) (Shanghai Shenggong), 0.5 μl of Mg2.3D. galeata 16S rDNA and CO I gene sequences in the NCBI database to ensure the reliability of our sequences. Before the combined analysis of 16S rDNA and CO I gene sequences, the homogeneity of the two gene sequences was tested by using PAUP*v4.0, and the results showed that the topologies of the two data sets were consistent (p = .14). Therefore, the two gene sequences can be combined.We compared the alignments of the 16S rDNA and CO I gene sequences was performed by using Mega 7.0, and the missing sequences of the primer binding sites at both ends were removed after manual proofreading. The conversion/transversion ratio and base composition of the sequences were calculated with Mega 7.0. The genetic distances among the individuals and among the populations of D. galeata from the middle and lower reaches of the Yangtze River were calculated. A neighbor‐joining phylogenetic tree was constructed by using Mega 7.0 on the basis of the pairwise genetic distances among populations, and population groups were identified according to the genetic relationships among populations. D. magna  was used as outgroups. Principal component analysis (PCA) was performed on the genetic distance matrix among D. galeata individuals using Canoco 5.0. Molecular variation analysis was performed with Arlequin v3.5  were also calculated. The “standard AMOVA analysis” method and 20,000 permutations were used to test the significance of the covariance components associated with different levels of genetic structure. A Mantel test was performed to identify significant correlations between population genetic differentiation coefficients (FST) and geographical distances (km) using 1,000 randomization replicates in IBD.The homologous alignment of H), haplotype diversity (Hd), nucleotide diversity (π), average number of nucleotide differences between whole sequences (K), Tajima's D value, and Fu's Fs value. Haplotype networks were constructed in Haploviewer  is X × Nm = Ɵ × M, where X is the inheritance parameter and depends on the molecular marker, which is usually 1 for mt DNA data  were considerably higher than the C + G contents (35% and 43%) in the 16S rDNA and CO I gene sequences.A total of 219 16S rDNA and CO I genes, 936 base pairs and 935 identifiable sites, including 881 invariable sites, 54 variable sites, 22 single sites, and 32 parsimony‐informative sites, were found. The overall transition/transversion ratio was 4.4. The average A, T/U, G, and C contents were 27.3%, 34.1%, 21.8%, and 16.8%, respectively. The A + T content (61.4%) was significantly higher than the C + G content (38.6%).Under the combined sequences of the 3.216S rDNA and CO I gene sequences. Both indicated high levels of haplotype diversity (Hd = 0.37 and 0.67)  and 35 haplotypes (Hap1–Hap35) were, respectively, obtained Figure a,b in th7) Table .16S rDNA gene sequences of the D. galeata populations in nine water bodies in the middle and lower reaches of the Yangtze River, the haplotype diversity ranged from 0.13 to 0.63. The highest haplotype diversity was detected in the AH population (Hd = 0.63), and the lowest was found in the JS and WC populations (Hd = 0.13). In the CO I gene sequences, a high haplotype diversity (0.33–0.81) was found, and the highest and lowest haplotype diversity appeared in the DS population (Hd = 0.81) and JS population (Hd = 0.33), respectively. In the 16S rDNA gene sequences, the highest nucleotide diversity appeared in AH population (π = 0.50%) whereas the lowest occurred in JS and WC populations (π = 0.02%). The overall nucleotide diversity π of nine D. galeata populations was 0.28%. In the CO I gene sequences, the highest nucleotide diversity appeared in the AH population (π = 1.57%) whereas the lowest occurred in the JS population (π = 0.08%). The overall nucleotide diversity π of nine D. galeata populations was 0.91%  were obtained from the D. galeata populations of nine water bodies in the middle and lower reaches of the Yangtze River  and nucleotide diversity (π = 0.56%). The highest haplotype and nucleotide diversity all appeared in the AH population , whereas the lowest of both diversities occurred in the JS population  showed that the D. galeata populations from the middle and lower reaches of the Yangtze River deviated from neutral evolution. The PH population evidently deviated from the neutral evolution . Haplotype networks based on 16S rDNA, CO I gene sequences, and the combination of two gene sequences showed that D. galeata individuals from nine water bodies in the middle and lower reaches of the Yangtze River shared the same mitochondrial haplotypes , Hap39 was distributed in the populations in the middle reaches of the Yangtze River (LG and PH populations), and Hap19 and Hap26 were distributed in the populations in the lower reaches of the Yangtze River . Hap12 was mainly located in the AH population although it was also found in the CH population in the junction of the middle and lower reaches of the Yangtze River , and significant differences (p < .001) was all observed among and within populations  values of the D. galeata populations among the JS, LG, and WC populations were all relatively high. Lake Chaohu was far away from Lake Taihu and Dianshan, but the F‐statistics (FST) values between CH and TH and DS populations were all small. No significant relationship was observed between the FST and geographical distance of nine D. galeata populations   Figure .3.4D. galeata populations, the effective population size of Group I  was 502.82, and the effective migration rate (M) from Group I to Group ΙΙ  in the middle and lower reaches of the Yangtze River was 851.49. The effective migration rate (M) was 685.96 from Groups I to ΙΙΙ , which are located in the Huaihe River Basin. This finding suggested a strong gene flow from Group I to II and Group ΙΙΙ. The effective population size of Group ΙΙ was 827.31. The effective migration rate (M) from Group ΙΙ to Group I was 141.47 and from Group II to Group ΙΙΙ was 402.62. The effective population size of Group ΙΙΙ was 622.56, and the effective migration rates (M) from Group ΙΙΙ to Group I was 79.62 and from Group ΙΙΙ to Group ΙΙ was 596.63. Therefore, compared with Group I, the gene flows were relatively weak from Group ΙΙ or Group ΙΙΙ to other groups.Based on the genetic differentiation of nine D. galeata populations showed that a strong gene flows from the populations in the middle reaches of the Yangtze River  and the CH population in the junction of the middle and lower reaches of the Yangtze River to the TH and DS populations in the lower reaches of the Yangtze River. In the Huaihe River Basin, a certain degree of genetic communication exists between HN and AH population. The AH and HN populations in the Huaihe River Basin showed strong gene flows with the PH, CH, and TH populations in the middle and lower reaches of the Yangtze River.The directional gene flow of nine D. galeata populations spread from the middle to the lower reaches of the Yangtze River, and the diffusion of the gene flows from the Huaihe River to the middle and lower reaches of the Yangtze River was also observed  and nucleotide diversity (π) of mitochondrial gene sequences. In this study, nine D. galeata populations in the middle and lower reaches of the Yangtze River showed high genetic diversity . However, the genetic diversity among populations was significantly different. In Lake Junshan, strong predation pressure of fish evidently inhibited the population density of D. galeata  of JS population. Previous reports showed that floods had frequently occurred in the middle and lower reaches of the Yangtze River from 1921 to 2000  of the LG, PH, WC, CH, and DS populations of D. galeata. Furthermore, the HN population of D. galeata had high haplotype and nucleotide diversity (Hd = 0.65 and π = 0.70%). Lake Nanhu, which is a small closed lake formed by coal mining collapse, is located in the warm temperate monsoon climate zone. With the continuous deterioration of the lake environment, Lake Nanhu had gradually developed at a eutrophic level  was evidently higher than those found in the lower reaches of the Yangtze River  and the Huaihe River Basin (HN and AH). This finding might be caused by the spread of D. galeata populations. Bohonak and Jenkins (Daphnia) and their resting eggs could easily expand to other water bodies through passive diffusions, such as bird migration  with distance for populations of Daphnia hyalina , and then expanded Daphnia populations . The dispersal capabilities of D. galeata are high . Therefore, the genetic differentiation of D. galeata populations in the middle and lower reaches of the Yangtze River might be mainly caused by geographical segregation rather than geographical distance.Similarly, the genetic differentiation of other , et al.  detected, et al.  discoverNone declared.Deng Daogui and Liu Qi conceived and designed the experiment. Liu Qi, Zhang Kun, He Ping, Sun Yuchen, Zhang Tingting, Yang Wei, and Liu Wei collected samples. Liu Qi performed the experiment. Liu Qi and Deng Daogui analyzed the sequence data and drafted the manuscript. All authors have read and approved the final manuscript.


In this study, we used a series of diallelic genetic marker insertion/deletion polymorphism (indel) to investigate three populations of Yao, Kelao, and Zhuang groups in the Guangxi region of China and to evaluate their efficiency in forensic application.p > 0.05/30 = 0.0017). The allele frequencies of the short allele (DIP-) for the above three populations were in the range of 0.0520–0.9480, 0.0950–0.8780, and 0.0850–0.915, respectively. The observed heterozygosity of the 30 loci for the three populations was in the ranges 0.0802–0.5802, 0.1908–0.6053, and 0.1400–0.5600, respectively. The cumulative power of exclusion and combined discrimination power for Yao, Kelao, and Zhuang groups were (0.9843 and 0.9999999999433), (0.9972 and 0.9999999999184), and (0.9845 and 0.9999999999608), respectively. The DA distance, principal component analysis, and cluster analysis indicated a clear regional distribution. In addition, Zhuang groups had close genetic relationships with the Yao and Kelao populations in the Guangxi region.No deviations for all 30 loci were observed from the Hardy–Weinberg equilibrium after Bonferroni correction (This study indicated that the 30 loci were qualified for personal identification; moreover, they could be used as complementary genetic markers for paternity testing in forensic cases for the studied populations. However, STR mutations have always troubled the paternity testing. Compared to the mutation rates of STR (about 10an 10–8) . Howeveran 10–8) . Insertian 10–8) ; moreovean 10–8) . In totaThe Investigator DIPplex kit was the first commercial kit developed by Qiagen  for the indel marker. This kit contains 30 autosomal indels and amelogenin, and it has been used to perform systematic investigations on worldwide populations of African , Asian , Europeahttp://www.stats.gov.cn/english/statisticaldata/censusdata/rkpc2010/indexch.htm). Moreover, the Zhuang language, Yao language, and Kelao language all belong to the Sino-Tibetan language. In this study, the genetic data of these myriad types of populations based on 30 indels was investigated, and the results were further analyzed to evaluate whether they could be used in forensic applications and population genetic studies.Guangxi Zhuang Autonomous Region is located in southwest China and adjacent to Guangdong, Guizhou, Yunnan, and Hunan provinces. One of the settlements of Chinese ethnic minorities is located in this region; moreover, the immigration and symbiosis of multiple ethnic groups have witnessed a fine balance in this region. Among the 11 ethnic minorities in the Guangxi region, the Zhuang group is the native inhabitant of Guangxi region; the tribe members are descendants of the Xi’ou and Luo’Yue clans that have been distributed in Guangxi since ancient times . The ZhuN = 162), Kelao (N = 152), and Zhuang (N = 200). As described earlier by Bloodstain samples were collected from unrelated volunteers living in the Guangxi Zhuang Autonomous Region of China. The population sample sizes were as follows: Yao (http://www.qiagen.com/products/investigatordipplexkit.aspx) in a GeneAmp PCR system 9700 thermal cycler . The PCR products were detected with ABI 3130XL Genetic Analyzer . Genotypes were determined by GeneMapper ID-X software v1.3 . In addition, 9948 DNA  was employed as a positive control in amplification.In this study, PCR amplification was performed using the Investigator DIPplex kit handbook in accordance with the manufacturer’s instructions , observed heterozygosity (Ho), power of exclusion (PE), discrimination power (DP), match probability (MP), polymorphic information content (PIC), typical paternity index (TPI) were calculated using the modified powerstate (version 1.2) spreadsheet. Single nucleotide polymorphism Analyzer v2.0 was used to analyze linkage disequilibrium (LD) . DISPAN The allele frequencies and forensic parameters of 30 indel loci in the three ethnic populations are presented in p > 0.05/30 = 0.0017), we could not observe any deviation from HWE. As displayed in In the three populations, the departures from the HWE were observed at the HLD125 locus in Yao population, whereas the same parameter was observed at HLD45, HLD83, HLD114, HLD124, and HLD136 loci in Kelao population. After applying the Bonferroni correction  as exceptions, the r2 values of all loci were less than 0.8 in Yao samples. This means that HLD88 were linked with HLD92 in the Guangxi Yao samples. This probably occurs because the sample size of Yao individuals is too small.An analysis of linkage disequilibrium that occurs between 30 indels was performed by SNPAnalyzer software. As is shown in A distance) were calculated among Yao, Kelao, and Zhuang samples, and the 20 referenced groups included Beijing Han  also exhibited a mixture of yellow, blue, and green components, where in the proportion of green component was higher. In addition, the main constituent is yellow in the African groups. At K > 4, we did not observe any further substructure. Hence, the 30 indels can distinguish ancestries of the studied population and other populations to some extent; however, this result warrants further investigation.Population structure analysis was performed using the STRUCTURE program with 10,000 burn-in period and additional 10,000 Markov Chain Monte Carlo replication; and the plicates . As showThe allele frequencies of 30 indel loci could distinguish different populations to a certain degree, and the results revealed that Guangxi three ethnic groups were similar to the East Asians, especially with most Chinese populations in allele frequency distributions. In the forensic application field, CPE and CPD values are common indicators to estimate the forensic efficiency. In this study, the CPE and CPD values for the Yao, Kelao, and Zhuang samples were 0.9843 and 0.9999999999433, 0.9972 and 0.9999999999184, and 0.9845 and 0.9999999999608, respectively. The high CPD value clarified that these 30 indel loci could be regarded as efficient genetic markers in forensic identification cases, while the relatively lower CPE value indicated that the 30 loci could only be used as complementary genetic markers for paternity testing.The indel loci are suitable for forensic applications as independent loci if no relevance exists between two loci from the same chromosome or two random chromosomes . The LD A distance is considered an effective analysis method to reveal the genetic divergences between populations within a species (DA genetic distance were consistent with the geographic locations of these populations. Compared with East Asian groups, Eurasian groups, European groups, Mexican groups, and Nigeria groups were found had more differences with Guangxi three ethnic groups. The results of the STRUCTURE analysis were roughly in line with population differentiation. From this analysis, 28 populations were classified into geographic patterns.Genetic distance reflected by D species . Detaile species . As showAccording to A distance, PCA, and cluster analysis all showed that Zhuang groups had very close genetic relationships with Yao and Kelao groups in the Guangxi region. Furthermore, studies were performed in order to compare the three ethnic groups in Guangxi with more reference groups. All these results were helpful in providing a better understanding of the genetic background of people in the Guangxi region.In conclusion, we obtained the allele frequencies and forensic parameters of 30 autosomal indels loci for Yao, Kelao, and Zhuang groups in Guangxi. The set of 30 indels showed high efficiency in individual identification of these samples. The results of D10.7717/peerj.6861/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.6861/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.6861/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj.6861/supp-4Supplemental Information 4Click here for additional data file.10.7717/peerj.6861/supp-5Supplemental Information 5Click here for additional data file.10.7717/peerj.6861/supp-6Supplemental Information 6Click here for additional data file.


Labeo rohita) is a significant freshwater aquaculture species with approximately 1.8 Mt produced annually. Fin clips obtained from the founders of a newly established Bangladesh-based breeding population  were used to identify 9157 SNPs and 14 411 silicoDArT markers using the Diversity Arrays Technology (DArT) genotyping-by-sequencing platform known as DArTseq. After quality control, 1985 SNPs were retained and used to examine population structure within and among river systems. Examination of genomic relationships revealed evidence of full- and half-sibling relationships among founders. Accordingly, sibship and dummy parents were assigned within each river population using a maximum likelihood approach with COLONY software. Founders that had no dummy parents in common were then identified for population genetic analyses. Only 40 unique dummy parents and 17 founders with no common dummy parents were identified from the Halda river, compared with 206 (96) from the Jamuna and 184 (83) from the Padma. Overall pairwise FST estimates among rivers were low (<0.005) and the optimum number of clusters using unsupervised K-means clustering was one, indicating little genetic divergence among the river populations in our SNPs. These results suggest that observed sibship among founders should be accounted for in future pedigree-based analyses and it cannot be assumed that fertilized spawn collections are representative samples of river populations.Rohu ( Labeo rohita Hamilton) is a member of the Indian major carps and has a natural distribution encompassing rivers in Bangladesh, Myanmar, Nepal, and Pakistan, as well as the tributaries and branches of the Ganges river in northern India  technology that uses a combination of genome complexity reduction methods implemented by Diversity Arrays Technology Pty Ltd. (DArT) . DArTseqan 0.043 . A numbean 0.043 . In addiIn 2012, fertilized rohu spawn was collected using commercial spawn harvesting methods  from twoFor the purpose of the current study, archived fin-clip samples of all but four founders, for which fin-clips were not available, were genotyped using the DArTseq platform in 2016. The laboratory procedures and analytical pipelines outlined in Appendix S1 of Sequences generated from each lane were processed using proprietary DArT analytical pipelines which have been deployed over the last decade to produce marker data for thousands of species. In the primary pipeline the fastq files were first processed to filter away poor quality sequences, applying more stringent selection criteria to the barcode region compared to the rest of the sequence. In that way the assignments of the sequences to specific samples carried in the “barcode split” step were very reliable.https://doi.org/10.7910/DVN/PWC5EY  and ‘Min Phred pass score 10, Min pass percentage 50′ (Whole read). The mean number of sequences identified per sample was approximately 2.45 million. For SNP calling, all tags from all libraries included in the DArTsoft14 analysis were clustered using DArT PL’s C++ algorithm at the threshold distance of three, followed by parsing of the clusters into separate SNP loci, checking the balance of read counts for the allelic pairs was within a small range (up to 4-fold difference). The co-efficient of variation in sequences per sample was around 8% and variation in counts among samples was low, therefore selecting markers based on average read depth for the whole experiment was deemed sufficient. This assertion was tested by proving Mendelian behavior of markers called by DArTsoft14 from DArTseq libraries in over 1000 controlled genetic crosses. Rejection of very large clusters  and checking for feasible SNP-reference allele proportions facilitated selection of true allelic variants from paralogous sequences. In addition, multiple samples were processed from DNA to allelic calls as technical replicates and scoring consistency was used as the main selection criteria for high quality/low error rate markers. Only markers with average reproducibility of 95% were accepted, although 99% of markers were completely concordant among technical replicates. Confidence in calling quality was supported by high average read depth per locus, high marker score reproducibility and high call rate percentages . DArTseqN/PWC5EY . Data fo2 was then excluded iteratively until all pairwise r2 values were ≤0.2 .Analyses of genomic data were primarily conducted in R  was generated according to the first method proposed by Post SNP quality control, a genomic relationship matrix , P is a matrix that specifies allele frequencies expressed as a difference from 0.5 and multiplied by 2, and pi is the frequency of the second allele at locus i. This method was implemented using code from Where A) from the COLONY-derived pedigree using the “makeA” function of the “nadiv” package  to other individuals in A and then removing these individuals from A; (iii) appending to the list generated in step ii the individual remaining in A with the lowest average relationship with the other individuals and then removing this individual and its relatives (aij > 0) from A; and (iv) iteratively repeating step iii until no individuals remained in A  and expected (Hexp) heterozygosities by SNP were estimated, for each sampled population, using the “summary” function of the “adegenet” package. The significance of pairwise population differences in mean Hexp were estimated using the “Hs.test” (n.sim = 999) functions of the “adegenet” package. The significance of the differences between Hobs and Hexp within rivers were tested with paired t-tests. Differences in allelic richness and private allelic richness among sampled populations were compared using the rarefaction method, implemented in ADZE . Analysis of molecular variance (AMOVA) was conducted using the “poppr.amova” function of “poppr”  clustering was then undertaken to investigate the possibility that a population structure, other than the predetermined structure , might better fit the data. The “adegenet” package was adopted for this purpose using the “find.clusters” function (default settings except that max.n.clust = 20 and n.start = 1000) and the output of principal component analyses (PCA) conducted using the “glPca” function (default settings except that nf = 500). The optimum number of clusters was identified as that with the minimum Bayesian Information Criterion (BIC).Observed , 9157 SNPs and 14 411 silicoDArT markers were identified . After qG, computed using observed allele frequencies from all founders from all rivers, as a heatmap . Consistent with the removal of SNPs deviating from HWE during quality control, differences between mean Hexp and Hobs were small within populations , albeit significantly different from zero in the case of the Jamuna and the Padma (P < 0.001). Rarefaction analysis revealed no substantive differences in either allelic richness or private allelic richness among rivers , 0.319 (Jamuna), and 0.317 (Padma) and no significant difference were detected between populations , represented less than 0.2% of the total molecular marker variance in AMOVA. In addition, unsupervised K-means clustering revealed an essentially linear increase in the BIC from 1 to 20 clusters (K), indicating the optimum number of clusters to be one and providing further evidence of a lack of substantive genetic structure within and among the sampled populations, once putative siblings had been purged from the data.The first principal component explained only 1% of the total variance and the three river populations were not clearly distinguishable from each other with respect to the three most important principle components . Overall <0.005; , furtherThe high degree of sibship among rohu breeding population founders observed in this study provides evidence that fertilized spawn samples are not necessarily representative of river populations. This has implications for the management of river-sourced hatchery broodstock , pedigreSamples of populations taken in early life stages of highly fecund species, including rohu , have prIn the absence of access to genetically- improved broodstock, Bangladeshi hatcheries currently relying on broodstock collected as spawn from rivers  should aA) was 0.0078 , within the level that would generally be deemed an acceptable increase in the average relationship per generation in a closed breeding population. An increase in average relationship of 0.0078 per generation equates to a future increase in inbreeding per generation (ΔF) of 0.0039  founders with no dummy parents in common were identified from the three sampled rivers, representing a sizable base population for breeding purposes . Indeed,f 0.0039  and an ef 0.0039 . AccordiST estimates among rivers, and lack of evidence for genetic structure within or among rivers based on unsupervised K-means clustering, is indicative of no or low levels of divergence in our SNPs among river populations. Previous estimates of pairwise FST between rivers for rohu, using a range of non-SNP genetic markers, were also generally low and/or not significantly different from zero, albeit universally higher than our estimates  previous estimates of FST were biased upward, as the possible over representation of siblings in river spawn samples was not accounted for  ounted for ; or  successfully identified and characterized single nucleotide polymorphisms (SNPs) and silicoDArT markers in rohu ; (ii) idAs stated in the Section “Materials and Methods.” In 2012, rohu spawn was collected from three major river systems in Bangladesh; the Halda, Jamuna and Padma (Ganges). At 2 years of age, approximately 140 individuals from each river were mated as the founders of a breeding population . All fouMH performed the analyses and wrote the first draft of the manuscript. WM oversaw the establishment of the founder population using fish collected as part of the Aquaculture for Income and Nutrition (AIN) project. AK oversaw the generation of SNP and silicoDArT data. All authors contributed to manuscript writing and revision.AK is the Director of Diversity Arrays Technology (DArT) Pty Ltd., who undertook genotyping for this study on a fee-for-service basis. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Clanga clanga), a large and globally vulnerable raptor, is extinct or in severe decline in most of its previous range in Europe. We assessed whether the remnants of European population are genetically impoverished, and isolated from each other. We evaluated levels of genetic diversity and population structuring by sequencing mitochondrial pseudo-control region and 10 introns from various nuclear genes, and estimated length diversity in 23 microsatellite markers. The European population has expanded since the late Pleistocene, and does not exhibit signs of a recent population bottleneck. The global genetic diversity in Europe was rather similar to that detected in other similar species. Microsatellites suggested shallow but significant differentiation between the four extant populations in Estonia, Poland, Belarus and Russia (Upper Volga region) populations, but introns and mtDNA showed that only the Estonian population differed from the others. Mitochondrial diversity was highest in the northernmost Estonian population, introns suggested lower diversity in Upper Volga, microsatellites indicated equal diversity among populations. A recent bottleneck was detected in Poland, which is consistent with the observed repopulation of the region. We conclude that significant gene flow and high genetic diversity are retained in the fragmented Greater Spotted Eagle populations; there is currently no need for genetic augmentation in Europe.Characterising genetic diversity and structure of populations is essential for effective conservation of threatened species. The Greater Spotted Eagle ( In the short term, inbreeding and genetic drift lower fitness of individuals and survival propbability of populations6, while in the long term populations that have lost their genetic variation have constrained evolutionary potential8. Hence, characterising genetic diversity and structure of populations is a research priority for threatened species because it can lead to more effective conservation and management strategies9.Human activities have caused the contraction and fragmentation of the distribution of many species. Small and geographically distinct remnant populations are often characterised by low genetic diversity, reduced growth, declines in numbers and increased extinction rates11. Moreover, persecution and environmental contamination have caused severe population constrictions13. These circumstances have led to genetic impoverishment of several species20, though there are also examples to the contrary: European White-tailed Eagles have retained genetic diversity despite a recent severe population bottleneck, which was probably short relative to that species’ long generational time21. Also, the effect of gene flow between populations of large, mobile animals is strong and can facilitate recovery of genetic diversity after a population constriction22. Indeed, although large raptors typically exhibit strong natal philopatry, which may favour genetic structuring, they also often range over large areas, and can overcome barriers like habitat fragmentation, which can promote increased genetic diversity. Moreover, individuals of migratory species may settle along migration routes23, which may further impede genetic structuring. To date, however, genetic studies have concentrated on non-migratory raptors, and the influence of migratory behaviour has remained poorly studied.Many populations of raptorial birds have declined due to habitat loss and fragmentationClanga clanga Pallas, 1811, is a large, long-lived, migratory raptor that breeds in temperate Eurasia, and winters in the southern parts of the continent and in Africa. Its population has been in continual decline for decades, and is considered globally Vulnerable and Endangered in Europe24. Although it is distributed from the Baltic Sea in the west to the Pacific Ocean in the east, this vast area is occupied by only a few thousand breeding pairs, with fewer than a thousand pairs breeding in Europe24. It has been extirpated throughout most of its historical European range, and the remaining populations are small and decreasing25  how diverse genetically are Greater Spotted Eagles across Europe, (ii) how much has fragmentation affected the genetic structure of the European population, (iii) how genetically isolated from one another are remaining populations, and (iv) have extant populations gone through a genetic bottleneck, which might have affected their evolutionary potential. The results will be examined within the context of Greater Spotted Eagle conservation, with an aim to provide advice on genetic management and look for potential opportunities for augmentation or translocation programmes.At the end of the 20Among the 85 mtDNA sequences of 470 nucleotides studied, 19 mtDNA haplotypes were defined by 18 polymorphic sites (14 transitions and 4 transversions). Additionally, two single-nucleotide indels and a 26-nucleotide indel was found. There was one dominant haplotype containing 42 individuals, but it differed by only a single mutation from another haplotype with 13 individuals; other haplotypes contained five or less individuals , expected heterozygosity  and observed heterozygosity  were very similar among the four populations . Mean values of allelic richness .Intron sequences resulted in 54 alleles (haplotypes), but the mean number of alleles per locus across populations (2.7) was lower than in microsatellites. Populations in Estonia, Belarus and Poland exhibited rather similar values of diversity, and although diversity values for Russia were about two times lower, the difference was not significant , less within populations 13% and 17%), and only a small fraction of variation was found among populations (7% and 4%). According to the mtDNA analysis above the individual level, 95% of variation was detected within, and only 5% between populations. The plots of Factorial Correspondence Analysis and Principal Coordinates Analysis of microsatellites suggested close relatedness between Belarussian, Estonian and Russian populations and distinctiveness of Polish population, but the distribution of plotted populations overlapped to a great extent  as at larger K values this parameter increased only slightly  detected no recent migrants in any of the populations  ranged between 0.50 and 0.71 (mean = 0.59) and with K = 3 between 0.34 and 0.61 (mean = 0.42). Similar results were obtained when introns and microsatellites were analysed in combination (K = 2: qmax = 0.58 (0.50–0.70); K = 3: qmax = 0.38 (0.43–0.50).In the STRUCTURE analysis of microsatellites, three groups (K = 3) were suggested by rus Fig. . The PolMedian joining network did not reveal any clear structure among European mtDNA samples Fig. . There wP = 0.008) and Fu’s Fs  were significantly negative, indicating population expansion. We obtained various estimates on the time of expansion, depending on the method and mutation rate used , and the proportion of different alleles in frequency classes followed a typical L-shaped distribution , suggesting a recent population bottleneck, although the proportion of different alleles in frequency classes was only slightly shifted  from the L-shaped distribution , and the proportion of alleles in frequency classes followed the L-shaped distribution  the control region, we obtained similar values for mitochondrial haplotype and nucleotide diversity to those recorded for several other species of bird of prey36. Results of microsatellite studies are often not easily compared because diversity values depend strongly on marker set selection. This is not the case in this study. Cross-species amplification has been rather successful in eagles, and several earlier studies have used the same  microsatellites used by us and obtained from White-tailed Eagle and two imperial eagle species . Because we did not analyse only the most polymorphic markers, but all available markers, our estimates could be somewhat lower than those from other studies. However, heterozygosities detected were not very different from imperial eagles18 or the White-tailed Eagles37. Still, interspecific comparisons should be taken with caution, because even in a population of the White-tailed Eagle population, various studies using different sets of microsatellites have resulted in different heterozygosity estimates37.The maintenance of genetic diversity is essential for supporting the survival probability of individuals and the evolutionary potential of populations3. For example, Spanish Imperial Eagle Aquila adalberti, an endangered raptor with a confined and fragmented distribution, has significantly lower genetic diversity than its sister-species, the more abundant and widespread Eastern Imperial Eagle A. heliaca18. A similar difference has been noted between the endangered Madagascar Fish-eagle Haliaeetus vociferoides and African Fish-eagle H. vocifer32. Surprisingly, the rare Greater Spotted Eagle has higher mitochondrial diversity than the Lesser Spotted Eagle30, a closely related species with rather numerous breeding populations across most of its distribution range. However, the range of the Lesser Spotted Eagle, which breeds almost entirely in Central and Eastern Europe and to some extent the Middle East, is rather restricted when compared to that of the Greater Spotted Eagle, which is distributed across a vast area of Eurasia. This suggests that range size, and probably historical population size, plays a larger role in determining current genetic diversity than the current population size. Indeed, both Spanish Imperial Eagle and Madagascar Fish-eagle have very restricted ranges compared to their widespread sister species. Moreover, Madagascar Fish-eagle has persisted for a long time despite low diversity32. Also Red Kite Milvus milvus, a species with a relatively small breeding range, exhibits significantly lower genetic diversity than the widespread Black Kite M. migrans19.Typically, rare species are genetically less diverse than abundant species. This has been demonstrated in various organisms by comparing pairs of threatened and non-threatened sister species30. One may suspect that the marginal populations studied by us would be atypically diverse, but this is probably not true; in an analogous study of the White-tailed Eagle, diversity values of peripheral populations were nearly a magnitude lower than in centrally located populations38. Surprisingly, the highest mitochondrial diversity was detected in the northernmost studied population, a situation also found in the White-tailed Eagle where the nordic Kola population was characterised by one of the highest diversity values38. The high diversity in White-tailed eagle was attributed to the high degree of admixture from various historical lineages38, and this may explain the unexpected pattern in the Greater Spotted Eagle. Also, the more recent population subdivision may have contributed to genetic diversity retention, as has been seen in the Mauritius kestrel31.Different diversities among sister-species may also arise from different patterns of colonisation, a feature that is more common in northern latitudes. During glacial times, the Greater Spotted Eagle population may have fragmented in multiple refugia, while the Lesser Spotted Eagle seems to be survived in only a single refugium26. Undoubtedly, introgression significantly affects the contemporary nuclear genetic architecture of the European Greater Spotted Eagle population, but in the current study all known hybrids were excluded from microsatellite and intron analyses. Although our methods reduced the effect of recent hybridisation on genetic diversity, we cannot exclude completely the influence of interbreeding on genetic diversity in the more distant past. However, relatively high diversity was detected also in mtDNA, which, due to its maternal heritability, is not affected by hybridisation. This confirms that high genetic diversity has been retained in the fragmented Greater Spotted Eagle populations, regardless of hybridisation, and, as of yet, there is no evidence of inbreeding depression.The two spotted eagle species hybridise extensively across Eastern Europe, and in some populations mixed pairs outnumber pure-species pairs37. However, this does not seem to be the case for the Greater Spotted Eagle, where admixture of various genetic lineages may have undermined this tendency. High mobility amongst birds of prey facilitates gene flow across populations. As a result, genetic differentiation between populations is typically shallow, although genetic differences between populations are often significant37. Moreover, and in concurrence with our findings, admixing of two groups across an east-west gradient has been found in several Eurasian raptors40. We studied only marginal populations of a widespread species, which may be more differentiated from one another than from central ones41. More extensive sampling across Russia is needed to reveal population structure across the range. Moreover, in the (currently defined) monotypic Greater Spotted Eagle several morphologically distinguishable groups have been separated42, which suggest that some genetic clusters might exist. The inclusion of more samples from eastern parts of the range is necessary before final conclusions can be reached about the significance of population structuring.Population structuring has been found in most raptor species that have been studied. Often this is attributable to the effect of isolation by distance—populations that are geographically more distant from one another are also genetically more different23. In spotted eagles the possibility of this is strikingly illustrated by pairs breeding far from their main distribution range. For instance, a Greater Spotted Eagle has bred in Germany, far from their closest extant breeding population in eastern Poland44. Similarly, single pairs of Lesser Spotted Eagles have bred in France and Spain, and probably in Algeria47, when their closest populations are located in north-east Germany and Slovakia. In our study we identified no recent migrants from other populations. This might have been due to the rarity of such events, as philopatry has relatively strong effect in the declining remnant, and thus unsaturated, populations. Indeed, an Estonian Greater Spotted Eagle, having spent at least five years out of the current breeding range of the species in Scandinavia, returned to breed in Estonia . However, a single immigration event could change significantly the genetic architecture of a population22.In migratory species, regular long-distance movements play an additional role in boosting gene flow, and subsequently mitigating population structuring. Philopatry forces most individuals to return to their breeding grounds, but some birds, especially in saturated populations, may settle and breed in suitable habitats along the migration route48. Individual Greater Spotted Eagles breeding in one area can spend their non-breeding time in places that are distant from one another. For instance, birds from the Estonian and Polish populations we studied, winter across a huge longitudinal range from south-western Europe to the Middle East and north-east Africa51. The two groups detected by microsatellites in the current study may also be linked to different migration routes and destinations, and this may have also increased the differentiation found in the east-west gradient. On the other hand, settlement of individuals along the migration route before reaching to their natal areas may lower differentiation along the north-south gradient. However, the patterns detected in this study need further exploration, as nuclear splits have been suspected to result from recent demographic dynamics, whereas mitochondrial differentiation, which was not detected here, has been assumed to be a consequence of disjunct Pleistocene refugia37.Although seasonal migration may facilitate gene flow between populations, as described above, migration is probably even more deeply linked with population differentiation because different migration routes probably reflect historical postglacial colonisation routesD and Fu’s Fs values indicate that the European Greater Spotted Eagle population has undergone demographic expansion in the past52. Although the time estimations were rather variable, depending upon method and mutation rate, the expansion of European Greater Spotted Eagle population dates back to the late Pleistocene. Several European species have expanded since the Last Glacial Maximum, some 10000 years ago53. However, the expansion of the Greater Spotted Eagle might have an earlier genesis if it initiated in Asia where glaciers were more northern in their distribution and species’ ranges were less constrained. Interestingly, diversity levels among Greater Spotted Eagle populations were similar for microsatellites, but somewhat more variable for introns. It may be due to faster mutations for microsatellites, and thus a more rapid recovery after historical bottlenecks than for the slowly mutating introns.A star-like network and significantly negative Taijma’s 21. Despite recently described population declines across most of Europe, the Garza-Williamson M index did not reveal any bottlenecks in the European Greater Spotted Eagles, and an excess of heterozygotes suggested only one local population decline in recent times. The bottleneck that was detected in Poland was suggested also by a subtle mode shift in allele frequency proportions. In fact, field observations corroborate the genetic indication of this population bottleneck, and suggest that the small Biebrza population is not just a remnant of a large population that has recently declined, but is the re-establishement of a population in the 1980s from few remnant pairs54.It is more difficult to detect recent bottlenecks, especially in long-lived species, if population levels have been low during a relatively short time period24. As field studies of the wetland-dwelling Greater Spotted Eagle are labour intensive, non-invasive genetic methods are potentially a more cost-effective approach in estimating actual and effective population sizes56.The current study focused on the European part of the global population, which stretches across Eurasia. Further studies that are spatially more extensive  would shed more light on genetic diversity and structuring among Greater Spotted Eagle populations. Moreover, although estimations of current global population size have been published, further validation of these estimates is needed57. For example, the declining Lesser Spotted Eagle population in Germany has been supplemented by translocating individuals from Latvia, where they are abundant58. Our study suggests that there is currently no need for genetic augmentation of European Greater Spotted Eagle populations because genetic diversity is not low, when compared to other, similar species. Instead, we recommend focusing on other conservation efforts, such as protection of nest sites, conservation of habitats and reducing poaching, which seem to be the major threats faced by this species54. However, translocations may be needed to increase current population sizes or re-establish populations in regions where they have gone extinct. Our results indicate that within Europe there are no restrictions on translocations from the genetic perspective as the differentiation between populations is low and there is ongoing gene flow between them. However, before any such actions are taken, other ecological, demographical and behavioural variables should be considered.Declines and extinctions of raptor populations have given rise to several reintroduction and augmentation programmes60 should not be considered as advocating concentration of conservation actions on a single population. As is the case in the other species (e.g. White-tailed Eagle37), much genetic diversity is retained in small Greater Spotted Eagle populations, and these should be conserved as well.On the other hand, our results supporting management of a European Greater Spotted Eagle population as a single conservation unit24) where, unlike seen in most published maps, the distribution is probably rather patchy . Belarus harbours 120–160 pairs, mostly in the south61. Approximately 40 pairs breed in Ukraine24. A small population (≤15 pairs) occur in Poland, but only in the north-eastern part of the country54. Up to 30 breeding territories were found in Estonia in the late 20th century62, but only a maximum of 10 territories remain63. There exists extensive hybridisation in Estonia, Poland and Belarus; the extent of hybridisation in Russia is largely unknown, although hybrids were detected in the only population in which it was studied in the Upper Volga26. Pure and hybridising pairs have been recorded also in Finland, Germany and Lithuania, but their numbers are low65.In Europe, the largest population of the Greater Spotted Eagle occurs in Russia  or those of one or both of the adults. Although nestlings may have lower genetic diversity compared to adults, differences are only subtle27. Therefore we paid special attention to species determination prior to population-level genetic analyses. Species identity of the samples was initially confirmed by assignment analysis, where a large (c. 1500 inds.) Pan-Eurasian data set of Greater Spotted Eagles, Lesser Spotted Eagles and various types of hybrids between the two species was included27, Ü. Väli, unpublished data. The software NEWHYBRIDS version 1.171 was used to calculate probability of Greater Spotted Eagle ancestry, q, for each individual using genotypes based on 24 microsatellite and 8 nuclear Single Nucleotide Polymorphism (SNP) markers (according to27). Only individuals with q values > 0.9 were included in further microsatellite and intron analyses. However, in the analysis of mtDNA we added also Greater Spotted Eagle-specific lineages from hybrid individuals. Comparatively, we conducted mtDNA analysis using only individuals identified as Greater Spotted Eagles, and obtained similar results.The Greater Spotted Eagle hybridises with the more common Lesser Spotted Eagle across the broad contact zone in Eastern Europe; the hybrids are viable and fertile72, 100 mM dithiothreitol (DDT) was added to improve lysis.DNA was extracted using proteinase K treatment followed by phenol-chloroform purification or using an extraction kit . When extracting DNA from the superior umbilicus of moulted flight feathers75, were amplified and genotyped as described by27; 7734 nucleotides from ten introns of various nuclear genes77 were sequenced according to77; 470 nucleotides from the hypervariable pseudo-control region of the mitochondrial DNA (mtDNA) were sequenced following29. We also sequenced and conducted global analyses on longer fragments (764 nucleotides) from 40 individuals. The results of which were similar to those of our study samples.Three types of genetic markers were analysed according to previously described protocols  evidence for null alleles was detected by MICRO-CHECKER. However, the results were consistent among populations for only IEAAAG11. Therefore, we removed this locus from further analysis, keeping the other five. We tested the effect of potential null alleles by estimating differentiation between populations using FreeNA software with and without ENA algorithm, which corrects for the bias induced by presence of null alleles79. No significant differences between the two sets of estimates were detected, which supports retaining loci for which the evidence of null alleles is inconsistent. Deviations from Hardy-Weinberg equilibrium were tested using ARLEQUIN version 3.5.2.280, but no significant deviations were found after applying a Bonferroni correction for multiple comparisons. Significance of linkage disequilibrium between intron and microsatellite loci was tested using FSTAT version 2.9.3.281 with 5000 permutations, but no significant linkage was found after applying a Bonferroni correction. Lack of linkage disequilibrium in the intron markers we used had been also verified by previous genomic studies77.No microsatellite genotyping errors, stutter peaks or allelic dropouts were detected by MICRO-CHECKER version 2.2.3 software82. Number of haplotypes (Nh), haplotype diversity (H) and nucleotide diversity (π) for nuclear introns and mtDNA were calculated for all samples, and separately for each of the four populations from which we had more samples, using DNASP version 5.1083. In order to avoid loss of information (an Estonian mtDNA haplotype contained a 27-nucleotide deletion), gaps were excluded only in pairwise comparisons. Number of alleles, allelic richness , observed and expected heterozygosities of microsatellites were obtained by ARLEQUIN and FSTAT. Between-population differences in allelic richness and heterozygosities were tested by analysis of variance.Haplotypes of nuclear introns were reconstructed for each intron separately using the software PHASE version 2.184. A median-joining network of mtDNA haplotypes was constructed with the software NETWORK version 585. Differentiation between populations  was further evaluated using ARLEQUIN. Significance was determined by 1000 permutations of gene copies between individuals. ARLEQUIN was also used to study partition of genotypic diversity among and within populations , and to conduct a Mantel test for the effect of isolation by distance. For the latter, geographical coordinates were determined as approximate middle points for each of the sampled populations.Relationships between genotypes were initially explored by conducting principal coordinates analyses (using GENALEX) and factorial correspondence analyses (using GENETIX version 4.0.5.2)86. The number of clusters (K) was detected with five independent simulations of potential K values up to four, using default parameters in the admixture model with correlated allele frequencies. Each run included 100,000 iterations for burn-in period, followed by 1,000,000 iterations for parameter estimation. Two approaches were used to choose the most likely number of clusters. First, number of clusters was estimated as a plateau-reaching values of log probability of data (Ln P(D) values87. Second, we calculated ΔK statistic, which is based on the rate of change in the log probability of data between successive K values; the correct number of clusters is the one with the highest ΔK value88. We assigned individuals to clusters using two approaches as per37 working on White-tailed Eagles. First, we used a strict 50% cut-off criterion, under which an individual is assigned to the cluster with higher membership; and second a more conservative 70% cut-off criteria, which retains individuals with intermediate values (for both clusters q = 31–69%) as intermediates between the two clusters. To identify recent migrants in the population, STRUCTURE was run employing a LOCPRIOR model, which uses information of the source population from which individual samples were taken.At the individual level, population structure was further analysed by studying microsatellite data using the software STRUCTURE version 2.3.4D89 and Fu’s Fs90. Mismatch distribution was investigated and τ values for demographic and spatial expansions were calculated using ARLEQUIN. Time of the expansion was calculated as t = τ/2ul, where u is the haplotype mutation rate per site per million years andlis the number of studied nucleotides91. We used two mutation rates. First, 6%, given the 3-fold faster mutation rate in pseudo-control region in eagles, compared to the 2% of cytochrome b sequence29. Second, 14.8%, which is the mutation rate of the non-coding control region in avian mtDNA92, and which has been commonly used in earlier avian studies (e.g.38).To investigate the demographic history, we tested for signals of sudden population expansion in mtDNA using ARLEQUIN and DNASP. We calculated Tajima’s 94 was run to detect recent bottlenecks in the total sample and in each population separately. Statistical significance of the heterozygosity excess was performed by Wilcoxon sign rank test under a two phase mutation model . The occurrence of a recent bottlenecks was evaluated also by checking mode-shift distortion in the distribution of allele frequencies95. Garza-Williamson indices, ratio of the allele number and the difference in allele lengths, were calculated using ARLEQUIN. Values above 0.82 have been found in populations that have not suffered a known reduction in size, and values below 0.70 have been detected in bottlenecked populations96.BOTTLENECK version 1.2.02Supplemetary Information