Anopheles sinensis is a competent malaria vector in China. An understanding of vector population structure is important to the vector-based malaria control programs. However, there is no adequate data of A. sinensis population genetics available yet.A. sinensis from 14 representative localities in China. All 5 microsatellite loci were highly polymorphic across populations, with high allelic richness and heterozygosity. Hardy–Weinberg disequilibrium was found in 12 populations associated with heterozygote deficits, which was likely caused by the presence of null allele and the Wahlund effect. Bayesian clustering analysis revealed two gene pools, grouping samples into two population clusters; one includes six and the other includes eight populations. Out of 14 samples, six samples were mixed with individuals from both gene pools, indicating the coexistence of two genetic units in the areas sampled. The overall differentiation between two genetic pools was moderate (FST = 0.156). Pairwise differentiation between populations were lower within clusters (FST = 0.008–0.028 in cluster I and FST = 0.004–0.048 in cluster II) than between clusters (FST = 0.120–0.201). A reduced gene flow (Nm = 1–1.7) was detected between clusters. No evidence of isolation by distance was detected among populations neither within nor between the two clusters. There are differences in effective population size (Ne = 14.3-infinite) across sampled populations.This study used 5 microsatellite loci to estimate population genetic diversity, genetic differentiation and demographic history of A. sinensis populations in China. The population divergence was not correlated with geographic distance or barrier in the range. Variable effective population size and other demographic effects of historical population perturbations could be the factors affecting the population differentiation. The structured populations may limit the migration of genes under pressures/selections, such as insecticides and immune genes against malaria.Two genetic pools with moderate genetic differentiation were identified in the  Anopheles sinensis Wiedemann 1828 is a widely distributed Oriental species A. sinensis was incriminated as a competent malaria vector and was responsible for the transmission during the recurrence of vivax malaria in recent years Plasmodium infection, survival and reproductive rates, degree of anthropophily, and the epidemiology of malaria in the human host may all be affected by genetic variation in vector populations A. sinensis exhibits variation in ecology A. sinensisA. sinensis was poorly susceptible to Plasmodium vivaxA. sinensis is more susceptible to P. vivax than to P. falciparum, and therefore it is an important vector in the areas where no other vector species exist A. sinensis was incriminated as a competent malaria vector A. sinensis has long been suspected to be the most important vector of malaria in temperate Japan, including Okinawa and Hokkaido Islands A. darlingiA. mouchetiA. gambiaeA. niliA. sinenesisA. sinensis to determine the population structure across its range in China.Despite its significance in malaria transmission, only a few studies on population genetics have been conducted A. sinensis were identified by a species diagnostic PCR assay Fourteen samples of wild mosquitoes were collected from 20 locations in China , Table 1A) as 12 (ANS15), 17 (ANS1 and ANS6), 18 (ANS11) and 19 (ANS5), respectively (OH) across all samples ranged from 0.398 (ANS5) to 0.757 (ANS11), the minimum Ho was in GUD (0.433), the maximum Ho in SIC (0.760). To determine if the null alleles impacted the population genetic analyses, we performed these analyses both before and after the dataset were adjusted for estimated null allele frequencies. The effect of this treatment was minimal and did not significantly change the degree or statistical significance of the estimated parameters.Polymorphism at five microsatellite loci varied, with number of alleles (ectively . The aveFIS), reflecting heterozygosity deficits  for all the samples assayed. At the population level, 26 out of 70 (37.14%) comparisons did not conform to Hardy-Weinberg expectations after sequential Bonferroni correction, and the deviations were associated with positive inbreeding coefficient . Two pairs were detected in HAN  and the other three pairs were in SHD (ANS6/ANS15), HUB (ANS1/ANS6), GUD (ANS6/ANS11), respectively .Fisher's exact tests were conducted for linkage disequilibrium (LD) within each of the 14 collections. Out of 140 comparisons only five pairs (3.57%) were at LD (ectively . No pairThe significant deviations from HWE with heterozygote deficiency and the presence of LD suggest the presence of population subdivision within samples (the Wahlund effect). We therefore examined if there were different gene pools in these samples. The Bayesian cluster analysis divided populations into two main subgroups (posterior probability of Bayesian clustering Ln(D) likelihood score optimal for k = 2 clusters) . One genF statistics (FIT (0.379) showed significant heterozygote deficits at the total population level, most likely due to the presence of null alleles. Individually, three loci, ASN1, ASN6 and ASN11, presented significant heterozygote reduction at the population level (P<0.05). The average value of FIS (0.264) showed heterozygote reduction at the subpopulation level. These results corroborated the homozygote excess detected with the individual HWE tests  while other loci ranged between 0.020–0.086. The average FST  = 0.156, indicating a moderate genetic heterogeneity between the two gene pools. An AMOVA analysis using the two clusters found that 15.6% of the variance was attributed to between populations and 84.4% to within populations.Overall genetic divergence between two gene pools was assessed. First, specimens were assigned to the cluster I or II at greater than 80% probabilities, which resulted in 126 individuals in the cluster I and 135 individuals in the cluster II. Then the two pools of individuals were analyzed by the Wright's atistics . The oveWE tests . Locus AFST estimates for pairwise comparisons among populations. Within the clusters, paiwise FST was low, 0.008 (HUB-GZI) to 0.028 (GZH-SHD) in the cluster I, and 0.004 (JSU-LIA) to 0.048 (HAN-GXI) in the cluster II. Between the clusters, higher level of differentiation was demonstrated with FST from 0.120 (FUJ-GZH) to 0.201 (SHD-GXI). Of 28 pairwise comparisons, 17 were significant including all 15 comparisons between clusters (P<0.05). Nm estimates among populations were higher (4.93 to 65.54) within the clusters, and much lower (0.99 to 1.84) between the clusters  from the cluster I and five  from the cluster II. clusters .A. sinensis populations.Tests of isolation by distance were performed for each population cluster and for all of the populations together. No statistically significant correlations were detected between genetic differentiation and geographic distances based on the Mantel test in all cases . The resHe<Heq) was detected in the GXI and SIC populations under SMM model (P<0.05), suggesting a recent demographic expansion.Estimates of expected heterozygosity under mutation-drift equilibrium (MDE) were calculated for detecting demographic instability. These statistics are expected to be equal in a neutral locus at MDE. Results of the heterozygosity tests  did not Ne varied considerably depending on the model used. Under the heterozygote excess model all of the Ne estimates were infinity. Under the linkage disequilibrium model, diverse Ne values were detected across populations  to 18.4 (SHD) in the cluster I and ∞ (HUB) to 14.2 (LIA) in the cluster II, respectively.Estimates of long-term ulations . The twoA. sinensis occur in most parts of China, a range as from 100° E to 120° E, and from 19° N to 54° N A. sinensis mosquitoes were collected from most localities across its range in China. The LIA was at the most northern limit, and HAN was at the most southern limit of the distribution.Sampling strategy and geographic coverage greatly influence the analysis and interpretation of the data generated from the samples. A. sinensisA. sinensis population genetic structure. Theses microsatellite loci have not been physically mapped to A. sinensis polygene chromosomes. Therefore, their location with respect to polymorphic chromosome forms is unknown, but linkage disequilibrium between loci was detected only in 3.6% of 140 comparisons suggests that they are at least statistically independent and might distribute across the genome. The high allelic diversity and expected heterozygosity were observed in most of populations, which was similar to the level of diversity in the A. darlingi in Peru and Brazil  A. albimanus in Latin America (OH  = 0.73) A. gambiae (OH  = 0.59) A. funestus  A. sinensis occurs in the temperate climate zones, populations undergo marked seasonal variations in abundance, reaching high densities only during the summer months. The high level of genetic diversity suggests that A. sinensis are able to maintain a relatively large effective population size in spite of the seasonality of low temperature in cold winter.The five microsatellites in In this study, significant deviations from HWE due to the heterozygote deficits were detected in most samples. These could be attributed to the Wahlund effect, inbreeding, selection or null alleles. Selection was not considered because it usually occurs in one locus, not systemically in multiple loci as detected in this study. Inbreeding has genome-wide effect. If inbreeding were important in our case, we would expect a similar heterozygosity deficiencies in all markers with each population studied, which we did not detect. The reduction of heterozygotes at several loci detected in this study is likely the results of presence of null alleles that are not amplified because of the mutations at the primer annealing sites. In this study, there were specimens that failed to amplify some alleles at certain loci, but efficiently amplified at other loci. The Wahlund effect, referring to subpopulations in a sample, is likely another cause for the heterozygote deficits. Indeed, the Bayesian analysis revealed two gene pools across the locations sampled , and theA. sinensis were represented by two population clusters. There is substantial differentiation between two gene pools, demonstrated by mean FST = 0.156 between the two clusters (FST = 0.120–0.201) among populations between the two clusters (Nm  = 1–1.7). The level of differentiation is comparable to that detected previously between A. sinensis populations in China using isozymes and RAPD markers (FST  = 0.069–0.111) A. albimanus populations from Central and South America (FST = 0.114) A. gambiae populations in west Africa separated by >200 km (FST  = 0.034–0.167) A. darlingi populations from Brazil and Peru A. funestus populations from west, central, and eastern Africa (FST  = 0.110) e.g., Yangtze River, Yellow River and Qinling Mountains).The two genetic divisions of clusters  and pairclusters . The genFST = 0.004–0.048). No isolation by distance was detected  and more genetic loci in order to thoroughly elucidate the forces that shape and maintain the population structure. More studies are required to characterize the two gene pools of A. sinensis regarding ecology and malaria susceptibility. The population structure of A. sinensis implies that the expansion and spread of genes responsible for immunity against malaria or insecticide resistance would be easier within than between the genetic units. Moreover, the estimate of effective population size would help to evaluate the effectiveness of mosquito control measurements This study revealed that A. sinensis were collected from 1996 to 2008, by using indoor light traps at livestock corrals. Human landing catches at human living room were tried in 1996 in Sichuan (SIC) and Hainan (HAN). Only two specimens were caught by human bait in SIC. A. sinensis is zoophilic, therefore, the human bait was no longer used in later collections. The 20 collection sites in 14 provinces in China were located from 109°58′ N to 120°25′ N, and 23°60′ E to 31°44′ E  values per locus between clusters and pairs of populations using ARLEQUIN 2.001 Nm) between localities was estimated from pairwise FSTFST. We focused on estimates of FST performed under the infinite alleles model (IAM) because this model is considered more reliable when fewer than 20 microsatellites are used P-values. The isolation by distance model was investigated as a potential explanation for the observed population differentiation. The significance of the regression of genetic differences on geographic distance between sample pairs was tested using a Mantel test Genetic differentiation was estimated by calculating Wright's K) in the data set without prior information of the sampling locations, implemented with STRUCTURE 2.2 K (K = 1 to 9), with a burn-in period of 100,000 iterations and 100,000 replications. The method of Evanno et al.ad hoc quantity, ΔK, based on the second order rate of change of the likelihood function between successive values of K.A Bayesian approach was used to infer the number of clusters (He), assuming Hardy-Weinberg proportions, and another based on the number of alleles and sample size (Heq), assuming MDE. At MDE, both estimates should be similar in the majority of loci analyzed (i.e. He = Heq). If a population experiences a bottleneck, rare alleles will be rapidly lost and therefore Heq will decrease faster than He (i.e. He > Heq). This apparent excess of heterozygosity in a significant number of loci is an indicator of a bottleneck, whereas the converse (i.e. He <Heq) may indicate a population expansion. Estimates of expected heterozygosity under MDE were calculated under the Stepwise Mutation Model (SMM) and Two Phase Models (TPM) with 10%–30% indels larger than the repeat unit. Calculations were done using the software BOTTLENECK 1.2.02. Ne) was estimated using NEESTIMATOR 1.3 Because demographic instability such as recent population bottleneck and/or expansion might bias genetic differentiation estimates to a significant extent Figure S1Allele distributions across populations for each of five loci. Alleles are denoted by length (bp), and populations are color coded.(TIF)Click here for additional data file.Figure S2Differentially distributed alleles across populations between two clusters. Top, ANS1; Middle, ANS5; Bottom, ANS6. Differential alleles are color coded, and populations were arranged based on their cluster assignment.(TIF)Click here for additional data file.


Limnadopsis in eastern and central Australia . Mitochondrial cytochrome c oxidase subunit I sequences of 413 individuals and a subset of 63 nuclear internal transcribed spacer 2 sequences were analyzed. Genetic differentiation was observed between populations inhabiting southeastern and central Australia and those inhabiting the northern Lake Eyre Basin and Western Australia. However, over large parts of the study area and across river drainage systems in southeastern and central Australia , no evidence of population subdivision was observed in any of the four Limnadopsis species. This indicates recent gene flow across an area of ∼800,000 km2. This finding contrasts with patterns observed in other Australian arid zone taxa, particularly freshwater species, whose populations are often structured according to drainage systems. The lack of genetic differentiation within the area in question may be linked to the huge number of highly nomadic water birds that potentially disperse the resting eggs of Limnadopsis among temporary water bodies. Genetically undifferentiated populations on a large geographic scale contrast starkly with findings for many other large branchiopods in other parts of the world, where pronounced genetic structure is often observed even in populations inhabiting pools separated by a few kilometers. Due to its divergent genetic lineages (up to 5.6% uncorrected p-distance) and the relaxed molecular clock divergence time estimates obtained, Limnadopsis parvispinus is assumed to have inhabited the Murray–Darling Basin continuously since the mid-Pliocene (∼4 million years ago). This means that suitable temporary water bodies would have existed in this area throughout the wet–dry cycles of the Pleistocene.Temporary water bodies are important freshwater habitats in the arid zone of Australia. They harbor a distinct fauna and provide important feeding and breeding grounds for water birds. This paper assesses, on the basis of haplotype networks, analyses of molecular variation and relaxed molecular clock divergence time estimates, the phylogeographic history, and population structure of four common temporary water species of the Australian endemic clam shrimp taxon  In Australia's arid and semiarid zone, which covers about two-thirds of the continent , permaneIn addition to organisms, which also occur in other freshwater habitats, the fauna of temporary water bodies comprises a range of characteristic organisms . A numbeThe evolution of species inhabiting the arid zone of Australia is closely coupled with the ongoing aridification, which started about 15 million years ago (mya) in the mid-Miocene The present study focuses on some of the most remarkable representatives of the Australian large branchiopods, those of the spinicaudatan taxon n length . Limnado studies . Limnado birchii  and L. t L. tatei. In an iLimnadopsis species is studied on the basis of two molecular markers  in order to assess the evolutionary and demographic history of species inhabiting Australian temporary water bodies. The main goal of this study is to identify the extent of population cohesion on various geographic scales . Due to the patterns of genetic differentiation observed in populations of taxa with similar life-history traits in other parts of the world , and those observed in species inhabiting isolated Australian freshwater systems (waterholes or groundwater springs), genetically differentiated populations are expected at geographic distances of much less than 100 km. Furthermore, a potential correlation existing between genetic structure and main drainage systems, as observed in many riverine species, is tested, because rare, severe floods may interconnect otherwise isolated temporary water bodies within river drainage systems, facilitating dispersal and gene flow within but not among drainage systems.In this paper, the phylogeography of four closely related Adult specimens were collected between 1998 and 2011 . The spe2 (50 mM) topped up with H2O to a total of 30 µL. The PCR program for COI consisted of an initial denaturation step of 1 min at 94°C, followed by 38 amplification cycles , and a final elongation step at 70°C for 5 min. For the amplification of ITS2, the annealing temperature was set to 50°C. PCR products were purified using magnetic beads  or by cutting out visible bands using the QIAquick Gel Extraction Kit  following the manufacturers’ instructions. PCR products were sequenced with the DCTS Quick Start Kit (Beckman Coulter) on an automated sequencer (CEQTM 800 from Beckman Coulter) or by the Qiagen Sequencing Service (Qiagen). All in all, 413 specimens were sequenced for COI and a subset of 63 specimens for ITS2. Sequence electropherograms were analyzed using Sequencher 4.1.4 (Gene Codes), and corrected sequences were aligned using the ClustalW algorithm , whereas “Buchanan” relates to populations in central and northern Queensland .In the following, certain terms are used to describe the hierarchical structure of geographic regions and populations. The smallest unit-population refers to the individuals living in the same pool. The term (sub)catchment refers to the catchment area of single rivers , whereas (main) drainage system denotes larger areas usually consisting of several interconnected (sub)catchments . In Owing to the more extensive COI dataset, most of the following analyses were performed solely with this gene fragment, meaning that most results and conclusions will be based on COI. To avoid repetition, COI will not be referred to in all instances.The number of variable and parsimony-informative sites was computed using Mega5  for eachL. parvispinus with uncorrected p-distances in COI of up to 9.9%. As the genetic distance between these two lineages might affect the topology within them, Median Joining networks were calculated for each lineage independently as well as for the whole L. parvispinus dataset.To assess the geographic distribution of genetic lineages, COI and ITS2 haplotype networks were calculated using the Median Joining algorithm in Network 4.6 (Fluxus Techno-logies Ltd.). One single network was produced for ITS2 while for COI a separate network was calculated for each species. A fundamental assumption underlying these network analyses  is that each haplotype evolved only once so occurrence of the same haplotype in different populations is due to historical dispersal events. L. birchii population from Onslow, Western Australia, from which only three individuals were available), as populations of fewer individuals could obscure the results. For each species, one AMOVA was run, with populations grouped according to subcatchments within drainage systems. Additional AMOVAs were performed for L. birchii and L. parvispinus in which the populations were grouped according to the main genetic lineages. For L. birchii, three groups were predefined: (1) all populations of the Murray–Darling Basin, the Bulloo River, and the Finke River , (2) Western Australia, and (3) the Cooper Creek . For L. parvispinus, two additional AMOVAs were run. In the first, two groups were predefined, which correspond to the two main genetic lineages “Paroo” and “Buchanan” described by To determine whether or not populations are structured according to drainage systems, several analyses of molecular variance AMOVAs;  were perFCT value (variation among groups), is, in theory, that which represents the maximum genetic differentiation among groups of populations. Only those populations included in the AMOVA runs were used in the SAMOVAs. For L. parvispinus, additional SAMOVA runs were carried out for each of the two main genetic lineages.Because the predefined population groupings used in the AMOVA analyses might not be representative of actual genetic structuring within the species, we also conducted a spatial analysis of molecular variance SAMOVA; , which dST for COI was calculated using Arlequin 3.5. Here again, only populations for which at least four individuals were available were included. In contrast to traditional FST, which only considers haplotype frequencies, ΦST also takes the differences between haplotypes into account. Mantel tests for each species were performed in Arlequin 3.5 to determine whether genetic differentiation is correlated to geographic distance (isolation-by-distance). Pairwise ΦST values were compared to the geographic distance in kilometer. Statistical significance was tested by 1000 permutations.To assess the degree of population differentiation, pairwise ΦLimnadopsis species was determined by means of neutrality tests and COI data mismatch distributions carried out in Arlequin 3.5. To test whether populations evolve under neutrality, Fu's Fs  of the four  Fu's Fs  and Taji's Fs  SSD and Hri values mark deviations from the sudden expansion model. Conversely, sudden expansion cannot be rejected if SSD and Hri values are nonsignificant (P > 0.05).Mismatch distributions, which represent the distribution of pairwise nucleotide differences between all the individuals in a given dataset and are used to test for sudden expansion events and infer their relative timing, were calculated using the sudden expansion model. The validity of the sudden expansion assumption was determined using the sum of squared deviations (SSD) and Harpending's raggedness index Hri; , both ofp-distances  using Mega5. To estimate the divergence times of different COI lineages, Kimura-2-Parameter (K2P) corrected distances were determined for the COI dataset. Divergence time estimates are, in the absence of reliable estimates of COI divergence rates specifically for branchiopods, based on the suggested decapod crustacean pairwise divergence rates of 1.4%  of over 200 was determined using Tracer 1.5 : “Buchanan,”“Paroo,” and the population furthest to the east of the Murray–Darling Basin , though this is largely due to the genetic differences between the two main lineages. A similar AMOVA including only the four subcatchments of the Murray–Darling Basin (an area that corresponds to the geographic distribution of the “Paroo” lineage) resulted in an FCT value of only 0.187 (deemed 73% of the genetic variation to occur within single populations)  was obtained when the Meandarra population  was grouped separately from all other populations and did not result any further groupings within the “Paroo” lineage. The genetic distinction of the Meandarra population was further strengthened by high ΦST values  . The SAMT values , indicatT values .FCT value of the SAMOVA analyses increased with the number of proposed groups until all the populations were assigned to separate groups. Pairwise ΦST values were generally greater than within the “Paroo” lineage  is the furthest away from the other populations at a distance of about 100 km. Geographic distances within the “Paroo” populations are much larger  featured a haplotype belonging to the L. birchii“Buchanan” lineage (Lb03). We double-checked this remarkable finding  and can exclude contamination. The Western Australian lineage and L. birchii“Buchanan” are separated by 10 and six mutational steps, respectively, from L. birchii“Paroo,” showing that the separation is not recent . The ITS2 haplotype network did not reveal any genetic structure in correlation to the geographic origin of the specimens ; grouping the populations according to subcatchments resulted in a lower FCT value (0.669). Gene flow among populations of these three lineages (or geographic areas) is restricted, as indicated by pairwise ΦST values of ≥0.80  and a significant correlation between ΦST and geographic distance in the Mantel test  were greatest in L. parvispinus, at up to 9.9% between the “Paroo” and “Buchanan” lineages and 5.6% and 3.3%, respectively, within these lineages  and specimens from central and eastern Australia.The COI genetic distances observed . Within L. birchii, the three main lineages diverged about 1–2.5 mya. The order in which they did so is ambiguous, going by genetic distances  dates to about 43 mya, with the lower bound of the 95% confidence interval extending to around 11 mya.The majority of haplotypes in all four species originated at some point within the last 0.5–0.8 million years, according to the relaxed molecular clock estimate. Deeper intraspecific splits occurred largely during the last 2.0–2.5 million years . However, the oldest intraspecific split within istances  and the istances , and it Limnadopsis species revealed overall congruencies in the spatial distribution of genetic lineages. The eastern Australian populations of L. parvispinus and L. birchii are differentiated into lineages inhabiting central Queensland  and lineages inhabiting the Murray–Darling Basin (Paroo). This finding ties in with the results of studies of river-inhabiting species such as fish, crayfish, and mollusks .A comparison of the phylogeographic patterns revealed by the COI dataset in the four mollusks ,b, whosemollusks . As dispmollusks . This isL. birchii, L. tatei, and L. paratatei, which inhabit the Bulloo catchment and southern (Lake Frome) and westerns (Finke River) areas of the Lake Eyre Basin (L. parvispinus has not been found in these areas), are genetically very similar or even identical to the populations of the Murray–Darling Basin. Similarly, no genetic subdivision corresponding to the subcatchments of the Murray–Darling Basin was revealed in any Limnadopsis species. The high number of haplotypes exhibiting wide spatial distribution indicates recent and numerous independent dispersal and gene flow events over large geographic distances  and across drainage systems borders. This cannot be a result of actual drainage connectivity between the river basins concerned. We suggest that the lack of genetic differentiation over large geographic distances is down to a mode of dispersal, which is independent of the direct connection of habitats and the connection of river systems . As genetic differentiation across drainage systems is low or nonexistent along the east–west axis , also the genetic differentiation of the central Queensland populations (Buchanan) may be due to other dispersal-limiting factors than the actual drainage division . Populations in the far west of Australia  appear to be genetically distinct from other populations, probably to the same extent as observed between L. birchii “Paroo” and “Buchanan,” but the limited number of individuals studied does not allow any clear conclusions to be drawn.However, the genetic differentiation (seemingly between drainage systems) is only evident along the north–south axis in eastern Australia. The populations of L. parvispinus “Paroo” and “Buchanan.” However, the very low overall intraspecific diversity, which has been reported for other crustaceans as well . However, due to the inherent uncertainties in the dating of diversification events, the actual diversification may have occurred much earlier. Nevertheless, the inferred time periods are associated with important changes in climate and thus vegetation. Glacial cycling started at the beginning of the Pleistocene and changed in the mid-Pleistocene to cycles of longer periodicity and greater amplitude. The alternation of wet interglacial and dry glacial periods was overlaid by an overall trend toward aridification and an expansion of the arid zone in Australia  permanent water bodies and reduced the number of adequate temporary habitats. Limnadopsis and other branchiopod species are therefore likely to have benefited from the progressing aridification of Australia, with the increase in available habitats resulting in expanding populations. All four species studied provide some indication of recent expansion events. Some deviations  may be explained by the retention of divergent “old” lineages and the existence of divergent haplotypes in refugial areas , L. birchii “Buchanan,”  and L. birchii “Paroo” . The combination of low genetic diversity and wide haplotype distribution in L. tatei and L. paratatei also indicates recent expansions of these species across central and southeastern Australia and explains why no clear pattern of genetic structuring is seen over these large areas of arid zone Australia. Limnadopsis . This is indicated by the high overall genetic diversity and deep haplotype divergence observed in these areas. According to the estimated divergence times of these haplotypes, L. parvispinus “Paroo” may have inhabited the Murray–Darling Basin continuously for the last 4 million years and L. birchii “Paroo” for the last 1 million years. Consequently, suitable habitats would have persisted in this area in both dry glacial and wet interglacial periods. Later immigration from other areas is another possibility, though it is unlikely that such a genetically rich and divergent assembly of haplotypes would be the result of immigration only, especially as no other areas, which may have acted as source populations, are known to be inhabited by L. parvispinus today , in which case the two species would have expanded into central and eastern Australia only recently. Under this scenario, either a lack of suitable habitats during the Pleistocene or factors limiting the dispersal ability of these species may have prevented them from inhabiting the Murray–Darling Basin earlier. Even today neither species is as prevalent in this area as L. parvispinus or L. birchii (L. tatei and L. paratatei was not made therein). Differences in their distribution and occurrence may reflect distinct habitat preferences that vary between species  are generally assumed to have a high potential for dispersal. Many of these species are among the first colonizers of new habitats , Eurasian Coot (Fulica atra), Black Swan (Cygnus atrus), and Australian Pelican (Pelicanus conspicillatus) feeding in the Macquarie Marshes (located within the Murray–Darling Basin). “Large branchiopod” resting eggs were found in 30% of the fecal samples and were retrieved from all bird species studied. The highest percentage (50%) of “large branchiopod” egg-containing fecal samples came from the Grey Teal, and Australian Pelican feces contained the largest absolute number of resting eggs. As all of these bird species are highly nomadic, they have the capacity to disperse freshwater invertebrates over large areas  and Meandarra in the east of the Murray–Darling Basin , were not interconnected with the other wetland habitats of the Lake Eyre and Murray–Darling Basins by migratory corridors suitable for water birds . Wind-mediated gene flow should thus be unidirectional from western areas to eastern areas. If wind was the main force behind the dispersal of resting eggs, haplotypes originating in eastern Australia would not disperse to the west. However, the haplotype distributions in the present study did not reveal such unidirectional dispersal, so it seems that while wind may disperse resting eggs over large distances at times, the effect of migrating birds is likely to be stronger.Wind may also play an important role in the dispersal of resting eggs . StudiesFloods may also disperse resting eggs on a local scale by connecting neighboring pools after exceptionally strong rain falls. However, the effect of flooding is probably restricted to neighboring pools within subcatchments. If floods were the main dispersal agent, populations should show some genetic structuring according to subcatchments and main basins, which was not the case.2), a phenomenon that has not been observed to this extent in any other branchiopod species, may be attributable to the dispersal capacity of the abundant, vagile migratory water birds that inhabit east and central Australia during wet periods. Wind and local floods may further facilitate the dispersal of resting eggs locally. The number of resting eggs being dispersed on a regular basis may be large enough to overrule founder effects and the buffering effects of resting egg banks. This may also explain why Limnadopsis exhibits patterns of range expansion similar to those of vagile arid zone taxa like the King Brown snake or the Spiny-cheeked Honeyeater .In summary, the congruent pattern of virtually no genetic differentiation over large areas  in both lineages is high. The high nucleotide diversity reflects the long differentiation history of the haplotypes, which is underlined by uncorrected p-distances of up to 5.6% in the “Paroo” lineage. The “Paroo” lineage in particular features a number of highly divergent haplotypes (Lp33–Lp55) that are separated by numerous mutational steps from all other haplotypes and found only in one or a few individuals. These divergent haplotypes might represent remnants of haplotype lineages, which are largely extinct. These “old” haplotypes may have been retained in the populations through the preserving effect of the resting egg bank.This effect may be evident in the


Salmo salar L.) exhibit natural variance in susceptibility levels to the ectoparasite Gyrodactylus salaris Malmberg 1957, ranging from resistance to extreme susceptibility, and are therefore a good model for studying the evolution of virulence and resistance. However, distinguishing the molecular signatures of genetic drift and environment-associated selection in small populations such as land-locked Atlantic salmon populations presents a challenge, specifically in the search for pathogen-driven selection. We used a novel genome-scan analysis approach that enabled us to i) identify signals of selection in salmon populations affected by varying levels of genetic drift and ii) separate potentially selected loci into the categories of pathogen -driven selection and selection acting upon other environmental characteristics. A total of 4631 single nucleotide polymorphisms (SNPs) were screened in Atlantic salmon from 12 different northern European populations. We identified three genomic regions potentially affected by parasite-driven selection, as well as three regions presumably affected by salinity-driven directional selection. Functional annotation of candidate SNPs is consistent with the role of the detected genomic regions in immune defence and, implicitly, in osmoregulation. These results provide new insights into the genetic basis of pathogen susceptibility in Atlantic salmon and will enable future searches for the specific genes involved.Mechanisms of host-parasite co-adaptation have long been of interest in evolutionary biology; however, determining the genetic basis of parasite resistance has been challenging. Current advances in genome technologies provide new opportunities for obtaining a genome-scale view of the action of parasite-driven natural selection in wild populations and thus facilitate the search for specific genomic regions underlying inter-population differences in pathogen response. European populations of Atlantic salmon ( Parasites are considered to be among the strongest selective forces driving the evolution of host populations , with populations differing widely in their susceptibility to the monogenean ectoparasite Gyrodactylus salaris. Stocking of salmon originating from one basin to another has inadvertently exposed numerous populations of Atlantic salmon in Norway and one population in Russia to this ectoparasite and subsequently decimated them , large freshwater reservoirs began to form G. salaris resistance observed in north European Atlantic salmon offers an opportunity to use an evolutionary approach to study the genetic basis of parasite resistance. This, combined with the availability of an Atlantic salmon SNP chip G. salaris-induced selection in the Atlantic salmon genome via approaches such as hitchhiking mapping The gradient in G. salaris-induced selection in Atlantic salmon populations. First, earlier research has indicated that Russian landlocked salmon populations, i.e., those with the highest level of G. salaris resistance, have relatively low population sizes and are hence strongly affected by genetic drift There are, however, several challenges for detecting G. salaris-induced selection in north European salmon populations results from the fact that the G. salaris resistance gradient co-varies with several environmental gradients, one of the most prominent being salinity G. salaris exposure and, e.g., salinity tolerance may be difficult. However, the phylogeographic history of the populations in the region enables the design of specific comparisons to potentially separate the signals of these alternative selective forces.A second challenge for detecting G. salaris)-driven selection and selection acting upon other environmental characteristics.In this study, we used a novel genome-scan analysis approach aimed at i) identifying signals of selection in salmon populations affected by varying levels of genetic drift and ii) separating the potentially selected loci into those affected by pathogen , the Baltic Sea (brackish) and landlocked lakes (freshwater); and they represent different susceptibility levels to the parasite G. salaris  or diversity (GD) and hence represent genomic regions potentially subjected to natural selection et al.et al.FST values were first calculated using the approach described by Weir and Cockerham FST or GD statistics to the regional average was estimated using local polynomials and a bandwidth of 3.5 cM . The width proved to be appropriate to reduce sampling variance while being small enough to detect narrow regions of differentiation (data not shown). A permutation procedure  was performed to statistically test whether the smoothed curve was significantly (P ≤ 0.01) higher or lower than expected by chance within a local genome region. This candidate genomic region approach was applied to the following population comparisons.We first identified groups of adjacent markers that deviated from the chromosome-wide average levels of divergence . Despite the very low genetic diversity in these populations  or from the Baltic Sea (design 3). When compared to populations from landlocked lakes, Barents and Baltic Sea salmon populations differ in their phylogeographic history and vary in their relative response to the parasite, salinity of the basin and, possibly, other environmental characteristics; therefore these designs may help to disentangle the effects. The landlocked population, V. Kuito lake (LL_PIS), was excluded from these designs as it originates from a different glacial refugium than the other landlocked populations FST by conducting six pair-wise population comparisons: design 2) between Ladoga (LL_SYS) & Onega (LL_PYA) and three Barents Sea populations , and design 3) between Ladoga (LL_SYS) & Onega (LL_PYA) and Baltic Sea populations . In each case, regions were identified as potentially under directional selection when elevated FST was observed in a particular genomic region for at least two comparisons out of six, with the additional criterion that both LL_SYS and LL_PYA should be represented in the significant comparisons. This ensured that observed elevations in FST statistics were not artefacts of a high genetic drift in a single landlocked population. Populations from the White Sea were not included in Design 2 in order to avoid a skew in number of individual tests between designs 2 and 3.Designs 2 and 3 focused on comparisons between two landlocked lakes (Ladoga and Onega) and either populations from the Barents Sea FCT - based “outlier” test that accounts for hierarchical population structure FCT), controlled for heterozygosity of the loci. The expected distribution of FCT values was obtained by 50,000 coalescent simulations, and loci falling outside the upper boundary of the 0.95 confidence interval were considered to be potentially affected by directional selection.To detect the potential effects of selection on a regional scale, we applied an G. salaris susceptibility levels. Populations were combined into three groups: susceptible Atlantic Ocean , tolerant Baltic Sea , and resistant landlocked populations . Three pair-wise outlier tests were performed: design4a) Atlantic group vs. landlocked, 4b) Atlantic vs. Baltic, 4c) Baltic vs. landlocked. Resulting single SNPs outliers could further be separated into a category of SNPs influenced by parasite and another category of SNPs influenced by salinity. To do so we applied the following logic. Populations from the Atlantic group are completely susceptible to G. salaris, whereas populations from the Baltic and landlocked groups are, to some extent, similar in their response to the parasite: with a certain level of effectiveness, they are able to tolerate the pathogen 4a (Atlantic group vs. landlocked) and 4b  designs, but not found in 4c , are considered more likely to be the result of parasite-driven selection occurring in landlocked and Baltic populations. Likewise, the Atlantic and Baltic groups both migrate to saline environments (marine and brackish water), whereas landlocked populations never experience a saline environment as they migrate to freshwater lakes. Thus, we predict that outliers common for both 4a (Atlantic group vs. landlocked) and 4c  designs, but not for 4b , are more likely to be affected by salinity-driven selection in the Atlantic and Baltic populations compared to the landlocked populations.The hierarchical structure was formed based on the The genomic regions in which “footprints” of selection were detected with each of designs 1–3 were compared identify overlaps in selection footprints between the designs. Cases where the same region was detected by at least two of the three designs were highlighted as promising candidates that are potentially affected by some form of selection. We then plotted outlier SNPs detected in design 4 along the genome to relate the linkage map position of these loci to the regions detected by designs 1 – 3. Genomic regions highlighted as promising candidates potentially affected by some form of selection in designs 1 – 3 and that also included “Parasite-influenced” outliers (common outliers from designs 4a and 4b) were considered promising candidate regions that are potentially affected by parasite-driven selection. The same logic was applied for “Salinity-influenced” outliers (common outliers from designs 4a and 4c) to identify genomic regions harbouring signals consistent with salinity-driven selection.et al.  from the GO database (www.geneontology.org). For further enrichment and functional analysis, a more recent version of the GO database (as of 08.10.2013) was used.Annotation of all 4631 SNPs to specific gene ontology (GO) terms was performed using a customised python-based procedure intended to maximise the number of successfully annotated markers. Briefly, the general steps included tblastx and blastx searches of SNPs flanking sequences against nucleotide and protein NCBI databases with a 1×10Enrichment analyses could only be conducted on the results of tests that identified single SNP outliers, as opposed to genomic regions i.e. design 4 and landscape genomics analysis. To determine whether sets of parasite- and salinity-affected single SNP outliers identified by design 4 and by landscape genomics analysis were significantly enriched or depleted for particular GO terms, a hypergeometric test for GO term over- and under-representation was performed in Cytoscape 2.8.3. Functional relatedness of GO terms included in the “parasite” and “salinity” lists of outliers resulting both from design 4 and landscape genomics analysis was assessed using the Cytoscape plugin ClueGo1.7.1 HO and GD , while populations from the Barents and White Seas were the most variable (from 0.293 and 0.275 (BW_SUM) to 0.355 and 0.356 (BW_TUL), respectively; Basic genetic statistics across populations and markers are presented in -5, STF values were observed within landlocked comparisons (from 0.32 LL_PYA vs. LL_SYS to 0.52 for LL_PYA vs. LL_PIS); Baltic Sea comparisons varied between 0.08 (BA_TOR vs. BA_VIN) to 0.23 (BA_VIN vs. BA_KUN), while the Barents and White Sea populations were the least differentiated: STF varied from 0.05 (BW_LEB vs. BW_TUL) to 0.18 (BW_SUM vs. BW_EMT).All pair-wise comparisons showed highly significant levels of population genetic differentiation P<10-5, . The higAn AMOVA analysis indicated a significant genetic variation among and within geographic regions . Notably, the amount of genetic variation explained by the within-region component  is higher than the variation explained by the among-region component , indicating markedly high between-population differentiation within regions .1: see 2) or Baltic Sea (design 3) exhibited significant differentiation at 7 regions across 7 chromosomes and 15 regions from 13 chromosomes, respectively.Regarding reductions in gene diversity within the landlocked populations (design 4a), 394 (8.5%) in the Atlantic vs. Baltic comparison (design 4b), and 523 (11.3%) in the Baltic vs. landlocked test (design 4c)  showed signals of selection when comparing Atlantic vs. landlocked populations (design sign 4c) _2. Usingsign 4c) .Results of all comparisons in designs 1 – 3 were combined to identify genomic regions suggested to be potentially affected by selection by at least two of the three designs. In total, seven genomic regions were identified : five reG. salaris-induced mortality, followed by the SNPs correlated with longitude (193 SNPs), salinity of the basin (179 SNPs), latitude (10 SNPs), and water temperature (6 SNPs). The same SNPs were frequently detected to be correlated with several environmental characteristics, with salinity and G. salaris-induced mortality associated with the same SNP on 124 occasions  was correlated with ccasions . DetaileA total of 2857 SNPs were successfully annotated (GO database as of 05.04.2013) and used further for the enrichment tests . For desFunctional network analysis conducted on outliers identified by design 4 however, grouped SNPs in the parasite- or salinity-driven selection sets into three significant and non-overlapping functional clusters . Terms iGyrodactulus salaris. We have identified a number of single SNP markers and several regions in the Atlantic salmon genome exhibiting signatures of directional selection. Based on the comparison and combination of results from different tests and methodological designs, we propose at least three genomic regions, on chromosomes 10, 11 and 23, potentially contributing to parasite resistance in Atlantic salmon and three regions, on chromosomes 4, 6, 9, presumably influenced by salinity-driven selection.The goal of this study was to reveal the genetic basis of Atlantic salmon inter-population susceptibility differences to the parasite 2, 15 in design 3, and 27 in design 1. Therefore, at least designs 1 and 3 are likely to have identified a number of regions potentially affected by selection. The likelihood of identifying the same genomic region in two tests purely by chance is likely to be considerably lower. However, as the designs implemented in this study are not completely independent it is not possible to make an exact calculation. Given that the same landlocked populations (lakes Ladoga and Onega) were included in all three designs, specific genetic features of one or more of those populations could potentially result in an increase in the detection of  positives in more than one design for this reason, rather than due to selection. On the other hand, as the landlocked populations are the only populations known to be totally resistant to G. salaris, there are no clear alternatives. That said, we attempted to limit the potentially biasing effects of a single landlocked population by only including outlier signals detected in more than one landlocked population in designs 2 and 3 . It has been proposed, however, that STF -based outliers might not only be an outcome of natural selection but can also indicate a presence of intrinsic barriers of gene flow, especially when the existence of intrinsically incompatible alleles is coupled with environmental or ecological constraints of gene flow CTF -based outliers  and Baltic  Atlantic salmon G. salaris resistance were different to those highlighted by our study. Marker-trait associations in the Gilbey et al.FST-based methods have a number of limitations if applied to search for signals of balancing selection, including high rates of false positives G. salaris resistance in Atlantic salmon.At least three genomic regions affected by parasite-driven selection have been identified in our study. Given the plausible implication of these regions in the mechanisms of immune response, our findings support the existing conception of polygenic control of immunity in Atlantic salmon. Differential expression studies in response to various pathogens including G. salarisG. salaris exposure by enhanced production of IL- 1β and IFN-γ cytokines and elevated proliferation of inflammatory cells, which, in turn, triggers the proliferation of epithelial and mucous cells that the parasite feeds on. Therefore, susceptible salmon launch an immune response that might actually be beneficial to the parasite G. salaris infection later in time and with up-regulation of genes encoding other molecules: IFN-γ, T-cell receptor-α and serum amyloid A G. salaris-mediated immune response, when, according to Kania et al.G. salaris infection. G. salaris, like other parasitic flatworms,  Functional network analysis of the design 4 “parasite” influenced outliers is consistent with an immune-related role for these regions. However, caution is warranted because it is possible that the observed signals of selection may be due to selection acting on a tightly linked gene with a different function. Recently, the potential dangers of ‘storytelling’ based on GO annotations has also been cautioned +/K+ ATP-ase et al.,CaSR+ concentration and activates the appropriate intracellular signalling pathways, acting as an internal salinity sensor CaSR has been shown to be upregulated during smoltification in Atlantic salmon, supporting its importance in osmoregulation et al.,CaSR appears to be a very strong candidate for involvement in salinity adaptation in salmonid fishes. Although the remaining two genomic regions we identified were not identified in the QTL study of Norman et al.,Three genomic regions presumably affected by salinity-induced selection were identified in our study. To our knowledge, an outlier approach has not previously been used to investigate the genetic basis of salinity tolerance in Atlantic salmon. However, there have been a number of investigations of this topic in salmonids and other fish species using alternative approaches. The effect of varying salinity levels on the RNA expression level of genes known to be involved in osmoregulation has been studied in species with varying salinity tolerance Slc9a3r1 (sodium-hydrogen antiporter), which functions in kidney of mice and controls phosphate and uric acid concentration in urine output PPP2R1A (serine/threonine-protein phosphatase regulatory subunit A) contributes to regulation of cell autophagy as part of an anti-bacterial response G. salaris. The landscape genomic analysis additionally highlighted that distinguishing between selection acting upon different environmental factors is not straightforward (see below).Functional network analysis suggested that the “salinity-selected” SNPs detected by design 4 were associated with GO biological process terms involved in the protein kinase B cascade and peptidyl-tyrosine phosphorylation , althougThe analyses used in our study were designed to provide a means to tease apart the effects of pathogen driven selection and other potential selective forces. We considered a simple scenario where the only environmental characteristic, salinity of the basin fish migrates to, was taken into consideration. Nevertheless, we also tested for associations between SNPs allele frequencies and other varying environmental factors using the LFMM approach.G. salaris  was correlated with mortality rate in presence of  salaris . When co salaris , indicatA surprisingly low number of SNPs were correlated with water temperature , given that temperature regime is often associated with population genetic diversity in salmonids rev. by  and otheG. salaris resistance in Baltic Sea and landlocked freshwater Atlantic salmon, compared to susceptible salmon from the Atlantic Ocean. We detected three additional genomic regions exhibiting signatures of selection that are possibly salinity-related. These genomic regions are a good starting point for further research to identify the specific genes contributing to the signals of directional selection. For instance, targeted sequencing of detected regions might be a promising approach, especially given that the Atlantic salmon genome sequence is soon to be publically available G. salaris. The validity of the separation of the effects of parasite-driven vs. salinity-driven selection was, to some extent, supported by a functional network analysis of GO terms associated with the detected outliers. Given the unlikeliness that the observed results could be explained by chance alone, our study highlights the suitability of a carefully planned multi-testing strategy for whole-genome screens for signatures of directional selection in natural populations.We identified at least three genomic regions potentially contributing to Figure S1Overlap between SNPs outliers detected by the landscape genomics analysis for each of the environment characteristics. Numbers within Euler diagram sectors represent number of SNPs, shown to be associated with an appropriate environment characteristic or combination of characteristics.(TIF)Click here for additional data file.Figure S2SNPs outliers based on the landscape genomics analysis results, plotted along the genome. Both all detected SNPs (orange) and unique SNPs (violet) for each environmental characteristic are shown as vertical lines plotted along the genome. For easier comparison of results, global results based on Designs 1-4 are shown as in (TIF)Click here for additional data file.File S1SNP allele frequencies per population.(XLSX)Click here for additional data file.Table S1Gene diversity and observed heterozygosity per SNP marker and per population.(XLSX)Click here for additional data file.Table S2Pairwise population differentiation as measured by FST.(XLSX)Click here for additional data file.Table S3Analysis of molecular variance (AMOVA) results.(XLSX)Click here for additional data file.Table S4Significant genome region/outlier tests results for each of the 4631 SNPs.(XLSX)Click here for additional data file.Table S5Detailed list of all significant single test results with design 1, 2 or 3.(XLSX)Click here for additional data file.Table S6Significant genome region/outlier tests from each design that contributed to the six identified regions suggested to be affected by "parasite" or "salinity"- influenced selection.(XLSX)Click here for additional data file.Table S7List of the 2857 SNP loci with a significant BLAST match and GO annotation.(XLSX)Click here for additional data file.Table S8Significant BLAST match and GO annotation for the single outlier SNPs identified using Design4 and landscape genomics analysis.(XLSX)Click here for additional data file.Table S9Results of an enrichment test conducted for GO terms associated with the "Parasite" and “Salinity” subsets of SNP outliers identified using Design4.(XLSX)Click here for additional data file.Table S10Results of an enrichment test conducted for GO terms associated with the "Parasite" and “Salinity” subsets of SNP outliers identified using landscape genomics analysis.(XLSX)Click here for additional data file.Table S11Functional network analysis: comparison of GO terms associated with "Parasite" and "Salinity" sets of SNP outliers as identified by Design 4 and landscape genomics analysis.(XLSX)Click here for additional data file.


Human settlement and migrations along sides of Bay-of-Bengal have played a vital role in shaping the genetic landscape of Bangladesh, Eastern India and Southeast Asia. Bangladesh and Northeast India form the vital land bridge between the South and Southeast Asia. To reconstruct the population history of this region and to see whether this diverse region geographically acted as a corridor or barrier for human interaction between South Asia and Southeast Asia, we, for the first time analyzed high resolution uniparental (mtDNA and Y chromosome) and biparental autosomal genetic markers among aboriginal Bangladesh tribes currently speaking Tibeto-Burman language. All the three studied populations; Chakma, Marma and Tripura from Bangladesh showed strikingly high homogeneity among themselves and strong affinities to Northeast Indian Tibeto-Burman groups. However, they show substantially higher molecular diversity than Northeast Indian populations. Unlike Austroasiatic (Munda) speakers of India, we observed equal role of both males and females in shaping the Tibeto-Burman expansion in Southern Asia. Moreover, it is noteworthy that in admixture proportion, TB populations of Bangladesh carry substantially higher mainland Indian ancestry component than Northeast Indian Tibeto-Burmans. Largely similar expansion ages of two major paternal haplogroups (O2a and O3a3c), suggested that they arose before the differentiation of any language group and approximately at the same time. Contrary to the scenario proposed for colonization of Northeast India as male founder effect that occurred within the past 4,000 years, we suggest a significantly deep colonization of this region. Overall, our extensive analysis revealed that the population history of South Asian Tibeto-Burman speakers is more complex than it was suggested before. Bangladesh is bordered by Eastern India in west, Northeastern India in north and by the Bay of Bengal in the south. It also shares a narrow boundary with Myanmar on the southeastern rim . Its geoIn the present study, we have carried out a detailed molecular analysis of three major tribal populations  living in Chittagong Hilly tract of Bangladesh. These populations are described as true aboriginal tribal populations of Bangladesh The aim of this study was to trace footprints of initial Paleolithic colonization and the much later Neolithic expansion of populations among the aboriginal tribal populations living in Chittagong Hill Tract region of Bangladesh. We also assessed whether South Asian TB speaker's genetic variation was structured by the language or by their geographical placement. We also examined the relationships of the studied populations with Indian, Southeast Asian TB and other linguistic groups.Marma and Tripura populations share high frequency of Indian and low frequency of East Asian specific maternal haplogroups, than Chakma . East AsThe haplotype based comparison with neighboring Indian and Southeast Asian populations showed a closest match with Northeast India, implying their close genetic connection . The hapTo learn more about the maternal relation of studied tribal populations with the neighboring populations, we performed Principal Component Analysis (PCA). PC1 was basically segregating Indian and E/SE Asian populations, while PC2 was setting apart loosely packed Indian and TB populations of Bangladesh from E/SE Asians and Indian groups . All theTo gain insight into their paternal gene pool, we have analyzed Y chromosomal data from 89 Chakma, 60 Marma and 88 Tripura individuals. Over 35 biallelic markers and 17 short tandem repeats (STRs) loci were genotyped to produce a high-resolution dataset of Y chromosomes from these groups. We observed considerable differences in Indian specific NRY haplogroup frequencies among the three groups, Chakma has the least, while Tripura has the highest Indian-specific haplogroups . More thNotably, none of the studied populations carry Indian-specific Paleolithic haplogroup H or any of its branches , which iSimilar to mtDNA PCA, in Y chromosome haplogroup based PCA, PC1 differentiates Indian with E/SE Asian clusters, while, PC2 sets apart Indian-Indian and E/SE Asian-E/SE Asian populations . The BanTo assess whether geography or linguistic affiliation was a better predictor of genetic variation, we conducted AMOVA analysis . We noteBecause variation accumulates with time, a relative chronology can be constructed by assessing haplogroup STR variance, haplotype diversity (HD), ρ (rho), and mean pairwise differences (MPD)  and S3. To compare them with the surrounding populations, we have redone NETWORK analysis of O2a1 and O3a2c1 haplogroups based upon common STRs from published sources The coalescent ages of haplogroups O2a1 and O3a2c1 were almost similar ranging between 14 and 18 Kya , suggestst among studied population showed a consistent pattern for all the studied loci  ied loci . Chakma ulations . The PCAulations . The STRulations . First sIn conclusions, our extensive analysis on uniparental and biparental markers led to the more precise identification of Indian and E/SE Asian haplogroups among Bangladeshi aboriginal tribal populations and a better understanding of the extent of ancient and recent admixtures. We present substantial evidence for multiple genetic strata that arose likely through a series of distinct migratory processes. In contrast to Indian context, where gene is highly correlated with geography, we found that the Northeast Indian and Bangladeshi landscape is associated with language. The post-Neolithic incursions make a major impact on the composite gene pool of the studied tribal populations, consistent with the genetic impact of the language. In addition, by generating well resolved mtDNA and Y chromosomal lineages, we were able to detect the directional geneflow and their conglomerisation from east and west of Bangladesh.About 5 ml blood samples were collected from each individual belonging to three tribes  from Rangamatti and Khagrachori regions of Chittagong Hill Tract in Bangladesh. All the three populations are contributed by these two places together . These aOver 32 Y chromosome biallelic markers from previously published dataset www.phylotree.org.). Along with Y chromosome and mtDNA markers, 50 AIMs published elsewhere We sequenced the hypervariable segment I (HVS-I) of mtDNA and the variations were scored against the Reconstructed Sapiens Reference Sequence (RSRS) www.fluxus-engineering.com). The age of O2a1-M95 and O3a2c1- M134 was estimated from microsatellite variation within the haplogroup using the method described by Zhivotovsky et al. Fragment sizes of Y-STRs, were determined using the GeneMapper® Analysis Software v4.0 and allele designations were based on comparison with allelic ladders provided by the manufacturer. Out of 17 loci obtained, two DYS385 loci were excluded from the current analyses because they could not be distinguished using the typing method employed. DYS 389I (DYS 389cd) was subtracted from DYS389II and re-named DYS389ab. Thus, all the analysis linked with Y-STRs data were carried out with 15 loci. A median-joining network, resolved with the MP algorithm, was constructed using the Network package (version 4.5.0.2)  and selected the K (K = 4) that maximizes the posterior probability of the data, as explained by the developers Principal component analysis (PCA) for mtDNA and Y chromosomal haplogroups was performed using POPSTR, kindly provided by H. Harpending, and for autosomal SNP we used smartpca programme Figure S1Y chromosome haplogroups observed among the studied populations with major Y chromosome biallelic markers.(TIF)Click here for additional data file.Figure S2Unrooted phylogenetic network of a M214 derived individuals.(TIF)Click here for additional data file.Figure S3Unrooted phylogenetic network Tree of all Bangladeshi Tibeto-Burmans with eight common Y-STRs.(TIF)Click here for additional data file.Table S1mtDNA variations observed among the studied populations.(DOC)Click here for additional data file.Table S2Y-chromosome age estimates for haplogroups O2a and O3a3c among population groups of India E/SE Asia and Bangladesh.(DOC)Click here for additional data file.Table S3Y STR haplotype data of the studied populations.(DOC)Click here for additional data file.Table S4Pairwise population Fst values for the autosomal analysis.(XLS)Click here for additional data file.


Traits related to local adaptation by definition show high phenotypic differentiation. The underlying genetic patterns could be clines at individual loci or small effects and extensive linkage disequilibrium at the underlying loci. In any case, including many populations in an analysis provides more information, but may simultaneously induce problems due to genetic structure. Even if the neutral loci have little genetic structure, loci related to other clinally selected traits could show more structure. Here we have developed an approach to efficiently use the information along a latitudinal environmental gradient. Scots pine populations from central Europe to the species' northern range were sampled and patterns of phenotypic variation of both timing of budset and frost tolerance were measured in common garden experiments, . By hierarchical modelling of the phenotype's clinal variation and accounting for varying allele frequencies across the 10 populations, the statistical approach simultaneously exploits the genetic variation between and within populations to detect association signals. We apply shrinkage-based Bayesian variable selection to detect genetic associations between timing of bud set and ~450 SNPs in Scots pine.


Rhinolophus ferrumequinum displays a diverse call frequency and inhabits a heterogeneous landscape, presenting an excellent opportunity for this kind of research. We quantified geographic variation in resting frequency (RF) of echolocation calls, estimated genetic structure and phylogeny of R. ferrumequinum populations, and combined this with climatic factors to test three hypotheses to explain acoustic variation: genetic drift, cultural drift, and local adaptation. Our results demonstrated significant regional divergence in frequency and phylogeny among the bat populations in China's northeast (NE), central-east (CE) and southwest (SW) regions. The CE region had higher frequencies than the NE and SW regions. Drivers of RF divergence were estimated in the entire range and just the CE/NE region (since these two regions form a clade). In both cases, RF divergence was not correlated with mtDNA or nDNA genetic distance, but was significantly correlated with geographic distance and mean annual temperature, indicating cultural drift and ecological selection pressures are likely important in shaping RF divergence among different regions in China.Patterns of intraspecific geographic variation of signaling systems provide insight into the microevolutionary processes driving phenotypic divergence. The acoustic calls of bats are sensitive to diverse evolutionary forces, but processes that shape call variation are largely unexplored. In China,  Understanding the origins and maintenance of phenotypic diversity is a fundamental goal of evolutionary biology Acoustic divergence can be influenced by a variety of factors such as geographic distance and landscape barriers Patterns of geographic variation in acoustic signals occur in insects, frogs, birds and mammals e.g., . Some reRhinolophus monoceros echolocation calls. However, investigations combining species' phylogenetic history with acoustic divergence are rare, though such studies are important to help understand the varying factors that can affect acoustic geographic variation.Population genetic structure and dispersal routes could all play significant roles in leading to acoustic geographic variation. The effects of geographic isolation and population expansion could lead to species being distributed in different regions that vary in habitat and ecological characteristics, providing the possibility of either acoustic change due to drift in different populations, adaptation to different habitats, or both. Also, the population genetic structure may be related to call variation. Chen et al. Rhinolophus bats are associated with assortative mating, reproductive isolation and resource use R. monocerosR. cornutusR. pusillusR. clivosusHipposideros ruberH. larvatusCraseonycteris thonglongyaiRhinonicteris aurantiaBats mainly use echolocation for orientation and locating food Rhinolophus ferrumequinum , emits constant frequency echolocation calls and is a widely distributed species covering most of the Palaearctic from Europe to China and Japan R. ferrumequinum has a broad distribution from Jilin Province in northeast China to Yunnan Province in southwest China R. ferrumequinum in China, corresponding to different biogeographic regions. Bats were divided into eastern and southwestern groups, isolated by the Qinling Mountains, resulting in a restricted gene flow R. ferrumequinum from northeast China could be a distinct lineage differing from the eastern and southwestern lineages. The bats inhabit a heterogeneous landscape with their distribution covering ca. 18° latitude. Previous studies reported that the dominant frequency of R. ferrumequinum was 69 kHz in Jilin Province, northeast China R. ferrumequinum may exhibit a pattern of geographic variation in their echolocation calls. However, whether genetic drift, cultural drift, or adaptation to their environment (such as climate variation) drives the acoustic divergence of R. ferrumequinum remains unknown.The greater horseshoe bat, R. ferrumequinum across China. We also investigated the climatic data of each locality and patterns of neutral genetic variation at the mitochondrial control region and microsatellite loci among populations to test evolutionary forces related to RF divergence. Our specific objectives were: (1) to determine whether there was detectable geographic variation in RF of R. ferrumequinum; and (2) to test the fit between observed patterns of RF geographic variation in R. ferrumequinum echolocation calls and those predicted by the following three hypotheses to explain RF divergence: (i) random genetic drift, (ii) cultural drift and (iii) acoustic adaption to different environments. To support the genetic drift hypothesis, we predicted that acoustic distance in RF would be correlated with genetic divergence at neutral loci  of echolocation calls emitted by ci e.g., . For theci e.g., . Finallyci e.g., . Our resR. ferrumequinum is not an endangered or protected species, and is not on the list of state-protected or region-protected animal species. All sampling was conducted outside protected areas, and with permission of the forestry departments in the different areas. All field studies were approved by National Animal Research Authority in Northeast Normal University, China  and the Forestry Bureau of Jilin Province of China . Bats were captured using mist nets at cave exits. We then measured the bats, recorded the echolocation calls, sampled the wing membranes, and released them.According to the regulations of Wildlife Conservation of the People's Republic of China (Chairman Decree [2004] No. 24), permits are required only for those species included in the list of state-protected and region-protected wildlife species when it is collected. No specific permission was required because In China, 129 greater horseshoe bat samples were collected during 2006–2012 from 15 different localities . SamplinMAPW- mean annual precipitable water, MARH- mean annual relative humidity, MAT- mean annual temperature, mRF- mean resting frequency, N- sample size.Echolocation calls were recorded with a D980 Pettersson bat detector . This detector was positioned approximately 30 cm in front of hand-held bats. The recordings were transferred to a computer with a time expansion of ×10 using BatSound Pro 3.10 . The RF that the bats emitted under these conditions is the stable CF portion of the echolocation pulses emitted by the motionless bat, which coincides with the response frequency of the cochlear ‘acoustic fovea’ To test for an effect of population and sex on RF, we tested for differences in RF among localities and between sexes using a general linear model (GLM) with type III sums of squares, and both variables were treated as fixed effects in the model. We performed a Mann-Whitney U test on the mean call frequency values obtained at each location. This allowed us to evaluate gender differences in RF at each locality when the individuals in each population were greater than three.post-hoc multiple comparison Tukey tests were used to test the differences among groups. To test the effect of group and sex on RF we used a GLM with group and sex as fixed effects in the model.We used multidimensional scaling (MDS) to arrange acoustic distance between population pairs in a two-dimensional space. Variation in the RF of echolocation calls for males and females between different populations was calculated using Euclidean distances . MDS can be applied to any kind of distance matrix, and involves moving objects around in a defined space, using a number of dimensions, to maximizing the goodness-of-fit to the observed distance matrix. From the MDS plot, the RF of all populations clustered into three distinct groups. Therefore, R. ferrumequinum haplotypes and sequences were deposited in GenBank with accession numbers JN230556-JN230592.Samples for the genetic analysis were collected from bat wing membrane. Bat wing membrane (plagiopatagium) samples were acquired using biopsy punches (3 mm diameter) as outlined by Worthington Wilmer and Barratt FST values between populations were calculated based on sequence information using the model specified by Tamura and Nei All sequences were aligned using Clustal_X R. pearsoni and R. pusillus (GenBank: EU053159 and DQ916121). A minimum-spanning network  method implemented in PHYML v2.4.4 ork MSN;  for the Seven microsatellite loci, Reffer 15, 17, 19, 22, 24, 27 and 28, were amplified using fluorescently labeled primers. Primer sequences and PCR conditions followed those of Dawson et al. STF values All loci were screened for null alleles and large allele dropouts using Micro-Checker v2.2.3 K) was based on the number of lineages recovered in the mtDNA analysis. For K = 3, 10 runs were performed with 1,000,000 iterations after 30,000 iterations were discarded as “burn-in”. The graphical display of the genetic structure was produced by DISTRUCT We analyzed the genetic structure of our data set using two different methods. First, we used a phylogenetic tree based on the shared allele distance between individuals and reconstructed using a NJ algorithm using Populations v1.2.30 We used climate data from each locality to summarize environmental features relevant to sound propagation. Temperature and humidity have a direct influence on sound transmission STF (given by STF/(1– STF)) To assess whether differences in RF were related to geographic, genetic and climatic distance, we first calculated a dissimilarity matrix of acoustic distances using RF differences (kHz) between localities and calculated genetic distances using Slatkin's linearized R. ferrumequinum individuals comprising of 53 females and 56 males from 13 localities with 15 calls per bat. Across all samples, RF ranged from 68.41 to 77.65 kHz (SD  = 3.03) in females and from 68.41 to 77.05 kHz (SD  = 2.67) in males , except for JL2, SXi1 and HeN populations.We recorded and analyzed 1,635 calls from 109 adult in males , Fig. 2Anificant . Female r2  = 0.9996). The MDS revealed three distinct geographic groups: the northeast (NE), the central-east (CE) and the southwest (SW) . The CE showed the highest RF (76.16±0.77 kHz), while the NE group had the lowest RF (69.36±0.58 kHz). The RF in the SW group had an intermediate frequency (73.10±0.81 kHz). The NE group was restricted to northeast China, whereas the CE group was separated from the SW group by the Qinling Mountains  . Our resest (SW) , suggestountains .R. ferrumequinum from 15 populations. This included 13 populations with their frequency recorded and 2 populations  control region recovered 37 haplotypes for 100 W groups , which wW groups . The SW W groups . The NE K = 3, corresponding to the three major groups  identified in the mtDNA and nDNA trees  microsatellite loci. Across all sampling localities, the number of alleles per locus varied from 1 to 8 with mean number of alleles per locus at 2.3–4.4. All individual-based analyses of nDNA supported the presence of the three divergent evolutionary groups  that were identified in the mtDNA data. However, 15 individuals (14.5%) between adjacent populations of different lineages were identified in unexpected groups based on their genotype . For exaNA trees . AMOVA wNA trees . The mtDNA trees .R. ferrumequinum populations, Mantel tests showed that RF variation was significantly and positively associated with geographic distances  is smaller that would be expected to forage for different prey sizes Hipposideros armiger has a lower calling frequency of ∼67–69 kHz).In this study we sought to determine whether there was detectable geographic variation in RF of R. ferrumequinum populations, indicating more variation than previously documented R. ferrumequinum populations studied clustered into three groups, NE, CE and SW, corresponding to biogeographic regions. The large differences in RF among groups suggested a significant acoustic variation in R. ferrumequinum.The fine-scale analysis of RF variation conducted in this study revealed significant regional divergence in echolocation calls in R. hipposiderosR. cornutusR. monocerosR. capensisR. ferrumequinum suggests that call frequency is probably a poor signal for sex recognition The differences between males and females in RF should not have influenced the groups identified from the MDS since the variation between the sexes was relatively small and is less than that among groups . The difR. ferrumequinum, we studied the acoustic geographic variation in the total range and CE/NE region. Using this, we attempted to identify the drivers of frequency divergence of R. ferrumequinum across different spatial scales.The three groups identified from the RF of echolocation calls corresponded to the three phylogenetic groups, NE, CE and SW, identified from the genetic data . The CE A correlation between acoustic and genetic distances indicates that the observed pattern of geographic variation could be largely due to genetic drift. Several studies have demonstrated that genetic drift can lead to acoustic divergences in some animals Cultural transmission and copying errors are major drivers of stochastic divergence in learned vocal signals R. ferrumequinum, the RF of a developing infant is influenced in part by that of its mother R. ferrumequinum had a low correlation with both mtDNA and nDNA, and that of mtDNA was even lower than that of nDNA  of the group vocal signature might be greater than the effect of the vocal transmission of from mother to offspring.One factor that may drive cultural drift is mother-offspring transmission  of nDNA . These rR. ferrumequinum. While this does not account for other environmental factors that may impact sound propagation, such as vegetation types, ambient noise and air turbulence In addition to cultural drift, environmental selection pressure might also contribute to RF variation. Both temperature and humidity have a direct influence on sound transmission and absorption R. ferrumequinum region (R. ferrumequinum, though we recognize there may be other ecological variables, such as habitat structure, that are important as well.In our study, we found a positive correlation between acoustic and MAT distance, controlling for latitudinal or longitudinal distance, which is consistent with those found in a terrestrial muroid rodent m region . StudiesR. ferrumequinum in China, which corresponded to three phylogenetic groups identified from genetic data. By integrating genetic structure, geographic variation, and environmental differences, it appeared that divergence in R. ferrumequinum calls across China were most likely due to cultural drift and ecological selection, but not genetic drift. These results were consistent with other studies that have examined geographic variation in calls (e.g., Our study demonstrates significant geographic differences in RF of ls e.g.,  in sugge


Haliotis discus hannai, is a popular food in Eastern Asia. Aquacultural production of this species has increased because of recent resource declines, the growing consumption, and ongoing government-operated stock release programs. Therefore, the genetic characterization of hatchery populations is necessary to maintain the genetic diversity of this species and to develop more effective aquaculture practices. We analyzed the genetic structures of five cultured populations in Korea using six microsatellite markers. The number of alleles per locus ranged from 15 to 64, with an average of 23.5. The mean observed and expected heterozygosities were 0.797 and 0.904, respectively. The inbreeding coefficient FIS ranged from 0.054 to 0.184 (mean FIS = 0.121 ± 0.056). The genetic differentiation across all populations was low but significant . Pairwise multilocus FST tests, estimates of genetic distance, and phylogenetic and principal component analyses did not show a consistent relationship between geographic and genetic distances. These results could reflect extensive aquaculture, the exchange of breeds and eggs between hatcheries and/or genetic drift due to intensive breeding practices. Thus, for optimal resource management, the genetic variation of hatchery stocks should be monitored and inbreeding controlled within the abalone stocks that are being released every year. This genetic information will be useful for the management of both H. discus hannai fisheries and the aquaculture industry.The Pacific abalone,  Nearly 100 Haliotis species inhabit both tropical and temperate waters worldwide  size standard , denatured at 95 °C for 2 min, and immediately chilled on ice. Fragment analysis of the reaction products was performed using an ABI 3130 Genetic Analyzer (Applied Biosystems) and GeneMapper software . To improve accuracy when determining allele sizes, a control DNA sample was included in each set of samples for each run.2.3.H. discus hannai. The data were tested for PCR errors due to null alleles, stuttering, and allele dropout using MICRO-CHECKER ver. 2.2.3 (1000 randomizations) [FST-outlier detection method. Two genetic diversity parameters, the number of alleles per locus (NA) and the number of unique alleles (U), were determined for each local sample at each locus, using the program Genepop ver. 4.0 (http://kimura.univ-montp2.fr/~rousset/Genepop.htm). Allelic richness (AR) was corrected for the smallest sample size (n = 39), using the rarefaction method of FSTAT ver. 2.9.3.2 [He) and observed (Ho) heterozygosity values [FIS) [FIS can measure deviations from the Hardy–Weinberg equilibrium (HWE) within a population. Deviations from the HWE were tested using probability tests or exact tests by the Markov-chain procedure of Arlequin.Statistical genetic analyses were conducted for five populations of zations) . Scoringzations) ,23, whic 2.9.3.2 . Using F 2.9.3.2 . For the 2.9.3.2 , compone 2.9.3.2 . The sigy values  were cales [FIS)  for eachFST values [RST values (1000 permutations) [FST based on an infinite alleles model (IAM) was estimated and RST values based on a stepwise mutation model (SMM) were calculated using Arlequin. The index RST incorporates the correlation of the weighted mean allele size, expressed as the number of tandem repeats. Significance levels were adjusted for multiple tests using the sequential Bonferroni correction technique [The extent of population subdivision was examined by calculating global multilocus T values  and RST tations) . The indechnique .DCE [DCE is one of the most efficient distance measures in obtaining correct tree topology from allele frequency data [http://bioinformatics.org/~tryphon/populations/). Bootstrap values were calculated using 1,000 replicates. The UPGMA tree was visualized using the Tree Explorer program (http://evolgen.biol.metro-u.ac.jp.TE/).The genetic distance between populations was estimated on the basis of the chord distance, DCE . DCE is ncy data . To examhttp://www.anu.edu.au/BoZo/GenAlEx/), which was based on the covariance matrix of gene frequencies.Relationships between geographical populations were assessed using principal component analysis (PCA) of GenAlEx 6.3 . However, all six loci were used in this study because no null alleles affected all populations.Micro-Checker analysis did not detect any allele scoring errors caused by stuttering or large allele dropout, but it indicated that five of six loci could carry null alleles . In total, 56 alleles were found to be unique to a single population. The greatest number of unique alleles was detected in NH (20), while TA had only seven unique alleles. The inbreeding coefficient FIS of the hatchery populations ranged from 0.054 to 0.184 (mean FIS = 0.121 ± 0.056).Estimates of genetic variability for the five Pacific abalone populations are summarized in P values using the sequential Bonferroni method (P < 0.01), and these disequilibriums (DHWE) were detected at all loci except Hdh114B. Ten DHWE cases resulted from a deficiency of heterozygotes and two from an excess of heterozygotes . Significant deviations were not evenly distributed among samples or loci, nor were they associated with a particular locus or sample. DHWE was observed at one locus in WD, but at four loci in GJ.The observed genotype frequencies were tested for agreement with HWE . Among tozygotes . Eleven In total, 233 different alleles were observed across all loci and all samples, ranging from 15 for the Hdh154 locus to 64 for the Hdh513 locus. No population had a diagnostic allele. In the five populations, 64.0 to 76.6% of the alleles were rare alleles with a frequency <5%. They were detected at most loci and were not associated with a particular locus in either population.P > 0.05) through the sign test, standardized differences test and Wilcoxon sign rank test, suggesting that five hatchery populations have not experienced a recent bottleneck.In testing the departure from mutation-drift equilibrium based on heterozygosity excess or deficiency for five populations, bottleneck analysis was conducted using Bottleneck software under the TPM of microsatellites. No population displayed significant heterozygosity excess  was observed among populations, indicating that the level of genetic heterogeneity among them was low. DCE and mean pairwise FST and RST values using composite allele frequency data. Values of pairwise FST among populations were significantly different from zero in all pairwise comparisons except one . On the other hand, the pairwise RST among populations was significantly different from zero in only five pairwise comparisons. Estimates of FST (range 0.0040 to 0.0176) were generally lower than RST values (range −0.0082 to 0.1016). The DCE values were similar to the FST and RST values, in the range of 0.018 to 0.026. The AMOVA from the distance matrices for 233 individuals permitted partitioning of the overall variation. The amount of variation attributable to differences between individuals within populations and within-individual differences were large , while only a low but significant proportion of variation  was detected among populations. The UPGMA tree and the PCA scatter plot constructed on the basis of the DCE values showed that no population formed a separate cluster  [H. discus hannai. This remarkable level of cross-species microsatellite conservation is a clear indication of the close molecular relatedness of the two species. Similar results were observed in the microsatellite analysis of H. discus discus [Although the two loci, Hdd114B and Hdd229, used in this study were originally described and designed for a congener  was significantly higher than previously measurements from Japanese and Chinese hatcheries  [et al. [NA = 28.13, average He = 0.916; An et al., data not published). Most of microsatellite markers used in above studies were same; then, can be compared on an equal basis. These values are similar to our results, indicating that a high level of genetic variation has been preserved during the domestication of these five cultured Pacific abalone populations in Korea. The maintenance of genetic diversity in hatchery populations has been reported for other marine organisms. For example, no differences in genetic diversity could be detected between wild and hatchery populations of Pacific oysters in Australia and China [The genetic variability of the six microsatellite loci proved to be extensive. Genetic variability in the five hatchery populations (average o 0.774) ,20. Howe [et al.  reportednd China ,37. The FIS in this study can attribute to the founder effect. The limited dispersal nature of abalones together with the sweepstake style of reproductive success may easily lead to such a non-random mating of population [et al. [Significant deviations from HWE were observed in 12 of the 30 population-locus cases. Ten cases had lower heterozygosity than predicted by HWE. Heterozygote deficiency has been reported for many other marine invertebrates ,39. Howepulation . The hetpulation . In our pulation ,43. Howepulation . Li et a [et al.  and Hara [et al.  observedFST and RST values were low but significant beteen most pairs of hatchery populations. Although we cannot trace the origins of the significant differences between the hatchery populations with no records from the farms, this differentiation observed among cultured populations is likely be due to a low number of founding individuals in hatcheries, which increases the effect of genetic drift, as has previously been reported for other hatchery-reared species including abalone [FST = 0.009, P = 0.040) of the genetic variance occurred among the hatchery samples, with the remainder of the variance occurring between individuals within populations and within individuals. To our knowledge, this is the lowest among-population variance component reported for this species. Previously reported FST values for hatchery Pacific abalone  [DCE genetic distances did not indicate a regional structure, i.e., individuals from nearby regions were not grouped together   all excetogether  and 3. A5.In conclusion, an understanding of the genetic structure and diversity among marine resources, especially for species for which artificial stocks are produced by hatchery-produced seed for natural resource restoration, are critical for the establishment of suitable guidelines for resource management and selective breeding. No detailed information is available to date on the genetic diversity of hatchery populations of Pacific abalone in Korea. In this study, we reported that relatively high genetic variability and significant but minor genetic differentiation were detected among the hatchery populations of Pacific abalone in Korea based on analysis of six microsatellite loci. The lack of a relationship between the geographic and genetic distances implied extensive abalone aquaculture. The allelic composition and diversity of Pacific abalone should be carefully considered regarding the seedling and stocking practice of hatcheries in order to conserve the genetic diversity of the natural population. Such information will be of assistance in the genetic management of fisheries and the successful implementation of stock-enhancement programs.