Panthera tigris) conservation their survival is threatened by poaching, habitat loss and isolation. Currently about 3,000 wild tigers persist in small fragmented populations within seven percent of their historic range. Identifying and securing habitat linkages that connect source populations for maintaining landscape-level gene flow is an important long-term conservation strategy for endangered carnivores. However, habitat corridors that link regional tiger populations are often lost to development projects due to lack of objective evidence on their importance. Here, we use individual based genetic analysis in combination with landscape permeability models to identify and prioritize movement corridors across seven tiger populations within the Central Indian Landscape. By using a panel of 11 microsatellites we identified 169 individual tigers from 587 scat and 17 tissue samples. We detected four genetic clusters within Central India with limited gene flow among three of them. Bayesian and likelihood analyses identified 17 tigers as having recent immigrant ancestry. Spatially explicit tiger occupancy obtained from extensive landscape-scale surveys across 76,913 km2 of forest habitat was found to be only 21,290 km2. After accounting for detection bias, the covariates that best explained tiger occupancy were large, remote, dense forest patches; large ungulate abundance, and low human footprint. We used tiger occupancy probability to parameterize habitat permeability for modeling habitat linkages using least-cost and circuit theory pathway analyses. Pairwise genetic differences (FST) between populations were better explained by modeled linkage costs  compared to Euclidean distances, which was in consonance with observed habitat fragmentation. The results of our study highlight that many corridors may still be functional as there is evidence of contemporary migration. Conservation efforts should provide legal status to corridors, use smart green infrastructure to mitigate development impacts, and restore habitats where connectivity has been lost.Even with global support for tiger ( By virtue of being at the top of the food chain, large carnivores occur at low densities, have large home ranges and therefore require vast areas to harbor viable populations Panthera tigris) acts as a flagship species for the conservation of forested ecosystems throughout its range in Asia c. 200 years ago, and habitat contiguity that permitted genetic exchange between the various regional tiger populations in the area The tiger , with tiger densities ranging from about 0.1 to 20 individuals per 100 km2Currently in India, the once contiguous tiger population is now fragmented with source populations primarily restricted to tiger reserves. A tiger reserve is legally mandated to designate a critical core area wherein human habitation and resource extraction is not permitted  within 185,100 km2 of Central India. Based on our understanding of tiger ecology, we predicted a priori that tigers should occur in vast, undisturbed, productive forest patches, with high density of large wild ungulate prey, which would be negatively impacted by human disturbances a priori hypotheses by spatially explicit modeling of tiger occupancy that accounted for imperfect detections, using covariates obtained by remote sensing and ground surveys covering all forest patches within our study area. We then used this spatially explicit information of tiger occupancy as a resource selection probability function We extensively and intensively collected scat and a few tissue samples across seven tiger reserves in the Central Indian Landscape and first identify tiger individuals by genotyping the DNA extracts using eleven autosomal microsatellite loci. Next we assessed spatial genetic structuring and gene flow in the identified individuals through individual clustering methods. We use likelihood based The majority of field sampling was conducted non-invasively from tiger scat, without animal capture and handling. Permits for collection of tiger scat samples were obtained from the National Tiger Conservation Authority and the State Forest Departments. Capture and radio collaring of tigers required the approval of the Ministry of Environment and Forests, Government of India and the Chief Wildlife Warden, Madhya Pradesh State, under the Wildlife (Protection) Act 1972. The permissions define the conditions required for capture of tigers, which include an approved protocol and participation by a Park Official and supervision by a qualified veterinarian in the capture and collaring exercise. Both these permissions were obtained and strictly adhered to. Capture operations were conducted by trained veterinarians and wildlife biologists as per the protocols of the Wildlife Institute of India and the National Tiger Conservation Authority. A tiger tissue sample was obtained from Satpura Tiger Reserve where the tiger died due to natural causes . This research project was conceived and radio collaring reported in this paper was done prior to the formation of an animal ethics committee at the Wildlife Institute of India.2 (20.1–23.5°N and 76.5–81.5°E) covering the seven tiger reserves of Melghat, Satpura, Pench, Kanha, Tadoba, Achanakmar, and Bandhavgarh along with their buffer zones, corridor habitats and adjoining forested habitats were sampled. Sampled sites covered different types of tiger habitats found in Central India ranging from the tropical moist-deciduous Sal (Shorea robusta) forests in Kanha and Bandhavgarh to tropical dry-deciduous teak (Tectona grandis) dominated forests in Pench, Tadoba and Melghat. The topography varied from about 200 meters above sea level (m a.s.l.) in the low-lying hills to the dadar plateaus and meadows in Kanha (500 m a.s.l.) and the rugged Satpura ranges . The rainfall, primarily restricted to the monsoon season (late June to September end) ranged between 1,000 to 2,200 mm per year. The large mammal fauna found in the region included tigers (Panthera tigris), leopards (P. pardus), sloth bears (Melursus ursinus), dholes , gaurs (Bos gaurus), wild pigs (Sus scrofa), sambar deer (Rusa unicolor), chital deer (Axis axis), barking deer (Muntiacus muntjak), swamp deer (Rucervus duvaucelii) and nilgai antelopes (Boselaphus tragocamelus).The present study was carried out in the global priority tiger conservation landscape of Central India within the states of Madhya Pradesh, Maharashtra, and Chhattisgarh . A foresBlood from 16 radio-collared tigers, one tissue sample from a dead individual and 587 putative tiger scat (faeces) samples were collected between 2006 and 2011 from the seven Tiger Reserves, and at a few intervening forest corridors in the area . The numBamHI restriction enzyme digestion of the mitochondrial DNA cytochrome b gene  statistic Identity analysis option in CERVUS 3.0 FSTRSThttp://cran.r-project.org).In order to investigate the power of the eleven microsatellite loci to distinguish among closely related individuals in the same population, the conservative sibling probability of identity (PhiPT genetic distances www.ncss.com) to understand broad spatial patterns of populations structure in the landscape. Next, we used the Bayesian individual clustering approach in STRUCTURE 2.3.3 K) based on the multilocus genotype data alone. The clustering process ensures that for identified population clusters, deviations from Hardy Weinberg and linkage equilibrium are minimized. We analyzed our data in STRUCTURE by using the admixed model and correlated allele frequencies option to carry out thirty independent simulations at each K (K = 1 to 10), with a burn-in length of one million Monte Carlo Markov Chain (MCMC) steps and data collection phase of five million MCMC iterations. These run times were sufficient to ensure convergence of the Markov chains, and all runs were carried out both with (locprior  = 1) and without (locprior  = 0) using prior sampling locality information. The true K or most likely number of population clusters in the dataset was inferred from (i) the ad hoc parameter of log-likelihood change in probability of individual assignments to K clusters (Ln P(K), K values  and among subpopulations within each group.We used two types of individual-based analyses to assess genetic differences among individuals and assignment patterns of tigers to populations. First, a Principal Coordinates analysis (PCoA) based on pair-wise delta K, . Both thdelta K,  in GENALa priori designation of the migration rate (MIGPRIOR  = 0.05). All other parameters and run times followed previous population clustering runs . We detected no biases in a priori assignment of the migration rate as the selection of particular MIGPRIOR values (0.001 to 0.1) did not substantially influence the STRUCTURE output, therefore only results for MIGPRIOR  = 0.05 are presented here.We used three Bayesian approaches implemented in STRUCTURE 2.3.3 h/LmaxL in combination with the resampling algorithm of Paetkau et al.et al.h/LmaxL, is the ratio of hL, the likelihood of a given individual being assigned to its sampled population to maxL, the greatest likelihood among all sampled populations et al.α levels of 0.01 and 0.05) of h/LmaxL. We considered a minimum log likelihood h/LmaxL ratio of 2.0, which corresponds to a 100 times probability of being cross-assigned, as the threshold level for determining putative migrant status of an individual Next, we used the likelihood-based estimator Third, the non-equilibrium Bayesian assignment test of BAYESASS was used to trace each individual's immigrant ancestry within the last two to three generations. Unlike STRUCTURE and GENECLASS analyses which require loci to follow Hardy-Weinberg and linkage equilibrium, BAYESASS is robust to violations from Hardy-Weinberg equilibrium as it measures contemporary gene flow within the last few generations based only on multilocus allele sharing among individuals Lastly, to corroborate the results of the above assignment based migrant decisions, a likelihood based parentage analysis was carried out in CERVUS 3.0 P<0.01, Lh/Lmax ≥2.0); (ii) observance of>50% migrant or cross-assignment probability in STRUCTURE and BAYESASS; (iii) high assignment probability to first generation immigrant ancestry state (gen1>50%) in both STRUCTURE and BAYESASS; and (iv) high membership (Q>0.8) to a single non-home cluster in the STRUCTURE analysis without prior population information. Further, in most cases of putative migrants, the successful parentage assignment corroborated the migrant status of the individual tiger. We considered a conservative approach by identifying individuals as putative migrants only if all three programs suggested evidence of immigrant ancestry.Designation of migrant status to an individual was contingent upon - (i) significant assignment of first generation migrant status in GENECLASS (m) can be inferred. In BAYESASS, a Bayesian approach incorporating an MCMC sampling scheme is used to estimate migration rates between pairs of populations over the last few (approximately <5) generations back. With an estimated generation time of four to five years in tigers Neθ  and asymmetric mutation-scaled immigration rate M (m/µ). The mutation-scaled immigration rate M, which is the immigration rate m divided by the mutation rate µ, is a measure of the importance of immigration events over mutation in contributing to variation in the population emN, is the product of theta and M. MIGRATE assumes mutation-migration-drift equilibrium with values of M and theta constant over time and parameter estimates in MIGRATE date back nearly 4Ne generations into the past (approximately thousands of years ago). Hence, these migration rates provide estimates of gene flow that post and pre-date the estimated time (approximately 600 years ago) when humans began to significantly alter the habitats in which these tigers currently live.In order to study gene flow across different timescales, we used the programs BAYESASS 1.3 6 MCMC iterations were carried out, by discarding the first 2×106 steps as burn-in and sampling at every 2,000 iteration intervals of the remaining 6×106 MCMC chain. Runs were carried out with a migration rate prior of 0.05 while other parameters were kept at default settings. The average result from three independent BAYESASS runs is presented. Estimates of historical gene flow and effective population size were carried out in MIGRATE by using the Bayesian approach and the Brownian motion model as an approximation for the step-wise microsatellite mutation model. Following initial trial runs, the Bayesian search criteria for the MCMC sampler was set at 10 replicates of one long chain of 50,000 steps with every 100 steps of the chain being recorded, producing a total of 5×107 visited parameter values. The initial 107 steps of the MCMC run were discarded as burn-in, and the remaining 4×107 runs were sampled. To increase efficiency of the sampler, we used four chain-heating temperatures of 1, 1.5, 3 and 10,000, which allows a more efficient exploration of the genealogy space. We used parameter estimates from the initial run to calculate starting values of theta, for use as new parameters during subsequent runs. Parameter estimates from the final run were similar to the results of the initial runs. All MIGRATE runs were carried out on the Bioportal cluster computing facility at the University of Oslo, Norway .For both the BAYESASS and MIGRATE runs, we used the STRUCTURE defined population clusters to estimate pairwise migration rates. BAYESASS runs were performed as described in the preceding methods section on detecting migrants. A total of 8×10M ratio test eN generations in the past. The mode-shift test is more suited for detecting bottlenecks within the last few dozen generations et al.P-values from the Wilcoxon's test were used to examine bottlenecks at each timescale and were assessed at the alpha 0.05 level. The M ratio (M = k/r+1) was calculated from the mean ratio of the number of alleles (k) and the allele size range (r). Assuming loci follow a generalized stepwise mutation model, the loss of rare alleles would diminish the value of k at a faster rate than r thereby a drop in the M ratio below a threshold of 0.68 would be suggestive of populations that experienced a recent bottleneck To detect past occurrences of genetic bottleneck in the sampled populations, we evaluated three summary statistics - (i) Wilcoxon's sign rank test and (ii) mode-shift test, implemented in the program BOTTLENECK 1.2.02 2 of habitat. Surveys were not random, but instead conducted along features that were likely to have tiger sign  so as to maximize detections et al.The entire study area was divided into 10×10 km grids. Each grid that contained potential tiger habitat  was surveyed by replicate search paths for tiger sign. The number of surveys per grid ranged from 3 to 35, and was proportional to the amount of tiger habitat within each grid. Areas under agriculture, industry, and human habitation that were known to be non-habitat for tigers were not surveyed in an occupancy framework. Each survey consisted of a 5 km search for tiger signs with approximately one survey for every five km2 grid ranged from 1 to 24, and were proportional to the quantum of tiger habitat within that grid. The total effort invested in transect surveys was 71,468 km of walking on 26,688 occasions.Within each forest beat, one or two permanent line transects of two to four km in length were delineated. Each transect was walked on two or three subsequent mornings (06:30 to 08:30 hrs) by two observers to record encounter rates of wild ungulates and domestic livestock. Data on number of each species seen and the length of transect were recorded to compute encounter rates of each species. In disturbed forests (outside of protected areas) wild ungulate densities were low, animals were shy, and difficult to record using line transects. Therefore, at every 400 m along the line transect we also sampled a plot of 20×2 m to record ungulate dung. Dung was visually distinguished to species 2 grid ranged from 5 to 120. The total number of plots sampled across the landscape was 51,073.At every 400 m along transects established for ungulate assessment a plot of 15 m radius was sampled to assess indices of human impact. Presence of (a) human/livestock trails within the plot, and (b) sighting of humans and livestock from the plot were recorded 2 Advanced Very High Resolution Radiometer (AVHRR) data, acquired from the National Aeronautics and Space Administration's (NASA) Television Infrared Observation Satellite (TIROS) . Road and drainage information were obtained from Digital Chart of the World . Protected area shape files were obtained from the wildlife database at the Wildlife Institute of India, National Tiger Conservation Authority and State Forest Departments of India. The Shuttle Radar Topography Mission has produced the most complete, high-resolution digital elevation model of the earth 2 grid, this information was used for computing average elevation and the coefficient of variation of elevation used as a measure of terrain ruggedness. Night light data was obtained from the United States Air Force Defense Meteorological Satellite Program (DMSP) and National Oceanic and Atmospheric Administration's (NOAA) Operational Linescan System (OLS) . Density of roads (length of paved road per km2), and Euclidean distances to roads, protected areas and night lights were computed in ArcGIS 9.3 (www.esri.com) software.Remotely sensed data that depict landscape characteristics and human impacts were obtained from various sources and extracted at the 10×10 km grid resolution. Forest cover was obtained from the Forest Survey of India 2) so as to ensure an even distribution of sampling effort across the landscape, analysis was done at the scale of 1,851 grids, each of size 10×10 km. This grid size was chosen since it was larger than the average home range size of a tiger Though sampling was done at the level of the forest beat (an administrative unit of about 15 kma priori expected to be positively influenced by (a) prey abundance, and (b) amount and quality of tiger habitat, and negatively influenced by (c) human disturbance http://www.proteus.co.nz/). Model selection was done using AIC, and model fit was assessed by comparing the actual detection histories with simulations generated from 50,000 parametric bootstrap runs of the target model in PRESENCE. The over dispersion parameter Ĉ close to one suggests that the model provides an adequate description of the data, values of Ĉ greater than one suggests more variation in the data than expected by the model, and values less than one suggest less variation in the data. The standard errors of model estimates were adjusted by the square root of Ĉ as recommended by MacKenzie et al.Ψ) in each grid.Tiger site occupancy was We used two approaches to estimate population extents and area of occupied habitats; (i) a more conservative approach wherein we considered only those grids that detected tiger sign as being occupied (the naïve estimate) and (ii) model inferred occupancy that corrected for detection bias and covariates in PRESENCE. Herein, landscape scale occupancy was computed by sum of cell occupancy probability values and divided by the total number of cells. Tiger habitat (forested area) in each grid was weighted by the tiger occupancy probability of that grid and summed across all grids to arrive at occupied tiger habitat for the landscape Ψ) obtained from PRESENCE was used as a measure of habitat suitability for tigers Ψ. For the non-tiger habitat (human land uses) where tiger occupancy values were not available, we considered them permeable to tigers at higher costs than forested habitats, although human habitations (townships) were considered impermeable to tigers  that result in long-term (FST) genetic differences between populations due to tiger movement across the landscape.The genetic structure between populations is a consequence of the amount of genetic drift to which each local population has been subjected, due to its local effective size, and/or due to its degree of demographic, geographic and ecological isolation FST, RST and PhiPT genetic distance estimates, obtained from AMOVA analysis in GENALEX, were linearized using the formula FST/(1 - FST), as given by Rousset 10 GGD), least-cost pathway distances (LCPD) least-cost corridor distances (LCCD), and resistance distances (RD). In addition, the effectiveness of the modeled corridors and spatial distance matrices was compared using partial correlations that allowed one model to be tested, while controlling for other competing models Pairwise As patterns of isolation-by-distance (IBD) are known to bias tests of hierarchical structuring and vice-versa, we used the population clusters as a covariate in a partial Mantel test, to model the partial correlations between pairwise genetic distances and spatial distances, while controlling the effect of population clusters (following Meirmans b (mtDNA cyt b) primers, of which 275 were identified as tiger scat based on BamHI restriction enzyme digestion profile of the PCR products . The total number of individuals identified here constitutes roughly 49% of the estimated total tiger population in the entire sampled area (PI-sib) of 1.5×10−4  of the samples was sufficiently low to determine unique individuals.Out of 587 scat samples, 330 scats were successfully PCR amplified using the felid specific mitochondrial DNA cytochrome products . From thled area . Sex ide1.5×10−4 , indicatp>0.05 at 1,000 permutations). Except for deviation at three loci, Fca441, Pati09 and Pati18 (p<0.001), all other loci were in Hardy-Weinberg equilibrium (HWE) in the pooled population after adjusting the critical P-value using the Bonferroni correction procedure (No significant evidence of linkage disequilibrium (LD) among all pair-wise loci combinations was observed, when all sampling locations were pooled . Partial correlations between estimated population size and the allele diversity indices, after controlling for the effect of sample size, were positive and highly significant . Heterozygosity values were not correlated with population size . Correlation of estimated population size with actual sampled size was highly significant , meaning that our samples were in proportion to the size of the population.Most tigers genotyped in this study showed high heterozygosity . The minpulation . A summapulation . The numpulation . EstimatPhiPT genetic distance estimator, individuals in the area were clustered into roughly four groups with varying degrees of population partitioning  based on itioning . The thrK from the output of the STRUCTURE runs using prior population information (locprior  = 1), produced the largest modal value of the statistic at K = 4, suggesting pronounced population subdivision at K = 4 (L(K) value reached an inflection point at K = 4 before gradually plateauing at K = 6 to 7 and finally leveling off at K = 8 (L(K) increased at higher values of K, as reported previously with other studies K and log-likelihood values occasionally occurs in cases where the FST values are significant Q summary barplots (K = 2 to 4 (carried out both with and without prior sampling location information), and distinct population saturation, indicative of population subdivision was evident at K = 4 in conformance with the delta K approach. Based on the above, the four cluster solution (K = 4) best describes the levels of genetic subdivision in our sample of the Central Indian tiger population  between the STRUCTURE identified clusters (p<0.01). Both FST and PhiPT values showed highly significant structuring (p≤0.001), and had relatively similar trends in magnitude with low sampling variance. In contrast, RST estimates showed no variation between groups, and had unreliably high sampling variances and mean square error estimates. The results of pair-wise FST and RST calculations indicated significant (p<0.05) and varied  genetic structuring between all sampled populations in Central India  to high (0.079 to 0.102), compared to pairwise estimates between different clusters (0.127 to 0.217).Partitioning of genetic variation in the AMOVA test indicated low but significant differentiation  and showed variable Q (0.289 to 0.758), and with the majority of samples having cluster memberships to more than one non-home locality.Eight putative migrants were identified in STRUCTURE . Of thesP<0.01), with high log likelihood of cross-assignment (h/LmaxL>2.0). Lowering the likelihood threshold (h/LmaxL<2.0) yielded further six putative migrants (P<0.05).GENECLASS also identified eight individuals  as putatP>0.8), and four individuals had intermediate migrant assignments (P = 0.509 to 0.617). In general, the posterior probabilities of migrant assignment were higher in BAYESASS compared to the STRUCTURE analysis. Additionally, BAYESASS identified six other individuals  as potential migrants (P = 0.509 to 0.926) that were not assigned as migrants by either STRUCTURE or GENECLASS.A total of fifteen individuals with likely immigrant or admixed ancestry were detected in the BAYESASS analysis . Of the Identification of offspring-candidate parent pairs in CERVUS yielded parentage relationships in thirteen out of seventeen putative migrants . No evidh/LmaxL>2.0 were classified as 100% first generation migrants. Two individuals  with>90% gen1 assignment and three individuals  with relatively high gen1 assignment probability (0.5 to 0.7) were considered as migrants. Five individuals  showed moderate levels of migrant assignment and immigrant ancestry patterns are indicative of admixed status. The assignment status of seven more individuals  was equivocal. While STRUCTURE could not assign them as migrants, they were identified as potential second generation migrants or admixed individuals in BAYESASS.The detection of migrants by the above methods yielded a total of seventeen individuals with putative immigrant ancestry . Identicm = 0.09) and Kanha to Pench (m = 0.07) where the rates were more than 5% (m = 0.015). Asymmetric migration was also visible between the Pench and Kanha population clusters, as the proportion of migrants from Pench to Kanha was negligible (m = 0.006). The Bandhavgarh population was devoid of migrants as suggested by the lack of gene flow between other populations (m<0.01). As in the locality-wise analysis, similar asymmetry and rates of migration were obtained between the various STRUCTURE defined population clusters (The mean posterior distributions of pairwise immigration rates depicting contemporary gene flow estimates in BAYESASS are shown in  than 5% . Gene flclusters .θ) and historical mutation scaled immigration rate (M) suggesting very low overall migrant proportions in the area over the long-term (M ranged from a high of 6.17 (Pench to Bandhavgarh) to a low of 0.9 (Bandhavgarh to Melghat-Satpura-Tadoba), revealing limited to no migration among populations in the landscape , to the population with the smaller theta value (Melghat-Satpura-Tadoba and Bandhavgarh clusters). The number of migrants per generation ranged from almost zero (Bandhavgarh to Melghat-Satpura-Tadoba cluster) to nine (Pench to Bandhavgarh). The Pench and Kanha clusters represented the largest source populations for immigrants in the area. Though marginally higher migration from Pench to Kanha was visible compared to migration in the other direction, both population clusters had overall symmetric gene flow.Results from the MIGRATE analysis showed low estimates of relative effective population size  regardless of the mutation models used and all localities tested showed normal L-shaped allele distributions in the mode-shift test, indicative of stable non-bottlenecked populations (p<0.05) evidence of heterozygosity deficiency  PC1 that represented availability of large undisturbed good canopy forests, (b) PC2 and PC4 that represented grids that had low human and livestock disturbances, (c) PC3, PC6 and PC9 that represented high ungulate prey especially in the form of chital, sambar, wild pig and gaur, (d) PC5 representing lower elevation, (e) PC7 representing grids within or in the proximity of legally protected areas and (f) PC8 that gave greater loadings for grids with higher rainfall , Ĉ statistic was estimated at 1.4 and standard errors shown in 2 area of the sampled grids, 76,913 km2 was forested or potential tiger habitat. The naïve estimate of grid occupancy was 17.5% while the model-averaged estimate of occupancy was 20.87% giving a 3.4% increment over the naïve estimate. The naïve estimate of tiger occupied habitat was 19,845 km2 while the model inferred occupancy of forests was 21,290 km2 giving an increment of 1.8% in occupancy estimate. The habitat variables of forest area, forest core area, rainfall, NDVI, and terrain ruggedness had significantly higher values in tiger occupied grids compared to unoccupied cells. All wild tiger prey indices were higher, while livestock abundance indices were lower in tiger occupied grids. Relevant human impact indices had significantly lower values in tiger occupied grids  had Eigen values greater than one and together explained 86.6% of the variability of the original variables . The facrainfall  and 8. Trainfall . The gooed grids .2 with intermittent tiger presence recorded throughout this patch even outside the legal reserve boundaries. The Satpura-Melghat forest patch was 12,720 km2, while the forest patches that contained Tadoba and Bandhavgarh Tiger reserves were smaller 2,088 km2 and 1,902 km2 respectively and connected to larger adjacent patches by fragmented forests  was recorded in Achanakmar  genetic distances vs. resistance  and least-cost corridor distances . In comparison to Euclidean geographic distance correlations, this corresponded to a 29.5% and 25.7% increase in model fit for the circuit theory based IBR and the LCC models respectively. The proportion of migrants was higher between population pairs that had lower corridor costs  and resistance distances . This meant a 46.2% and 31.6% increase over log-transformed Euclidean distance correlations for the circuit theory and corridor models respectively.Mantel's timators . The higor costs . LineariFST: r>0.48, p<0.02; linearized PhiPT: r>0.39, p<0.05). In contrast, after controlling the effect of resistance distance, significant positive correlation was only observed between linearized FST vs. geographic and least-cost corridor distances, while partial correlations between all other spatial and genetic distance metrics were either non-significant or negative. Although significant positive correlation was observed between FST and geographic distances in the standard Mantel tests, genetic and geographic distances were uncorrelated in the partial Mantel tests, after controlling the effect of population clusters, thus affirming that isolation-by-distance (IBD) pattern was absent in the data. Only resistance (IBR) and LCC distances retained a significant, positive partial correlation with genetic distances, when controlled for the effect of population clusters .Correlations in partial Mantel tests showed a significant, positive relationship between genetic and resistance distances, after controlling for all competing spatial distance metrics (linearized n = 169) representing about 49% of the estimated population The present study is illustrative of the general strengths and challenges in using non-invasive genetic samples to assess spatial distribution patterns in an endangered and cryptic species living in fragmented habitats. Although maternally inherited mitochondrial DNA could elucidate patterns of female gene flow, the sequencing of mitochondrial genes was not attempted as the primary objective of the study was to assess the functionality of corridors through the detection of first and second generation migrants. Hence, highly polymorphic, bi-parentally inherited, co-dominant and selectively neutral nuclear loci such as microsatellites were most suited for this purpose. Microsatellite marker amplification success was low from scats, reflecting the often poor quality of samples, typical of tropical environments Most individuals genotyped in this study, possessed heterozygous genotypes at>50% of typed loci, and there was no discernible differences in heterozygosity between migrant and resident individuals . HoweverHo = 0.701±0.059, He = 0.754±0.039) observed in our study is typical of genetic diversity prevalent among tigers in the Indian subcontinent He) and mean number of alleles (A) to be lower in other subspecies, viz. P. t. altaica , and allelic diversity (A = 7.76±1.96), and very low genetic subdivision (mean FST = 0.013±0.006). Another study in the southern parts of the Central Indian landscape, focused primarily in peripheral habitats just outside the scope of this study area Ho = 0.54, He = 0.81), and allelic diversity (A = 11.71) levels at fourteen microsatellite loci. A range-wide study Ho = 0.70±0.16) and allelic diversity (A = 12.4±3.6) compared to all other tiger subspecies . The study also reported low and non-significant genetic structuring of the Central Indian tigers with the Northern  and Southern Indian populations . This could be attributed to a historically large effective population size and inter-population connectivity in the region of Central and Peninsular India, explaining why, despite centuries of immense trophy hunting and continued habitat fragmentation, extant tiger populations in the region currently retain close to 60% of the global genetic variation in the species The allelic diversity (9.1±2.2) and heterozygosity  resulting in higher hierarchical levels of population subdivision at K = 8. In contrast, the runs carried out with population information  did not detect unnecessary genetic structuring, and ignored the prior sampling location information if the ancestry of individuals was uncorrelated K = 4 appeared optimal for the following reasons. In cases where STRUCTURE detects multiple clustering options with similar probabilities, typically the lowest K value which captures much of the biological complexity in the sample is considered to be the most conservative K value FST statistics indicated significant pairwise structuring across all populations in the area, the pattern shared certain similarities to the population clusters identified by STRUCTURE and the ordination results in PCoA. FST values were generally lower for localities within the same cluster compared to pairwise estimates between different clusters. We therefore treated the genetic distance estimators including FST and its analogues as relative measures of population differentiation. The estimation of these parameters requires prior identification of populations and unless the population units are clearly known, such a priori designation may not reflect realistic biological patterns as they would only be representing ad hoc division of populations FST and its analogues are based may not be suited for estimating genetic differences between populations that have undergone fragmentation or demographic fluctuation events only recently. On the other hand, the results of the model based STRUCTURE clustering approach, which partitioned individuals into relatively distinct clusters based on iterative assignments, made much more sense of the biological realities of the area as the structuring observed here is likely an artifact of recent population fragmentation.By using a combination of classical population differentiation and individual clustering approaches, we were able to detect patterns of population sub-structuring in the region. Results from both the PCoA and STRUCTURE analyses suggest the presence of four genetic clusters in the area. The clustered localities occur in close proximity to each other and the overall pattern of genetic structuring observed in the landscape is concordant with existing habitat connectivity and indicative of the role that habitat fragmentation plays in shaping the distribution of allele frequencies across populations. Significant genetic structuring was also detected in the population pairwise FST and RST values can provide valuable insights into the balance between genetic drift and mutation events in the studied populations FST values compared to RST values. The FST statistic is based on allelic identity and accounts for gene flow between different populations as the basic premise under which it estimates pairwise genetic differences RST relies on allele size and single stepwise mutations are the primary contributors of genetic variation for this statistic RST estimations produced non-significant results across many pairwise comparisons, and had higher sampling variances compared to FST. This suggests that populations in the area were not isolated long enough for mutations to have caused the genetic differences between populations and the primary cause of genetic structuring in the area is due to recent genetic drift. Some measure of this drift is evident from the number of private alleles found in each population, especially Bandhavgarh, which has the highest individual allele frequency compared to all other localities .Although the STRUCTURE and PCoA results suggest subdivision at four clusters for the Central Indian tiger population, the significant 2) to low (Achanakmar 0.1 tigers/100 km2) are juxtaposed in a matrix encompassing a range of land-use regimes from undisturbed natural forest to high density human settlements . In these situations it was difficult to tease apart actual migration events from similar allelic patterns that arose from shared population histories and low genetic separation. On the other hand, migrant identification between localities in different population clusters produced robust assignments because of distinct genetic differences. Migrant detection was highest between localities that had discernible genetic separation and had relatively intact habitat corridors with confirmed tiger presence, such as between Kanha and Pench populations where four migrants and seven likely admixed individuals were detected. Other studies by Joshi Q>0.8) to a single non-home cluster, as first generation migrants. Further, a fourth male individual (D1853) that had high cross-assignments, albeit with marginally lower Q value (0.527) compared to the above four migrants, was also designated as a first generation migrant. The immigrant ancestry status in the remaining ten putative migrant individuals was classified as admixed based on - (i) intermediate levels of migrant probability in STRUCTURE; (ii) membership to more than one cluster in the individual Q assignments; and (iii) high probability of assignment to second and not to first generation immigrant ancestry state in STRUCTURE and BAYESASS.Based on our strict criteria of defining migrants, we designated four individuals, three males  and one female (D958) that had high cross-assignments , confirming that dispersal is male-biased in tigers et al.et al.et al.2) likely serving as a source population, while the population density in Melghat is lower (2 tigers/100 km2). Asymmetric contemporary rates of gene flow were also apparent between Pench and Kanha, as migration from Kanha (6 tigers/100 km2) to Pench was much higher compared to movement in the opposite direction from Pench to Kanha. Joshi et al.et al.et al.Both the contemporary and historical analyses detected low estimates of migration rate between populations in the landscape. While this result is superficially similar to the findings of Joshi Contemporary density estimation studies using photographic capture-recapture techniques in the region Ne past generations, As parameter estimates of migration rate and theta in MIGRATE average across at least 4et al.M ratios (<0.68) suggestive of bottleneck, but the evidence was equivocal since no significant heterozygosity excess or a mode-shift in allele frequencies was detected. Though bottlenecks were not observed in this study, the analyses may be undermined in a few populations because of low sample sizes , as tests such as the mode-shift in allele frequencies is known to be affected by sample size variances et al.Alternatively, the low estimates of historic migration rate raise concerns whether these results are artifacts of sampling or population related. MIGRATE parameter estimations assume coalescent-based models of constant population sizes and mutation-migration-drift equilibrium. Departures from the equilibrium model such as recent and sudden declines in population sizes can negatively bias the posterior parameter distributions of theta and hence migration rate estimations et al.et al.et al.et al.et al.Our study and the recent study by Joshi 2 of tiger habitat. Thus a 10×10 km2 grid had anywhere between 3 to 35 replicate surveys depending on the amount of tiger habitat present within that grid. The detection probability of tiger sign was high in comparison to other studies Occupancy has often been used as a straightforward and economical state variable in place of abundance to monitor populations a priori predictions based on our understanding of tiger ecology. Since PCs are orthogonal to each other a priori predicted effects and to the coefficients obtained by the multivariate best model obtained from their PCs by PRESENCE. These results indicate that tigers are a conservation-dependent species, requiring areas having effective legal protection, good forest cover with least human impacts and high prey availability.Detection of tiger sign was better modeled by including encounter rate of sign per km walk. Intensity of sign was found to be a reliable index of tiger abundance Although both LCP and circuit theory used tiger occupancy probability as a base layer to parameterize landscape resistance surfaces, LCP defines the optimal minimal route that is required to connect two tiger reserves FST). Instead, the LCCs which were delineated by matching and buffering the pathway results from the raw LCP model with existing ground forest cover maps of the area provided a more realistic relationship of the ecological costs with observed genetic differences. In contrast, Euclidean distances (GGD and log10GGD) did not correlate significantly with genetic distances, due to the null model assumptions of spatial homogeneity where habitats are arrayed in an infinite lattice FST (but not PhiPT or RST) with geographic distance is an artifact of metapopulation structure and discrete population clusters present in the area. This observation was affirmed by non-significant partial correlations between genetic vs. geographic distances (but not LCCD and RD) using the STRUCTURE identified population clusters as a control. Apart from fragmentation induced spatial heterogeneity, genetic structure is also a result of population history.In our study, the ecological distances that were generated using information from habitat heterogeneity and landscape resistance surfaces (LCCD and RD), correlated significantly with pairwise genetic distances, which validated the effectiveness of the modeled linkages in representing realistic biological scenarios. In particular, IBR models depicted well the ecological costs of movement as they accounted for multiple pathways, irregular patch effects, landscape heterogeneity and wider habitat swaths connecting populations The results of our study imply that population subdivision and genetic structure across most localities in the area was strongly associated with habitat features that offer resistance to dispersal at different intensities such as agricultural land, roads, railway lines, high density human settlements and urban infrastructure and not only by geographic distance between populations. Mantel tests showed significant positive correlation of genetic differentiation with least-cost corridors and landscape resistance surfaces associated in traversing corridors, further confirming this observation. The tiger habitats in the region are patchy with some populations still having connectivity, while being reduced or conspicuously absent in others. The best patches of contiguous forested habitat are present in the corridor between Pench and Kanha, which extends eastward to Achanakmar. Likewise, the Satpura and Melghat populations are also connected through swathes of degraded forests, which are interspersed with agricultural land and medium density human settlements. Connectivity between Pench and Satpura is fragile as parts of the linkage are disrupted by mining activities, and broken up in places by agriculture, habitations, major highways and railway lines. The population in Tadoba is linked with Kanha in a stepping stone connectivity through patchily distributed forests. Even though the intervening matrix between Tadoba, Pench and Melghat is heavily human dominated, the populations are tenuously linked by degraded forest patches and tiger occupied habitats such as Bor Wildlife Sanctuary. Bandhavgarh has linkages with forest habitat and tiger reserves further east It is indeed surprising that in spite of being highly fragmented Elephas maximus), gaurs (Bos gaurus), leopards (Panthera pardus), and dholes . They need to be offered legal protection with smart green infrastructure Conservation efforts in such a fragmented and dynamic human-dominated landscape presents enormous challenges, but should be attempted in all areas where substantial habitat is still present. Our study gives renewed hope to tiger conservation efforts within the Central Indian Landscape and similar habitats with small fragmented tiger populations. We show that minimal habitat connectivity permits gene flow between populations, which is essential to maintain metapopulation connectivity. Our findings suggest that tigers could negotiate passage through stepping stone dispersal Figure S1A) MtDNA cyt b alignment with GenBank and reference sequences showing polymorphism at the particular BamHI restriction enzyme between tiger and leopard. (B) Enzyme digested bands of the 187 bp PCR product, targeting this region, showing different profiles in tiger and leopard for species identification.PCR-RFLP identification of tiger scats. ((TIF)Click here for additional data file.Figure S2Frequency of heterozygous genotypes observed at each individual multilocus genotype in all tiger individuals (n = 169) in this study.(TIF)Click here for additional data file.Figure S3A). Difference in delta K and mean LnP(K) for an estimated number of K populations, in models run with (locprior  = 1) and without (locprior  = 0) prior sampling location information. (B). Summary barplots depicting prior and non-prior STRUCTURE runs (assumed K = 2 to 8), of sampled populations in central India showing cluster affiliations according to individual Q values. Cluster saturation at K = 4, indicative of four population clusters, is observed in runs carried out both with and without a priori location information. At K>4, increased sub-structuring is detected, but there is no concordance in clustering between the prior and non-prior runs.Results of STRUCTURE analysis. ((TIF)Click here for additional data file.Figure S4Differences in tiger present (n = 311) and tiger absent (n = 1540), 10×10 km grids shown as violin plots in the Central Indian Landscape. All variables are normalized by z transformation to make the scales comparable.(PDF)Click here for additional data file.Table S1Ho) and expected (He) heterozygosity, Hardy-Weinberg equilibrium (HWE) tests, null allele frequencies and sibling probability of identity (PI-sib) values obtained across 169 tiger individuals. Null allele frequencies>+0.05 are italicized.Information on the 11 microsatellite loci used in this study. Allele diversity statistics, observed ((DOCX)Click here for additional data file.Table S2k), number of individuals typed (N), observed (Ho) and expected (He) heterozygosity, Hardy-Weinberg equilibrium (HWE) test significance and null allele frequencies (Null). The Achanakmar (n = 4) and Kanha-Pench corridor (n = 5) individuals are included in the Kanha cluster.Summary of population cluster-wise genetic diversity statistics at each locus. Depicted are number of alleles ((DOCX)Click here for additional data file.Table S3List of diagnostic alleles present in the sampled populations.(DOCX)Click here for additional data file.Table S4AMOVA results.(DOCX)Click here for additional data file.Table S5FST  and RST  estimates.Population pair-wise (DOCX)Click here for additional data file.Table S6P values <0.05 are depicted in italics.Bottleneck test results for loci under different mutation models. (DOCX)Click here for additional data file.Table S7Variable attributes used as site covariates in modeling tiger occupancy.(DOCX)Click here for additional data file.Table S8Habitat corridors, major roads, corridor cost between tiger reserves.(DOCX)Click here for additional data file.Table S9r) and probability (p) are shown using three different genetic distance estimators. Significant values (p<0.05) are indicated by an asterisk (*).Results of standard and partial Mantel tests for correlation between pairwise genetic and spatial distance metrics. The correlation coefficient ((DOCX)Click here for additional data file.Table S10b PCR amplification in reference samples using felid-specific (187 bp) and universal primers (309 bp). + indicates all samples which amplified. NA – not amplified.Details of cyt (DOCX)Click here for additional data file.Table S11n = 65) for species identification of tiger samples by PCR and BamHI restriction enzyme digestion.Information on the pilot test carried out on scats ((DOCX)Click here for additional data file.Method S1Species identification from scat samples.(DOCX)Click here for additional data file.Method S2Generating the cost surface for PATHMATRIX and CIRCUITSCAPE.(DOCX)Click here for additional data file.


There is a growing interest among geneticists in developing panels of Ancestry Informative Markers (AIMs) aimed at measuring the biogeographical ancestry of individual genomes. The efficiency of these panels is commonly tested empirically by contrasting self-reported ancestry with the ancestry estimated from these panels.Using SNP data from HapMap we carried out a simulation-based study aimed at measuring the effect of SNP coverage on the estimation of genome ancestry. For three of the main continental groups  ancestry was first estimated using the whole HapMap SNP database as a proxy for global genome ancestry; these estimates were subsequently compared to those obtained from pre-designed AIM panels. Panels that consider >400 AIMs capture genome ancestry reasonably well, while those containing a few dozen AIMs show a large variability in ancestry estimates. Curiously, 500-1,000 SNPs selected at random from the genome provide an unbiased estimate of genome ancestry and perform as well as any AIM panel of similar size. In simulated scenarios of population admixture, panels containing few AIMs also show important deficiencies to measure genome ancestry.The results indicate that the ability to estimate genome ancestry is strongly dependent on the number of AIMs used, and not primarily on their individual informativeness. Caution should be taken when making individual  inferences based on AIMs.The online version of this article (doi:10.1186/1471-2164-15-543) contains supplementary material, which is available to authorized users. The term ancestry refers to “the origin or background of something” (http://oxforddictionaries.com). Accordingly, in human genetics, ancestry is generally understood as the origin or background of our genomes. However, the question is far from trivial. Considering the way in which the DNA material is inherited through generations, most of it from both parents , entire blocks of our genome can have different ancestral origins. In the words of Svante Pääbo, “to understand what make us unique, both as individuals and as a species, we need to consider the genome as a mosaic of discrete segments, each with its own unique history and relatedness to different contemporary and ancestral individuals” [With the publication of the Human Genome Project  [Although genetic variation in humans shows gradients of allele frequencies extending over the entire world , there mes.org) , 4. Howemes.org) –8, humanmes.org) –11, and mes.org) –14. The http://www.illumina.com/products/dna_test_panel.ilmn) aimed at measuring the ancestry of samples as a screening method before proceeding with their high-throughput genotyping or massive parallel sequencing.Measuring ancestry is important in biomedical studies for a number of reasons. For instance, it has been demonstrated that population stratification represents an important confounding effect in case-control association studies of complex and multi-factorial diseases –19. EstiThe search for autosomal ancestry has also been a focus of attention in the forensic community , 20, 21.At the same time, many private companies offer direct-to-consumer-tests (DTCT) specifically designed to measure ancestry , 23. AltMost of the AIM panels available in the literature have been designed by way of selecting SNPs from large genomic databases (e.g. HapMap) showing skewed population frequencies between the ancestral populations targeted. Usually, researchers do not evaluate the amount of genetic informativeness provided individually by the selected SNPs. An exception is the study by Galenter et al. , who useThe ability of an AIM panel to measure ancestry is generally evaluated empirically, that is, by examining its performance on a given set of DNA samples for which a given ancestry is already assumed. Several statistical techniques  are then used to evaluate their efficiency. An AIM panel is generally considered to be efficient if e.g. it can differentiate the targeted populations in the Euclidian space represented by two or three principal components (PC) or if the inferred ancestry is consistent with some expectation (e.g. self-reported ancestry). The majority of the panels are designed with the aim of distinguishing main continental groups  owing to the known difficulties of using small SNP panels to classify individuals when they belong to closely related populations.The number of SNPs incorporated into AIM panels varies from a few dozens to a few hundreds Table ; and thihttp://hapmap.ncbi.nlm.nih.gov). This database contains 1,440,616 SNPs genotyped in a total of 1,218 individual samples belonging to the following main continental groups: 472 Africans, 58 Americans, 101 Central-South Asians, 364 East Asians and 223 Europeans. Unless specified, for most of the simulation experiments, only three populations representing the main continental groups were taken from the full HapMap data, namely CEU (European ancestry), CHB (East Asian ancestry) and YRI (African ancestry), with 50 individuals in each group. This decision was based on the fact that most of the AIM panels available were designed to identify ancestry from main population groups.The HapMap SNP database was retrieved from its repository  is 1/3 of ancestry from each of the main continental groups .The dependence of ancestry inference on sample size was estimated through simulation experiments using a similar procedure to that in Heinz et al. . In brieK value .The software Admixture v. 1.22  was usedPLINK v.1.07  was usedST distances as carried out in Shriver et al. [ST values were taken from SPSmart and ENGINES [Locus specific branch length (LSBL) statistics was estimated using pairwise Fr et al. . LSBL ai ENGINES , 35.http://www.r-project.org) and Perl (http://www.perl.org) scripts were used to display results obtained from the different software packages used.In-house R 2.13.0 (http://www.illumina.com/products/dna_test_panel.ilmn). All of these panels were originally designed to differentiate the three main continental groups , some of them also including other ancestral groups ; that is, for example, a Yoruban profile receives a ~100% membership in a cluster that groups all samples of African ancestry  genome ancestry of continental groups. Admixture analysis shows an optimum value of The estimates above were obtained considering the three main continental groups: Europe, Asia, and Africa. However, the number of SNPs needed to infer ancestry strongly depends on the evolutionary relatedness of the populations being considered: the closer the population under study, the larger the number of SNPs needed. The PCA plot in Additional file Ancestry estimates were obtained using different pre-designed panels and compared to the genome ancestry inferred using sets of 10,000, 1,000 and 500 rSNPs. As shown in the bar-plots in Figure LSBL can be used to measure the informativeness of specific AIMs in the panels and their potential to measure different ancestries when applied to the HapMap populations. To the best of our knowledge, only the GAL panel was designed using LSBL as a criterion to balance the informativeness of the AIMs incorporated in the panels Table . As showWe also followed the LSBL criteria for selecting the best AIMs from the HapMap database ; in eacSimulations were carried out in order to estimate the effect of population sample size when inferring genome ancestry using different AIM panels. These analyses are significant because geneticists are often interested in measuring the average genetic ancestry of a given population , but saThe hybrid AA-genomes were created in order to allow some simulations to investigate the number of SNPs needed to capture genome ancestry in admixture scenarios. Figure The standard deviation can be used to measure the error of the estimated ancestry obtained from panels, compared to global genome ancestry Figure . The errMeasuring genome ancestry is an issue of interest in different fields of biomedical research, including case-control association studies, forensic casework and police investigation, and anthropological studies. It is also of interest for private companies, given the growing social interest in knowing more about ancestry coupled with the progressive reduction of the cost of DNA tests.The present study aims to estimate the number of SNPs needed to reliably infer genome ancestry using unbiased sets of SNPs (rSNPs) and sets of pre-fabricated AIM panels.The results indicate that 10,000 SNPs selected at random from an individual can be used to infer genome ancestry with negligible error when considering the three HapMap populations CEU, CHB, and YRI. Even so, panels of 500 rSNPs perform reasonably well in this population scenario. Below this number, errors in the inference of ancestry increase noticeably as the number of rSNPs is reduced. As expected, the number of rSNPs needed to infer ancestry strongly depends on the evolutionary proximity of the populations under study. For instance, we made simulations to test the number of rSNPs needed to differentiate ancestry in two different East Asian populations, Chinese and Japanese. Here the number of rSNPs needed to differentiate these populations increases significantly more than one order of magnitude; therefore, the need for searching panels of highly discriminating AIMs is more justified. The distinction between individual ancestries within Asian populations (or other closely related groups) would require genome-wide screenings  or very During the last few years, several panels of SNPs have been designed in order to estimate ancestry using only a few markers (AIM panels). Analyses were carried out in the present study in order to assess the performance of these panels when applied to three main HapMap continental populations, CEU, CHB, and YRI. The results indicate that inference of ancestry can be seriously compromised when using panels containing small numbers of AIMs. For instance, out of the panels tested in the present study, those showing the best performance are ILU and GAL, that is, those that have more AIMs, while the ones including only a few dozen AIMs show higher errors and variability  could be biased if using panels containing a small number of AIMs. Second, DTCT should consider employing panels containing large amounts of markers in order to provide the most accurate service to the public . Third, the appearance of clustering is a function of how populations are sampled, of how criteria for boundaries between clusters are set, and of the level of resolution used. In the same way that the earth can be described by many different kinds of maps —from topological to economic—so, too, can the naturally occurring genetic variation among populations be divided in numerous ways and be made to highlight any chosen similarity or difference”. This conclusion is particularly important for the general public, who is often not aware of the limitations of ancestry DNA tests; and also in police investigation, where over-interpretation of an ancestry test could have important consequences on the investigation of forensic DNA evidence.Caution should be exercised when inferring ancestry using AIM panels. The concept of ancestry is a complex one and although it can be operational for particular purposes, it can lead to erroneous perceptions of human variability. As stated by Sankar and Cho : “the apPCA analysis of three HapMap populations, YRI, CHB, and CEU using all the SNPs in HapMap.(TIFF 2 MB)Additional file 1:PCA plots of East Asian populations using the whole set of SNPs in HapMap and 500 re-samples of rSNPs taking at random 100,00, 50,000, 10,000, 1,000 and 500 SNPs. As in Figure Additional file 2:The distributions on the left show the cumulative LSBL values for the different AIM panels compared to the distributions generated by the best AIMs in the HapMap datasets (indicated in the figure as “present study”). The bar-plots on the right mirror the ancestry inferred using these panels on the HapMap populations TSI (representing Europe), CHD (representing East Asia) and LWK (representing sub-Saharan Africa), in order to reflect the ‘portability’ of the different SNP panels in other population groups. (TIFF 8 MB)Additional file 3:Correlation values between the standard errors computed when comparing genomic ancestry with the estimated ancestries using different AIM panels, and considering admixed and non-admixed genomes. These values correspond to the distributions in Figure Additional file 4:


DI*A allele of the Diego blood group system was instrumental in proving Native American populations originated from Siberia. Since then, it has received scant attention. The present study was undertaken to reappraise distribution of the DI*A allele in 144 Native American populations based on current knowledge. Using analysis of variance tests, frequency distribution was studied according to geographical, environmental, and cultural parameters. Frequencies were highest in Amazonian populations. In contrast, DI*A was undetectable in subarctic, Fuegian, Panamanian, Chaco and Yanomama populations. Closer study revealed a correlation that this unequal distribution was correlated with language, suggesting that linguistic divergence was a driving force in the expansion of DI*A among Native Americans. The absence of DI*A in circumpolar Eskimo-Aleut and Na-Dene speakers was consistent with a late migratory event confined to North America. Distribution of DI*A in subtropical areas indicated that gene and culture exchanges were more intense within than between ecozones. Bolstering the utility of classical genetic markers in biological anthropology, the present study of the expansion of Diego blood group genetic polymorphism in Native Americans shows strong evidence of gene-culture comigration.Six decades ago the  Genetic diversity in Native Americans is an inexhaustible field of investigation. In the pre- molecular biology era, protein, enzyme, and red cell antigen polymorphisms were extensively studied , 2. An oa, and anti-Dia , where f(Di(a+)) is the proportion of Dia antigen carriers in the population [By reviewing previous studies on Dipulation . By assupulation .Zea genus [Manihot genus) [The following cultural and environmental traits were collected for each of the 144 populations: linguistic family, subsistence strategy, crop landraces, climate conditions, and mosquito infestation . As a baea genus ) or casst genus) . Lastly,t genus)  and const genus) .DI*A allele frequency data were plotted onto a single map using the Kriging algorithm of SURFER software 8.0. Spatial autocorrelation analysis was performed with the GENALEX software [DI*A frequency between pairs of populations showing geographic separation falling within a specified distance class.All software . Using tsoftware . We wereDI*A allele frequency amongst geographical, cultural or ecological groups, we first ran an analysis of molecular variance (AMOVA) using ARLEQUIN software 3.5 [DI*A variance across the Americas, a preliminary AMOVA was performed assuming that the 144 Native American populations were a single group. Subsequent analyses tried to identify the highest proportion of variance among several pool groups according to In order to detect a pattern of the ware 3.5 . To ensuDI*A variance even when geography was held constant. This was done by considering geographic groups that showed significant proportion of variance of DI*A and running three procedures, i.e., a two-way ANOVA  around SLC4A1 in 28 Native American populations previously screened for 678 genome-wide STRs [SLC4A1. Information can be found in the Mammalian Genotyping Marshfield Screening Sets 16 and 54 (http://research.marshfieldclinic.org/genetics/). We ran STRUCTURE [In order to distinguish patterns of variation amongst carriers of ide STRs . GeneticTRUCTURE  based onTRUCTURE  S2 Fig)DI*A and DI*A allele frequency in the Americas based on study of 144 Native American populations. High frequencies were observed in the Peruvian Andes, on the Guyanese Plateau, in the southeastern Amazonian Basin, in a region comprising the Brazilian state of Pará, and along the Tapajos, Xingu and Araguaia tributaries of the Amazon River. Low frequencies occurred mainly in North America, in Chaco and at southern tip of South America. The DI*A allele was totally absent in the Arctic (Eskimo and Tlingit), Panama Isthmus (Bribri and Teribe), Tierra de Fuego , and a few pocket areas in North America (Cherokee), Northern Brazil/Southern Venezuela (Ninam and Yanomama), and the Chaco area . DI*A frequency and geographic distance in paired populations. The observed pattern refutes the null correlation hypothesis (p<0.001) by showing an increasingly significant positive correlation as the distance between populations decrease. This finding suggests the existence of locations with similar genetic features, i.e., where populations have similar DI*A allele frequencies.DI*A frequencies were observed for the Equatorial-Tucanoan and Ge-Pano-Karib branches of the Southern Amerind linguistic family, particularly among Tucanoan speakers (mean frequency = 0.229), Panoan (0.223), Karib (0.194), Tupi (0.184), and Ge (0.171). The DI*A allele was almost completely absent (less than 2%) in Eskimo-Aleut, Na-Dene, and Keresiouan speakers from North America and Chibchan speakers from Lower Mesoamerica. Portrayal of polymorphism in relation to subsistence mode and ecological conditions indicated that DI*A was more frequent in hunter-gatherer/forager (mean frequency = 0.133) than in farmer (0.061) and marine hunter/gatherer (0.018) groups. Equatorial regions accounted for up to 12% of DI*A and frequencies decreased in gradient fashion from warm temperate (mean = 0.076) to arid (0.045), snowy (0.032) and polar (0.000) regions. A higher frequency of DI*A was observed in populations living in areas where the domesticated crop was cassava (0.176) rather than maize (0.081) or neither (0.039).When the Diego polymorphism  was portDI*A allele frequencies occurred randomly or rather coincided with geography, linguistics, lifestyle, or ecoregions. DI*A allele frequency variation obtained using various one-way models to identify the factor most closely associated with allele variance. Overall, the Diego blood group exhibited a significant variation in the Americas accounting for 11.95% of the total frequency variance between the 144 Native American populations. Geography accounted for significant between- group allele variation . Grouping according to the three main linguistic phyla, i.e. Eskimo-Aleut, Na-Dene, and Amerind, failed to reveal a significant genetic structure for the system of interest  whereas grouping according to Native American linguistic family as defined by Ruhlen was significant . Lifestyle accounted for 3.61% of DI*A allele frequency variation among Amerindians, with highest values being reached whether the populations inhabited areas with wild taxa of cassava or not . Grouping according to putative mode of subsistence and alimentary crop in relation to maize showed little (Zea species: p = 0.029) and no significant pattern of variance of DI*A (presence/absence: p = 0.489). Climate did not account for the observed DI*A allele differences  but ecological areas defined by anopheles mosquito was correlated with DI*A allele frequency .In the second phase of study particular attention was paid to determine if areas with similar DI*A amongst geographically determined populations. DI*A allele frequency. All tests indicated that language was the most explanatory factor .In the third phase of study, we tested the gene-culture concept with respect to the effects of four cultural traits in the dispersal of SLC4A1 locus could help understand the observed variation  with environmental and cultural factors. Our assumption was that expansion of the Diego blood group from North to South America was also driven by culture.Six decades ago, demonstration that the Di unclear . This stDI*A allele frequencies show striking contrasts between areas and that variations tend to correlate significantly with environmental and cultural traits. The strongest correlations were observed in the Amazon region as opposed to the rest of the continent and in function of linguistic family. Analysis according to linguistic family revealed a significant correlation between with the phylogenetic structure of the Amerind language family with high DI*A levels in Southern Amerind language speakers. High correlation with cassava and Tupi-speaking areas agrees with expansion driven by agriculture [Whether or not human genetic diversity occurred randomly is a fundamental question in the field of biological anthropology. Mapping genetic variation amongst populations has proven to be a useful technique to identify meaningful concordances with historic populations or ecological niches , 40. Theiculture . The curiculture , 42, 43.iculture , 44, 45.DI*A levels. Local genetic differentiation associated high genetic drift and uneven gene flow could explain this finding especially in the Yanomama populations [Another striking finding of the present study involves areas with low ulations  who provulations –49.Previous studies have described the special genetic and craniometric characteristics of the Ayoreo (a.k.a. Moro) in the Chaco region. Like the Aché, Emerillon, and Yaghan people, the Ayoreo exhibit limited within-population diversity that is among the lowest in South America. These genetic peculiarities are most likely due to a severe founder effect, but the exact causative events remain unclear , 51.a antigen in the Isthmus of Panama would be consistent with the long-term preservation of the Chibchan genetic substratum.In the Isthmus of Panama, craniofacial, classical and molecular investigations have documented the genetic distinctiveness of present-day Chibchan-speakers suggesting arguing that their predecessors have lived in isolation from Central and South Amerindians since the early Holocene , 52, 53.DI*A amongst South Amerindians reinforced by spatial autocorrelation analysis suggests that isolation by distance and genetic drift in Eastern South Amerindians populations also played a non-negligible role. These factors may have led to random accumulation or depletion of DI*A in the same way as for private polymorphisms [Based on dispersal patterns coupled with the demographic history of Native Amerindians, it is not unexpected to see a loss of genetic diversity in most of the Eastern South Amerindians , 45, 56.orphisms , 57. ThiDI*A allele frequency and warm tropical conditions, domesticated crop type, and presence of disease-carrying vector species. The circumscribed areas defined by these factors compose a mosaic of specific biocenoses and pathocenoses [SLC4A1 (the closest being D17S2180 at >1.6 Mb). An alternative explanation for the correlation with tropical conditions is that ecology may have contributed to higher gene flow rates within a similar environment and induced distortion in the distribution of the DI*A allele frequency between distinct ecological centers [Our study also revealed a significant correlation between ocenoses , 32. It ocenoses . However centers . Analysi centers .Unraveling the genetic structure of Native Americans holds the key to depicting the patterns of the initial peopling of the Americas. Deeper screening of the variation of the ABO-RH blood group, classical genetic markers, or Y-chromosome failed to show clear-cut genetic patterns amongst the O+ phenotypes or Q-M3 male lineages , 60. TwoDI*A in the northernmost and southernmost regions of the American continent is another noteworthy finding. While isolation by distance with ensuing drift likely accounts for genetic variation in Fuegians [DI*A is a shared characteristic of Eskimo-Aleut, Na-Dene, and Yeniseian-speakers [DI*B allele carriers occurred in the northernmost areas of North America.Absence of Fuegians , an alteFuegians , 42. SecFuegians , reverseFuegians , therebyFuegians , 70. IntDI*A and DI*B were sufficiently informative for preliminary study of Native American population dispersal. In this era of fine-grain genetic screening [DI*A carriers will be needed to confirm our approach.Whether or not it is realistic to attempt to reconstruct the genetic history of peopling from a single locus is pertinent question. It is difficult to ascertain that deviation of allele frequency is due to population displacement only and not to genetic drift , 60. Paucreening , additioDI*A allele of the Diego blood group system in Amerindians. Our data demonstrates large variations in frequency ranges and a significant correlation with environmental and cultural parameters. Our findings suggest that DI*A carriers crossed into Americas from Siberia and have diverged along the lines of the main linguistic families. Subsequent genetic differentiation appears to have been stronger in Lower Meso and South Amerindians depending on environment conditions. In the Isthmus of Panama, Chibchan-speakers appear to have experienced intense cultural interactions with neighboring populations without relevant gene flow. A secondary migration by mostly DI*B carriers may have covered North America. In addition to providing strong evidence of gene-culture comigration in the Americas, the present study illustrates the extent to which geographical maps of genetic and cultural phenomena dovetail.The aim of this study was to assess the geographical distribution of the S1 File(DOC)Click here for additional data file.S1 TableThe file is a MS Excel spreadsheet 97–2003 (.xls) and is coupled with a Readme file .(XLS)Click here for additional data file.S1 FigLeft panels: three main areas where controlled. Right panels: eight main areas where controlled. Ruhlen's linguistic classes . Main Pre-Colombian subsidence strategies . Areas with/without cassava . Areas with/without maize or cassava crops . See (TIF)Click here for additional data file.S2 FigSamples were genotyped by .(TIF)Click here for additional data file.


Tetraogallus tibetanus) using two segments of mitochondrial gene  and eight microsatellite loci by characterizing population differentiation and gene flow across its range. Combined (Cyt-b + CR) datasets detected several partially lineages with poor support. Microsatellite data, however, identified two distinct lineages congruent with the geographically separated western and central regions of Qinghai-Tibetan Plateau (QTP). The phylogeographic patterns that we observed might be explained by a combination of vicariance events that led to local isolation of T. tibetanus during warm periods and range expansions and population intermixing during cold periods. The results of this study add to our knowledge of population differentiation and connectivity in high altitude mountain ecosystems.Most of the temperate species are expected to have moved to lower altitudes during the glacial periods of the Quaternary. Here we tested this hypothesis in a cold-tolerant avian species Tibetan snowcock ( Historical refugia have long been thought vital in shaping contemporary patterns of biological diversity . The preTetraogallus tibetanus [www.redlist.org 2013) and as class II Protected Status under Chinese law, feeds mainly on the roots, shoots, grass, leaves and insects, and inhabits fluvial rocky hills, alpine meadows, hilly pastures, and barren shrubby grasslands[in situ? (2) Have recent climate-induced habitat shifts shaped patterns of population connectivity and gene flow in this evolutionary hotspot [Cold-tolerant avian species with limited dispersal capabilities present potentially good candidates to test whether they have persisted in central refugia or retreated to the edge of QTP during Pleistocene glaciations. The Tibetan snowcock ibetanus  is the oibetanus . Tibetanrasslands, this st hotspot –18?The study of high altitude species requires hard and difficult fieldwork. In total, 80 individuals of Tibetan snowcock were collected from 14 sampling sites in the QTP at elevations from 3700 to 6000 m a.s.l. These sites cover most of the distribution range of the species. The sample size of each population is shown in Cyt-b) and Control Region (CR) were PCR-amplified. Two new pairs of primers were designed for Tibetan snowcock using Primer3 [Gallus gallus) genome: . Primer sequences and information on markers can be retrieved from http://www.ncbi.nlm.nih.gov/sites/entrez. The published microsatellite data from the other 4 groups  have been involved in this study[Total genomic DNA was extracted from the samples using the DNA whole genome kit Qiagen, Inc., Valencia, CA). Two mitochondrial DNA (mtDNA) fragments from the Cytochrome-b  and nucleotide diversity (π) for each population and all populations combined diversity using DNASP 5.0 .R), observed heterozygosity (HO), heterozygosity expected from the Hardy-Weinberg assumption (HE) and exact tests of linkage disequilibrium (LD) between pairs of loci for each population were calculated with ARLEQUIN 3.5 [For microsatellite data, we estimated the standard genetic diversity for each site using the following indices: the number of alleles at each locus (NA), average allelic richness  was calculated to ascertain the conditions of gene communication among populations, and was estimated as follows: Nm = (1- FST)/4 FST[. FST and Nm were calculated using the software ARLEQUIN.A hierarchical analysis of molecular variance (AMOVA) was performed to compare levels of mitochondria and microsatellite data genetic diversity within and among several possible population groupings of Tibetan snowcock using ARLEQUIN. Genetic differentiation between regional groups was evaluated based on Pairwise values of FST)/4 FST. FST and6 generations, sampling every 100 generations. A 50% strict consensus tree was computed for the 1000 bootstrap trees.Evolutionary relationships among all haplotypes were performed by constructing phylogenetic trees using Bayesian (BA) Markov chain Monte Carlo (MCMC) phylogenetic analyses . The comPhylogenetic analyses were also run in PAUP 4.0b10  using maTetraogallus himalayensis) . We further constructed unrooted haplotype networks using the median-joining algorithm [http://www.fluxus-engineering.com). This method allows the visualization of mtDNA haplotype relationships and frequencies.The phylogenetic trees were rooted using homologous sequences derived from the Himalayan snowcock   was emplCyt-b gene (1.6 × 10–8 per site per year) [The dynamics of population size fluctuations were estimated using the Bayesian skyline plot (BSP) method implemented in BEAST. This approach incorporates uncertainty in the genealogy by using MCMC integration under a coalescent model, in which the timing of dates provides information about effective population sizes through time. Chains were run for 100 million generations and the first 10% discarded as ‘burn-in’. The substitution model was selected according the result of jMODELTEST. We applied 10 grouped coalescent intervals and constant growth for the skyline model. Because no fossil data were available to calibrate the mutation rate, we assumed a conventional molecular clock for the avian mtDNA er year) . Demograer year) .Fs [Fs value.Demographic histories of each clade were inferred by pairwise mismatch distribution analyses  and compFs  and TajiFs  test in To determine whether populations remained isolated in individual habitats (multiple refugia), or merged into a single gene pool (single refugium) during glaciations and interglaciations, we used coalescent simulations in MESQUITE 2.75 to test Ne) using the θ-values calculated by the ML and coalescent-theory approach in the program MIGRATE 1.7.3[Ne using the formula θ = 2 Neμ using same mutation rate in demography time estimate. When running the coalescent simulations, we set the overall Ne to equal the empirically estimated values, and constrained the Ne of the putative refugial population to a size proportional to the overall empirically estimated Ne.For coalescent simulations, the effective population size . A total of 1589 bp of mtDNA was obtained, which contained 136 polymorphic sites, 124 of which were parsimony informative. These polymorphic sites defined 49 unique haplotypes, 36 of which occurred only once. 7 haplotypes were shared among individuals within the same population and six haplotypes were shared between populations. Within sampling locations, haplotype diversity values were from 0.73 to 0.98, and nucleotide diversity ranged from 0.0019 to 0.036 (All sequences are accessible at GenBank (Accession nos: JX136799- JX1368336 for CR and JX136834—JX136846 for to 0.036 .O) and expected heterozygosity (HE) ranged from 0.49 to 0.66 and 0.65 to 0.81, respectively , which equated to a MLE estimate θtotal of 0.0367 with lower and upper bounds of 0.027 and 0.0496, respectively. For the observed gene tree we computed Slatin & Maddison’s S = 31.We then performed simulations under continuous migrations using the module ‘Coalescent in current tree with migration’ in Mesquite. All simulated datasets similarly rejected the first hypotheses (P < 0.01), but supported the two-refugium assumption, and the highest P-value (0.46).We conducted coalescent simulations to test two hypotheses concerning the glacial refugia of Tibetan snowcock: (1) all current populations of the species were derived from a single refugium present towards the end of the LGM (c. 12 ka) and located either at the western edge of the QTP or in the interior of the plateau ; (2) theEospalax baileyi [Potentilla fruticosa [Aconitum gymnandrum [The glacial-interglacial alteration during the Quaternary greatly influenced the present distribution pattern and phylogeographic structure of alpine species of QTP . The Ple baileyi , an alpiruticosa  and an amnandrum .Fs and Tajima’s D statistic were negative expected under rapid population growth at about10,000 year ag [Geographic complexity and environmental heterogeneity are likely to have shaped the genetic structure among Tibetan snowcock populations. Such a landscape effect is evident in the western and central of QTP. Although Tibetan snowcock has a rather restricted geographic distribution, mitochondrial data support the intermixing lineage with poor support, while microsatellites indicate a clear subdivision into a western and a central lineages. The discordance between mitochondria and microsatellite data is most likely explained by the different mutation rate of these markers, which reflects different processes acting at different times in the evolution of this species. We speculate this maybe is the evidence of ongoing divergent process. This pattern of population divergence between western Kunlun Mountains and central of QTP is consistent with the other dispersal-limited bird the twite (irostris). AlthougThe Qinghai-Tibetan Plateau is well known as the highest plateau in the world and one of world biodiversity hotspot. As occurring on the plateau and adjacent regions, Tibetan snowcock showed lack of evidently phylogeographic structure on mitochondria data and historical population expansion, suggesting instead historically wide gene flow and a relatively stable demography, as found in other plateau species or populations . This reS1 FigThe solid line represents the expected distribution; the dashed line represents the observed distribution. Mismatch distribution for the following groups are shown: QLS, QDM, WKL, BKL and TGL.(TIF)Click here for additional data file.S1 TableAll vouchers are deposited in the Lanzhou University. Location names correspond to those on the map in (DOC)Click here for additional data file.S2 Table(DOC)Click here for additional data file.


AbstractPageBreakArtemesialonginaris is a marine shrimp endemic to the southwestern Atlantic and distributed from Atafona, Rio de Janeiro (Brazil) to Rawson, Chubut (Argentina). In recent years, this species has become an important target of the commercial fishery as a consequence of the decline in the fishery of more traditional and profitable marine shrimps. In addition, phenotypic variations have been documented in populations along its distribution. Therefore, investigations on the genetics of the fishing stocks are necessary for the development of sustainable management strategies and for understanding the possible sources of these variations. The mitochondrial gene Cytochrome Oxidase I (COI) was used to search for evidence of genetic structure among the populations of Artemesialonginaris and to analyze the phylogenetic relationships among them. A total of 60 specimens were collected from seven different localities, covering its geographical range. The final alignment showed 53 haplotypes , with no biogeographical pattern. The low genetic divergence found, with a non-significant FST value, also suggests the absence of population structure for this gene. These findings indicate a continuous gene flow among the populations analyzed, suggesting that the phenotypic variation is a consequence of different environmental conditions among the localities. Artemesialonginaris Spence Bate, popularly known as Argentine stiletto shrimp, plays an important role in the marine trophic chain of the southwestern Atlantic, as food for different species of fish and cephalopods  and Farfantepenaeuspaulensis (Pérez-Farfante), the white shrimp Litopenaeusschmitti (Burkenroad) and the seabob shrimp Xiphopenaeuskroyeri (Heller)  .Artemesialonginaris in Brazil. The offseason in south and southeast coast of this country for this species and other commercial shrimps is based on the period of juvenile recruitment of Farfantepenaeus species, without taking account the possibility of the existence of more than one stock for these species  to Rawson  (43°18'S) . Althoug43°18'S) . In addi43°18'S) .Artemesialonginaris. For example, in the Ubatuba region  the temperature (16–30 °C) and salinity (28–38) vary widely because of the intrusion of different water masses  increase with the latitude, from Ubatuba  to Mar del Plata , but decrease with latitude from the Farol de São Tome  to Ubatuba , Faculty of Philosophy, Sciences and Letters at Ribeirão Preto (FFCLRP), University of São Paulo (USP)  Table .PageBreakThe protocols for DNA extraction, amplification and sequencing followed An ~700-bp region of a partial sequence of the mitochondrial COI gene was amplified by the polymerase chain reaction (PCR) using the pair of primers: HCO1 (5’-TAAACTTCAGGGTGACCAAAAAATCA-3’) and LCO1 (5’-GGTCAACAAATCATAAAGATATTGG-3’) . The PCRPageBreakgenetic distances, and to construct a Neighbor-Joining dendrogram, both based on the Kimura 2-parameter substitution model  and Rimapenaeusconstrictus (Stimpson) (GenBank accession number KF783863). We also attempted to use a sequence of the same portion of the COI gene of Artemesialonginaris available in GenBank (accession number EU400383.1)  . However00383.1) .The haplotype number was calculated in the program DNASP 4.10.9 . The hapArtemesialonginaris was 28.41% (A), 30.99% (T), 19.47% (G), and 21.12% (C).A total of 60 sequences of the COI gene from individuals sampled in the seven localities was obtained. The final multiple sequence alignment included 645 base pairs. The number of variable sites was 66 (10.23%), 8 (12.12%) in the first codon position and 58 (87.88%) in the third position, and 30 of the variable sites were phylogenetically informative. Adding three species as the outgroup, the number of variable sites was 143 (28.49%), 72 of which were phylogenetically informative. The average nucleotide composition for Artemesialonginaris ranged from 0 to 2.7%, and the average distance was 1.1 ± 0.2%. The interspecific genetic distance, including the outgroup, ranged from 21.3 to 27.1%. Average distance among individuals in each population ranged from 0.81 ± 0.25% at Cananéia to 1.42 ± 0.24% at Macaé  did not detect structure among the localities, and the observed variation occurred predominantly within the localities. The FST indices were not significant p > 0.05)  is much lower than the interspecific distance between Artemesialonginaris and the out-group species (21.3–27.1%). This result not only confirms Artemesialonginaris as a single taxon throughout its distribution, but also supports the utilization of this methodology in the identification of penaeid shrimps from the Brazilian coast. The difference between the intra and interspecific genetic variation of the barcode region of the COI gene is termed the “barcode gap”  (The intraspecific genetic distance for ode gap” . It is aode gap” . The gening 20%) .Artemesialonginaris along its entire geographical distribution. The FST value obtained reflects this absence of geographical genetic structure. In species with high genetic variation and few shared haplotypes, negative FST values are probably associated with the imprecision of the algorithms used in this type of analysis, and can be interpreted as zero . The determination of an individual phenotype is a consequence of the interaction between genotype and environment , on the Argentine coast, are able to travel for distances between 120 and 300 nautical miles , transported by the coastal currents , for instance, is a water mass that cover the geographical range of this study  in Ubatuba, the number of animals in the larger size classes increased. It was associated with the coming of migrants into the population , on the Australian coast, 930 km from the site where the specimen was marked.According to pulation . Thus, tArtemesialonginaris migrates over long distances, and is able to establish populations in different areas when conditions are appropriate. It is therefore possible to consider Artemesialonginaris as a metapopulation, which fits the model of source and sink proposed by Our results, encompassing samples from its entire distribution, support the hypothesis that PageBreakrange of its distribution. The role of marine protected areas in enhance fisheries in adjacent regions depend if they act as sources or as sinks (Thus, these localities, where the populations are considered sources, would be strategic for the implementation of management measures such as the creation of protected areas or offseason periods, in order to maintain the fisheries in these areas and also in all as sinks . Even coas sinks , so the Studies investigating the larval dispersal and the migration of juveniles and adults of this species must be conducted in order to verify whether the model described by Artemesialonginaris as a single taxon, with no genetic differentiation among the populations through its entire geographical distribution, we showed the importance of the effect of the environmental conditions specific to each locality in the expression of the phenotypic characteristics of the individuals in a population.Our results confirm that the DNA barcoding technique is an efficient tool for the identification of penaeid shrimps from the Brazilian coast. In addition to the validation of The genetic homogeneity is maintained by the larval dispersal and high migratory capacity, which assure gene flow among populations. These characteristics make it possible for individuals to be transported by water masses and currents of the southwestern Atlantic.In addition, this study also indicate the importance of populations of south Brazil and Macaé as sources, to provide individuals to other areas. Thus these populations should be considered essential in developing management strategies for the species.