Ne and LD indicated that these populations diverged from East Asians during the late Pleistocene (14.5 to 8 KYA). The continuum in divergence time from Negritos to Senoi and Proto-Malay in combination with ancestral markers provides evidences of multiple waves of migration into SEA starting with the first Out-of-Africa dispersals followed by Early Train and subsequent Austronesian expansions.Indigenous populations of Malaysia known as Orang Asli (OA) show huge morphological, anthropological, and linguistic diversity. However, the genetic history of these populations remained obscure. We performed a high-density array genotyping using over 2 million single nucleotide polymorphisms in three major groups of Negrito, Senoi, and Proto-Malay. Structural analyses indicated that although all OA groups are genetically closest to East Asian (EA) populations, they are substantially distinct. We identified a genetic affinity between Andamanese and Malaysian Negritos which may suggest an ancient link between these two groups. We also showed that Senoi and Proto-Malay may be admixtures between Negrito and EA populations. Formal admixture tests provided evidence of gene flow between Austro-Asiatic-speaking OAs and populations from Southeast Asia (SEA) and South China which suggest a widespread presence of these people in SEA before Austronesian expansion. Elevated linkage disequilibrium (LD) and enriched homozygosity found in OAs reflect isolation and bottlenecks experienced. Estimates based on  The events and period of prehistoric peopling of Southeast Asia (SEA) have been controversial. Human remains from archeological sites such as Callao Cave in Philippines  and NiahThe rich ethnological diversity that exists in Peninsular Malaysia provides a great opportunity to study SEA prehistory. The current Malaysian population comprises three major ethnic groups including Malay, Chinese, and Indians. In addition to these groups, Peninsular Malaysia is home to other ethnicities including several minor indigenous communities collectively known as “Orang Asli” (OA) or “Original People.” Making up approximately 0.6% of Malaysian population, OA has been classified into three groups, namely Negrito (Semang), Senoi, and Proto-Malay  based on linguistic, physical, and anthropological characteristics. Each OA group could be further subdivided into six subgroups based on their lifestyle and geographical location.Malaysian Negritos are Austro-Asiatic (AA) speakers and inhabit in northern parts of Peninsular Malaysia. The tradition of these hunter-gatherers involves northern Aslian dialect of AA language, egalitarianism, and patrilineal descent system. On the basis of their hunter-gathering lifestyle and physical characteristics including their small body size, dark skin pigmentation, cranio-facial morphology, and frizzy hair, Malaysian Negritos traditionally are grouped with other Negrito communities in South Asia and SEA such as Andaman islanders, Mani in Thailand, Philippine Negritos, and other phenotypically similar populations in Papua New Guinea and Australia. These similarities have led to the general idea that all Negrito populations of SEA and Oceania originated from a common ancestral group which entered SEA during the earliest human dispersals into Asia . HoweverSenoi, who are AA speakers, make up the largest group among the OA populations. They traditionally practice slash-and-burn farming and their phenotypic features are intermediate between Australoid and Mongoloid people. The origin of the Senoi is obscure; however, based on archeological and limited genetic studies, they have been linked with AA agriculturists from mainland SEA or South China who arrived in Peninsular Malaysia in the mid-Holocene . Proto-MPrevious studies of these Malaysian populations have relied on relatively small sample sizes and low density genetic markers, limiting the power of the analysis. Here, we provide a more comprehensive insight and better estimate of divergence time for populations in SEA, by leveraging on larger sample sizes on very high-density Illumina HumanOmni 2.5 BeadChip arrays. We first investigated how distinct OAs are from other Asian populations, quantifying genetic structure within the Asian continent. We also examined linkage disequilibrium (LD) decay and runs of homozygosity (ROH) to study population history and consanguinity. Finally, we examined gene flow between OA population and other populations in East Asian (EA) and estimated the divergence time for these populations to elucidate events involved in the peopling of SEA.This study was approved by the Ministry of Health Malaysia under National Medical Research Registry MNDR ID #09—23-3913, JAKOA  and Monash University Human Research Ethics Committee.Following consultation with JAKOA officers in the various districts in different states, courtesy visits were made to OA community elders and the rationale of the study and the procedure of sample collection explained. Once they had agreed and informed their communities, field visits were carried out. Individuals who provided informed consent and also answered questionnaires were included.Peripheral blood samples were collected from 169 individuals belonging to Negrito , Senoi (MahMeri and CheWong subgroup), and Proto-Malay  groups . GenotypP < 10−6) and displayed missing rates >0.05 across all samples in each population were removed. Additionally, samples with call rate <0.99 were excluded. Gender concordance was examined using PLINK v1.07 (P < 10−6) test and missing rates more than 0.05 across all samples.Quality controls were applied to the data obtained from each OA community separately to exclude problematic samples and single nucleotide polymorphisms (SNPs). All SNPs that failed the Hardy–Weinberg exact (HWE) test , a total of 291,096 overlapping autosomal SNPs remained for downstream analysis.The OA genotype data were merged with data from Human Genome Diversity Project (HGDP) , 89 Malar2 > 0.5 were pruned out in order to avoid the effects of excessive LD between SNPs. After this pruning a total of 204,426 SNPs remained for analysis. Pairwise Fst distance between populations in same data set were calculated using EIGENSOFT v3.0, and a Neighbor-net tree was constructed by SplitsTree v4 software . SNPs with software . ADMIXTUsoftware .r2 using PLINK. Pairwise LD between all possible SNPs was calculated and mean LD was measured in bins of 5 kb.PLINK v1.07 was used to estimate ROH in selected populations. PLINK takes 5,000 kb (50 SNPs) sliding windows across the genome and allows for 1 heterozygous and 5 missing calls in each window. To minimize the effects of LD on ROH, minimum ROH length was set to be 500 kb because it is unusual for LD to extend beyond 500 kb. LD decay for each population was calculated as D statistics using ADMIXTOOLS (Ne) and divergence time between OAs and Yoruba in Ibadan (YRI), Han Chinese in Beijing (CHB), and Japanese in Tokyo (JPT) samples were estimated according to the method suggested by r2 using PLINK v1.07. In order to minimize the effects of small sample size, all individuals were pooled together in their respective OA groups. Admixture time between OAs and EA was estimated by rolloff package using 399,971 SNPs by HapMap3 and OAs.TreeMix v1.12  was usedMIXTOOLS  to examiMIXTOOLS . Effectisupplementary fig. S1, Supplementary Material online). As presented in figure 2A, the first component, which captures 32% of total variation, clearly distinguishes South Asian populations from those in the East. From PC2, the Onge and Jarawa, both Negrito subgroups, clustered together and were distinct from other populations. However, they appeared closest to Papuans and Melanesians. The Malaysian Negrito subgroups, while clustering closer to East Asians, showed a tendency toward other Negrito subgroups in Oceania and Andaman islands. The rest of OAs such as Senoi and Proto-Malays as well as Singaporean Malays were located between Malaysian Negritos and East Asian clusters indicating that these groups might be admixed between these two populations. However, both Senoi and Proto-Malay groups lay closer to East Asians on PC4 suggesting that all these populations may have a common origin.To understand population structure across Negritos, other OA subgroups, and their relationship with neighboring populations in Asia and Oceania, a PCA was performed  and EA (yellow) and the latter appears to be the major component in all OA groups. From K = 3, Andamanese component (pink) appeared. This component also presented considerably in Oceanians and in lesser extent in Malaysian Negritos. At higher K = 4 and K = 5, Negrito (dark green) and Oceanian (dark blue) components appeared respectively. The best model which had the lowest cross validation error suggests nine major ancestral groups which gave rise to the 40 distinct populations included in our study. At K = 9, all Negrito subgroups showed similar ancestral patterns. However, we observed small portions of other ancestral components (shown in yellow and purple) in some Negrito individuals .In order to determine critical ancestral components that may have shaped the genetic architecture among the OAs, we applied ADMIXTURE analysis. The results of ADMIXTURE from K = 9 also showed that two Senoi subgroups had different ancestral patterns. The purple colored ancestry component is highest in MahMeri, but also present in the Proto-Malay and Malay. The CheWongs appear to have MahMeri, Negrito, and East Asian components. At K = 11, CheWong appeared distinct.Results of ADMIXTURE at K = 9, Jakun and Temuan had similar ancestral components, but there was a unique substantial component (shown in light blue) only present in the Seletar from K = 6. The ADMIXTURE results further support the uniqueness of OAs.Different patterns of ancestry were identified in Proto-Malays. At supplementary fig. S3, Supplementary Material online). Using Yoruba as root, the graph that best fits our data (99.4% of variances) inferred six migration events. The tree topology was consistent with geographical distribution of populations and with previously shown Neighbor-net tree. Andamanese and Oceanians grouped together in a deep clade, while all OA groups formed a distinct cluster. Focusing on migration events, a migration (migration weight 0.37) directed from root Onge and Jarawa toward Malaysian Negrito root. The resulting tree also highlighted another migration (0.39) from the root of Bateq and Jehai to CheWong.To understand the relationship between our populations and examine the gene flow between them, we used TreeMix . The computed D statistics demonstrated significant gene flow between Andamanese and Malaysian Negritos but there was no significant gene flow detected between Andamanese and other OA groups. This suggests that an earlier gene flow occurred before other OA groups arrived in Peninsular Malaysia. The D statistics supported admixture between different OA groups, as gene flows between Negrito/Senoi, Negrito/ Proto-Malays, and Senoi/Proto-Malays were evident. We also traced admixture in AA-speaking OAs and those of Mainland SEA and Lahu and Dai, ethnic groups from South China.To further investigate gene flow between OAs and other populations, we used A shows the distribution of ROH in these populations. As expected, all Negrito groups generally showed long and high ROH compared with other OA groups. This is indicative of small population size or consanguinity. Interestingly, Seletar had the longest ROH among all OA groups which may reflect higher levels of autozygosity.Focusing on OAs in Malaysia, we determined inheritance of parental genome components, and calculated ROH in all OA groups against Malay from Singapore. Figure 5B shows the LD decay in OA subgroups and Singaporean Malays. LD in all OA groups was markedly higher even for long pairwise SNPs distances.To further examine the genetic isolation and admixture between OA groups, we calculated pairwise LD between all autosomal SNPs. LD is the nonrandom association of two SNPs and its decay can be affected by factors like drift, admixture, and inbreeding. Figure 5T) of OA groups and Africans to be around 67 KYA assuming generation time of 25 years which is a good agreement with other reported estimations of EA and African divergence previously  “belonging” to Malaysian Negritos was also spread among Southeast Asian and Southern Chinese populations. However, although Negritos predominantly shared this ancestral component, the Mendriq shared more portions of other ancestral components with East Asians and Senoi. This suggests more recent gene flow between them and their neighboring populations, most likely Malays. A similar observation was reported in Jehai, a Negrito subgroup using a less SNP .K = 6 in ADMIXTURE and Neighbor-net tree showed an affinity to the Oceanian. Anthropological information regarding origins of the Seletar is scarce and anecdotal. There is a paucity of information about this community. It is plausible that Seletar might have experienced a recent bottleneck as suggested by the long stretches of LD in their genome. The low levels of mtDNA diversity  and Southern Chinese populations. Existence of Negrito ancestral components in some MSEA has been reported by previous studies .


Dianthus gratianopolitanus exhibits a highly fragmented distribution range comprising many isolated populations. Based upon this pattern of distribution, we selected a study region in Switzerland with a lower magnitude of isolation (Swiss Jura) and another study region in Germany with a higher degree of isolation (Franconian Jura). In each region, we chose ten populations to analyze population structure, reproduction, and genetic variation in a comparative approach. Therefore, we determined population density, cushion size, and cushion density to analyze population structure, investigated reproductive traits, including number of flowers, capsules, and germination rate, and analyzed amplified fragment length polymorphisms to study genetic variation. Population and cushion density were credibly higher in German than in Swiss populations, whereas reproductive traits and genetic variation within populations were similar in both study regions. However, genetic variation among populations and isolation by distance were stronger in Germany than in Switzerland. Generally, cushion size and density as well as flower and capsule production increased with population size and density, whereas genetic variation decreased with population density. In contrast to our assumptions, we observed denser populations and cushions in the region with the higher magnitude of isolation, whereas reproductive traits and genetic variation within populations were comparable in both regions. This corroborates the assumption that stronger isolation must not necessarily result in the loss of fitness and genetic variation. Furthermore, it supports our conclusion that the protection of strongly isolated populations contributes essentially to the conservation of a species' full evolutionary potential.The endangered plant species  Rare plant species are largely subjected to the effects of isolation , near to the potential center of the distribution range, and the other study region was located in Germany (Franconian Jura), more closely to the periphery of the distribution range. From each region, we analyzed each ten populations . In Switzerland, D. gratianopolitanus occurred on average in 33.5 quadrants around the selected populations, whereas in Germany, the species was recorded on average in 12.2 quadrants around the study populations. The magnitude of isolation was, therefore, higher in Germany than in Switzerland.The magnitude of isolation was characterized based upon the quadrants of the grid of topographic maps , which is officially used for the design of species distribution maps in Germany. This grid was extended to Switzerland and the locations, where the species occurs in Switzerland were transferred from the Swiss distribution map in the accordant quadrants. Based upon this dataset, we determined the number of quadrants within a radius of 30 km around the selected study populations, where In both regions, we studied 15 individuals (cushions) per population, which were randomly chosen across the colonized habitat and represents about 50% of the number of individuals per population. However, in the smallest population, only 12 cushions were available  was estimated using topographic maps. Based upon the size of the area and the number of individuals, population density was calculated as cushions per square meter and is, therefore, an indicator for habitat occupancy. Furthermore, width and length of each individual was measured to calculate the cushion size in cm². Cushion density was estimated as the number of shoots per square meter, counted in small plots of 10 × 10 cm and extrapolated to 1 m².In the field, the number of flowers and capsules of each investigated cushion were counted in May and July, respectively. Using these data, we calculated fruit set, as number of capsules divided by number of flowers. Seeds were collected from the selected individuals with regard to nature conservation, which means that we did not collect all available seeds, but only 20% of the seeds to allow natural reproduction in the population. As populations of the species are generally small, we obtained not enough seeds to test all germination treatments for all populations separately. Therefore, tests were conducted with pooled seeds from the two study regions, respectively, to receive a sufficient number of replications. Different treatments were used to evaluate the germination response of seeds from Swiss and German populations associated with different environmental conditions which indicate a gap detection mechanism and longevity of the seeds. In this way, we tested whether differences between the two study regions occur at different treatments. We analyzed germination at four treatments, which were generally used by default in germination ecological analyses, and have turned out to be the most suitable conditions to test the germination of central European plants. They are derived from typical temperatures at day and night  lined with a double layer of filter paper  moistened with 5 mL of distilled water. To prevent evaporation, each petri dish was sealed separately with parafilm. Per treatment eight petri dishes as replicates with 10 seeds each were kept in stacks of eight and rotated daily. All experiments were placed in an incubator  with a constant humidity of 65%. For germination in darkness, the petri dishes were kept in a black box and scored under green light (25 W) to exclude any influence of light of a different wavelength. Germination was recorded daily for the first 10 days, then twice a week for a total of 6 weeks. Germinated seeds were removed.μL and used for the analysis of amplified fragment length polymorphisms (AFLPs), which were conducted concordant with the protocol from Beckmann Coulter as described previously  and 0.2 μL CEQ Size Standard 400 (Beckman Coulter), selective PCR products were separated by capillary gel electrophoresis on an automated sequencer .Shoots with young, green leaves were sampled of 15 cushions per populations for genetic analysis, and in total, 297 individuals were analyzed. Single shoots were placed in permeable bags, immediately stored in sealed containers and dried on silica gel. Genomic DNA was isolated from dry leaf material using the CTAB-based method (Rogers and Bendich Results were examined using the CEQ 8000 software (Beckman Coulter) and analyzed using the software Bionumerics 6.6 . From the computed gels, only those fragments were taken into account that showed intense and articulate bands. Samples yielding no clear banding pattern or obviously representing PCR artefacts were repeated or finally excluded. Reproducibility of molecular analyses was investigated with 10% of all analyzed samples by means of estimating the genotyping error rate 2) and as Shannon's Information Index SI (SI = sum(pi)ln(pi); pi = allele frequency). The apportionment of genetic variation within and between populations and subpopulations was assessed by hierarchical AMOVA with the software GenAlEx 6.3  was correlated with a matrix of the respective geographic distances (km) among populations  matrix was created wherein the presence of a fragment of a given length was counted as 1 and the absence as 0. The final matrix and all further calculations comprised 297 samples. Employing the software PopGene 1.32  were computed for the group mean difference and considered credible, when the HDI excluded zero. All estimated parameters had an effective sample size (ESS) of >10 k.Population parameters were compared between regions by means of Bayesian two-group models. The analysis was carried out using Markov Chain Monte Carlo sampling (MCMC) with the JAGS 3.4.0 software package Plummer . HabitatFor the analysis of the seed germination experiment, a Bayesian approach was chosen for both its higher flexibility and informative value as compared to classical NHST procedures. Only data of day 7 were analyzed, when overall germination was close to 50%. Modeling and interpretation were carried out using the software packages R 3.2.0  for Windows.D. gratianopolitanus occurred in credibly smaller habitat areas in Switzerland than in Germany. However, the number of individuals (12–60) was quite consistent in both regions  than in Germany (1435 cm²). Generally the size of the cushions varied between 181 and 2017 cm². However, mean cushion density  than among German populations (ΦPT = 0.37). A mantel test revealed no significant correlation of genetic and geographic distances between populations in Switzerland  in Germany (Fig.AFLP analysis resulted in 308 fragments. Genetic variation within populations Table with ΦPTland Fig.A, but a many Fig.B.Generally, we observed in all analyses Table a signifConsidering all populations from both study regions Table, we obseIf we analyzed the two regions separately Table, we founIn German populations Table, we obseD. gratianopolitanus. In contrast to our assumptions, population density was higher in Germany, although the magnitude of isolation was higher in this study region. This supports the findings of a previous study on Hornungia petraea (Kluth and Bruelheide Cirsium acaule and C. heterophyllum (Jump and Woodward In the study presented here, we analyzed the impact of isolation on the population structure, reproduction, and genetic variation of the rare plant species Succisa pratensis, similar observations were reported. In this survey, plants from more strongly isolated habitat islands also produced on average more flower heads and rosettes than plants from connected habitat islands (Hooftman et al. Increased population and cushion density affect growth and flowering traits (Hooftman et al. D. gratianopolitanus, similar germination rates were found in both analyzed study regions. It is already known that increased habitat fragmentation and the associated isolation may cause a decline in seed yield (Steffan-Dewenter and Tscharntke Juniperus communis, the production of filled seeds declined toward the limits (García et al. Narthecium ossifragum produced more, but smaller seeds that failed to germinate (Tsaliki and Diekmann Lychnis viscaria revealed no difference between more and less isolated populations (Lammi et al. Draba aizoides, strongly isolated relict populations exhibited even higher germination rates than populations from the center of the distribution range (Vogler and Reisch D. gratianopolitanus accompanied by a strong potential for long-distance pollination (Erhardt Although we expected a negative impact of stronger isolation on the reproduction of  Erhardt  may guarD. gratianopolitanus was within the range observed for rare, perennial and outcrossing plant species (Reisch and Bernhardt-Römermann The level of genetic variation within populations of D. gratianopolitanus, genetic variation within populations seems to be much more affected by population density and size than by isolation. We could show that genetic variation decreases with increasing population density across all studied populations. This may be traced back to the equalizing effects of gene flow within populations, which should be stronger in dense populations. In the German study region with a higher magnitude of isolation, genetic variation within populations depended furthermore on population size, which is already known as most important factor for genetic variation within populations (Leimu et al. In the case of D. gratianopolitanus in Germany than in Switzerland. Moreover, a correlation of genetic and geographic distances occurred only between German but not between Swiss populations. It has been postulated that genetic drift is 2–30 times higher in isolated peripheral populations (Vucetich and Waite Genetic variation was higher between populations of D. gratianopolitanus populations should, therefore, be an integral part of the strategy to conserve this central European endemic plant species.Based upon our results, it can be concluded that the isolation of naturally fragmented populations must not necessarily have a negative impact on population structure, reproduction and genetic variation within populations. However, genetic variation between populations increases with geographic isolation due to restricted gene flow. Isolated populations make, from this point of view, due to their genetic uniqueness an essential contribution to a species' full evolutionary potential. The protection of isolated


Sitobion avenae is a major agricultural pest of wheat in China. Using microsatellite markers, we studied the potential gene flow, genetic diversity, genetic differentiation, and genetic structure of seven S. avenae populations from different regions of China . The populations from Henan, Shandong, and Jiangsu showed high levels of genic and genotypic diversity. By contrast, the genic diversity in the Beijing and Hebei populations was much lower. Despite this low genic diversity, the genotypic diversity of the Beijing population was higher than that of all of the other populations, except those from Jiangsu and Shandong. Overall, the genetic divergence among the seven S. avenae populations tested was high, though there was almost no differentiation between the Shandong and Henan populations. We observed significant negative correlation between the strength of gene flow and the geographic distances among populations. Based on genetic analysis, the seven S. avenae populations studied can be divided into four distinct clusters; (i) Hubei, (ii) Shanxi, (iii) Beijing and Hebei, and (iv) Shandong, Henan, and Jiangsu. The present results provide a basis for potentially optimizing integrated pest management (IPM) programs in China, through adapting control methods that target biological traits shared by various populations of the same genotype. Sitobion avenae, Rhopalosiphum padi (L.), Schizaphis graminum (Rondani), and Acyrthosiphon dirhodum Walker S. avenae is the most dominant and destructive wheat aphid in China 2 per year, and yield losses can be up to 40% Several wheat aphid species are major agricultural pests in China, notably Rhopalosiphum padi had higher genetic variation in fitness compared to the sexual genotypes Wheat aphids have complex life cycles that are known to be highly affected by climate S. avenae can survive on numerous plant species, including all of the cereals, many other monocots, and certain dicots S. avenae collected from oat and barley showed that their fitness traits differed significantly, indicating a genetic basis for their differentiation Aphids have complex lifecycles and adaption to diverse hosts; this means that any given geographical population of aphids will not be homogeneous Clonal selection is very important in the study of population genetic structures of wheat aphids. Ecological adaption may occur quickly because of the rapid propagation of asexual offspring It is difficult to understand the ecology of wheat aphids, as they tend to migrate with the wind In the past 20 years, many studies addressing the flying behavior and genetic structure of aphids populations have been conducted using microsatellite markers S. avenae populations from seven different geographic regions in China. To interpret the population structure and dynamics of S. avenae, we need to better understand the importance of migration and clonal selection, which may have relevance in forecasting aphid outbreaks. Our results also provide a foundation for optimizing integrated pest management (IPM) programs through adapting control methods according to biological traits shared by various populations of the same genotype.In the present study, we used five microsatellite loci , which had been used previously S. avenae were collected from seven sites across six provinces  and the Beijing municipality of China. These are the main wheat producing provinces of China. The site locations are presented in S. avenae collected from, are agricultural experiment fields belong to plant protection research stations at Cangxian , Liaocheng , Xihua , the Yanhu distriction of Yuncheng , Dongtai , Zaoyang  and the Agricultural Experiment Station of China Agricultural University , respectively. 30∼40 individuals were collected from each site, and each aphid was taken from a different field in order to minimize resampling of the same clone. Samples that were collected from the same region were considered as one ‘population’. The collected specimens were taken back to the laboratory in 100% ethanol.Wingless adults of Genomic DNA was extracted from wingless adult aphid individuals using DNAVzol , according to the manufacturer's instructions.S. miscanthiS. avenae. Sm11 is x-linked, and the other three loci are autosomal 5′-TCTGCTGCATTACTGTTGGC-3′, 5′ -TCGTCTACTTCGCCGTCA-3′; Sm11: 5′-TCTGCTGCATTACTGTTGGC-3′, 5′ -TCGTCTACTTCGCCGTCA-3′; Sm17: 5′-TCTGCTGCATTACTGTTGGC-3′, 5′ -TCGTCTACTTCGCCGTCA-3′; Sa∑4: 5′-TCTGCTGCATTACTGTTGGC-3′, 5′ -TCGTCTACTTCGCCGTCA-3′) were reported by Simon et al. (1999) 5′-CACCATCGCGTTTCATCTTA-3′; 5′-ACTCCCAACCTCTGATGAGC-3′) were reported by Llewellyn et al. (2003) Two hundred forty six aphids were examined at five microsatellite loci . The microsatellite loci of Sm10, and Sm11, Sm12, Sm17 were isolated from et al. (1999) The PCR conditions were the same as those of Simon et al. (1992) aN), the richness of alleles (rA), the observed heterozygosity (OH), the expected heterozygosity (EH), gene diversity (SH), and the inbreeding index (ISF) for each population were calculated using FSTAT v2.9.3 Na is the mean number of alleles in each sample. It can be calculated according to the following equation:The genotype data is presented in jN is the number of alleles at microsatellite loci j in one sample. r is the number of all loci. The richness of alleles (rA) is the mean of sR  across all microsatellite loci. sR is a measure of the number of alleles and is independent of sample size, hence allowing one to compare sR between different sample sizes. However, the observed number of alleles in a sample is highly dependent on sample size. To bypass this problem, El Mousadik and Petit (1996) n genes, given that 2N genes have been sampled (N≥n). In FSTAT, n is fixed as the smallest number of individuals typed for a locus in a sample. Allelic Richness is then calculated according to the following equation:iN is the number of alleles of type i among the 2N genes. Note that each term under the sum corresponds to the probability of sampling allele i at least once in a sample of size 2n. In FSTAT, estimates of gene diversity per locus and sample use an unbiased estimator Where n is the size of a sample. ip is the frequency of allele Ai in a sample. oH is the observed proportion of heterozygotes in a sample. The multilocus genotypes (MLG) were classified based on the length of alleles, using software that we developed in house. The genotype diversity (K) of each population was calculated as follows: K = G/N, where G is equal to the number of MLGs, and N is the number of samples Where F-statistics  and pairwise STFSD) AD) STF and geographic distance, a Mantel test was carried out. The matrices of pairwise STF and the geographic distance between two populations was transformed by Genepop  of geographic distances in kilometers), and a regression was performed using SPSS 17.0.AD) using the Dispan program. Second, factorial correspondence analysis (FCA) was implemented in Genetix v4.04 To identify the population structure, two clustering methods were used. First, we constructed phylogenetic trees (dendrograms) using the unweighted pair group-method with arithmetic mean (UPGMA) An analysis of molecular variance (AMOVA) was implemented in Arlequin v3.0 ISF values of populations were positive, except for the Beijing and Shanxi populations. The ISF of the Shanxi population was the most negative  were higher than the values for the other populations. The genic diversity level of the Beijing population was the lowest; the values of Na, Ar, and SH of the Beijing population were 6, 3.65, and 0.39, respectively. However, the genotypic diversity of Beijing population was higher (K = 0.925) than all of the other populations, except for the Jiangsu and Shandong populations.Our results suggested that We observed 215 multilocus genotypes from the 246 aphids tested. The same genotypes were observed within the Hubei, Henan, Shanxi, Hebei, and Beijing populations, but the same genotype was not found within the Jiangsu and Shandong populations. However, one individual in 31 aphids from Shandong was found to share the same genotype with one individual from Jiangsu province. There were not any genotypes shared among the other five populations.STF values ; high levels of differentiation were also detected between Hubei and the other populations (0.2137∼0.3626). The pairwise STF value between the Shandong and Jiangsu populations (STF = 0.1012) was close to the value between the Beijing and Hebei populations (STF = 0.1055), which were lower than the values for the other populations, with the exception of the value between the Henan and Shandong populations (FST = 0.0383).Population differentiation was analyzed using pairwise T values . The paiS. avenae populations sampled had high levels of genetic differentiation (STF = 0.2547) , the  0.2547) . GeneticmN) mN>1. However, if mN<1, it means that gene flow could not counteract the divergence caused by genetic drift S. avenae populations was mN = 0.899 (mN = 6.277) was observed between the Henan and Shandong populations .The migration number among different geographical populations has been proposed to be represented as gene flow  with geographic distance, according to the results of the Mantel test.Overall, the genetic divergence of the populations had significant positive correlation  HubSecond, the FCA results divided the seven populations into four groups ; (i) HubSD and AD)   between CTF = 0.18399). This result indicates that gene flow among the four groups was strictly restricted. The AMOVA results thus confirmed the conclusions of the clustering analysis.We classified the seven populations into 4 groups , and conducted AMOVA to detect the variance components. The results showed that the variances among groups and among populations within groups accounted for 18.34% and 9.53% of the total variance, respectively , and thaS. avenae is a migratory pest insect that is widely distributed throughout the wheat growing regions of China S. avenae geographical populations in China using microsatellite marker technology. These research results provide a basis for efforts to optimize integrated pest management (IPM) programs through adapting control methods which target the biological traits shared by various populations from the same genotype.et al. (2005)  The genetic diversity of aphids is influenced by many factors such as climate, host plants, topography, and physiognomy EH) of the Hebei, Henan, and Shandong populations was lower than their observed heterozygosity (OH). According to previous aphid genetic studies, factors like inbreeding, the Wahlund effect, and the presence of null alleles and/or clonal selection have been implicated in the generation of heterozygote deficits S. avenae has the ability for long-range dispersal, and their host plants are common and widespread, strong inbreeding and the Wahlund effect might not occur in this aphid ISF value of the Shanxi population was extremely negative. This might be associated with the geography of Yuncheng of Shanxi. Shanxi province is mountainous, which might restrict aphid migration.Many factors can affect the heterozygosity of populations. The expected heterozygosity . STF≤0.05, indicates little differentiation among populations; (ii). 0.05<STF≤0.15, middling differentiation; (iii). 0.15<STF≤0.25, high differentiation; (iv). STF>0.25, significant divergence The genetic structure of populations reflects the interaction of genetic drift, mutation, gene flow, migration, and natural selection STF = 0.2547) present in the S. avenae populations of China. Various factors might affect the divergence of S. avenae populations in different sites, including reproductive mode, clonal selection, migration, and so on. Additionally, genetic differentiation might be accelerated by the high level of variation in S. avenae population size In the present study, there was a high level of genetic differentiation , which might be attributed to large scale migration and/or the local pest management strategies. The Mantel test showed that the genetic divergence of the populations had a significant positive correlation with the geographic distances between the populations. This means that the gene flow between populations was significantly associated with the distance among the locations, and that the gene exchange among the different geographic populations could be affected by geographical isolation. For example, the genetic divergence between Beijing (the most northern site in our study) and Hubei (the most southern site in our study) populations was the highest as compared to the others.A study showed that the aphid clones on the winter host plant did not significantly contribute to the spring/summer population build-up in the cereal fields over short distances, but that the density of sources for early migrants, on a regional scale, is important for the establishment of the population et al. (2013) S. avenae populations might be impacted by the barrier of the Qinling Mountains. In the present study, the Qinling mountains and Dabie Mountains separate Zaoyang city of Hubei from the other 6 sites. There was great genetic differentiation between Hubei and the other populations, which might result from a limited dispersal of aphids. Similar phenomena of genetic isolation resulting from by geographical barriers were also found in Eriosoma lanigerumMacrosiphoniella tanacetaria and Metopeurum fuscoviride) Diuraphis noxiaHowever, geographical distance didn't explain the study results completely. According to the results published by Huang S. avenae in China might be classified into 4 clusters based on our research results. The population from Shanxi had less communication with other populations because of Taihang mountain, which may have obstructed the migration of the wheat aphids. However, the geographic location of Shanxi is adjacent to Henan, Hebei, and Shandong, and the location of sample collection for Shanxi was Yuncheng, which is at the southern limit of the Taihang mountain range, so gene flow was affected relatively little and there was overlap between Shanxi, Henan, and Shandong in the FCA analysis  Hubei, (ii) Shanxi, (iii) Beijing and Hebei, and (iv) Shandong, Henan, and Jiangsu. The present results provide a basis for efforts to optimize Integrated Pest Management programs through adapting control methods which target the biological traits shared by various populations from the same genotype.In the present study, we analyzed the population genetics and predicted the gene flow among the seven different File S1Sitobion avenae populations in China. “individual ID”, the tag of each tested aphid individual. “sampling location”, the corresponding location where each aphid individual was collected. The other ten columns were the corresponding allele sizes of each aphid individual at each microsatellite loci.Microsatellite genotypes of the seven (XLSX)Click here for additional data file.


Ursus arctos L., 1758 population of the Cantabrian Mountains (northwestern Spain) became isolated from other bear populations in Europe about 500 years ago and has declined due to hunting and habitat degradation. At the beginning of the 20th century, the Cantabrian population split into eastern and western subpopulations, and genetic exchange between them ceased. In the early 1990s, total population size was estimated to be < 100 bears. Subsequently, reduction in human-caused mortality has brought about an increase in numbers, mainly in the western subpopulation, likely promoting male-mediated migration and gene flow from the western nucleus to the eastern. To evaluate the possible genetic recovery of the small and genetically depauperate eastern subpopulation, in 2013 and 2014 we genotyped hair and faeces samples (116 from the eastern subpopulation and 36 from the western) for 18 microsatellite markers. Data from the annual count of females with cubs of the year (COY) during the past twenty-six years was used to analyze demographic changes. The number of females with COY fell to a minimum of seven in the western and three in eastern subpopulations in the biennium 1993–1994 and reached a respective maximum of 54 and 10 individuals in 2013–2014. We also observed increased bear dispersal and gene flow, mainly from the western to the eastern subpopulation. Of the 26 unique genotypes detected in the eastern subpopulation, 14 (54%) presented an admixture composition, and seven (27%) were determined to be migrants from the western subpopulation. Hence, the two separated and clearly structured subpopulations identified in the past currently show some degree of genetic admixture. This research shows the partial demographic recovery and a change in genetic composition due to migration process in a population of bears that has been isolated for several centuries.The brown bear  Ursus arctos may be a good model for study of the impact of population distribution on the genetic diversity of large mammals  of the eastern subpopulation.Complementary to the genetic data, data concerning females with COY from twenty-six years of field-based monitoring in both subpopulations were used to evaluate demographic changes.Bear population monitoring in the Cantabrian Mountains was carried out from 1989–2014, counting females with COY as described by Non-invasively sampled material from the eastern subpopulation was collected from June 2013–August 2014 in the Cantabrian Mountains . SimilarDNA was extracted from the hair root using the QIAamp DNA Investigator kit (Qiagen) and from faeces using the QIAamp DNA Stool kit (Qiagen) following manufacturer’s instructions and eluting the DNA in 40 μl of water for hair and 100 μl of water for faeces. The DNA extraction was performed in a room designed for ancient DNA extraction at the Museo Nacional de Ciencias Naturales of Madrid, Spain (MNCN-CSIC), using a tube with no DNA as a negative control for the extraction. No more than 16 samples, including respective negative controls, were processed in one set.Ursus arctos  and expected (HE) heterozygosity (NA), and the allelic richness standardized for the smallest sample size (NAR)  runs of 1,000 batches, each of 2,000 iterations, with the first 500 iterations discarded before sampling   were calze (NAR)  and FSTAze (NAR)  programssampling . Correctsampling  with thesampling . SamplesK) with the best value of the mean lnProb (D) was calculated assuming an admixed model and a uniform prior probability of K. We performed a series of independent runs for K of from one to five populations. MCMC consisted of 5 × 106 burn-in iterations followed by 5 × 105 sampled iterations. The modal value of lambda, ΔK  to defir) between individuals in both subpopulations was calculated based on five commonly used estimates of relatedness estimators  was used as a threshold to classify individuals as UR ≤ 0.25 < HS < 0.5 ≤ FS. Results were presented as the percent of pairs in each classification.The pairwise relatedness (ANCESTRY ). These crit) than 0.02, 0.01, and 0.001. Secondly, we implemented an approximate Bayesian computation method to estimate current effective population size (Ne) in ONeSAMP (Effective population size (Ne) is one of the most important parameters to estimate in small and endangered populations, since it can be used to predict extinction risk and early detection of fragmentation and population decline . To dete ONeSAMP , which c ONeSAMP . Differep < 0.0001) for the western subpopulation and 10.4%  for the eastern subpopulation . The PID-Sib values obtained with the number of loci used provided higher statistical confidence than the PID-Sib < 1 × 10−4 suggested in distinguishing between full siblings  was higher when compared to the values obtained for each subpopulation separately  were similar to those of the western subpopulation    despite = 0.467) . Tests fK = 2, corresponding to the subpopulations analysed. Accordingly, the modal value of ΔK  and the principal coordinate analysis (PCoA). Results of the PCoA separated samples into the two main groups. Some individuals from the eastern subpopulation overlapped with the western samples, indicating a degree of continuity between the two regions  among individuals within each subpopulation, we observed a high proportion of pairs with relatedness values above the half-sib level (> 0.25). The percent of HS and FS pairs ranged from 20.6–45.5% and 16.7–39.4% for the eastern and the western subpopulations, respectively, depending on the coefficient used  did not alter the Ne values obtained with LDNe. Similarly, the estimate of the effective population size using ONeSAMP for the eastern subpopulation was not sensitive to the prior used, since the values obtained did not vary substantially. Overall, the LDNe software generated higher estimates for Ne (mean 22.4) when compared with the values obtained with ONeSAMP (mean ranging from 12.1–13.7)  .The results reveal a steady increase in the number of females with COY for both brown bear subpopulations on the Cantabrian Mountains. Reported migration of some individuals  was alsoID and PID-Sib values obtained were low, indicating high discriminatory power. We consider that this difference is the result of an increase in subpopulation size rather than difference in loci used and number of samples analyzed, since the obtained percentage of samples giving a unique genotype was similar among studies.We have attempted to overcome some of the limitations and challenges of non-invasive sampling, including low quality or degraded DNA, by minimizing the genotyping errors and applying robust statistical analyses . To thisFST estimates, PCoA and SAMOVA correspond to the genetic pattern identified by STRUCTURE.For the first time, after 26 years of monitoring, we detected changes in the genetic composition in the eastern subpopulation. Previous studies supported the existence of two genetically differentiated subpopulations of brown bear in the Cantabrian Mountains , as was Migration between subpopulations was first detected in 1992 based on genotype composition , when a The effective population size (Ne) obtained in our study represents the number of individuals that effectively contribute to the population . Our resQuercus pyrenaica forests is improving the habitat of the potential corridor between the subpopulations  was similar to that reported for other small brown bear populations, such as the HO of 0.50 in Italy in 2011 (n = 45) (Improved connectivity and increasing population size are presumed to increase the genetic diversity and the long-term viability of populations . When ge(n = 45) . Althoug(n = 45) . HoweverPuma concolor (As shown in other studies , the migconcolor . In spitUrsus arctos has led to increased understanding of changes in a fraction of the bear population. The genetic monitoring programs represent a step forward and could detect demographic and genetic trends and other factors to aid the recovery of this isolated, but seemingly increasing, population.The demographic monitoring carried out for more than 25 years in the Cantabrian population of 10.7717/peerj.1928/supp-1Supplemental Information 1Unique genotypes obtained for Cantabrian brown bear individuals collected at the eastern and western subpopulations respectively. The name of the microsatellite loci used is also indicated.Click here for additional data file.10.7717/peerj.1928/supp-2Supplemental Information 2Click here for additional data file.


Amphiprion perideraion, is analyzed by applying 10 microsatellite loci as well as sequences of the mitochondrial control region to also allow for a direct comparison of marker-derived results. Both marker systems detected a strong overall genetic structure  and best supported regional groupings  that suggested a differentiation of the Java Sea population from the rest of the archipelago. Differentiation of a New Guinea group was confirmed by both markers, but disagreed over the affinity of populations from west New Guinea. Mitochondrial data suggest higher connectivity among populations with fewer signals of regional substructure than microsatellite data. Considering the homogenizing effect of only a few migrants per generation on genetic differentiation between populations, marker-specific results have important implications for conservation efforts concerning this and similar species.To enhance the understanding of larval dispersal in marine organisms, species with a sedentary adult stage and a pelagic larval phase of known duration constitute ideal candidates, because inferences can be made about the role of larval dispersal in population connectivity. Members of the immensely diverse marine fauna of the Indo-Malay Archipelago are of particular importance in this respect, as biodiversity conservation is becoming a large concern in this region. In this study, the genetic population structure of the pink anemonefish,  Reproductive population connectivity in spatially separated subpopulations of sessile marine species is shaped primarily through larval dispersal and mortality , across the Indo-Malay Archipelago (IMA) and one Philippines site. Nonconcordance between nuclear and mitochondrial markers is common in fish and other animals .The coral reefs of the Indo-Malay Archipelago, which support the highest global marine biodiversity , and to include samples from all major central and peripheral basins of the archipelago. A. perideraion individuals could not be found during expeditions in west Sumatra and Singapore (Batam), although these locations lie within the suggested range of this species. Fin clip samples were stored in 96% ethanol at 4°C. Genomic DNA was extracted with a commercial kit .With the use of SCUBA, 305 samples of A. perideraion sequences were subsequently deposited in GenBank.A 420-bp fragment of the D-loop segment of the mitochondrial control region was amplified with primers CR-A (TTC CAC CTC TAA CTC CCA AAG CTA G) and CR-E (CCT GAA GTA GGA ACC AGA TG)  were aligned with Clustal W   and pairwise population differentiation (pairwise ΦST) were determined with an analysis of molecular variance (AMOVA). Corresponding P-values for pairwise computations were adjusted to control for the false discovery rate (FDR) according to Benjamini and Hochberg .Unless otherwise stated, all following analyses were carried out with Arlequin  were included in the construction of a minimum spanning tree (MST)  and interpopulation (pairwise mean Dest) genetic differentiation in the dataset , and corrected for the FDR. The inability of FST to accurately reflect population differentiation when diversity within populations is high (as with polymorphic microsatellites) has been repeatedly discussed and confirmed  was run for 10 iterations, and probabilities were calculated on the basis of the median estimated ln probability of the data. The proportion of samples assigned to different clusters at each sampling location was visualized by means of pie chart diagrams, superimposed on the map of the sampling area. An artificial q-value threshold difference (≥0.25) was enforced for a clear assignment of samples to one of the proposed groups. When this value could not be reached, samples were treated as potential descendants of mixed ancestry and marked as such in the corresponding pie charts.A hierarchical AMOVA was run for several different scenarios of population groupings. Inferences drawn from pairwise distance calculations were used for grouping decisions, although only a fraction of all tried groupings is presented here. The population structure within and among ST and Dest) of sampled populations by applying a Mantel's test, providing a one-tailed P-value for significance of the matrix correlation and the corresponding R-square. A third matrix component was added to include information on whether population pairs stemmed from the same (score = 0), adjoining (score = 1), or nonadjoining (score = 2) “discreet clusters of (genetic) exchange” according to divisions proposed by Kool et al.  were successfully amplified and subsequently deposited in GenBank  when both loci were excluded. Therefore, these loci where included in the further analysis.Heterozygosities were high 0.714–0.906) in all loci except AC1578 (0.395). The values for observed and expected heterozygosities were close, but observed heterozygosities tended to be lower in most cases  is unlikely, and the scale of sampling appears to capture discrete populations  indicate linkage disequilibrium in three different populations . If markers are truly linked, this linkage is expected to carry across populations, which was not the case here. Therefore, all loci were expected to assort independently.h = 0.81–1.00) and nucleotide diversities (π = 0.037–0.078) were consistently high among populations, being lowest in Karimunjava  than in the rest of the archipelago. Most populations located at the northern , western (Ka), and southern peripheries (Ku) of the sampling area had nucleotide diversities at the lower end of the spectrum, potentially suggesting that founder events with subsequent expansion may have shaped the diversity of these populations. Allelic richness reflects the same pattern found in gene diversity  Table. TypicalST = 0.096, P < 0.0001; mean Dest = 0.17; FST = 0.015, P < 0.0001). Despite the low sample size from Karimunjava, the hierarchical AMOVAs for both datasets best supported a differentiation of Karimunjava from the central archipelago and eastern populations, as well as an isolation of eastern populations from the center. However, the CR dataset finds the highest between-group variation when the eastern group includes east and west New Guinea populations  , while the Msat dataset best supports a smaller eastern group with only Biak (and Misool), grouping Papisol and Triton Bay with the central populations . A summary of the examined group configurations is provided in TableAMOVAs with the CR and Msat datasets showed highly significant deviations from panmictic population conditions  between geographic and genetic distance with Msat data and no significant correlation with the CR dataset. R-squared values were very low (explaining <10%), and the regression lines did not describe the spread of the data. Controlling for the effects of potential dispersal-retarding current features, as modeled by Kool et al.  are listed in TableDest = 0.111 [Sp] – 0.373 [Ka], ΦST = 0.169 [Pa] – 0.267 [Do]) and Karimunjava  to the rest of the populations, while only the Msat dataset indicated differentiation of the Philippine population (range Dest = 0.106 [Sp] – 0.373 [Ka])  on larval transport. Pairwise population differentiation measures . Among them are the two most proximate upstream and downstream locations in the datasets, Sangalaki and Spermonde. A similar situation is seen in Luwuk, which is significantly different from its closest northern (LS) and southern (Ke) neighbors in the dataset (Msat data only), and in Kupang and Komodo, which are significantly different from one another, as well as to samples from Bali and populations just to the north .The minimum spanning tree (MST) shown in FigureClade A holds the central position in the star-like topography of the tree, with all other clades directly or indirectly diverging from it. Due to its central position and its presence in all sampled populations (except Papisol) Fig., this clMost of the sampled populations were found to contain clade C and/or clade D haplotypes, the exceptions being Karimunjava, Luwuk, Bira, and the Banggi Islands. Clade E and clade F haplotypes were found in northeastern populations , along the Celebes Sea  and in some of the populations lining the Java and Flores Seas , but were absent from New Guinea, the Banda and Maluku Seas, the Makassar Strait, and the South China Sea. Both clades contain only singleton haplotypes with internal divergences of up to nine ns.Clade G haplotypes are found throughout most of the IMA although the haplotype from its most southern expansion in Kupang is 11 ns removed from the next-closest clade member. A similar situation is found in clade H, where the haplotype from Kupang diverges by ten mutational steps. The high number of ns for these and other outliers makes their direct clade association questionable. However, within the given dataset, no alternative connections were suggested by the analysis. Clade H is dominated by haplotypes from Karimunjava, Donggala, and Manado, with Karimunjava at the clades' most basal position.A. sandaracinos and A. akallopisos, these haplotypes clearly group with A. perideraion (data not shown). Clade J also includes the haplotype from the Solomon Islands, which was included from GenBank. Haplotypes from Biak and the western New Guinea populations are distributed throughout this clade, occupying both central and very divergent peripheral positions.The smallest divergence between haplotypes (mostly 2–3 ns) is found in clade I, which is predominantly found along the northern, western, and southern coastline of Sulawesi and in other populations situated along the ITF. Moving into and across the Banda Sea, the presence of clade I haplotypes decreases, while that of clade J haplotypes increases Fig.. The latk = 4) is most probable  a genetic break between the Java Sea population (Karimunjava) and all other locations, (2) a unidirectional (north to south) connectivity of the Philippines to the rest of the archipelago, (3) mixing of central populations along the ITF and within and across the Sulu Sea, and (4) a gradual eastern displacement of the black cluster genotypes by gray genotypes.A. perideraion in the IMA. Potential barriers to gene flow acting on the sampled populations were identified, and the found structure was placed in its historic and phylogenetic context. The study found substantial population structure overall for both marker types , confirming expectations derived from genetic population structuring seen in other anemonefish , and along other coastlines could indicate that barriers to gene flow are acting on these populations. The analysis revealed significant substructures within the IMA and barriers to gene flow that may need to be considered for conservation purposes.Population breaks between eastern, central, and western IMA populations detected in this study mirror similar breaks in a congener of Amalgamation of previously isolated and secondarily admixed divergent gene pools, which is characteristic for populations in highly fragmented and repeatedly fused habitats produced during Pleistocene glaciations, is here supported by a significant Chakraborty's test of amalgamation and a large negative Fu's F Table. ExcludiA. ocellaris at three different spatial scales (southeast Sulawesi/Sulawesi/entire IMA) also produced “trimodal” mismatch distributions at all scales  marked Misool as a divergent population, forming its own group. In A. ocellaris, the population from Misool grouped with other west New Guinea populations and did not appear as distinct . Considering that the central position of the haplotype network is dominated by east New Guinea and Banda Sea haplotypes  with CR data, but isolates east New Guinea (Bk) (and Misool) from all other populations using Msat data. The low sample number from Misool does not allow conclusions to be drawn with any reliability about this population, but its association with east New Guinea, instead of more proximate west New Guinea locations, may give some indication that this population could be subject to other dynamics  population and Biak, and increasing connectivity across the Banda Sea moving up the west coast. Pairwise differences in the CR data identified the eastern IMA group  as the most divergent population  and the Kota Kinabalu (Msat) site  samples from the central and eastern IMA populations. Despite the seasonally oscillating currents Fig. that appite Table.A. perideraion is only 18 days, much less than the max PLD (30 days) used for virtual larvae in the model, so that the trajectory of A. perideraion larvae can be expected to be even more restricted. This strengthens the case for a divergence of the A. perideraion population in Karimunjava even under contemporary oceanic conditions, suggesting that a continued isolation of the Java Sea population from the rest of the IMA should be considered and accounted for in management plans.From a conservation standpoint, it is also important to know whether phylogeographic barriers still persist today in order to adjusted ecosystem management strategies accordingly. Model simulations by Kool et al.  of larvaA. ocellaris  were unsuccessful, although all three locations lie within the suggested distribution of A. perideraion  , and the continued degradation of coastal habitats through pollution and unchecked exploitation may override any conservation efforts. The lack of suitable settlement habitats outside of reserves can prevent an overspill effect from reserve areas and negate efforts to increase marine resources for local populations. In the case of Species response to Pleistocene sea-level oscillations and more recent examples of complete marine habitat annihilation (Barber et al. None declared.DNA sequences have been deposited in GenBank . Microsa


P. t. tigris) subspecies. Maximum likelihood phylogenetic analyses of the mtDNA data revealed reciprocal monophyly. Genetic differentiation was found stronger for mtDNA than nuclear DNA. Microsatellite markers indicated low genetic variation in Sundarbans tigers (He= 0.58) as compared to other mainland populations, such as northern and Peninsular (Hebetween 0.67- 0.70). Molecular data supports migration between mainland and Sundarbans populations until very recent times. We attribute this reduction in gene flow to accelerated fragmentation and habitat alteration in the landscape over the past few centuries. Demographic analyses suggest that Sundarbans tigers have diverged recently from peninsular tiger population within last 2000 years. Sundarbans tigers are the most divergent group of Bengal tigers, and ecologically non-exchangeable with other tiger populations, and thus should be managed as a separate “evolutionarily significant unit” (ESU) following the adaptive evolutionary conservation (AEC) concept.The Sundarbans tiger inhabits a unique mangrove habitat and are morphologically distinct from the recognized tiger subspecies in terms of skull morphometrics and body size. Thus, there is an urgent need to assess their ecological and genetic distinctiveness and determine if Sundarbans tigers should be defined and managed as separate conservation unit. We utilized nine microsatellites and 3 kb from four mitochondrial DNA (mtDNA) genes to estimate genetic variability, population structure, demographic parameters and visualize historic and contemporary connectivity among tiger populations from Sundarbans and mainland India. We also evaluated the traits that determine exchangeability or adaptive differences among tiger populations. Data from both markers suggest that Sundarbans tiger is not a separate tiger subspecies and should be regarded as Bengal tiger ( Panthera tigris) conservation remains an enormous challenge at both national and global scales and its long-term persistence depends on effective conservation and management strategies. Five extant tiger subspecies have been recognized on the basis of geographic distribution and morphological characteristics [Tiger (eristics . Howevereristics  elucidatP. t. tigris) [On the basis of tiger distribution and the potential for connectivity, India is divided into six tiger landscape complexes . One of  tigris) . The for tigris) . By comb tigris) . Few stu tigris) , 8. Howe tigris) , 9. In o tigris)  comparedIdentification of population units within species is a crucial step for guiding management authorities and policy makers in management practices . Delinea, which is currently not connected to any other tiger population from mainland and can contribute to their conservation management [Previous genetic studies on mainland Bengal tigers have evaluated tiger populations from different landscape complexes (or geographic regions) in India, such as North, northwest, central, South and northeast, and found moderate to high levels of genetic diversity , 27, 28.nagement . TherefoOur ultimate goal was to use a multi-population comparative approach to: (i) determine whether the tigers in Sundarbans have reduced levels of genetic variability relative to other populations of mainland tigers, as might be expected given their small contemporary population size and insular distribution, (ii) estimate the degree of genetic differentiation, historical and recent migration, (iii) determine time since separation from the mainland and effective population sizes of Sundarbans and mainland tiger populations, (iv) evaluate the traits to determine the exchangeability or adaptive differences among tiger populations, and (v) discuss whether Sundarbans tiger population should be considered as a separate ESU and MU, and implications for their conservation or management.All tiger samples (blood and tissue) used in this study were provided by forest department of Sundarbans tiger reserve during the routine monitoring, translocations and radio collaring of the conflicted animal. Hence, sample collection did not require any extra handling of the animals. All necessary permissions to get the samples to the National wildlife reference sample repository at Wildlife Institute of India (WII) were obtained from the Field Director of the respective forest reserve /SBR/C-208/09, 401(4)/SBR/C-172/10, 1906/FD/2M-96/06 and 4439/WL/2W/567/06).n = 5), tissue (n = 1), hair (n = 1), and scat (n = 9) from the forest department of Sundarbans tiger reserve (hereafter Sundarbans)  from the tiger reserves located in the Central and Eastern Ghats of India , and  from the tiger reserves located in northern India . We pooled all the tiger samples from the Central and Eastern Ghats, and are referred to as “Peninsular India”.In order to adequately assess genetic status of Sundarbans tigers, it is critical to obtain as many biological samples as possible. However, because most of the area in this tiger habitat is inaccessible swamp, it is difficult to non-invasively collect samples, such as feces or hair. We overcome this problem by using opportunistically collected samples by the forest officials. We obtained sixteen tiger samples: blood (darbans) . In ordeTM) was extracted following Smith and Burgoyne [Genomic DNA from blood spots stored on FTA Classic cards (WhatmanBurgoyne . DNA froND2 (960 bp), ND5 (1139 bp), ND6 (443 bp), and cytb (555 bp) regions. Fragments were chosen based on previously sequenced regions of other tiger subspecies  that were established as variable and informative, and correspond to that used by Luo et al. [2, 200 μM of each dNTP, 1× BSA, 0.5 U of Taq DNA polymerase (Applied Biosystem), and 0.2 μM of each primer. PCR amplification conditions were the same as reported by Luo et al. [In order to check for the existence of new haplotypes in the tiger samples from Sundarbans, we amplified and sequenced four mtDNA fragments comprising o et al. . AmplifiNine microsatellite loci, of which four loci were originally developed for Bengal tiger  , two (Pun = 25) and Mondol et al. [n = 21). The GenBank accession numbers of mtDNA sequences used are Cytb: AY736634-AY736658, EU661630-EU661650, ND2: AY736684-AY736733, EU661651-EU661671, ND5: AY736734-AY736783, EU661672-EU661691 & FJ228452, and ND6: AY736784-AY736808. Since the length of the mtDNA sequences analysed in the two studies , number of segregating sites (S), mean number of pairwise nucleotide differences, or nucleotide diversity (π) were estimated with 1063 bp mtDNA sequence  using DnaSP 5.0 [In order to examine the phylogenetic relationships among haplotypes, all published tiger mtDNA sequences  were retrieved from GenBank and compared with CLUSTALW in BioEdit, and then manually edited to achieve the best alignment . The seql et al.  n = 21)1n = 25) The median-joining networks (MJ Network) were constructed with Network 4.6.1.1  by usingFST values based on haplotype frequencies were estimated using Arlequin 3.5.1.2 [The program ModelGenerator was used to determine the most appropriate model of substitution . ModelGe 3.5.1.2 , 43.To assess error rates for genotypes derived from fecal and tissue samples, we randomly selected 10 fecal samples from the field (from Kanha tiger Reserve) and 10 tissue (from Corbett tiger reserve) samples and reanalyzed them . This was done to calculate allelic drop out (ADO) and false allele (FA) error rates using PEDANT 1.0 involving 10,000 search steps for enumeration of per allele error rates . We perfNa), mean number of alleles (MNA), expected (He) and observed (Ho) heterozygosity for the nine microsatellite markers were estimated with FSTAT 2.9.3.2 [AR) was adjusted for discrepancies in sample size by incorporating a rarefaction method, and was estimated for each site using FSTAT 2.9.3.2 [F-statistics for microsatellite dataset according to the method of Weir and Cockerham [Number of alleles  or whether divergence is attributable to genetic drift only. We applied also this analysis to the genotyped data for the tiger populations in Peninsular (n = 73) and northern India (n = 62). The principle of this test is to compare the RST value with the distribution of RST values  obtained after 1000 random permutations of allele size among allelic states. The observed RST values significantly larger than the pRST indicate that stepwise mutations contributed to the genetic differentiation among subpopulations. Exact tests of population differentiation among the populations were conducted as described by Raymond and Rousset [We used SPAGeDi  for an a Rousset  in ARLEQ Rousset .K clusters, according to their genetic similarity. The Bayesian clustering analyses were done both with and without prior knowledge of sampling locations (with and without LOCPRIOR) [K between 1 and 10 with a burn-in period of 50,000 iterations and collected data for 500,000 iterations. The most likely value of K was assessed by comparing the likelihood of the data for different values of K and by the rate of change in the log probability of the data between successive K values (Delta K) [K [To identify possible distinct genetic clusters and to assign individuals to these clusters, we utilized the Bayesian clustering approach implemented in software STRUCTURE 2.3.3. . The sofOCPRIOR) , 52. We Delta K) . We ran Delta K)  was usedta K) [K . Assignmta K) [K .K 2–10. The preferred K was selected by comparing the individual assignment results and the deviance information criterion (DIC) for each K [. DIC values averaged over 100 independent iterations were plotted against K, and the most likely value of K was selected by visually assessing the point at which DIC first reached a plateau and the number of clusters to which individuals were proportionally assigned.We also used a spatially explicit clustering method to identify possible genetic clusters as implemented in the program TESS 2.3  that buir each K . DIC valNe) for different tiger populations  from genetic data using the approach based on linkage disequilibrium (LD) as implemented in LDNe 1.31 [Pcrit = 0.02, which generally provides a good balance between precision and bias from rare alleles [Pcrit) lower than 0.01 and 0.05.We estimated short-term effective population size , with 1000 simulations at an assignment threshold  of 0.01.We estimated the number of migrants between all pairs of sample sites using GENECLASS 2.0 . A Bayes6 iterations, with a burn-in of 106 iterations, and a sampling frequency of 2000 to ensure that the model's starting parameters were sufficiently randomized . Delta values were adjusted to optimize terminal proposed changes between chains  to ensure sufficient parameter space was searched [BayesAss1.3 programme  was usedsearched . BayesAsIn order to infer whether dispersal between tiger populations  is sex-biased, we estimated the relative amount of male and female gene flow following the approach of Hedrick et al. . We firsk et al. ND2, ND5 and cytb genes of Sundarbans tigers with sequences of northern and peninsular populations. To estimate divergence times and population history, we also used the coalescent-based approximate Bayesian computation (ABC) algorithm in the program DIY-ABC [Demographic analyses was performed to calculate the divergence time and population history of Sundarbans tiger by using different molecular markers . Bayesian skyline plots  of mitoc DIY-ABC  with theIn order to investigate if Sundarbans tigers have characteristics that distinguish them from other mainland tiger populations, we examined a large sample of the published literature in order to find evidence of adaptive differences or traits, such as morphology, habitat type, size of prey, competition with other predators, tiger density , 68–71.ND2, ND5, Cytb, and ND6) were successfully sequenced only from six Sundarbans samples. However, this was not the case with the nuclear DNA amplification. In case of microsatellite markers, out of the 16 samples genotyped, three (two scat and one liver tissue) did not yield results and 13 unique genotypes were identified in the Sundarbans samples. Therefore, mtDNA and microsatellite data from six and thirteen Sundarbans samples were used in further analyses.Large sized fragments of the mtDNA for at least one fragment of the four mitochondrial genes  and false alleles (FA), however, the fecal-extracted DNA showed that ADO ranged between 0 and 11% and FA between 0 and 4%. Genotyping error rate also varied among loci . Only twP. t. tigris [ND5 region and was shared among all six Sundarbans samples. However, the other variable site at cytb region was found only in three samples. These substitutions differentiated Sundarbans tigers from rest of the other tiger subspecies. In median joining network, the Sundarbans haplotypes composed a monophyletic sister group with other Bengal tigers  to Bengal tiger subspecies. The mtDNA amplification target includes 13 single nucleotide polymorphisms (SNPs), of which 3 are diagnostic (fixed differences) for l tigers .cytb region. However, the authors did not analyse the complete ND5 region from their samples from Sundarbans. Therefore, the other haplotype (at ND5 region) is reported here for the first time. This new haplotype have been submitted to GenBank under accession numbers JX074818-JX074823. We also compared mtDNA sequence data  of tigers from Northeast tiger populations  that were published in Sharma et al. 2008, 2010 [Further comparison of Sundarbans tiger haplotypes with the other Bengal tiger sequences obtained from northern and Peninsular tiger populations revealed that these haplotypes are specific to Sundarbans samples and there were no other shared haplotypes between the samples. Interestingly, one of the two Sundarbans haplotypes observed in the present study corresponds to the unique haplotype (TIG29) as previously reported by Mondol et al. . The stu08, 2010 , 23 and 08, 2010 . We did h), nucleotide (π) diversity was higher in the Peninsular India tiger populations than in the Sundarbans  for Sundarbans tigers was 3.33, which is lower than in Peninsular and northern India tiger populations . Allelic richness corrected for the sample size was lower in Sundarbans (3.24) than in Peninsular (4.80) and northern India population (4.18) (r = 0.88) was observed. Average expected heterozygosity (He across loci) values were higher in the northern and Peninsular India tiger populations than the Sundarbans and ranged from 0.58 in Sundarbans to 0.70 in Peninsular India populations, whereas average observed heterozygosity (Ho) ranged from 0.40 in northern to 0.49 in Sundarbans and Peninsular India tigers.All nine microsatellites were polymorphic, with between 3 and 4 alleles per locus in the Sundarbans tiger samples. The mean number of alleles (n (4.18) . Also, aFIS values over all loci were significantly different from zero and positive in the northern and Peninsular tiger populations, ranging from 0.109 (Sundarbans) to 0.394 (northern)  . The mosP = 0.1125), therefore we used only F-statistics.The allele size permutation test  indicateFST (PhiST) calculations indicated that all tiger populations were genetically differentiated from each other, with moderate to high FST (PhiST) values ranging between 0.116 and 0.250 . AMOVA test revealed that variation within populations, between populations, and among regions accounted for 90.12%, 9.40%, and 0.47% (p < 0.001) of the total variation, respectively.Using mtDNA, the pairwise K analyses [K) was found at K = 5. It is observed in many studies that delta K does not produce a proper resolution of population structure [K calculation and there is always a potential of including in the calculation Ks of several chains that have not converged leading to unreliable results. Based on their observation, Faubet et al. [K, but we should aim for the smallest value of K that captures the major structure in the data. On the basis of above observations we found maximum assignment at K = 5, as indicated by the LnPD. However, for K > 2, the likelihood values showed only a slight increase, suggesting the presence of five differentiated populations. Hence, we showed assignment probabilities of the tigers for K value at 2 to 5  and there was no evidence of admixture  = -2882.64), when data were analysed across a large geographical scale . However, delta analyses  suggest tructure . Faubet tructure  pointed t et al.  concludet et al.  method dt et al. ” and prot et al. . They sut 2 to 5 . At K = dmixture . Interesdmixture . We alsoi.e. suggested existence of five genetic clusters under both models (CAR and BYM) using the DIC criterion. In these models, all the individuals from the geographically isolated Sundarbans were grouped together in one cluster both at K = 5 and K = 6  and the lower limit of 95% credible intervals of both estimates were 0, suggesting that there were no migration out of the Sundarbans population. The 95% credible intervals of the estimates from Peninsular and northern into the Sundarbans population overlapped and thus the differences in asymmetric migration rates were not statistically significant.We did not detect first generation migrants in Sundarbans population with GENECLASS using a threshold Ne) for Sundarbans samples were negative values and hence, were unattainable using the LD method. This was as a result of the sample size to effective population size ratio being too small and therefore the sampling error overpowered the effect of drift within the sample. However, estimates of Ne in the Peninsular tiger population (73.3) are higher in comparison to the northern (47.9) India tiger populations (Estimates of effective population size (ulations .FST(m)” between the Northern and Sundarbans populations was 0.215 which was lower than amount of female differentiation (FST(f) = 0.250). The ratio of male to female gene flow between Sundarbans and Northern India was mm/mf = 1.21, suggesting slightly higher amount of male than female mediated gene flow between these populations in the past. However, the estimates of male gene flow and ratio of male to female gene flow between Peninsular and Sunderbans populations were not workable using Hedrick et al. [FST(m) and negative for mm/mf) suggesting that all the assumptions of the method were not valid in this case.The estimated amount of the genetic differentiation from male gene flow, “k et al.  equationFor the mtDNA, the Bayesian skyline plots revealed slight, but steady growth of the mitochondrial effective size during the last tens of thousands of years. However, the median in the skyline plot shows a decrease of the population size beginning about 1000–2000 years ago .Interestingly, our results from microsatellite data unambiguously point to a scenario similar to the one revealed by mtDNA. The DIYABC analyses suggested that recent divergence explains the observed genetic structure. With the data set divided according to STRUCTURE and TESS, all the explored coalescent scenarios involved estimates of a recent to ancient divergence . The pos2) in comparison to other landscape (16 tiger per 100 km2) [Further assessment of exchangeability using ecological data, such as differences in morphology, habitat type, size of prey species, and competition with other predators revealed that Sundarbans tiger landscape is distinct from other tiger landscapes in India . For exa100 km2)  reduces FIS for the tiger populations in the northern and Peninsular India, indicating a deviation from HWE. The most likely explanation for deviation from HWE in our study is the presence of multiple sub-populations sampled within a single population across a broad geographical area in the Northern and Peninsular part of India (Wahlund effect) [FST for each locus between the subpopulations. As expected, we found that the excess of homozygotes was caused by subdivision; those loci with showed the greatest differentiation between subpopulation also showed excess of homozygosity. Genotypic clustering computer programme STRUCTURE also suggested that there are three to four distinct subpopulations in northern and peninsular tiger population. On the basis of all these observation and detection of many subpopulations in the Northern and Peninsular population concluded that, Wahlund effect was the most probable reason for the HWE deviation.We observed a statistically positive  effect) , 12. We  effect) , we alsoP. t. tigris) subspecies. MtDNA analyses undertaken first time with 2600 bp on Sundarbans tiger samples revealed the presence of known diagnostic sites (SNPs) for Bengal tiger subspecies and a close phylogenetic relationship of haplotypes found in Bengal tigers [cytb, ND2, ND5 and ND6 genes revealed that the haplotypes typical to the Sundarbans tigers comprise a separate group that stems from Peninsular India . The absence of the haplotype unique to Sundarbans from other tiger populations indicates that practically no emigration of females has taken place from Sundarbans to Peninsular, northern or north east India after geographical isolation.He = 0.59) are lower than that observed in other tiger populations from the mainland (He = 0.67–0.70). The observed heterozygosity of Sundarbans (Ho = 0.49) was slightly higher in comparison to northern tiger populations  than nuclear DNA (Fst = 0.07). It could be due to strong philopatry, territoriality among females, and low or no immigration in Sundarbans. These levels of genetic differentiation also imply a strong impact of local genetic drift , indicating that the effective population size of Sundarbans population is very small and that current gene flow among them is likely very low. Several factors likely contribute to this common pattern including the larger effective population size of nuclear genes, differences in the rate and mode of mutation, and sex-biased dispersal in tigers [A closer look at the population genetic pattern inferred from our mitochondrial and microsatellite data analyses suggest that Sundarbans tigers are the most divergent group of Bengal tigers. Both the applied mtDNA and microsatellites demonstrate genetic differentiation and structure between tigers in the different geographical regions, with the Sundarbans being significantly differentiated from the northern and Peninsular populations. Pairwise n tigers , 81. Ourn tigers  approachn tigers , 82 and This finding was also supported by the detection of Sundarbans as a separate population by the Bayesian clustering analysis (STRUCTURE and TESS) for microsatellite markers. These results are not surprising, given the field-based knowledge on the current and historical landscape connectivity between Sundarbans and mainland areas , 10. FurWe observed that the microsatellites have considerable allele sharing among different tiger populations. The lack of population specific autosomal markers could mean the tiger populations are not as evolutionary isolated as the mitochondrial phylogeny suggests. However, the complete reciprocal monophyly, high divergence, and geographically structured lineages for the mitochondrial data would also mean that if ongoing gene flow were responsible for allele sharing, migration would have to be strongly male biased. The absence of first generation migrants, high assignment probabilities to the population of origin, and low long-term migration between Peninsular and Sundarbans populations as estimated by the Bayesian methods may suggest sex-biased dispersal between these populations until very recent times.Ne = 1000) and it might be due to the fact that the founder individuals of Sundarbans were with high levels of genetic variation. It is possible because present population of Sundarbans was part of a large contiguous population of Central India (Peninsular) in the recent past. Hence it does appear that the population has still retained the signals of high effective population size of Peninsular India. Indeed Mondol et al. 2009 [Interestingly, the demographic analyses using different markers (mtDNA and microsatellites) showed similar results and suggested a recent rather than an ancient divergence of Sundarbans tiger population. Bayesian skyline analysis provided clear evidence of a recent historical reduction in effective population size. These results are supported by Approximate Bayesian analyses (ABC), which suggest that Sundarbans tiger population might have diverged from the mainland tiger population within last 2000 years. The DIYABC estimates involved wide posterior distributions but unanimously suggested that recent divergence explain the observed genetic structure of Bengal tigers in India. ABC analysis suggests high effective population size of Sundarbans , and settlement of people in and around Sundarbans [It is striking that for both marker types the data point towards a contraction that took place in the last 2000 years and that is difficult to explain by recent anthropogenic activities alone. Several independent lines of evidence indicate that series of climatic changes profoundly influenced the geography and vegetation in many parts of Sundarbans, leading to shifts in the extension and distribution of different habitat types. These biogeographical changes are thought to have had a profound impact on the geographic distribution of the Sundarbans fauna, including tigers . During g places , and extndarbans . Severe ndarbans , 4.The recent deforestation in the last couple of centuries has most certainly affected the Sundarbans tigers, though we may not be able to detect and date such very recent factors. Indeed, it is also possible that all these ancient climatic and recent anthropogenic factors combined have shaped the history of present day Indian tiger populations, including the Sundarbans ,26, 28. Consequences of the level of ecological exchangeability from other Bengal tigers reinforce the distinctiveness of Sundarbans tiger population, and suggest that morphological and behavioral changes in Sundarbans tigers might be adaptations to new mangrove habitat and availability of small-sized prey . Based oAltogether, both historical and genetic evidence support the fact that Sundarbans tigers were connected by gene flow with other mainland tigers from peninsular India until very recent. Hence, we finally conclude that isolation from mainland tiger population; subsequent gene flow and local adaptation have jointly shaped the genetic architecture of Sundarbans tiger in this marshy ecosystem.This study gives the first description of the genetic diversity and population structure of Sundarbans tigers and as such should be used for their conservation management. A closer look at the population genetic pattern inferred from our mitochondrial and microsatellite data analyses suggests that Sundarbans tigers are the most divergent group of Bengal tigers. Hence, it should be managed as a separate conservation unit (CU). According to the definitions of Ryder , Waples S1 Fig(a) Selection of best possible number of genetic clusters on the basis of DIC criterion for BYM and CAR, detecting 5–6 genetic populations. (b) Individual assignment probabilities of Bengal tiger to genetic clusters using the model-based program of TESS (run K = 5 and 6).(DOCX)Click here for additional data file.S1 FileDemographic analyses methodology.(DOCX)Click here for additional data file.S1 TableObserved Null allele frequency.(DOCX)Click here for additional data file.