There is no doubt that the dramatic worldwide increase in obesity prevalence is due to changes in environmental factors. However, twin and family studies suggest that genetic differences are responsible for the major part of the variation in adiposity within populations. Recent studies show that the genetic effects on body mass index (BMI) may be stronger when combined with presumed risk factors for obesity. We tested the hypothesis that the genetic variance of BMI has increased during the obesity epidemic.The data comprised height and weight measurements of 1,474,065 Swedish conscripts at age 18–19 y born between 1951 and 1983. The data were linked to the Swedish Multi-Generation Register and the Swedish Twin Register from which 264,796 full-brother pairs, 1,736 monozygotic (MZ) and 1,961 dizygotic (DZ) twin pairs were identified. The twin pairs were analysed to identify the most parsimonious model for the genetic and environmental contribution to BMI variance. The full-brother pairs were subsequently divided into subgroups by year of birth to investigate trends in the genetic variance of BMI.The twin analysis showed that BMI variation could be explained by additive genetic and environmental factors not shared by co-twins. On the basis of the analyses of the full-siblings, the additive genetic variance of BMI increased from 4.3 [95% CI 4.04–4.53] to 7.9 [95% CI 7.28–8.54] within the study period, as did the unique environmental variance, which increased from 1.4 [95% CI 1.32–1.48] to 2.0 [95% CI 1.89–2.22]. The BMI heritability increased from 75% to 78.8%.The results confirm the hypothesis that the additive genetic variance of BMI has increased strongly during the obesity epidemic. This suggests that the obesogenic environment has enhanced the influence of adiposity related genes. The prevalence of obesity has increased strongly in Sweden during the last decades, and 10% of all Swedish men and women are now obese There is no doubt that environmental factors have initiated the epidemic, since the gene pool in the population changes at a rate that is much too slow to explain the observed pattern. Nevertheless with heritability  of body mass index (BMI) in the range of 50–80%, twin studies suggest that genetic differences account for the majority of variation in body fatness within populations FTO gene on BMI So far various presumed environmental risk factors for obesity have been investigated as potential effect modifiers of genetic effects on adiposity. Twin studies conducted in several populations have found lower heritability of obesity in physically more active individuals These results all suggest that genetic effects on adiposity are modifiable by environmental influences, which are related to the obesogenic environment. However, the research is still at an early stage and it is possible that the discovered interactions apply to other genetic loci and to other environmental factors than physical activity and fat and carbohydrate intake. On this background we tested the more general hypothesis that the genetic variance of BMI has increased during the obesity epidemic among Swedish young men. If the hypothesis is confirmed it may imply that the obesogenic environment reinforces the effect of genes related to adiposity. Although a similar hypothesis has been investigated previously on Danish twin data This study has been ethically approved by the Swedish ethical approval system . Informed consent was not considered necessary by the Swedish ethical approval system since the study relies purely on non-identifiable register-based data.2 (m2). Extreme BMI values (15 kg/m2>BMI>50 kg/m2) were excluded to reduce the risk of misclassification due to measurement or data entry errors. During the years covered by this study, conscription examination was compulsory by law for all young men with Swedish citizenship. Only men with severe diseases and disabilities were exempted based on a certificate issued by a physician with information on diagnosis.The data comprises height and weight measurements of all Swedish men born between 1951 and 1983 who underwent military conscription examination. Height was measured using a wall-mounted stadiometer and weight using an analogue or digital scale. BMI was calculated as weight (kg)/heightInformation on sibling status was acquired by linking the conscript data with the Swedish Multi-Generation Register and the Swedish Twin Register using the Swedish personal identification number (ID) unique to each subject. The ID numbers of the biological parents were included in the record of their offspring and on this background 264,796 pairs of full-brothers and 1,736 MZ and 1,961 DZ twin pairs were identified among the conscripts. Data on zygosity were obtained from the Swedish Twin Register and from the Swedish Young Male Twins Study The sibling pairs were analysed through quantitative genetic modelling, in which the total phenotypic variance of a trait is partitioned into environmental and genetic components. The disentangling of genetic and environmental variance is made possible through structural equation modelling of covariance within pairs of different types of family relations. The relations included in the modelling are genetically informative if they differ in the degree of genetic or environmental similarity The genetic variance can further be decomposed into parts due to additive genetic effects (A) and dominance genetic effects, i.e. interaction between alleles on the same locus, (D) over all relevant loci. Epistatic effects refer to interaction between different loci. If the loci are unlinked, e.g. by being on different chromosomes, these effects are modelled as part of dominance genetic effects. In the case of linked loci, epistatic effects will be modelled as additive genetic effects since the loci segregate together and form a unit. The environmental variance can be divided into factors shared by co-twins (C) and factors that are unique to each twin individual including also any measurement error (E). Hence, the total phenotypic variance can be decomposed into four components: the additive genetic (A), dominant genetic (D), common environmental (C), and unique environmental (E) variance component.BMI was used as a measure of body fatness. Two sets of statistical analyses were conducted to calculate the variance components in BMI. The first is a classical twin analysis using MZ and DZ twin pairs Assuming that the AE model adequately explains BMI variance, the second set of analyses investigated the secular trends in the A and E components using only full-brother pairs. The twin study complements the full-brother pair analysis by testing whether this assumption is reasonable. The advantage of using full brother pairs in this second phase is that this data set is considerably larger which improves the statistical power to detect possible secular changes. The brother pairs were divided into birth cohorts defined by the birth year of a randomly selected brother in a pair. The BMI variance and brother pair covariance was then calculated for each birth cohort. Assuming the absence of common environmental and dominant genetic influences, or assuming that these influences are very small, the covariance multiplied by two is an estimate of the additive genetic variance in BMI since brothers share, on average, half of their segregating genes such as DZ twins. The remaining variance can be attributed to the non-shared environmental influences. A sensitivity analysis approach was used to investigate whether the same results could be obtained when normalising the BMI distribution with the logarithmic transformation.To compare the development in the variance components with the general course of the Swedish obesity epidemic, we calculated obesity prevalence for each birth year using the WHO defined cut-point of BMI≥30 The prevalence of obesity increased more than five-fold from 0.8% to 4.4% from the 1951 to the 1983 birth cohort . Less stWhen analysing the twin data, the ACE model provided at marginally better fit of the data than the ADE model. The standardized A, C and E components were estimated at 0.80 [95% CI 0.73–0.84], 0.02 [95% CI 0.02–0.03] and 0.18 [95% CI 0.18–0.19], respectively. The ACE model was compared to the AE model. With a chi-square difference of 0.385 and a difference in degrees of freedom of 1, the p-value was 0.535 indicating that the ACE model did not fit the data better than the more parsimonious AE model. Hence, evidence for the contribution of common environmental influences to BMI variation was not found in the current data. The standardized A and E components were estimated at 0.82 [95% CI 0.82–0.84] and 0.18 [95% CI 0.18–0.19], respectively, under the AE model. Since the AE model will be used, we will, from now on, simply refer to the additive genetic variance as genetic variance.Among full-brothers the total BMI variance increased from 5.7 in the 1951 birth cohort to 9.9 in the 1983 birth cohort. The BMI covariance increased in a similar proportion from 2.1 to 3.9 within the period studied. The correlation coefficients for BMI within the brother pairs increased slightly from around .375 [95% CI .340–.410] to .394 [95% CI .345–441], which translates into an increase in BMI heritability from 75% to 78.8%, although not statistically significant.Since we can assume an AE model the genetic variance is twice the covariance within full-brother pairs. Hence, the genetic variance increased from 4.3 [95% CI 4.04–4.53] to 7.9 [95% CI 7.28–8.54] over the period studied . The incIn The analyses were also conducted with the logarithm of BMI to test whether the same results would be obtained when the slight right skewness of the BMI distribution were taken into account. This transformation did not lead to different results and to ease the interpretation only the untransformed results are presented.The results of structural equation modelling of MZ and DZ twins showed that a model including additive genetic and non-shared environmental factors adequately explains variance in BMI. These findings are also consistent with results from most adoption studies of non-biological siblings The results support the hypothesis that the genetic variance has increased in Sweden between 1969 and 2001. In parallel with the increasing prevalence of obesity, the increase in the genetic variance was moderate from the first observations in birth cohorts from 1951 until around 1973. From around birth cohort 1973 and onward the increase became stronger. The trend in the genetic variance was highly correlated (.921) with the trend for the prevalence of obesity in the same population, supporting the notion that it may be the obesogenic environment that drives the upward trend in the genetic variance . The cloThe results show that the strong increase in BMI variance primarily is driven by the increase in genetic variance and only to a small extent by an increase in the environmental variance. This implies, that although the influence from the obesogenic environment has increased during the last decades 2, where “p” denotes the population prevalence of an arbitrary obesity-related allele and “a” is the effect of that allele The increase in the genetic variance can be decomposed on basis of the mathematical formula for the contribution from one genetic locus which equals p(1-p)aThe influence of the obesogenic environment on the genetic variance of BMI is possibly the result of GxE, which could occur in several ways. The genetic effects could be modified if the strength of an association in a biological pathway from genotype to phenotype is influenced by environmental circumstances. Furthermore, it can be argued that the causal pathway from genotype to phenotype goes beyond the limits of our physical organism. If a behavioural pattern mediating the genetic influence on obesity is either suppressed or facilitated under certain environmental conditions, it is reasonable to assume that the strength of an association between genotype and phenotype will change. Other types of mediating phenotypes under genetic influence could play a role, e.g. that an individual's perceived level of stress is under genetic influence and that, in turn, stress is a predictor of body fatness. If the environment changes and becomes more stressful it will have the strongest impact on individuals with a genetic susceptibility to stress. The association between genotype and BMI would then increase and thereby contribute to genetic variation in BMI. Finally, the GxE may involve a combination of different mechanisms.This study is primarily exploratory and currently we have very limited knowledge concerning which mechanisms and environmental factors are actually underlying the findings. Considering the magnitude of change in genetic variance in a more obesogenic environment, the findings strongly encourage research in how the environmental influences alter the genetic contribution to differences in BMI, which would possibly allow a better targeting of preventive efforts in the future.The results align with a recent study using quintile regression of genetic associations with BMI in children CHRNA5-CHRNA3-CHRNB4 gene region (chromosome 15q25) is associated with BMI in ever smokers, but not in non-smokers, which implies that also smoking modifies the genetic variation in BMI Several studies have examined whether putative risk factors for obesity modify the genetic variation in BMI. Although the direction of causation between obesity and physical activity is unclear, the level of physical activity may still be an indicator of the exposure to the obesogenic environment. Hence, the consistent evidence for an interaction between physical activity and the additive genetic variance in BMI, where higher genetic variance and stronger candidate gene effects are found in groups with a lower physical activity, points in the same direction as the current results In addition to possible confounding from the C and D components, assortative mating may play a role as well. The assumption of equality of gene frequencies across birth cohorts relies on the assumption that mating occurs at random An important strength of the current study is that all participants were measured at approximately the same age, i.e. around 18–19 y and through the use of standardized measurement techniques. The genetic influence on adiposity may be age-dependent and measurement error is modelled as unique environmental variance, which attenuates the estimate of heritability. Hence, keeping these factors nearly constant allows a more accurate calculation of the genetic contribution to BMI. Furthermore, the collection of data from mandatory conscription examinations implies that the cohort is not self-selected, but is highly representative of the general population.Another strength is that this study utilizes the impressive size of the Swedish conscript database. The classical twin design would tend to suffer from statistical power problems in addressing the secular trend in variance components. For example, the current population of nearly 1.5 million conscripts included merely 3,699 twin pairs eligible for analysis. Since the AE model proves to be the most parsimonious model for decomposing BMI variance, the analysis of covariance in full-brother pairs is by far the most statistically powerful approach to study secular changes in the variance components of BMI.The study is subject to various limitations. Using full-brothers instead of twins comes at a price. While twin pairs are born at the same time full-brother pairs can be years apart. Although we limit our analyses to pairs with a maximum age difference of 3 years, the obesogenic environment may still change slightly between the births of two brothers. In periods with rapid secular changes in the obesogenic environment, this may influence the correlation and covariance estimates and hence bias the results. However, it seems unlikely that such biases could alone produce the relatively uniform secular trends and would probably rather tend to dilute the results. Likewise the twin analyses may have produced biased estimates of heritability. Perhaps the most critical assumption is the ‘equal environments assumption’, implying that MZ twins do not share a more similar environment than DZ twins The findings may have both theoretical and practical perspectives. Firstly, they may elucidate the mechanisms of GxE during the last decades and thereby improve our general understanding of the role of adiposity-related genes in the obesity epidemic. It may also nuance our understanding of genetic factors as, rather than being fixed from conception, are quite flexible units in terms of their influence on phenotypes. Secondly, the findings may have a practical relevance if they are used instrumentally in genome wide association studies (GWAS). Since the total genetic variation is much higher in populations under a strong influence from the obesogenic environment, the GWAS samples may be selected to yield the highest genetic variance thereby improving the chances of finding statistically significant genetic effects. Furthermore, since the increase in genetic variance could be explained both by an increase in the influence from genes as well as by the activation of novel gene effects, this approach may also allow the discovery of new adiposity related genes.In summary, the twin analyses showed that a model including additive genetic and unique environmental variance adequately explains BMI variance. On basis of the AE model the variance and covariance of BMI within Swedish brother pairs confirmed the hypothesis that the genetic variance in BMI has increased strongly throughout the obesity epidemic, suggesting GxE. The influence of genes can potentially be modified by the environment in any part in the causal pathway linking genotype to phenotype, including the restriction of facilitation of behavioural patterns related to obesity. The findings may have both theoretical and practical perspectives. Firstly, they may be a step towards a better understanding of the general role of genes during the obesity epidemic. Secondly, the findings could be used instrumentally in GWAS studies. It is plausible that if study population with the highest genetic variance is selected the chance of finding new genetic variants is improved. Thirdly, the results strongly encourage research in how the environmental influences alter the genetic contribution to differences in BMI, which would possibly facilitate development of better targeted preventive efforts in the future.


Penelopides panini) and the Walden’s Hornbill  are two threatened hornbill species endemic to the western islands of the Visayas that constitute - between Luzon and Mindanao - the central island group of the Philippine archipelago. In order to evaluate their genetic diversity and to support efforts towards their conservation, we analyzed genetic variation in ~ 600 base pairs (bp) of the mitochondrial control region I and at 12–19 nuclear microsatellite loci. The sampling covered extant populations, still occurring only on two islands , and it was augmented with museum specimens of extinct populations from neighboring islands. For comparison, their less endangered (= more abundant) sister taxa, the Luzon Tarictic Hornbill (P. manillae) from the Luzon and Polillo Islands and the Writhed Hornbill  from Mindanao Island, were also included in the study. We reconstructed the population history of the two Penelopides species and assessed the genetic population structure of the remaining wild populations in all four species.The Visayan Tarictic Hornbill (Penelopides species, but also unravel sporadic over-water movements between islands. We found evidence that deforestation in the last century influenced these migratory events. Both classes of markers and the comparison to museum specimens reveal a genetic diversity loss in both Visayan hornbill species, P. panini and A. waldeni, as compared to their more abundant relatives. This might have been caused by local extinction of genetically differentiated populations together with the dramatic decline in the abundance of the extant populations.Mitochondrial and nuclear data concordantly show a clear genetic separation according to the island of origin in both P. panini and A. waldeni as compared to their sister taxa P. manillae and A. leucocephalus. Because of the low potential for gene flow and population exchange across islands, saving of the remaining birds of almost extinct local populations - be it in the wild or in captivity - is particularly important to preserve the species’ genetic potential.We demonstrated a loss in genetic diversity of  The Philippine archipelago consists of more than 7,000 islands, mostly of volcanic origin, with a well known geological and plate tectonic history . AccordiBecause of their species richness, the Philippines belong to the 25 biodiversity hotspots on earth, but are simultaneously the one with the lowest percentage (3%) of remaining primary vegetation . AccordiThe distribution of hornbills in the Philippines largely corresponds to the composition of the PAICs. These birds are a prominent faunal element of the Archipelago such that, based on the occurrence of the different hornbill species, Kemp  divided Penelopides panini) and the Walden’s Hornbill , occur in the Panay-Negrosian region  from the Luzonian (Greater Luzon) and the Writhed Hornbill  from the Mindanaoean (Greater Mindanao) are included in this study [The focal species of this study, the Visayan Tarictic Hornbill , Guimaras, Negros, Masbate, and Ticao Island . Todticaensis-21, EC pticaensis. P. paninational .A. waldeni used to occur on three islands, Panay, Guimaras, and Negros  is held in a breeding facility on Panay. In 2005, for the first time, a total of three offspring were successfully bred by two pairs [P. panini have bred there successfully since 2003 [Penelopides could have also include P. panini, but due to uncertainty about species level in this genus [Captive breeding of both threatened Visayan hornbill species is very rare and incipiently. The world’s only captive population of wo pairs . P. panince 2003 . Former is genus ,18, the is genus .P. manillae is classified into two subspecies, separated morphologically by size, the width of the tail band and the glossiness of the upperparts [P. m. manillae occurs on Luzon and smaller adjacent islands, such as Marinduque and Catanduanes, P. m. subniger on Polillo and Patnanungan Islands  and their less threatened sister taxa  in order to test, whether the decline in population sizes in the endangered species is also reflected in lower genetic diversity.In order to support conservation efforts for Philippine hornbill species, an assessment of the genetic population structure of the remaining wild populations is a basic requirement. Not many population genetic studies have so far been carried out in the Philippines; this is especially true for birds. Patterns of colonization and diversification in Passeriformes have been studied in a phylogeographic context by Jones and Kennedy ,29 and SP. panini and 7 feather samples of A. waldeni were collected from molted feathers in nest holes of wild birds on Panay. Furthermore, 24 museum samples from The Natural History Museum, Tring  and the Museum für Naturkunde, Berlin  - all dating back to the 19th century - were included in the study (Table One drop of blood was taken from captive hornbills and stored in Queen’s Lysis Buffer . The numDNA extraction of blood samples was performed using the DNeasy Tissue Kit (Qiagen) according to the manufacturer’s instructions for blood samples. Feather samples were digested with 15 μl proteinase K (20mg/ml) in the presence of 40 μl DTT and 5 μl Carrier RNA (1 μg/μl) in 450 μl buffer solution . After incubation at 55°C over night, we added again 15 μl proteinase K (20mg/ml) and incubated the mix for one more hour. Afterwards, we purified the samples according to the manufacturer’s instructions of the DNeasy Tissue Kit (Qiagen), but increased EtOH to 400 μl and the washing solutions to 600 μl. DNA was eluted twice in 50 μl of the buffer solution provided with the kit.DNA extraction from toe-pad samples of museum specimens were performed using the DNeasy Tissue Kit (Qiagen), according to the manufacturer’s instructions, adding 5 μl Carrier-RNA (1 μg/μl). In all steps, solution volumes were doubled. DNA was eluted twice, initially in 50 μl and then in 30 μl of the kit buffer solution. DNA extraction from museum samples was carried out in a dedicated ancient DNA laboratory, physically separated from the laboratory where contemporary DNA was handled.The mitochondrial genome of Philippine hornbills contains two control regions of which we amplified the 5’end of the first control region (CRI) as described for fragment 3 in Sammler et al. . ProductP. panini, Penmanman_CRI-spec-185rev for P. m. manillae, Penaff_CRI-spec-186rev for museum specimen ZMB 21773, and Acewal_CRI-spec-180rev for A. waldeni). The second fragment was amplified with a CRI specific forward primer  and the reverse primer AcePen_644rev or, when amplificates did not yield sufficient results, also with AcePen_426rev ) were set up as follows: 6 μl dNTP Mixture (2.0 mM each), 1.2 μl of each primer (10 μM), 3 μl DNA template, 0.25 μl Taq polymerase . The reaction was performed under the following conditions: initial denaturation at 94°C for 5 min; 50 cycles: 94°C for 90 s, 50°C for 1 min, 72°C for 1 min; and a final extension at 72°C for 10 min. Products were sequenced as described in Sammler et al. [The 37.5 μl PCR reaction volumes , we performed an analysis of molecular variance (AMOVA) with the same software. Significance of FST/AMOVA results was evaluated by permutation analysis . Diagnostic mutations of each island population were identified visually in the alignment. ARLEQUIN was also used to assess the fit of the demographic expansion model [P-SSD) was determined by a parametric bootstrap with 10,000 replicates. Mismatch distributions were not calculated for A. leucocephalus because of the small sample size of the single population of this species included in the study.Standard measures of genetic diversity , were cST. When moon model  to hornbST values were calculated with ARLEQUIN. Significance of these values was evaluated by permutation analysis . Private alleles (PA) were assessed with the Excel Add-In software GENALEX 6 [O and HE, respectively) and to test for deviation from Hardy-Weinberg equilibrium (HWE) for each locus in each population using Fisher’s exact test and the Markov chain method . Levels of significance of the HWE and linkage disequilibrium tests were Bonferroni corrected for multiple comparisons. A Bayesian clustering was performed using the software STRUCTURE version 2.3.2 [The program FSTAT 2.9.3.2  was usedENALEX 6 . ARLEQUIon 2.3.2 . Geneticon 2.3.2  and Earlon 2.3.2 .Demographic signatures of bottlenecks were investigated using the BOTTLENECK 1.2.02 software  under thST=0.572, p<0.001), such that 57% of the mtDNA variation was apportioned to divergence among populations. All single populations had significantly diverged from each others . Three individuals originating from Negros  carry two unique haplotypes that differ by 18–24 substitutions from all other individuals coming from the same area. Both haplotypes are genetically more similar to haplotypes originating from Panay/Guimaras  between these populations is 0.665 (p<0.001), but rises to 0.765 (p<0.001) when calculated excluding the three individuals exhibiting deviant haplotypes (Pp11/Pp40 and Pp55). Haplotype diversities range from 0.900 (Negros) to 1.000 (Masbate). The Negros population has a nucleotide diversity higher than that found on Panay , defining 62 unique haplotypes Figure  GenBank,49, 22 sP-SDD ≤ 0.773) with the only exception of the population from Negros (P-SDD = 0.048).The museum specimens from Guimaras and Masbate are all represented by unique haplotypes that cluster closer to haplotypes from Panay than to those from Negros Figure . There aBased on 591 bp sequence in 32 specimens, we found 98 polymorphic sites , defining 26 unique haplotypes Figure  [GenBankP. m. manillae und P. m. subniger, are separated by at least 18 base substitutions in the network and can be distinguished from one another by 7 diagnostic mutations in the alignment. The FST between the two subspecies is 0.502 . P. m. subniger has a lower haplotype and nucleotide diversity than P. m. manillae  carrying a haplotype that does not fit with its morphological identification. Pmm28 is assigned morphologically to P. m. manillae, but its haplotype clusters closer to those of P. m. subniger (4–11 base substitutions), while there are at least 21 substitutions between Pmm28 and the closest P. m. manillae haplotype. Mismatch distributions never deviate from the purely demographic expansion model (0.156 ≤ P-SDD ≤ 0.371).Similar to what was observed in Based on 593 bp sequence of 35 specimens, we found 33 polymorphic sites , defining 17 unique haplotypes Figure  [GenBankP-SDD ≤ 0.373).With nine individuals, one haplotype is the most abundant; all others are only represented by four or less individuals Figure . mtDNA dBased on 592 bp sequence in 4 specimens, we found 41 polymorphic sites . All individuals bear unique haplotypes Figure  [GenBankPenelopides. Their number of alleles per locus varied from 1 to 8 in A. waldeni, 1 to 6 in A. leucocephalus, 2 to 13 in P. panini, and 2 to 19 in P. manillae. We confirmed that no linkage disequilibrium could be found between any pair of loci.The characteristics of the 14 Pp loci and their usability for population genetics analyses are presented by Sammler et al. . Six of STRUCTURE results of independent runs with K ranging between 1 and 5 supported two as the most likely number of clusters. The clear division into two groups Figure  correspoThe number of private alleles and the allelic richness over all loci is higher in the Panay than in the Negros population  [P. m. subniger a shifted mode indicates a recent bottleneck.The number of private alleles and the allelic richness over all loci is higher in ve size) , whereasE is slightly higher than HO in the species . Hes Table  and one P. panini show a clear division between the two populations from Panay and Negros , the hath century Guimaras museum specimens could not be found in our sampling. These Guimaras lineages have probably gone extinct. However, in this regard, a Guimaras origin of PpNe11/40, 55 seems the most plausible.In the last 100 years, both Panay and Negros suffered from severe deforestation; Guimaras became completely denuded ,14. ThisToday, the shortest distance between the remaining forest patches of Panay and Negros is about 100 km, a barrier that has become increasingly difficult for forest species to cross. As a consequence, we assume that gene flow between the two areas is currently strongly reduced or completely interrupted. The clear subdivision in the microsatellite data Figure  supportsHypsipetes philippinus, despite a small sample size of only three individuals from Negros, mtDNA suggested multiple independent colonization events for this island and a lack of a direct link between populations from Panay and Negros [Eumyias panayensis, the populations of Negros and Panay are clearly separated and form sister clades [Interestingly, for another forest bird species, the Philippine bulbul d Negros . These ad Negros ,29. Hower clades . GeneralP. panini on Panay shows no indication of a recent bottleneck, the one on Negros does. This comes along with lower values of genetic diversity in the Negros population  lineage, which still survives with a few last individuals ,22 or miP. m. manillae and P. m. subniger. However, divergence within one subspecies (manillae) is comparable to divergence between them  and P. m. subniger. More importantly, it is as distant from P. m. manillae as the latter subspecies is from P. m. subniger. While the instances of deviant mt haplotypes with inconspicuous microsatellite genotypes are indicative of historical gene flow (see above), the microsatellite genotypic compositions of Pms1, Pms9, Pms6, Pms3 might suggest ongoing gene flow, as these specimens presumably represent direct hybrids between a P. m. subniger female (inferred from the mtDNA type) and a P. m. manillae male. Although these samples originate from a non-breeding facility, we cannot exclude with certainty that these hybrids have been produced in captivity [In contrast, haplogroup III Pmm24/25) stands out in both the mitochondrial and nuclear data sets, suggesting the occurrence of a further distinct genetic lineage on Luzon.  stands oP. m. manillae and P. m. subniger became separated when the sea level rose at the end of the Pleistocene and the Polillo Islands became disconnected from the main island of Luzon. While these islands were land-bridge connected to southern rather than central Luzon [Apart from these hybrids, we hypothesize that al Luzon , they aral Luzon , migratial Luzon . Thus, gal Luzon ,57.P. m. subniger as compared to that of P. m. manillae might be the result of the small size of the Polillo Islands combined with the loss of adequate habitat. This would explain the inferred recent bottleneck and the low values of genetic diversity of P. m. subniger  mitochondrial variants that probably got lost when the population went extinct.In the haplotype network Figure , all thrP. panini, this individual indicates a separation between the Negros and the Panay/Guimaras population , similar to what was observed in n Figure . The samP. manillae is genetically more structured at the mtDNA level than P. panini. The latter is undergoing a very rapid continuing decline, associated with genetic depletion. Appreciable populations survived only on Panay and Negros. These two populations are genetically differentiated, as are those extinct or almost extinct from Guimaras and Masbate. This means, at least in the case of Guimaras, that part of the original genetic diversity of the species has already got lost.P. panini from Negros, but also for P. m. subniger (Polillo Islands). While the latter population might be small in size because the islands it colonizes are also small, Negros is a large island and its population shrank solely because of loss of adequate habitat.At the microsatellite level, we found evidences of a recent bottleneck for A. leucocephalus, our data suggest that this species is (or was) genetically highly structured. A. waldeni has very rapidly decreased in number [A. waldeni generally exhibits lower genetic diversity values, both at microsatellites and mtDNA, than its less endangered sister species A. leucocephalus. Nevertheless, no sign for a recent bottleneck was found for A. waldeni. For the extinct population on Guimaras we could show that an independent (yet closely related) lineage probably has already got lost. From what we found in P. panini,we are inclined to hypothesize that the extinct or almost extinct population of A. waldeni on Negros is most likely genetically differentiated from that still extant on Panay. This hypothesis is further supported by the mt haplotype of the single museum specimen from Negros.For n number . It survn number . A. waldA. waldeni on Negros and P. panini on Masbate.To sum up, the populations of the analyzed hornbill species are generally structured by the geological history of the area, although we found a few indications for across-island dispersal. By comparing patterns of genetic divergence and variability in both endangered Visayan hornbills species to that of their (less endangered) sister taxa, we revealed lower genetic diversity associated with their dramatic population decline and the extinction of genetically differentiated populations. Conservation efforts maintaining the surviving wild birds of virtually extinct populations are therefore particularly important, as well as the preservation of their genetic potential in captivity. This applies especially to the potentially still existing populations of The authors declare that they have no competing interests.SS designed the study, conducted most of the lab work, carried out the sequence alignments, performed most of the genetic analyses, and drafted the manuscript. VK participated in the discussion of results, performed part of the population genetics analyses and the phylogenetic analyses for the online supplement, wrote the corresponding text parts, and critically revised the manuscript. KH and UK performed part of the lab work. EC organized most of the sampling, participated in designing the study, and revised the manuscript. RT supervised and participated in designing the study, participated in the discussion of results, and critically revised the manuscript. All authors read and approved the final manuscript.Note on the museum label of ZMB 21773.Click here for file


Summary: GenAlEx: Genetic Analysis in Excel is a cross-platform package for population genetic analyses that runs within Microsoft Excel. GenAlEx offers analysis of diploid codominant, haploid and binary genetic loci and DNA sequences. Both frequency-based  and distance-based  analyses are provided. New features include calculation of new estimators of population structure: G′ST, G′′ST, Jost’s Dest and F′ST through AMOVA, Shannon Information analysis, linkage disequilibrium analysis for biallelic data and novel heterogeneity tests for spatial autocorrelation analysis. Export to more than 30 other data formats is provided. Teaching tutorials and expanded step-by-step output options are included. The comprehensive guide has been fully revised.Availability and implementation: GenAlEx is written in VBA and provided as a Microsoft Excel Add-in . GenAlEx, and supporting documentation and tutorials are freely available at: http://biology.anu.edu.au/GenAlEx.Contact:rod.peakall@anu.edu.au GenAlEx 6 was originally developed as a teaching tool to facilitate teaching population genetic analysis at the graduate level . GenAlExF-statistics, Nei’s genetic distance, population assignment and relatedness. The latter includes Analysis of Molecular Variance (AMOVA), Principal Coordinates Analysis (PCoA), Mantel tests, TwoGener, multivariate and 2D spatial autocorrelation. Readers are referred to GenAlEx offers population genetic analysis of diploid codominant, haploid, haplotypic and binary genetic data from animals, plants and microorganisms. It accommodates a wide range of genetic markers, including microsatellites (SSRs), single-nucleotide polymorphisms (SNPs), amplified fragment length polymorphisms and DNA sequences. Both allele frequency-based and distance-based analysis options are provided. The former includes estimates of heterozygosity and genetic diversity, GenAlEx 6.5 maintains backward compatibility, but it provides access to the expanded spreadsheet of Excel 2007 onward. Thus, the maximum numbers of loci and samples are vastly expanded and only constrained by memory. More than 30 different Excel graphs summarize the outcomes of genetic analyses. Graphics can be further manipulated with Excel options and easily converted to pdf or other publication-quality formats.FST as a measure of population genetic structure . GenAlEx offers the calculation of a series of Shannon indices, including the mutual information index FST, F′ST, G′ST, G′′ST, Dest and SHUA. This will allow informed comparison of the new estimators of population structure.The Mantel test capability of GenAlEx has been extended to allow multiple comparison among pairwise population statistics such as GenAlEx 6.5 introduces novel heterogeneity tests  around the world.More than 150 pages of documentation are provided. This includes Appendix 1 that outlines the statistical analyses used and their supporting references. The revised guide to GenAlEx 6.5 fully cross-links with the GenAlEx tutorials and Appendix 1.GenAlEx 6.5 offers a wide range of population genetic analysis options for the full spectrum of genetic markers within the Microsoft Excel environment on both PC and Macintosh computers. When combined with its user-friendly interface, rich graphical outputs for data exploration and publication, tools for data manipulation and export options to many other software packages, we believe that GenAlEx offers an ideal launching pad for population genetic analysis by students, teachers and researchers alike.


Pungitius pungitius), the exact location of the contact zone between lineages often remains poorly known. We assessed the genetic differentiation and diversity in the nine-spined stickleback within Europe using 1037 base pairs of cytochrome b sequence for 320 individuals from 57 locations, including pond, lake, river, and coastal habitats. Our main aims were (i) to locate the contact zone between the previously recognized western and eastern lineages, (ii) investigate latitudinal patterns in genetic diversity, (iii) compare genetic diversity among different habitat types, and (iv) date the known split between eastern and western lineages. The data revealed the split between eastern and western to be located across the Danish Straits and roughly following the Norway/Sweden border to the North. Reference sites from Canada form their own clades, and one of the Canadian sites was found to have a haplotype common to the Eastern European lineage, possibly representing an ancestral polymorphism. The split between the two European clades was dated to approximately 1.48 million years ago (Mya), and between Canada and Europe to approximately 1.62 Mya. After controlling for habitat effects, nucleotide (but not haplotype) diversity across populations decreased with increasing latitude. Coastal populations showed significantly higher haplotype diversity (but not nucleotide diversity) than pond populations, but there were no detectable differences in haplotype diversity among different freshwater habitat types , or between coastal and lake/river populations. Sequences were found to cluster according to their geographic proximity, rather than by habitat type, and all habitat types were found within each major clade, implying that colonisation and adaptation between the coastal and freshwater environments in different regions must have occurred in parallel.As a consequence of colonisation from different glacial refugia, many northern European taxa are split into distinct western and eastern lineages. However, as for the nine-spined stickleback ( Towards the end of the Pleistocene (approximately 10 k years ago), the ice sheets that had covered most of Northern Europe began to retreat, and temperate species began to re-colonise the newly habitable Northern regions Thymallus thymallus) fall into two major clades within Fennoscandia; an eastern lineage that occurs in Finland, Russia and Sweden, and a western lineage that occurs in Sweden and Denmark Pungitius pungitius), revealing two main lineages within Fennoscandia; a low diversity Eastern lineage found in Finland, Russia, Estonia and Sweden, and a higher diversity Western lineage that is found in Denmark, the UK, France, Belgium, and Norway Salmo salar) populations on the Swedish West coast differ from those close-by in the Southern Baltic, indicating no inward , and little outward gene flow through the Danish straits One of the youngest marine regions in Northern Europe is the Baltic Sea; a large and somewhat isolated brackish water inlet. The Baltic Ice Lake, now the Baltic Sea, formed as the glaciers retreated The process of colonisation is usually associated with a loss of genetic diversity towards the leading edge of the colonizing front, caused by founder effects and serial bottlenecks b sequence analyses to expand on the current knowledge Pungitius pungitius) - a small (ca. 4–6 cm) cold-adapted fish species which has lives in both coastal and freshwater habitats of the northern hemisphere  to see whether the evidence for reduced autosomal genetic diversity in freshwater populations The main aim of this study was to use cytochrome misphere ; Fig. 1.The samples used in this study were collected in accordance with the national legislation of the countries concerned.The current distribution of nine-spined stickleback in Europe approximately follows the Northern coast-line of Europe down to France, including most of Fennoscandia and the UK/Republic of Ireland b amplification were from Shikano et al. Total DNA was extracted from ethanol-stored fin-clips with either a silica-fines/microtitre filtration plate method 6 generations with four chains (average standard deviation of split frequencies <0.01). We excluded the first 25% of samples as burn-in, and coded the chosen model of nucleotide substitution. A published Japanese sample from the same species (GenBank accession number GU227782) was used as an outgroup to root the tree  and three-spined stickleback  were incorporated into the analysis. Due to the possible heterogeneity of evolutionary rates in different timescales 7 generations under an HKY+I+G substitution model, assuming a Yule speciation process 3 generations. After discarding the first 5×106 generations as the burn-in, the TMRCA of the four haplotypes of nine-spined sticklebacks was estimated from the two MCMC chains using Tracer v1.5 7 generations, sampling every 5×103 generations. The mean and 95% highest posterior density (HPD) for the dates of divergence were summarized from the two MCMC chains using Tracer v1.5.Time to the most recent common ancestor (TMRCA) was estimated using an uncorrelated lognormal relaxed molecular clock model, implemented in Beast v1.5.4 ix is the haplotype frequency, and n is the sample size Haplotype diversity was calculated globally, within the Eastern and Western clades, and for each population (excluding EE-TAG as there was only one sample for this population), using the formula b sequences consisted of 93 haplotypes , and 98 variable sites. Haplotypes E1–39 (n = 39) had previously been identified by Shikano et al. The 1037 base pair cytochrome AC = 8.506, rAG = 75.551, rAT = 8.506, rCG = 1.000, rCT = 27.296, rGT = 1.000, scaled to rGT), proportion of invariable sites = 0.670, and a gamma shape distribution = 0.910. The Bayesian tree revealed three main clades, namely Canadian, Eastern and Western European clades  was estimated to be 1.62  Mya. The split between the two European clades was dated at 1.48  Mya.The best-fit model of nucleotide substitution, as determined by jModeltest, was the TIM2+I+G model with unequal base frequencies , unequal substitution rates (rn clades . The Easn clades . Four Eun clades . The twoST based trees did not provide evidence for clustering within clades by habitat type (results not presented). Using the complete dataset, there was no evidence of any effect of longitude  = 0.204, p = 0.654), or latitude  = 1.847, p = 0.180) on haplotype diversity, but there was significant evidence for an effect of habitat type on haplotype diversity  = 2.812, p = 0.049). Sequentially dropping terms had little effect on these results. Tukey's tests revealed that all pairwise comparisons among habitat types were non-significant, except in the case of the Sea-Pond comparison . When limiting the analyses to include only freshwater samples, or to include only the Western or the Eastern clade samples, all the results became non-significant. Using the complete dataset, there was no evidence that nucleotide diversity is affected by longitude , and borderline evidence that it is affected by habitat type , but there is strong evidence that nucleotide diversity declines with increasing latitude . For freshwater samples only, the results remain qualitatively the same . However, all tests become non-significant when only looking within only the Eastern or Western clades, most likely simply due to the reduction in sample size. Box plots showing the distribution of haplotype and nucleotide diversities for each habitat show that in general coastal populations exhibit higher diversities than freshwater populations, and that the ponds had the lowest median diversities among the freshwater habitat types .Resolution within the main clades remains low, and there was no evidence of clustering of haplotypes by habitat type . Likewisat types . CorrelaOur data confirm a clear split between the eastern and western clades of the nine-spined stickleback in Europe, and now clarify the region where these clades meet and overlap. Interestingly, the age of these clades (ca. 1.5 Mya) appears to date back to far before the last Pleistocene glacial period (ca. 110-10 Kya), to the mid Pleistocene epoch (ca. 2.6 Mya–10 Kya), suggesting long independent evolution of the two clades. Furthermore, in accordance with microsatellite data A previous broad-scale study of three-spined sticklebacks revealed a large clade which ranges from the East coast of the USA across to continental Europe, and forms the basal clade to European lineages Our increased sampling intensity  and density (number of sampling sites) – and in particular the addition of a number of samples from Norway, Sweden, and Germany – allowed us to clarify the location of the secondary contact zone between the two main European clades identified by Shikano et al. A loss of nucleotide diversity from south to north was identified, but this pattern was not apparent when analysing haplotype diversity. This difference in results could be because the haplotype diversity measure is subject to large sampling variance (n = 4–7 per population), whilst the nucleotide diversity measure may be less affected by this stochasticity as it integrates variance over a number of variable nucleotide sites. A loss of diversity from south to north is expected due to the broad pattern of recolonisation from south to north, as new populations at the leading edge of a recolonisation are founded from a small number of individuals (the founder effect We found evidence that the genetic diversity (as measured by haplotype diversity but not nucleotide diversity) of coastal populations significantly exceeded that of pond populations, but not that of lake or river populations. However, though not statistically significant, our data shows that coastal populations generally have higher diversity (both haplotype and nucleotide) than all types of freshwater populations . This reWe found no evidence for clustering of haplotypes by habitat type . HaplotyIn summary, our results have helped to clarify the location of the contact zone between the Eastern and Western lineages of the nine-spined sticklebacks in northern Europe, and indicate that the Danish Straits act as a semi-permeable barrier to migration in this species. We have demonstrated that the genetic diversity in this species declines towards the North, as expected from a South to North route of postglacial colonisation. Furthermore, as the sequences cluster by geographical proximity rather than by habitat type, it appears that adaptation to different habitat types must have occurred in parallel at different locations, rather than as a single adaptation event followed by expansion events. As expected, freshwater populations show lower genetic diversity than coastal populations, but there is little difference among the different freshwater habitat types. The results of this study also demonstrate that the footprints of post-glacial colonisation process are still clearly visible in the mitochondrial genome of the nine-spined sticklebacks, and in particular, that the fish in western and eastern parts of the Fennoscandia originate from different glacial refugia with little mixing between the long-differentiated lineages.File S1et al.'s Samples used in the study, including information on geographical location, habitat types, number of individuals (those followed by a star were included in Shikano (DOCX)Click here for additional data file.


Shinisaurus crocodilurus is a critically endangered species, listed in Appendix II of CITES. Its populations and habitat in China have undergone significant changes in recent years. Understanding the genetic variability and phylogeography of this species is very important for successful conservation. In this study, samples were taken from 11 wild ponds and two captive populations in China. We sequenced mitochondrial CYTB, partial ND6, and partial tRNA-Glu and genotyped 10 microsatellite loci. Our analyses of these data showed low genetic variability, no strong isolation caused by distance, and a lack of a phylogeographic structure in this species. Based on our results, the basal divergence between two clades of S. crocodilurus in China may have been caused by the formation of the Pearl River system. We found a population expansion in one of these clades. Microsatellite analysis indicated the presence of three clusters, separated by significant genetic differences. We found that most individuals in the two captive populations were from the Luokeng (Guangdong) and Guangxi wild source populations, respectively.The Chinese crocodile lizard  Shinisaurus crocodilurus, was first collected in 1928. In 1930, Ahl established a new family (Shinisauridae) to accommodate the monotypic genus and species S. crocodilurus is currently distributed in the east part of the Guangxi (Kwangsi) Zhuang Autonomous Region, the west and north parts of Guangdong province in southern China, and in mountainous areas of northern Vietnam The Chinese crocodile lizard, S. crocodilurus has decreased rapidly, and its habitat has been widely destroyed. In the main parts of its distribution, including Jinxiu county, Zhaoping county, and Guiping county, as well as Hexian county in Guangxi (Kwangsi) Zhuang Autonomous Region, S. crocodilurus has suffered 70%–80% population declines. The overall number of individuals in China has decreased from 6,000 in 1978 to 950 in 2008 Illicium verum and tea shrubs. Such changes in vegetation directly contribute to the decrease in aquatic resources in streams. Small–scale dam construction has changed the distribution of streams and influences the survival of the crocodile lizard. Mining is another source of habitat destruction because it can pollute streams. In these cases, human activities have made the habitat no longer suitable for the crocodile lizard In recent years, the population size of S. crocodilurusS. crocodilurus at different geographic scales has not been conducted. In this study, we analyzed mitochondrial CYTB, partial ND6, and partial tRNA-Glu, as well as microsatellite genetic markers, to determine the evolutionary relationships and structure of S. crocodilurus populations across the species’ ranges in China. Our primary aims were as follows: (i) to assess the level and partitioning of genetic variation within S. crocodilurus, (ii) to analyze the demographic history and the events and factors that might have had an influence, (iii) to examine the extent to which habitat fragmentation has affected genetic variation, and (iv) to discuss management strategies for this species.Although there have been several recent ecological studies of S. crocodilurus were captured by hand on clear nights in the field while they were relatively inactive, lying on branches. Saliva samples were collected by buccal swabbing. The cotton swabs used to collect saliva samples were stored in 1.5 ml centrifugal tubes containing 100% ethanol. Saliva samples from 216 individuals were collected from 13 ponds (two captive and 11 wild) throughout the current range of the species in China . The amplification conditions were as follows: 95°C for 5 min, 35 cycles at 94°C for 35 s, Ta (55°C–61°C) for 35 s (DNA was extracted using the DNeasy Tissue Kit (Qiagen) according to the manufacturer’s protocol with slight modifications. We amplified 10 microsatellite loci from nuclear DNA using 5′-fluoro-labeled forward primers (MedProbe) for 35 s , 72°C fofor 35 s . Allele 2O, and 15 μl of Premix Taq DNA polymerase (Takara). Initial denaturation was performed at 95°C for 5 min, followed by 35 cycles for 45 s at 94°C, 1 min at 56°C, and 1 min at 72°C, with a final elongation step for 10 min at 72°C. The PCR product was run on a 1.5% agarose gel for 30 min at 100 V. The PCR products were purified using a PCR purification kit , sequenced using the ABI PRISM BigDye Terminator Ready Reaction kit (Applied Biosystems), and run on an ABI 377 genetic analyzer according to the manufacturer’s protocol.A subset of 13 ponds selected to represent each region was used for the mitochondrial DNA analysis, and five individuals from each pond were sampled. A 1364 bp region of the mitochondrial DNA  gene was amplified using primer 1 (5′GCAATTGAATAAGCAAAAACCAC3′) and primer 2 (5′TAGTTTATTAAAAATGCTAGTTTTGGG3′). Amplification was performed in a total volume of 30 μl containing approximately 2 μl of template DNA, 1 μl of each primer, 11 μl of Hp values, which represents an unbiased estimate of Fst Microsatellite variation within each pond was measured using allele frequency data, from which the average allelic richness, inbreeding coefficient (Fis), and gene diversity were calculated using the method of Arlequin K = 1–10 were performed using 1,000,000 Markov Chain Monte Carlo steps, with a burn-in period of 100,000. We used no prior information and assumed correlated allele frequencies and admixture. The log likelihood was used to select the most likely value for K. In addition, we estimated the ΔK statistic, which measures the second-order rate of change in the log likelihood of the data between successive values of KK for K = 1, we investigated K = 2 to K = 9.Population structure was also analyzed with STRUCTURE 2.3.3 The significance of the hierarchical partitioning of genetic structure among the geographic groups was examined using an analysis of molecular variance (AMOVA). This analysis was done in Arlequin http://www.genecodes.com). The sequences were then aligned using Clustal X [18], and all alignments were visually inspected. Departures from neutrality for each population were assessed using Tajima’s statistic as implemented in Arlequin v3.1 π), haplotype diversity (h), and number of polymorphic sites (S) within each pond was also calculated in DnaSP The DNA sequence data were edited using Sequencer v4.10.1 , Uromastyx benti (NCBI Reference Sequence: NC 014182.1), Takydromus tachydromoides (NCBI Reference Sequence: NC_008773.1), Gekko gecko (NCBI Reference Sequence: NC_007627.1), and Phrynocephalus mystaceus (NCBI Reference Sequence: NC_021131.1) were used as outgroups. The best-fitting substitution model was determined using MODELTEST 3.06 Bayesian phylogenetic analyses of the mitochondrial sequences were conducted using MrBayes v3.1.2 Genetic structure among geographic regions was assessed using the AMOVA method in Arlequin. The AMOVA calculations were based on haplotype frequencies and pairwise differences between the haplotypes. The population history was inferred from a comparison of π and Tajima’s D test of neutrality using Arlequin and DnaSP v3.0 E values ranging from 0.300 to 0.735. The average inbreeding coefficient (Fis) was 0.3, and ranged from 0.134 to 0.504. All Fis values were greater than zero.Micosatellite genotyping results are given in K = 3 and separates the three hydrographic systems , between populations within each region , and within populations , indicating that greater genetic variance was partitioned within individuals  than between populations (30.28%)  .S. crocodilurus individuals, 11 unique haplotypes were observed (h (haplotype diversity) and π (nucleotide diversity) are summarized in h was 0.710 and π was 0.007.Of the 65 mitochondrial DNA samples from observed  and theip = 0.001) and within ponds , but no significant variance was found among ponds within each region . This indicates that greater genetic variance exists among regions than among ponds within each region .Hierarchical structuring was also determined using an AMOVA. Significant variance was observed among regions  shows low genetic diversity, with a mitochondrial DNA nucleotide diversity (π) level of 0.007 and an average expected heterozygosity (HE) of 0.625 for the microsatellite loci. The nucleotide diversity of S. crocodilurus is lower than that of the ornate dragon lizard, Ctenophorus ornatus  Gnypetoscincus queenslandiae  E = 0.583) are lower than those in populations of the ornate dragon lizard (HE = 0.77) Oedura reticulata (HE = 0.79), and Gehyra variegata  Lacerta agilis  Mintonius gloydi  The Chinese crocodile lizard , as shown in S. crocodilurus suggest that there has been gene flow only over short distances.Genetic diversity is maintained by large population sizes and/or gene flow within populations S. crocodilurus is discontinuous S. crocodilurus, which is consistent with previously published studies. Gong et al. reported that the distribution of S. crocodilurus could be connected by the Pearl River system, with the exception of the MM S. crocodilurus population distribution in China may have been divided into two clades because the Yunwu Mountains are the watershed of the Pearl River system and Jianjiang River drainage.The distribution of During the Cenozoic era, Dayaoshan Mountain was located in the central region of the Guangxi Arcuate Mountains, which was an important pathway of animal migration in Guangxi province. Dayaoshan Mountain is at the intersection of the north-south species transition, and an ice sheet was not present during the Quaternary glacial period. Therefore, this intersection was an ancient species refuge with rich species diversity E = 0.66) was similar to that of the wild groups (HE = 0.61), and the average Fis values of the captive groups (0.33) were also similar to those of the wild groups (0.37)  and GPY , were derived from the LK and GX wild populations, respectively. The average expected heterozygosity of the two captive groups (Hs (0.37) . CaptiveS. crocodilurus on a large scale and includes 13 ponds throughout the studied range. The 11 haplotypes had low haplotype diversity (0.710), and the nucleotide diversity was also low (0.007). According to our study, all ponds had Fis values greater than zero, demonstrating that S. crocodilurus is extensively inbred. Urgent actions should be taken to protect this species.This study is the first to assess the genetic diversity in S. crocodilurus comprises three main genetic groups; GX, LK, and MM, which correspond to distinct geographic populations of Guangxi, China; Luokeng, Guangdong, China; and Maoming, Guangdong, China, respectively. On this basis, we propose the following three management units: , , and  Click here for additional data file.Table S2Microsatellite genotyping raw data, abbreviations are the same as those in . Locuses are the same as those in (XLS)Click here for additional data file.Table S3Summary of mtDNA CYTB, partial ND6, and partial tRNA-Glu region haplotypes distribution, abbreviations are the same as those in .(DOC)Click here for additional data file.


A total of 85 alleles and a very high level of gene diversity (0.776) were detected among the indigenous rice varieties of the region. Considerable level of genetic variation was found within indigenous varieties whereas improved varieties were monoporphic across all loci. The comparison of genetic diversity among different types of rice revealed that sali type possessed the highest gene diversity (0.747) followed by jum (0.627), glutinous (0.602) and boro (0.596) types of indigenous rice varieties, while the lowest diversity was detected in agronomically improved varieties (0.459). The AMOVA results showed that 66% of the variation was distributed among varieties indicating a very high level of genetic differentiation in rice varieties in the region. Two major genetically defined clusters corresponding to indica and japonica groups were detected in rice varieties of the region. Overall, traditionally cultivated indigenous rice varieties in NE India showed high levels of genetic diversity comparable to levels of genetic diversity reported from wild rice populations in various parts of the world. The efforts for conservation of rice germplasm in NE India should consider saving rice varieties representing different types with specific emphasis given to sali and jum types. The protection against the loss of vast genetic diversity found in indigenous rice varieties in NE India is crucial for maintaining future food security in the changing world.The Eastern Himalayan region of Northeast (NE) India is home to a large number of indigenous rice varieties, which may serve as a valuable genetic resource for future crop improvement to meet the ever-increasing demand for food production. However, these varieties are rapidly being lost due to changes in land-use and agricultural practices, which favor agronomically improved varieties. A detailed understanding of the genetic structure and diversity of indigenous rice varieties is crucial for efficient utilization of rice genetic resources and for developing suitable conservation strategies. To explore the genetic structure and diversity of rice varieties in NE India, we genotyped 300 individuals of 24 indigenous rice varieties representing The online version of this article (doi:10.1186/2193-1801-2-228) contains supplementary material, which is available to authorized users. Oryza sativa L.) is one of the most important crops and a major food source for more than half of the global human population. Phylogeographical and archeological evidence suggest that rice was domesticated over 10000 years ago from its wild ancestor O. rufipogon in the region south of the Himalayan mountain range, likely in the present day Eastern and NE India, extending Eastward to Nepal, Myanmar and Thailand to Southern China  in low-lying areas where sufficient water is available during the cold and dry months of the year. Thus, boro type rice varieties may contain genotypes suitable for cold adaptation. The dryland cultivated rice varieties, normally grown in slash and burn agriculture system, and locally known as jum type, show adaptations to a wide range of ecological conditions including low levels of soil moisture in areas at high altitudes reaching over 3000 m above sea level. The glutinous grain type rice is commonly cultivated throughout the region as a source of grain for breakfast and dessert for many ethnic communities in the region. In addition to cultivated indigenous rice varieties, natural populations of many wild rice species including O. rufipogon, O. granulata, O. officinalis, O. nivara, O. meyeriana, Hygrorhiza aristata, Leersia hexandra and Zizenia latifolia are also found in the Northeastern region of India  or to a narrow geographic region. In particular, no extensive studies have focused on the genetic structure of some of the widely cultivated indigenous types such as boro , jum (cultivated in upland areas in hill-slopes and low soil moisture condition), sali (most widely cultivated rice during monsoon season) and glutinous  covering the wider geographic area.The indigenous rice varieties cultivated by traditional farmers may contain a considerable genetic diversity that can serve as a source of germplasm for genetic improvements of cultivated varieties of rice. In general, diverse landraces traditionally cultivated by farmers around the centers of diversity and domestication of crops are considered as key natural resources  assess genetic diversity among indigenous rice varieties in the Eastern Himalayan region of the NE India, (2) compare the genetic diversity in indigenous varieties with agronomically improved varieties (3) assess distribution of genetic diversity among different types and (4) infer the population genetic structure of rice varieties in NE India.Oryza sativa) were collected from various regions of NE India , jum (4), boro (3), and glutinous (5) types and 5 agronomically improved varieties. The variety name, type and locality are given in Table O. rufipogon) accessions originally collected from Eastern India were obtained from the International Rice Research Institute (IRRI), Philippines. Either grains or fresh leaf samples were collected from the field and morphological characters were noted based on direct observation or interviewing the farmers. The agronomically improved varieties, released by the regional and central rice research institutes and widely cultivated for their higher yield were obtained from farmers of the region. Seeds were germinated in Petri dishes and transferred to small pots and grown in a greenhouse. Leaf samples from seedlings were harvested, air dried, and used for the study. Genomic DNA was extracted following a modified cetyltrimethyl ammonium bromide extraction protocol  followed by 35 cycles of 94° (2 min), 50° (1 min), 72° (2 min) and a final extension of 72° for 5 min. The amplified products were diluted (1:50) with loading dye (Formamide and Bromophenol blue), denatured at 94°C for 5 min and cooled on ice before loading to 6.0% denaturing polyacrylamide gels on a Li-COR automated DNA sequencer with a size standard  (Li-Cor Biosciences).Seven SSR loci  with relatively high polymorphism and distributed across the rice genome were selected for the genetic diversity analyses Table . The forThe size of each amplified fragment was determined by comparison with the size standard and scored to prepare the genotype matrix. To determine the optimum number of individuals per variety to be genotyped to capture the total diversity, the number of individuals analyzed were increased one by one until the number of alleles reached to a maximum with no further increase for a given locus. Accordingly, we determined that 10 individuals per variety was sufficient to capture the total genetic variation in a given variety. Therefore, we genotyped 300 individuals  at seven SSR loci for the present study.Na), average allelic richness (RS), population differentiation (FST) and Nei gene diversity (He) (Nei Ne) and Shannon information index (I) were calculated using PopGene version 1.31 , through different types and each variety. The among type genetic diversity was calculated by considering all genotyped individuals of a given type as one population while genetic parameters for among variety was calculated based on 10 genotyped individuals per variety. The observed average number of alleles per locus ((He) Nei  were cal(He) Nei . AllelicK) based on the multilocus genotypic data. The admixture model and correlated allele frequencies were applied for each run with 10,000 burn-in period (iteration) and 100,000 Markov Chain Monte Carlo (MCMC) replication. The optimum K value, which indicates the number of genetically distinct clusters in the data, was determined from 10 replicate runs for each value of K  was detected in the locus RM264 and the lowest (4) was in the locus RM130. The indigenous rice varieties were genetically variable, while agronomically improved varieties were monomorphic within varieties at all loci. The highest gene diversity value of 0.884 was detected at RM264 and the lowest value of 0.419 detected in RM130 (Table The seven selected SSR loci amplified DNA fragments from 29 FST = 0.754) was also detected among the rice varieties.Indigenous rice varieties in NE India showed high level of genetic diversity with an overall allelic richness of 10.205 per locus and a gene diversity value of 0.776, while the agronomically improved varieties had significantly lower average allelic richness of 2.857 per locus and gene diversity was 0.459. A very high level of differentiation (Joha and Bherapawa) to 3.29  while the corresponding value was only 1.00 for the agronomically improved varieties. Some of the elite traditional rice varieties  showed very high levels of genetic diversity as measured in average numbers of alleles, rare alleles and Nei gene diversity. Two of those varieties exhibited relatively high numbers of rare alleles . Locus wise allele frequencies are presented in Additional file Bherapawa) to 0.498  with an average of 0.223 across all indigenous varieties. Shannon information content varied widely across varieties from 0.072 (Bherapawa) to 0.854  and the average was 0.338 across varieties. The diversity parameters across varieties are presented in Table FST) ranged from 0.375 to 1.000 and highly significant (p < 0.001). The pairwise FST values are given in Additional file The average observed number of alleles among indigenous rice varieties ranged from 1.14  and 0.747 (±0.127) respectively. The next level of genetic diversity was detected among the jum type followed by the glutinous and boro types (Table HS(W)) was observed in jum (0.259) and the lowest was in glutinous type (0.189). Population differentiation study within different types showed very low FST values ranging from 0.023 in sali type to 0.036 in boro type (Table Different levels of genetic variation were observed in different types of indigenous rice from NE India. The highest diversity was detected among the f 7.585 ±.604 and f 7.585 ±.604 and indica sub-species. The other group (Group-II) consisted of a few indigenous varieties belonging to sali and jum types and could be considered as the japonica sub-species. O. rufipogon accessions appeared intermediate between indica and japonica groups  while the Group-II (japonica) formed two sub-groups corresponding the geographic locations  and Group-II (japonica)  formed a distinct group but showed closer affinity to the Group-I (indica). O. rufipogon accessions showed intermediate position between the two groups  and Group-II (japonica). However, independent STRUCTURE runs without agronomically improved varieties grouped these varieties into the groups concordant with UPGMA and PCA analyses  and O. nivara (0.64) populations of the Vientiane Plain of Laos   and gene diversity (0.459) in agronomically improved types. The levels of genetic diversity were also variable across different varieties and much higher than the agronomically improved varieties Table  and Indo670) Gao . A previ670) Gao . The higLallatoi, Mulahail, Aubalam and Mimutim . The local tribal group members in the Garo Hills of Meghalaya pointed out the superior agronomical qualities of Mimutim. Our study revealed high genetic diversity in Mimutum, one of the highly valued rice varieties by native tribal groups. This reflects the importance of traditional knowledge in evaluation and conservation of indigenous crop genetic resources ST values among different rice varieties of eastern himalayan region in Northeast India and wild rice (O. rufipogon). (XLS 24 KB)Additional file 2: Table S2: Pairwise Findica) and group-II (japonica) based on cultivation type, grain characteristics and geographic origin. Figure S2. STRUCTURE output (a) including agronomically improved varieties and (b) without agronomically improved varieties. Note that three varieties  interchanged between group-I (indica) and group-II (japonica) groups in (a) while all varieties of group-I (indica) and group-II (japonica) found in UPGMA and PCA analysis clustered together in (b). (DOC 168 KB)Additional file 3: Figure S1: Sub-groups of rice varieties within group-I (


Penaeus monodon) has a natural distribution range from East Africa to the South Pacific Islands. Although previous studies of Indo-Pacific P. monodon have found populations from the Indian Ocean and Australasia to differ genetically, their relatedness to South Pacific shrimp remains unknown. To address this, polymorphisms at eight shared microsatellite loci and haplotypes in a 418-bp mtDNA-CR (control region) sequence were examined across 682 P. monodon from locations spread widely across its natural range, including the South Pacific islands of Fiji, Palau, and Papua New Guinea (PNG). Observed microsatellite heterozygosities of 0.82–0.91, allele richness of 6.85–9.69, and significant mtDNA-CR haplotype variation indicated high levels of genetic diversity among the South Pacific shrimp. Analysis of microsatellite genotypes using a Bayesian STRUCTURE method segregated Indo-Pacific P. monodon into eight distinct clades, with Palau and PNG shrimp clustering among others from Southeast Asia and eastern Australia, respectively, and Fiji shrimp clustering as a distinct group. Phylogenetic analyses of mtDNA-CR haplotypes delineated shrimp into three groupings, with shrimp from Fiji again being distinct by sharing no haplotypes with other populations. Depending on regional location, the genetic structures and substructures identified from the genotyping and mtDNA-CR haplotype phylogeny could be explained by Metapopulation and/or Member–Vagrant type evolutionary processes. Neutrality tests of mutation-drift equilibrium and estimation of the time since population expansion supported a hypothesis that South Pacific P. monodon were colonized from Southeast Asia and eastern Australia during the Pleistocene period over 60,000 years ago when land bridges were more expansive and linked these regions more closely.The Black Tiger shrimp ( Scarus psittacus  geographic organizations Sinclair . In the Penaeus monodon)   has a na Palumbi  identifiP. monodon from Indian Ocean, Southeast Asian, and Australian regions have been examined in some detail, nothing is yet known about the genetic structure of P. monodon indigenous to South Pacific Island regions, the eastern boundary of its natural range, and when and how these regions were colonized. In addition, few attempts have been made to relate data on regional genetic divergences to how P. monodon have evolved and became distributed across the entire Indo-Pacific region.Although P. monodon from Fiji, Palau, and Papua New Guinea (PNG) as well as 14 other locations distributed widely across the Indo-Pacific. To examine genetic diversity, phylogenetic analyses were undertaken on a 418-nucleotide (nt) mtDNA-CR sequence and two multiplexed polymerase chain reaction (PCR) tests adapted from tests described previously , India (n = 23), Sri Lanka (n = 32), Brunei (n = 24), Thailand (n = 60), Taiwan (n = 33), the Philippines (n = 50), Palau (n = 50), PNG (n = 20), and Fiji . Shrimp from Australia were sourced from western (n = 10), eastern (n = 50), and northern (n = 40) regions and shrimp from Vietnam were sourced from Ca Mau , Can Tho , Ben Tre , Bac Lieu  Provinces. All P. monodon were sourced from the wild except those from Vietnam, which were hatchery stocks of postlarvae spawned from wild broodstock.A total of 682 -Pacific , includiPleopods, whole postlarvae, and hepatopancreas samples were received either preserved in 95% ethanol (or RNAlater) or as extracted DNA. DNA was extracted from tissue samples using the QIAamp DNA Mini kit  as described in the kit instruction manual and stored at −20°C until used.Each DNA sample was genotyped using two microsatellite marker panels adapted from the panels described previously  containing 40 ng DNA, 5.0 μL 2× Multiplex PCR Master Mix (QIAGEN), 1.0 μL 10× primer mixture, and 2.0 μL 5× Q buffer (QIAGEN). Each of the two 10× PCR primer mixtures contained concentrations of each primer pair optimized to reliably amplify a product. The thermal cycling conditions were 95°C for 15 min, 40 cycles of 96°C for 30 s, 60°C for 90 s, and 72°C for 1 min, followed by 60°C for 30 min to ensure complete adenylation of PCR products. All reaction sets included previously validated positive and template-free negative controls.All PCRs were undertaken in 96-well plates using a BIO-RAD Peltier Thermal Cycler. Each reaction (10 μl) of each PCR using either the System 1 or System 2 primers was diluted in 100 μL or 150 μL molecular-grade water, respectively, followed by the addition of 10 μL HiDi-formamide and 0.25 μL 1200Liz size standard . After heating at 96°C for 3 min, genotyping was performed using a 3130xl Genetic Analyzer (Applied Biosystems).An aliquot (1.0 A), the number of private alleles per loci (Ap), the allelic richness after correcting for differences in sample size (Ar), the expected heterozygosity (HE) in comparison to the observed heterozygosity (Ho) (Nei 1978), and the inbreeding coefficient (FIS) using Fstat version 2.9.3 . For confirmation, any ambiguous alleles were scored twice independently. To assess intra-population genetic diversity, the number of alleles  between any two populations was calculated from allele frequency comparisons  without considering sampling locations, Bayesian clustering analysis was performed using the Admixture Model of ancestry and correlated allele frequency parameters in STRUCTURE version 2.3  and for each value of K, five randomized data sets were generated. K was determined by plotting the posterior probabilities of 20 values examined. Comparing posterior probabilities predicted for each K value, the value immediately below the posterior probabilities values with the smallest differential was taken as the most likely number of population clusters.To investigate inter-population structures, the genetic distance  using the PCR primers described by Chu et al. (μL) contained 1× PCR buffer , 1.5 mM MgCl2 (Fisher), 5U Tth plus DNA polymerase (Fisher), 200 nM each dNTP , 2 μM each forward and reverse primer, and 100–130 ng DNA template. Thermal cycling conditions were 95°C for 3 min, 35 cycles of 94°C for 45 s, 50°C for 1 min, and 72°C for 1 min, followed by 72°C for 5 min. An aliquot (5 μL) of each PCR was run in a 1.5% agarose gel to select samples containing sufficient DNA of the expected size for sequence analysis. An aliquot (5 μL) of each DNA (20 μL) purified using the CleanSeq method (Agencourt) was sequenced in both forward and reverse directions in 12 μL reactions according to the recommended protocol for BigDye Terminator V3.1 (Applied Biosystems). DNA was sequenced using a 3130xl Genetic Analyzer (Applied Biosystems) and a 418 nt region internal to the PCR primers was selected to undertake phylogenetic analyses.A mtDNA-CR sequence was amplified successfully with DNA from a total of 369 shrimp , haplotype diversity (h), and nucleotide diversity (π) within each population, (2) quantify the levels of genetic differentiation (ØST) among populations based on Analysis of Molecular Variance (AMOVA) and (3) derive demographic evolutionary patterns based on neutrality tests for mutation-drift equilibrium as well as mismatch distribution analysis examining differences among pairs of mtDNA-CR haplotypes. Fu's FS test (Fu ST) between populations correlated with the geographical distance between the same populations. Analysis of 1000 data permutations was employed to determine the statistical significance of correlations identified by the Mantel test.Each mtDNA-CR sequence chromatogram was checked, edited manually, and trimmed to 418 nt in length before being aligned using Sequencher version 4.7  and ClustalX 2.0.12 Larkin . The Arl test Fu  and Tajiμk, where k is the number of nucleotides assayed, and the mutation rate per nucleotide per year (μ) was assumed to be the average calculated for mtDNA (5.7 × 10−8) , where τ is the age of the demographic change in mutation units estimated in the mismatch distribution  per locus varied from a high of 20.1 among the 32 shrimp from Sri Lanka to a low of 6.1 among the 22 shrimp from Vietnam-CM.Across the 615 all loci . Locus PHo) values determined across all eight microsatellite loci ranged from 0.90 for the 50 shrimp from Palau down to 0.74 for the 16 shrimp from Madagascar (see r) values were the highest for shrimp from Sri Lanka and India (11.3) followed by shrimp from Thailand (10.4) and Palau (9.7), and were the lowest for shrimp from Vietnam-CM (5.2). Shrimp from Thailand possessed the most (26) private alleles (Ap) unique to a population followed by shrimp from northern Australia (23) and the least (1) was possessed by the shrimp from Vietnam-CM.Within each of the geographic groupings of shrimp, mean observed allelic heterozygosity  values calculated for each of the eight microsatellite loci in shrimp from each of the 17 populations, only locus Pm8 showed no statistically significant (P < 0.05) departure from HWE , Vietnam-CT (45), northern Australia (41), the Philippines (34), Vietnam-BT (28), Sri Lanka (24), Vietnam-CM (15), Brunei (8), Taiwan (5), eastern Australia (3), Fiji (2), Palau (2), and PNG (1).In comparisons of inbreeding coefficient  across the 17 populations identified statistically significant differences (P < 0.05) between all except eight population pairs  of Fiji was extremely low (0.006 across the eight microsatellite loci) and not significant statistically, these Fiji shrimp were considered as a single group (data not shown). Other location pairs for which pair-wise FST values were not significant included India–Sri Lanka (0.001), Palau–Thailand (0.003), Palau–PNG (0.004), PNG–eastern Australia (0.005), PNG–Thailand (0.006), Taiwan–eastern Australia (0.009), Thailand–Western Australia (0.028), and Thailand–Vietnam-BT (0.050). In comparison to shrimp from all other populations, the most significant FST values were obtained for shrimp from northern Australia (0.067–0.178), Vietnam-CM (0.071–0.178), Madagascar (0.071–0.163), and Fiji (0.051–0.154). A Neighbor-Joining tree generated from the pair-wise FST data clustered P. monodon from the 17 locations into three clades comprising shrimp originating only from Pacific Ocean locations and three clades comprising shrimp predominantly from Indian Ocean locations   .ST) between the 17 shrimp populations, and identified that most were distinct (P < 0.05)  in pair-wise comparisons of shrimp from India and either Sri Lanka or Vietnam-BT, Sri Lanka and either Thailand, Vietnam-CM or Vietnam-BT, Brunei and either Thailand or Palau, Vietnam-CM and Vietnam-BT, the Philippines and either Taiwan or Palau, and from Taiwan and either Palau or PNG  was low in the 418 nt mtDNA-CR sequence examined, ranging from 1% for shrimp from either Vietnam-BL or Fiji to 8% for shrimp from Thailand. Analysis of Molecular Variance analyses of sequence differences were used to calculate genetic distances (Ø < 0.05) . The Man < 0.05) . Howeveru or PNG . These pexamined .P. Monodon mtDNA-CR haplotypes splits them into three primary grouping comprising shrimp predominantly from either Pacific Ocean (Pac) or Indian Ocean (Ind 1 and Ind 2) origins (Pac) group formed one large discrete clade (n = 273) including shrimp from Sri Lanka (n = 4), India (n = 1), Brunei (n = 10), Thailand (n = 21), Vietnam-CM (n = 5), Vietnam-CT (n = 1), Vietnam-BL (n = 10), Vietnam-BT (n = 3), the Philippines (n = 14), Taiwan (n = 22), northern Australia (n = 31), Western Australia (n = 1), eastern Australia (n = 25), PNG (n = 18), Palau (n = 27), and Fiji (n = 81) (A Neighbor-Joining phylogenetic tree constructed from 247  origins . The Pac(n = 81) .Ind 1 and Ind 2). Ind 1 clade comprised 26 shrimp including Vietnam-CT (n = 8), India (n = 2), Sri Lanka (n = 2), Thailand (n = 2), Vietnam-BT (n = 1), and Madagascar (n = 11). Ind 2 clade comprised 70 shrimp including Vietnam-BT (n = 8), India (n = 13), Sri Lanka (n = 10), Thailand (n = 17), Palau (n = 6), Brunei (n = 4), Vietnam-CM (n = 10), Taiwan (n = 1), and Madagascar (n = 1) (The Indian Ocean group segregated into two discrete clades ( (n = 1) .Examining the mtDNA-CR haplotype relationships showed them broadly to conform to one of three types in which (1) 80–90% of shrimp from a location clustered together , (2) 50–79% of shrimp from a location cluster together  or (3) <50% of shrimp from a location clustered together .P < 0.05) negative Fs values suggestive of previous population expansion events (P < 0.05) negative D values for shrimp from India, Vietnam-CT, and Fiji , analysis of mtDNA-CR sequences across the 17 geographic groups identified that six groups  generated significant , allelic richness (6.85–9.69), and mean number of alleles per locus (13.5–19.8) of microsatellites.Here, we have examined genetic relatedness of 615 P. monodon from its Indo-Pacific distribution range using microsatellite markers identified substantially more genetic structures than previous studies for this species , India and Sri Lanka (2), Thailand, Palau, PNG, Taiwan, Western Australia, eastern Australia, Philippines, Vietnam-BL and Vietnam-CT (3), northern Australia (4), Vietnam-BT (5), Brunei (6), Vietnam-CM (7), and Fiji (8). The results were well supported by an isolation-by-distance organization . A recent similar study of genetic diversity of P. monodon from Australia, Kenya, Madagascar, the Philippines, Taiwan, Thailand, and Vietnam delineated distinct Pacific Ocean, Indian Ocean, and West Thailand clusters , such genetic distinction was expected. This could also explain with the Member–Vagrant evolutionary model that restricted gene flows result in locally adapted gene pools and stable genetic structures.Microsatellite allele types and mtDNA-CR haplotypes detected among P. monodon from Sri Lanka and India could not be distinguished by either analysis method. The same was found with most shrimp examined from the several South-East Asian study locations, suggesting a common ancestry and higher levels of recent gene flow perpetuated by natural intermixing of proximal populations or intermixing due to shrimp translocations for aquaculture purposes. These fit well with a metapopulation evolutionary model, that is, highly frequent gene flows reduce genetic diversity between local subpopulations causing recurring extinction–recolonization events  deduced from microsatellite analyses suggest that these populations might be mixing perpetually due to their proximity and the relative shallowness of Torres Strait and its many Islands providing opportunities for Island hopping.Both analyses identified among P. Monodon from 17 locations using mtDNA-CR sequence. Although all three methods unanimously identified recent population expansion in the shrimp from Fiji, they also generated different results. For example Fu's Fs test predicted population expansions among P. monodon from Madagascar and India (Indian Ocean) as well as eastern Australia, PNG, Palau and more so from Fiji (Pacific Ocean). However, Tajima's D neutrality test only provided partial support to the Fu's Fs findings on India and Fiji, and mismatch distribution confirmed the results on Fiji only. Small population sizes may have contributed to the discrepancy of the results from different methods. On the other hand, the results were not unexpected. The study by Ramos-Onsins and Rozas  were performed to detect possible population expansion of nd Rozas  found thThe population expansions predicted from mismatch distributions were dated between 20,000 and 165,000 years ago, thus traversing the Pleistocene ice age period. Such time frames for population expansion generally agree with data on numerous Indo-Pacific distributed species that now display a wide variety of population structures, yet all share the impact of Pliocene/Pleistocene glacial cycles  has been significant or extensive, but they are unable to accurately estimate the direction and shape of population expansion through time. Therefore, additional analysis using Bayesian Skyline Plot would be needed to provide more information on estimated effective population sizes over time.P. monodon from the three South Pacific islands examined, their distinct genetic makeup was indicative of them having established more recently, most likely from small numbers of founder shrimp which has restricted gene pool sizes at any particular location. Together with the Malay Peninsula, the Philippines and Indonesia, PNG comprises part of the Malesia biogeographical region which shares tropical flora originating mainly from nearby Asia  being colonized relatively recently by neighboring populations closest in proximity. The findings clearly demonstrate that P. monodon distributed across the Indo-Pacific region have evolved following both Member–Vagrant and Metapopulation evolutionary models. The data are also consistent with the current dispersal patterns and population structures of P. monodon being mediated over long geological time frames most likely by global glaciation periods linking coastlines now well separated and possibly by ancient progenitor populations being dispersed by continental drift of northeasterly fragments of the Gondwana supercontinent.By examining heterozygosity among eight microsatellite loci and mtDNA-CR sequence differences among 17 populations of