Acacia aroma is a multipurpose species widely distributed throughout this country. In this study, we used the AFLP technique to study genetic diversity, population genetic structure, and fine-scale spatial genetic structure in 170 individuals belonging to 6 natural Argentinean populations. With 401 loci, the mean heterozygosity (HE = 0.2) and the mean percentage of polymorphic loci (PPL = 62.1%) coefficients indicated that the genetic variation is relatively high in A. aroma. The analysis with STRUCTURE showed that the number of clusters (K) was 3. With Geneland analysis, the number of clusters was K = 4, sharing the same grouping as STRUCTURE but dividing one population into two groups. When studying SGS, significant structure was detected in 3 of 6 populations. The neighbourhood size in these populations ranged from 15.2 to 64.3 individuals. The estimated gene dispersal distance depended on the effective population density and disturbance level and ranged from 45 to 864 m. The combined results suggest that a sampling strategy, which aims to maintain a considerable part of the variability contained in natural populations sampled here, would include at least 3 units defined by the clusters analyses that exhibit particular genetic properties. Moreover, the current SGS analysis suggests that within the wider management units/provinces, seed collection from A. aroma should target trees separated by a minimum distance of 50 m but preferably 150 m to reduce genetic relatedness among seeds from different trees.The identification of factors that structure intraspecific diversity is of particular interest for biological conservation and restoration ecology. All rangelands in Argentina are currently experiencing some form of deterioration or desertification.  Acacia aroma due to its ecological and economic importance in South America, is useful in conservation and management strategies for maintaining genetic diversity, particularly in stages of rapid habitat degradation [The spatial structure of the genomic variation among natural populations constitutes a central topic in evolutionary biology. The structure is primarily influenced by the population density, breeding system, and environmental heterogeneity, among other factors. For plants, the ability to extend the geographical distribution and maintain genetic variability within populations depends on the gene flow mediated by seed movement and pollen dispersal , 2. Thesradation .Habitat fragmentation and degradation may reduce the size and increase the spatial isolation of plant populations. This could lead to increased random genetic drift, elevated inbreeding and reduced gene flow , 5, 6. TAcacia s.l. is the second largest genus in the family Leguminosae (Fabaceae) and currently comprises more than 1,450 species in three subgenera. Additionally, in many dryland areas, Acacia s.l. is the dominant shrub or tree on which humans and animals depend, for example A. nilotica in Africa, A. farnesiana in Mexico, and A. aroma in Argentina, Bolivia and Peru [Acacia aroma Gillies ex Hook. & Arn. is an ecologically and economically valuable species facing considerable habitat degradation and is well distributed throughout the Chaco Region, where it is commonly known as “aromito” or “tusca”. This species is an outcrosser; it is pollinated by bees [Acacia s.l. species occur together with other legumes, and they provide pods for forage, lumber and medicinal products [and Peru . Acacia  by bees , and its by bees . The detproducts .A. aroma grows as a tree or shrub 4–6 m in height, which is economically and ecologically important because of multiple uses extending from its roots to its pollen. From an ecological point of view, the roots are good nitrogen fixers, and the fruits and leaves provide forage for cattle and goats. Economically, the fruits and bark are rich in tannins, the flowers are useful in the perfume industry, the seeds have medicinal uses and the pollen is used in honey production [A. aroma.oduction . CurrentA. aroma in the Chaco Biogeographical Region in Northwest Argentina, each with different levels of disturbance. The results of this study can be used to facilitate the sustainable management of A. aroma, and this methodology can be extended to other Acacia species.Thus, our main objectives are to analyse the genetic diversity and structure and characterize fine-scale spatial genetic structure (SGS) in five natural populations and a remnant of Acacia aroma plants were collected in the Chaco Biogeographical Region in Northwest Argentina .For each sampled tree, the spatial coordinates  were recorded using a GPS device , following the manufacturer’s instructions. The DNA was stored at -20°C.The AFLP assay was performed as described by Vos et al. , followiEach AFLP band was considered a single biallelic locus with an amplifiable (dominant) allele, scored (1), and a null (recessive) allele, scored (0).The Bayesian likelihood method implemented in the program BAYESCAN v2.1  was utilHE and pairwise Nei’s [To estimate the allele frequencies, the software AFLP-SURV  was emplse Nei’s  genetic se Nei’s .FST, was estimated with the package HierFstat [To estimate the distribution of genetic diversity, the analysis of molecular variance (AMOVA) was assessed by considering within-population and between-population components. The decomposition of variance by AMOVA was conducted following Excoffier et al.  while usierFstat , 25. Theade4 package [To assess isolation by distance, a Mantel test was performed using the function “mantel.randtest” from the  package , 26, by  package  and geogA. aroma, two Bayesian model-based cluster analyses were contrasted: STRUCTURE [K was set at 1–8, and K values were averaged across 10 iterations. Using the rate of change in the log likelihood, the ad hoc statistic ΔK described by Evanno et al. [To identify the population genetic structure in the populations of TRUCTURE  and GeneTRUCTURE . The ratTRUCTURE  was usedo et al.  was estio et al. . The reso et al.  and Disto et al.  to obtaiK) to be tested was set from 1–7. Each individual was assigned to one of K populations (1≤ K≤ 7) based on its multilocus genotypes and spatial coordinates. To confirm that the run was long enough, we obtained 10 different runs and compared the parameter estimates . The best result was chosen based on the highest average posterior density.The second method relied on the spatial cluster model implemented in the Geneland package  of the pFij). The assumed inbreeding coefficient was FIS = 0.19, based on the average of FIS estimates obtained for the same species from codominant allozyme markers [bF) was estimated.To analyse the fine-scale spatial genetic structure (SGS), the approach described by Hardy  was util markers . The numF1  and the bF, the upper and lower bounds of the 95% confidence interval of Fij were used, which were defined after 10,000 permutations of individuals within locations. The Sp statistic [Sp = −bF/(1−F1). The Sp statistic was expected to summarize the intensity of SGS, allowing for a quantitative comparison among species and/or populations [To determine the statistical significance of tatistic  was compulations . All estulations .Nb≡4πDEσg2, where DE is the effective population density and σg is the mean-squared parent-offspring distance and can be estimated as the inverse of Sp [Nb = (F1−1)/bF. In this study, we estimated Nb and σg using the census density of populations and three predicted effective values .An indirect estimation of gene flow from the SGS estimates was performed assuming an equilibrium of isolation by distance in the fine-scale genetic structure. In such cases, the extent of gene flow can be expressed in terms of Wright’s neighbourhood size as se of Sp , 36 yielIn this study, a total of 852 AFLP bands in the interval of 50–400 bp were generated with the four primer pair combinations used. From these bands, 401 with total reproducibility were selected for all analyses to yield a 0% error rate.FST-outliers, conducted with BAYESCAN within the 401 AFLP loci , did not detect selection for any locus.The scan for HE varied from 0.18 in TA to 0.24 in SJ (mean HE = 0.21), and PPL varied from 56.4% in TA to 67.1% in QU (mean PPL = 62.1%).The measurements of genetic diversity are summarized in Hw = 0.21), while the between populations component (Hb = 0.07) was lower. The analysis of population structure by means of Wright’s [FST statistic (0.42) was highly significant (P = 5x10-4). When comparing pairwise Nei´s [P = 0.38).When analysing the components of genetic diversity with AFLP-SURV, the highest component was within populations  was found within populations and that the remaining (39.3%) was found between populations .K was found at K = 3, based on the AFLP dataset analysis determining the presence of three clusters. In this analysis, individuals from MI, RO, SJ and TA were grouped, as they were similar from a genetic perspective.Using the software STRUCTURE, a high peak of ΔA. aroma, LA and QU, constituted two differentiated clusters. Admixture was observed in all populations but was less prevalent in MI and SJ  (bF) (Populations QU and RO showed the strongest SGS and have a negative log slope (bF) .The neighbourhood size was calculated for populations where SGS was significant and ranged from 15.2 in QU to 64.3 individuals in LA .σg) was conducted considering four different effective densities in populations where SGS was significant. The extreme values of gene dispersal, which corresponded respectively to the highest and lowest density estimates in each population, were 273 and 864 m in LA, 45 and 141 m in RO, and 67 and 211 m in QU  ranged from 86 to 273 m in QUA and 41 to 131 m in QUB, depending on the value used for the estimated density.Considering its patchy distribution, Quimilí (QU) was split into two groups of individuals: QUA  and QUB  . SignifiAcacia aroma species in South America, in this study, we explored genetic diversity and population structure and characterized fine-scale spatial genetic structure of this species in Argentina. In this work, we observed high levels of genetic diversity, showing that populations of this species tend to maintain the majority of variability within populations, as do other species of Acacia and perennial woody outcrossed species [Due to the importance of the  species , 38, 39.A. aroma seemed to be relatively high when compared with other species sharing the same life history traits (citar Nybom 2004) and was similar to the values observed in other Acacia species such as A. curvifructa [A. visco [A. senegal [FST statistics (0.42) showed a strong signal of genetic structure among populations. However, the lack of significant correlation between geographic and genetic distance suggested that the differentiation among populations did not fit the isolation by distance model. This may be explained by assuming that gene flow is limited and that populations have not reached the migration-drift equilibrium [A. aroma, that in this work showed that the majority of its molecular variance occurred within populations. This result also concurs with previous records for other South American and African species of Acacia [Habitat fragmentation is a significant threat to the maintenance of biodiversity in many ecosystems. In general, the genetic consequences of habitat degradation focus on the reduced size of populations and increased spatial isolation of remnant populations. However, in some circumstances, fragmentation appears to increase gene flow among remnant populations, breaking down local genetic structure . Therefovifructa , A. viscA. visco  and A. s senegal , which wilibrium . This apistics 0. showed af Acacia , 44, 45,K) was 3. Conversely, Geneland analysis showed that the optimal number of clusters was K = 4, which shared a similar grouping to that of STRUCTURE but divided in two one of the clusters identified by the aforementioned program. In the two cases, populations of SJ, RO, MI and TA constituted a group. This finding could partially be due to the geographical proximity of SJ, RO and MI. Although TA is geographically farther in distance, there is a route (used by vehicles and pedestrians) that might promote gene flow (mediated by human activities) between TA and the first three populations, leading to the increased genetic similarity. The population of LA constituted a second cluster in STRUCTURE, with low levels of admixture. The individuals from QU constituted a third cluster, with evidence of admixture with a sizeable contribution from LA. This situation, with several differences, seems to be reflected in the plot from Geneland  seems to be larger than the sampling area within sites, significant SGS was revealed in some populations, suggesting an isolation by distance pattern.Vekemans and Hardy  noted thSp statistic was proposed to synthesize SGS intensity and remains useful when comparing the strength of SGS in different populations. A. aroma is an outcrossing species, with its pollen and seed dispersed by animals. However, the mean Sp statistic estimated here (Sp = 0.057) was greater than the mean values presented by Vekemans and Hardy [Sp = 0.0126), species with animal-dispersed seeds (Sp = 0.0088), and species with pollen dispersed through animals (Sp = 0.0171). Additionally, this statistic was higher than the values observed for Schinus molle (Sp = 0.021) [Prosopis alba (Sp = 0.003) [A. aroma, since they are outcrosser trees with animals as the vectors responsible for seed and pollen dispersal. Spatial genetic structure can be affected by density, among other factors, because it can influence the rate of genetic drift [Sp value in QUB (0.23) than in the QU sample as a whole (0.07) and in QUA (0.03). The differences observed between the two groups could be partially attributed to the low level of disturbance in QUB. This group of individuals, although small, is situated in a more pristine and unmanaged area than those from QUA. Since the six populations studied here presented different levels of disturbance and ecological characteristics, our work suggests the need to evaluate the ecological aspects of the life history and landscape for each population.Previous research has demonstrated that SGS is correlated with the mating system, life history and population density . Thus, tnd Hardy  for outc= 0.021)  and Pros= 0.003)  which arChrysophyllum sanguinolentum, Eperua grandiflora and Virola michelii [A. aroma is a species pollinated by bees, with seeds dispersed by livestock and large mammals. Additionally, the distance of gene dispersal is usually inversely related to population density [When comparing the estimates of gene dispersal, we obtained similar values for the four possible densities considered here as those estimated with AFLP markers in other outcrossers, animal, pollen and seed dispersed trees, like michelii . Our res density , and thi density  proposedA. aroma, which are valuable for the management and conservation of this and other Acacia species. The current SGS analysis provides information that may be used during sampling of individuals and seed collection for ex situ conservation and reforestation programmes. Our findings suggest that for A. aroma reforestation and management programmes, sampling should consider a minimal distance of 50 to 150 m to minimize genetic relatedness among sampled seeds in each area. In cases of disturbed populations with low density such as LA, the minimum and maximum distances should be much greater . In summary, the findings of this study are important for managing and conserving the extant trees and populations of A. aroma in the fragmented landscapes, such as Lavalle and Quimilí, and provide baseline information on the spatial structuring and dispersal of genes in this species. The combination of molecular markers and robust statistical approaches constitutes an effective strategy for supporting programmes that mitigate the deterioration and desertification of semi-arid lands in Argentina.When aiming to design conservation, management, and sustainable use strategies for a species, it is important to understand the patterns of genetic diversity , 51, 52.S1 TableSample size (N); frequency of the AFLP fragment or marker (freq_frag); estimated frequency of the null allele ; estimated variance of the frequency of the null allele .(XLSX)Click here for additional data file.


Tridacna maxima, widely distributed from French Polynesia to East Africa, has faced population declines due to over-exploitation. Comoros islands are an important biogeographic region due to potential richness of marine species, but no relevant information is available. In order to facilitate devising effective conservation management plan for T. maxima, nine microsatellite markers were used to survey genetic diversity and population differentiation of 72 specimens collected from three Comoros islands, Grande Comore, Moheli and Anjouan. A total of 51 alleles were detected ranged from 2 to 8 per locus. Observed and expected heterozygosity varied from 0.260 to 0.790 and from 0.542 to 0.830, respectively. All populations have high genetic diversity, especially the population in Moheli, a protected area, has higher genetic diversity than the others. Significant heterozygote deficiencies were recorded, and null alleles were probably the main factor leading to these deficits. FST value indicated medium genetic differentiation among the populations. Although significant, AMOVA revealed 48.9 % of genetic variation within individuals and only a small variation of 8.9 % was found between populations. Gene flow was high (Nm = 12.40) between Grande Comore and Moheli, while lower (Nm = 1.80) between Grande Comore and Anjouan, explaining geographic barriers to genetic exchanges might exist in these two islands. Global gene flow analysis (Nm = 5.50) showed that larval dispersal is enough to move between the islands. The high genetic diversity and medium population differentiation revealed in the present study offer useful information on genetic conservation of small giant clams.Small giant clam,  Tridacna in the world: Tridacna. gigas , T. maxima , T. crocea , T. squamosa , T. derasa , T. tevoroa , T. rosewateri , T. costata   are sedentary as adults, reproduce by broadcast spawning with high fecundity , and have pelagic larval dispersal about 9 days  and classified as lower risk conservation dependent on the IUCN  Red List of Threatened Species. This status indicates that the population densities have declined in a large geographical region by their overexploitation and the degradation of their natural habitat Lucas . It seemT. maxima but no relevant information is available to now. Recently, a research was conducted to identify and determine the marine mollusks species in Comoros islands using the photo-identification method and documentation of previous studies . Likewise an ecologic description have been studied on Tridacnidea family from Mayotte island , Anjouan (An population) and Moheli (Mo population). At the same time, we investigated the population differentiation in order to implement the conservation strategies of the T. maxima.Comoros islands are separated from each other by a small distance, which indicate that the area is relatively narrow geographically Fig. . Despites et al.  and shows et al.  to estimn = 72) were collected randomly between intertidal and subtidal zones at depth range of 0–20 m either by snorkeling or hand picking in three different localitions through the coral reefs of Grande-Comore (Gc), Anjouan (An) and Moheli (Mo) in June 2015 , allele frequency, observed heterozygosity (HO), expected heterozygosity (HE), Nei’s unbiased genetic distance and genetic similarity between populations were calculated using POPGENE 1.32  was carried out using FSTAT 2.9.3  and gene flow (Nm) were calculated using GENETIX 4.05. Hierarchical Analysis of Molecular Variance (AMOVA) was conducted with ARLEQUIN 3.5 .Among 72 individuals, a total of 51 alleles were detected. The alleles number per locus ranged from 2 to 8 (mean = 5.6). Overall, Mo specimens showed the highest Significant deviations from HWE (P < 0.05) were detected in 21 cases of the 27 locus-population combination after Sequential Bonferroni correction  showed a moderate genetic differentiation among the three populations (Wright Nm) placed the mean value at 5.50.F-statistics over all loci among all populations fixed the average values for s Wright  with sigs Wright . The numThe distance matrix showed that populations from Gc and Mo had the smallest genetic distance (0.120) and the highest genetic similarity (0.885) values, whereas Gc and An populations indicated the highest genetic distance (0.480) and the smallest genetic similarity 0.620)  within populations. Grulois et al.  in New Caledonia. Comparing these two populations of T. maxima, one from Comoros islands in West Indian Ocean showed lower genetic diversity than the population from New Caledonia in Pacific Ocean. Vicariance process due to Pleistocene sea level fluctuation might be the main factor to affect the genetic diversity among populations of indo-Pacific Ocean  showed heterozygote deficiencies due to null alleles at microsatellites loci , while variation among populations is low, accounting for only 8.9 % of the total variations  is higher than Gc and An , respectively. The protection of the area could be the major factor to explain high genetic diversity in Mo population. As there is a Marine national park in Moheli (Beudard T. maxima. While they have a larval dispersal time about 9 days, specimens of T. maxima are able to travel a distance about 500 km. Even though the populations between Gc and An showed low larval dispersal, it is possible to detect a connectivity for populations among the three islands from Comoros because of their small scale area. Therefore, further studies based on oceanographic barriers and ecological barriers in addition to genetic data are more important to understand the marine organism movements and connectivity between the islands. The genetic diversity and population differentiation of T. maxima can offer useful information to establish an effective plan for conservation management.In our study, the genetic diversity in the three islands showed that population from Mo ( Beudard , species


Macquaria australasica. Using guidelines to assess the risk of outbreeding depression under admixture, we develop recommendations for population management, identify populations requiring genetic rescue and/or genetic restoration and potential donor sources. We found that most remaining populations of Macquarie perch have low genetic diversity, and effective population sizes below the threshold required to retain adaptive potential. Our simulations showed that under management inaction, smaller populations of Macquarie perch will face inbreeding depression within a few decades, but regular small‐scale translocations will rapidly rescue populations from inbreeding depression and increase adaptive potential through genetic restoration. Despite the lack of data on fitness loss, based on our genetic data for Macquarie perch populations, simulations and empirical results from other systems, we recommend regular and frequent translocations among remnant populations within catchments. These translocations will emulate the effect of historical gene flow and improve population persistence through decrease in demographic and genetic stochasticity. Increasing population genetic connectivity within each catchment will help to maintain large effective population sizes and maximize species adaptive potential. The approach proposed here could be readily applicable to genetic management of other threatened species to improve their adaptive potential.Genetic diversity underpins the ability of populations to persist and adapt to environmental changes. Substantial empirical data show that genetic diversity rapidly deteriorates in small and isolated populations due to genetic drift, leading to reduction in adaptive potential and fitness and increase in inbreeding. Assisted gene flow (e.g. via translocations) can reverse these trends, but lack of data on fitness loss and fear of impairing population “uniqueness” often prevents managers from acting. Here, we use population genetic and riverscape genetic analyses and simulations to explore the consequences of extensive habitat loss and fragmentation on population genetic diversity and future population trajectories of an endangered Australian freshwater fish, Macquarie perch  A much larger global effective population size, Ne ≥ 1,000, is required to retain adaptive potential, by maintaining an equilibrium between the accumulation of genetic variation for a selectively neutral trait by mutation, and the loss variation by random genetic drift  is recommended to avoid genetic swamping and outbreeding depression Hedrick, .Recent reviews provide guidelines:Macquaria australasica Cuvier 1830 is an endangered Australian endemic freshwater fish species that has undergone substantial population decline and range contraction and fragmentation. Prior to European colonization in the late 18th century, the Macquarie perch was widespread, occurring in the inland Murray–Darling Basin (MDB), and across three coastal drainage basins, Hawkesbury–Nepean (HNB), Shoalhaven and Georges  .Sequences of an 844‐bp mitochondrial DNA fragment including the complete control region were analysed for 339 individuals from 17 populations Appendix A. For fi2.3genepop 4.2 . Times of divergence among major mitochondrial lineages were previously estimated from complete mitogenomes . Three replicates were run for 100 million steps sampling every 100,000; runs were checked for convergence, combined and analysed in tracer 1.6.0  and ΦST (mtDNA sequences) between catchments. SPAGeDi ver 1.5  using the admixture model with correlated allele frequencies. Runs were summarized using the web server clumpak  a data set of 671 individuals of MDB origin  was run for Kmax = 16 to detect major population subdivisions within MDB; (ii) a data set of 375 individuals from the Murray and Yarra catchments  was run for Kmax = 7 to explore the origin of two previously detected genetic clusters in Dartmouth and Yarra , nucleotide diversity (π), the number of segregating sites (S) as well as Tajima's D ; genalex  under mutation–drift equilibrium and may not reliably estimate contemporary Ne associated with inbreeding and the probability of population persistence  implemented in onesamp  and effective size per generation (Ne), but when estimated from mixed‐age sample, it approximates effective size per generation, albeit consistently downwardly biased  . For species with Nb/Ne ~1, LD‐based estimates of Nb (and Ne) are expected to be unaffected by two‐loci Wahlund effect arising when parents from different cohorts are combined in a single sample , calculated for each individual using the Rhh package   were included in the final model . Age‐specific mortality was modelled based on Todd and Lintermans . Fifty percent of inbreeding depression was assumed to be due to recessive lethal alleles: inbred offspring with two copies of the same recessive alleles die before reproduction. The translocation scenario assumed that six individuals (3 of each sex) were moved from larger to smaller populations each year for 50 years to approximate the impacts of repeated, modest‐sized translocations; designing the most cost‐effective translocation programme is beyond the scope of this paper. All translocated adults were assumed to survive translocation. Genetic results were summarized for 1 mitochondrial and 19 microsatellite markers simulated based on observed allele frequencies. The first pair of populations had initial population sizes of 3,000 and 500 , and the second pair had initial population sizes of 300 and 100 (modelled using Cataract Dam and Murrumbidgee). Five hundred forward‐in‐time simulations were run for 100 years for each scenario using parameters outlined in Appendix vortex to model large female fecundities, calculations were restricted to adults  for two pairs of populations using an age‐structured population model in ntermans   and FST (microsatellites) values (details in Appendices p < 0.001) microsatellite FST values across almost all pairwise comparisons supported contemporary isolation of populations and strong drift effects, which is a novel result.Strong population differentiation among most HNB populations, all three MDB catchments and three populations of the Murrumbidgee catchment  was evident from generally high pairwise population Φ3.2N = 870), structure analysis assuming K = 2  assuming K = 12 yielded geographically meaningful genetic clusters  and 6 populations from the southern MDB (N = 375). Analysis of six HNB populations  assigned all HNB populations to different genetic clusters, except a single individual in the Little River had Q > 90% in the Kowmung cluster, suggesting a rare long‐distance dispersal event or undocumented translocation  had relatively high levels of genetic diversity  and nuclear (AR and He) genetic diversity ranged widely across populations in both basins Figure . Generaly Figure . The ShoNe (LDNe) for five populations with N > 50 were smallest in Cotter , slightly higher in Cataract River  and Cataract Dam  and moderate in Dartmouth  and Yarra  lower than LDNe estimates. onesamp Ne estimates were low (mean Ne < 100) for all populations except Yarra . Environmental variables could predict only c. 17% of the variation in HL , suggesting that stochastic processes  have a strong effect on genetic diversity of Macquarie perch and/or that one or more key variable impacting HL was not included in the model , heterozygosity (1.2‐ and 2.2‐fold) and number of alleles  will result in rapid and long‐lasting increase in genetic diversity (limited only by genetic diversity and divergence of the source(s)) and decrease in inbreeding and probability of population extinction. Although large source populations should not be impacted by appropriately limited harvest (e.g. for translocation or captive breeding), smaller sources may become more vulnerable to extinction, loss of genetic diversity and inbreeding. Therefore, where possible, management should consider reciprocal translocations and multiple sources.Significant investment in habitat restoration and protection has failed to lead to detectable recovery of some Macquarie perch populations . Although no data on fitness loss exist, it is reasonable to assume that inbreeding depression might have contributed to this failure. Our simulations suggest that inbreeding depression may contribute to population declines and impede population recovery in the near future. Our estimates of vortex model has a number of limitations: (i) offspring that do not survive to reproduction were not modelled (due to software limitation), but, given extreme fecundity  for at least 50 years (~7 Macquarie perch generations), it started to impact three smaller populations  within the first 10 years (i.e. <2 generations), supporting the short‐term inbreeding threshold Ne of 100  of a population in question and Heoutbred is heterozygosity of an outbred population  to rescue genetically depauperate populations from strong inbreeding depression  and hence should be interpreted with caution and re‐examined when genomic data become available  lower than LDNe Ne  lower than the true Ne  lower than the number of adult Macquarie perch N (Table Ne for each population could be ~4.5× (1.7–8.6) higher than onesamp Ne estimate  higher than onesamp Ne estimate (Appendix Contemporary Appendix .Ne is similar to variance Ne for this species (see Section 2), comparisons of the above approximations with 100/1,000 minimum Ne thresholds of Frankham et al. , implying that genetic restoration is required to boost population adaptive potential and avoid inbreeding depression within a few decades; (ii) Wollemi, Wheeny and Erskine have mean Ne < 100, implying they might experience inbreeding depression within a few generations even if they do not already; (iii) the majority of populations have mean sizes <3,000 , which our simulations suggest may lead to genetic problems in the next decade; (iv) Little, Cataract Dam, Dartmouth and Yarra have the largest Ne and mean sizes >3,000 and thus are the best sources for translocations. We note that for four populations with sample sizes of 30 individuals  LDNe estimates of Ne had means and ranges <100  should yield similar estimates. However, when conditions of the Wright–Fisher population model are violated , different methods yield estimates related to different time frames and spatial scales  or several generations in the case of onesamp, and declines are observed in some Macquarie perch populations . Despite much uncertainty around our approximations of population sizes, many of our results point to the need to restore genetic diversity by translocations in most Macquarie perch populations. Reconnection of several populations by gene flow might be required to achieve a global Ne > 1,000 necessary to maintain population adaptive potential and evolvability in the face of environmental change  and approximate population sizes  for the two Lachlan catchment populations suggests that under a do‐nothing scenario these populations will face inbreeding and loss of genetic diversity within a few generations. Although knowing with certainty the fitness consequences of cross‐catchment admixtures would be desirable, the need for management intervention to prevent loss of genetic diversity and inbreeding in the near future may necessitate gene flow from larger populations of fish of MDB genetic origins (e.g. Dartmouth or Yarra) before the risk of outbreeding depression can be assessed through breeding experiments  source populations do not exist, so there may be no option but to source individuals for translocation from a more strongly divergent population , genetic rescue could involve within‐catchment translocation of males. Assuming that on this timescale mitochondrial divergence did not drive mito‐nuclear co‐evolution . In the MDB, historical records indicate that prior to arrival of Europeans in Australia, Macquarie perch were common along the length of the Murrumbidgee River and in many tributaries of the Murray River , suggesting that the Murrumbidgee and Murray catchments were previously connected by gene flow  should be associated with very low risk of outbreeding depression or loss of local adaptation , translocating all remaining individuals into a more sustainable system might be more beneficial than attempting to maintain the original populations, for the overall chance of species survival and adaptive potential. Successful examples of fish rescue during extreme drought and bushfire runoff already exist  if they were (i) further downstream from a barrier  or had larger flow‐path lengths upstream, (ii) in a short‐to‐medium, but not long, stretch of a barrier‐free fragment of the stream, and (iii) in streams that are colder in winter. Lower genetic diversity in sites immediately downstream from dams could indicate that cold‐water pollution and/or other aspects of water supply releases  have a long‐lasting effect on Macquarie perch populations, as was previously inferred for Murray cod  northern HNB; (ii) southern HNB; (iii) the Murray catchment, where Yarra is recommended as an additional source of translocations; and (iv) the Murrumbidgee catchment, where Cataract Dam is recommended as an additional source. Several lines of evidence suggest that the mixing recommended above poses a negligible risk of outbreeding depression and swamping of local adaptation: (i) many populations are derived from a larger ancestral population with genetic diversity primarily lost through drift in populations that are small through human actions; (ii) these populations have only recently been isolated; thus, genomic incompatibilities are unlikely; (iii) the admixed Yarra population appears to be healthy and self‐sustainable. Managing populations within these regions as a metapopulation is likely to be beneficial for long‐term population viability.We found that most Macquarie perch populations are associated with low levels of genetic diversity and effective population size. Given that Macquarie perch continue to experience ongoing declines in spite of habitat restoration and threat amelioration efforts, these populations are at risk of extinction, at least in part due to negative genetic effects . To elevate genetic diversity, Genetic rescue/restoration is a powerful management tool for a number of reasons: (i) it can have a large and long‐lasting impact on genetic diversity and adaptive potential for relatively little additional work and expense  in specific cases is regarded as desirable, it should not be required to justify genetic intervention for small populations. Genetic augmentation alone often may not be sufficient to reverse population declines, but it must accompany other threat management actions for small populations .Data for this study are available as Appendices and Supporting Information . Sequence data are also deposited to GenBank (accession  Click here for additional data file. Click here for additional data file. Click here for additional data file.


DNA) and microsatellite markers generates a valuable tool in elucidating population structure and the impact of habitat fragmentation. We applied this approach to the study of an endangered montane newt, Euproctus platycephalus. Endemic to the Mediterranean island of Sardinia, it is threatened by anthropogenic activity, disease, and climate change. We have demonstrated a clear hierarchy of structure across genetically divergent and spatially distinct subpopulations. Divergence between three main mountain regions dominated genetic partitioning with both markers. Mitochondrial phylogeography revealed a deep division dating to ca. 1 million years ago (Mya), isolating the northern region, and further differentiation between the central and southern regions ca. 0.5 Mya, suggesting an association with Pleistocene severe glacial oscillations. Our findings are consistent with a model of southward range expansion during glacial periods, with postglacial range retraction to montane habitat and subsequent genetic isolation. Microsatellite markers revealed further strong population structure, demonstrating significant divergence within the central region, and partial differentiation within the south. The northern population showed reduced genetic diversity. Discordance between mitochondrial and microsatellite markers at this scale indicated a further complexity of population structure, in keeping with male‐biased dispersal and female philopatry. Our study underscores the need to elucidate cryptic population structure in the ecology and conservation strategies for endangered island‐restricted amphibians, especially in the context of disease and climate change.The identification of historic and contemporary barriers to dispersal is central to the conservation of endangered amphibians, but may be hindered by their complex life history and elusive nature. The complementary information generated by mitochondrial (mt Amplification was performed in a G‐Storm GS1 thermal cycler (Gene Technologies), with initial denaturation 95°C 5 min, 45 cycles of 94°C 30 s, 57–59°C 90 s, 72°C 90 s, and final extension 72°C 10 min. Primer sequences and locus‐specific PCR conditions are summarized in Table Microsatellite markers were developed from  Gemmell . In brie2.3Consensus 491‐bp sequences of noncoding D‐loop mtDNA were aligned using ClustalW . To test the assumption of a constant rate of nucleotide substitution, log‐likelihood (lnL) values of trees including with and without molecular clock constraints were compared using MEGA5. The null hypothesis of equal evolutionary rate throughout the tree was not rejected . This is in keeping with the rate constancy reported in interspecific studies of salamandrids  using the median‐joining method  between the ancestral haplotype and the other haplotype sequences using the Tamura 3‐parameter model in MEGA5 to calculate the number of base substitutions per site between sequences. The rate variation among sites was modeled with a gamma distribution (shape parameter = 0.3). This was used to derive the rate of nucleotide substitution (r) and time (T) of divergence between haplogroups, according to T = DML/2r  in salamanders of 0.8%/My, as estimated by Steinfartz et al. , for K = 1–9, with MCMC 1,000,000 after burn‐in 50,000. Mean log‐likelihood values for each value of K were computed in Structure Harvester  values as a function of the number of clusters (k) for each case. The dapc function was then applied to the same groupings of sites, using cross‐validation and α‐score functions to determine the optimum number of principal components to retain in each analysis.Hierarchical clustering was further investigated using discriminant analysis of principal components , all of which had been extracted from swab samples. This failure rate reflects both low yield, and a DNA extraction method which had been optimized for the detection of 3.2A D‐loop fragment was amplified and sequenced in 96 individuals, including a minimum of eight from each of the nine main sampling sites Table . The finE. montanus, and rooted with T. cristatus. Maximum‐likelihood topology and bootstrap support for trees computed with MEGA 5 yielded equivalent results (not shown). All trees showed a very strongly supported node with clear divergence of a clade comprising haplotypes from northern populations from a clade encompassing both south and central regions. Both the northern and the south/central clades showed further divergence with strong nodal support, generating partial separation of central and south haplotypes.Figure E. montanus sequence was positioned in the network at 63 mutations from the central E. platycephalus haplotypes. Equivalent topology was seen in the network yielded using the parsimony method in TCS software (not shown) although the E. montanus sequence could not be connected in the same network as E. platycephalus.Deep division was also apparent in the median‐joining haplotype network implemented in NETWORK Figure , in whicE platycephalus and E. montanus. By this approach, the divergence of the northern clade from the central/south was estimated at 1.13 Mya. Further divergence within the north was dated to 0.56 Mya, similar to the onset of divergence within the central and southern haplogroups (0.58 Mya). Based on the maximum‐likelihood genetic distance between E. montanus and the C1 haplotype, identified in TCS as the likely ancestral haplotype, the nucleotide substitution rate was calculated to be 0.5% per million years.The table in Figure D* and F* neutrality statistics , the results did not suggest the presence of null alleles affecting specific loci or populations , allowing the null hypothesis of panmixia to be rejected. Population structure thus provides the likeliest explanation for the observed deviation from HWE.We generated microsatellite genotypes for a total of 168 individuals sampled at 13 GPS‐defined sites. Successful amplification across all eight loci was achieved in 145 individuals (86.4%), with failure to amplify at a single locus in 17 (10.1%), and at 2 loci in 6 (3.5%). There was no association of failure to amplify with individual loci or populations and no significant linkage disequilibrium between any pairs of loci. Polymorphism was apparent at all loci in all populations, with allele numbers ranging from 9 to 31 for individual loci Table . While tp < .05), observed heterozygosity (p < .01), relatedness (p < .05), and FIS (p < .01). An estimated inbreeding coefficient (F) value of >.5, the equivalent of three generations of full‐sib mating . There was no significant difference in genetic diversity between south and central regions. There was no significant difference in relatedness, FIS or mean inbreeding coefficient between males (n = 41) and females (n = 26) pooled from all southern sampling sites (not shown). In the north and central regions, the number of individuals of known gender was insufficient to allow comparison.The two northern populations (HP and BIA) had significantly lower genetic diversity than those from the center or south, as measured by allele richness  were used, comprising two from the north, three central, and four from the south  observed in a single individual from the south. However, striking differences were apparent in the pattern of distribution of mtDNA haplotypes between individual sampling sites from within the same region. This mtDNA/microsatellite discordance is also illustrated by the differences in genetic distance between populations for mtDNA in comparison with microsatellite markers  with respect to both mtDNA and microsatellite patterns. By contrast, the mtDNA haplotype distribution in SES and PF was very similar, despite strong microsatellite divergence. Unfortunately, there was insufficient DNA to allow sequencing of mtDNA haplotypes in the smaller site (PGC) in the central region.3.53.5.1E. platycephalus. Model scenarios were informed by the mtDNA phylogeny results indicative of an early split of the northern population from an ancestral central (center–south) population, with later onset of divergence of the center and south. The model scenarios used and their posterior probabilities are detailed in Figure S1. As predicted, a scenario of early divergence of the north from an ancestral center–south population, with subsequent divergence of the center and south, was strongly supported over an alternative scenario of simultaneous divergence of all three regions. A third scenario including an unsampled or extinct population was not supported. The timing of the northern divergence, inferred from the posterior distribution of parameters for the supported scenario, was around 3 million generations ago and that of the center–south split around 0.5 million generations ago  were used to screen for bottlenecks at the time of divergence of the northern clade, which might underlie its observed reduction in genetic diversity, as well as for demographic expansion affecting the center–south population, suggested by pairwise mismatch distribution , consistent with the mtDNA‐based model. However, the model check showed a less good fit for this scenario  than for the composite center–south scenario, without the northern population but otherwise identical . In addition, parameters inferred the posterior distributions were inconsistent with the mtDNA‐supported phylogeny in that the divergence of the north from the center–south ancestor appeared to be more recent than that of the center and south. There were also discrepancies apparent in the timing of intraregional divergence of the south and center. . These results may be explained by differences in the inferred mtDNA mutation rate in scenarios incorporating the north. While the posterior mutation rate was equivalent for scenarios with only the north microsatellite dataset and for those including the center and south, all scenarios including north with the center and south had a significantly lower inferred mutation rate . This is in keeping with the time of coalescence being influenced by the occurrence of homoplasy, in which populations share alleles identical in size but not by descent . The supported central/south scenario, not including the northern population, was used as the basic model in which to screen for bottlenecks around the inter‐ and intraregional splits. Posterior support was shown for a bottleneck scenario at the time of the center–south split, affecting both the south and central ancestral populations, but not for bottlenecks around the later intraregional divergence events . To prevent distortion caused by homoplasy, the northern microsatellite dataset was screened separately for demographic change. To ensure comparability with results from the full scenario, this approach was initially applied to the separate south and central mtDNA datasets. Results supported the occurrence of bottlenecks 1–2 × 104 generations ago, affecting both south and central regional populations. This timing is similar to that inferred for the center–south split in the full scenario. A bottleneck affecting the north population was also supported. The inferred timing appeared more recent, around 5 × 103 generations ago, but with confidence intervals overlapping those of the center and south .Inferred values for stable Individual regional datasets were also used to screen for population expansion. There was good support for demographic expansion events affecting the center and south. Posterior distributions for the expansion models were in keeping with very similar timing of expansion for both center and south regions, predating the divergence of the center and south. For the north dataset, the expansion scenario received lower support, but with a good fit on model checking, including no outliers in distributions of summary statistics. The inferred timing for expansion was equivalent to those for the center and south. Posterior probabilities were close to zero for scenarios with expansion occurring more recently than the center–south split (not shown).4E. platycephalus across its range of distribution. With more extensive sampling, we have been able to build on and extend the mtDNA results of Lecis and Norris . Neutral genetic divergence may also occur as the indirect result of local adaptation, in which natural selection against immigrants limits gene flow , and much of its habitat is now included in Sardinian National and Regional Parks, the consequences of historical fragmentation of habitat and loss of connectivity between component parts of metapopulations may already be critical (Cushman, E. platycephalus in association with measurements of habitat quality, including ambient and water temperature (Lecis & Norris, Although Cushman, . An effeCushman, . For theE. platycephalus, with a degree of microendemicity that might not otherwise have been anticipated. There remains a still unexplored complexity of metapopulation structure, including sex‐biased philopatry and migration, indicated by our combined use of microsatellite and mtDNA markers. The statement that “any model that endeavours to explain island‐wide patterns must necessarily be complex,” made in reference to the distribution of herpetofauna in Madagascar (Brown, Cameron, Yoder, & Vences, Our study has demonstrated a complex hierarchical genetic structure in None declared.http://dx.doi.org/10.5061/dryad.b60c1Sequence data: GenBank accession numbers KY172000–KY172014.Microsatellite genotype dataset: Data available from the Dryad Digital Repository:  Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


We have analyzed Y-chromosomal variation in populations from Transoxiana, a historical region covering the southwestern part of Central Asia. We studied 780 samples from 10 regional populations of Kazakhs, Uzbeks, Turkmens, Dungans, and Karakalpaks using 35 SNP and 17 STR markers. Analysis of haplogroup frequencies using multidimensional scaling and principal component plots, supported by an analysis of molecular variance, showed that the geographic landscape of Transoxiana, despite its distinctiveness and diversity  had no strong influence on the genetic landscape. The main factor structuring the gene pool was the mode of subsistence: settled agriculture or nomadic pastoralism. Investigation of STR-based clusters of haplotypes and their ages revealed that cultural and demic expansions of Transoxiana were not closely connected with each other. The Arab cultural expansion introduced Islam to the region but did not leave a significant mark on the pool of paternal lineages. The Mongol expansion, in contrast, had enormous demic success, but did not impact cultural elements like language and religion. The genealogy of Muslim missionaries within the settled agricultural communities of Transoxiana was based on spiritual succession passed from teacher to disciple. However, among Transoxianan nomads, spiritual and biological succession became merged. Transoxiana is a historical region of Central Asia Fig. . It coveGeographic landscape of Transoxiana. This includes a desert located between the Amu Darya and Syr Darya river basins. The riversides are densely populated by Kazakhs, Uzbeks, Karakalpaks, Kyrgyz and Turkmens  the region was at the center of the Sogdian culture. In the 4th century BC Alexander the Great turned it into a Hellenistic province, naming it Transoxiana . Later the region was part of the Seleucid Empire (4th century BC), the Greco-Bactrian Kingdom (3rd - 2nd centuries BC), the Kushan Empire (1st century BC – 5th century AD), the Hephthalite Empire (5th century AD), and the Sasanian Empire (6th century AD). In the 5th century AD the direction of migration changed to Central Asian sources: with the expansion of Turkic tribes, the region becomes part of the Turkic Khaganate (7th century AD) and succeeding realms. The source of migration returned to Western Asia in the 8th century AD: due to the expansion of Islam, the region fell under the influence of the Arab culture and became known as Mawarannahr. In the 13th century AD the migration vector switched again to a Central Asian source: most of the region was under Mongol cultural influences, becoming the Chagatai Khanate - part of the Mongol Empire6. Besides these major cultural influences, minor ones such as a small settlement of Han Chinese mentioned by the Buddhist monk Xuanzang in 630 BC took place4.8 or linguistic shifts11 on genetic structure.Thus the major landmarks in Transoxiana’s history were: the development of settled agriculture and nomadic pastoralism, the influence of Western Asian Empires, Turkic nomads, Arabs and Mongols. The expansion of Islam by Arabs and preservation of both types of economy  had the biggest impact on the cultural landscape. Though cultural expansion itself is of course nothing more than changes of cultural elements , it is often associated with demic expansion (human migration) that has direct influence on the gene pool. A number of studies have exemplified influences of religiousGenetic landscape of Transoxiana. Previous genetic studies in Central Asia have used autosomal13, mtDNA16 or Y-chromosomal markers24. All these studies stressed the pronounced heterogeneity of the genetic landscape in Central Asia. Most recent studies were dedicated to Y-chromosomal variation, in particular regions or clans within Central Asia33.17. 150 samples from Kazakh, Kyrgyz, Uzbeks, Turkmens, and Tajiks were studied using 16 STRs and 16 SNPs20. Subsequent studies24 were updated recently30, presenting variation of 8 Y-STRs and 31 SNPs in 461 samples. Here, we double this number by genotyping 17-STRs and 35 SNPs in 780 samples, including four regional Kazakh populations along the Syrdarya river, three regional Uzbek populations, Karakalpaks, Turkmens and Dungans.In line with these studies, we also concentrate here on a single region within Central Asia. The Y-chromosomal variation in this Transoxiana region has been insufficiently studied so far. 133 samples from Kyrgyz and Kazakh populations were studied using 8 Y-STRs33. The areas studied here are predominantly populated by the Kazakh tribe Konyrat, the Kazakh clans Alimuly, Kozha (Khoja) and Sunak, as well as the Turkmen tribe Yomut. A brief summary of the genealogy and history of these clans is given in the Supplementary Text.The key element of Transoxiana populations that influences both genetic and cultural landscapes is the tribal-clan structure prevalent among nomadic populations. Many modern Kazakh and Turkmen individuals identify themselves not only with an ethnic group, but with a clan as well. Genealogical lineages are combined into clans, and clans are combined into tribes. The tribal-clan structure is based on the traditional belief in the unified bloodline of an ethnic group and has long regulated the ethnosocial order of nomadic populations. This tradition has endured through all the cultural expansions and has incorporated the new elements into the tribal-clan structure. Often the name of the lineage, clan and tribe are inherited through the male line just as the Y-chromosome is. Therefore, it is important to study them togetherThis study aims to examine the genetic landscape of Transoxiana and explore its connection to geographical and cultural landscapes. To achieve this aim, we examined Y-chromosomal variation in Kazakhs, Uzbeks, Karakalpaks, Turkmens and Dungans with reference to their cultural and geographical landscapes. The results were used to determine whether or not the two last major expansions (Arab and Mongol) were demic and influenced the Y-chromosomal variation in Transoxiana.780 samples from 10 populations - Kazakhs  is available in Supplementary Fig. 30 despite their sample coming from the same region of Uzbekistan as our sample. Third, Dungan populations of Uzbekistan (DUN) are genetically closer to the populations of China (genetic distance d = 0.178) and northeastern India (d = 0.152) than to their neighboring Uzbek populations . This is explained by the historically recent migration of Dungans from China and the maintenance of their Sino-Tibetan language, prevalent in China and northeastern India. Fourth, most of the Kazakh populations studied cluster with Mongols, Pakistani Hazaras (HAZ1) and Afghan Hazaras (HAZ2) due to their high frequency of haplogroup C2-М217. This correlates with the historically well-known Mongol origin of the Hazaras35. In addition, Fig. First, Uzbek and Tajik populations practicing settled agriculture, as well as Kyrgyz, are genetically distant from most nomadic populations . Second, despite originating from three countries , Turkmen populations form their own firmly separated cluster. The reason lies in high frequency of haplogroup Q-M242 , tribe Uysun (6%) and Karakalpaks (5%). The estimated age of the μ cluster (1100 ± 300 years) aligns with previous estimations of ~1000 years39. It may be assumed that modal haplotype 3 was the “proto-Mongolian haplotype”, inherited, among others, by Genghis Khan, his descendants and patrilineal relatives. It is important to mention that Temujin (Genghis Khan) belonged to the Kiyat clan, which in turn is a branch of the Borjigin tribe, part of the Nirun Mongols. Subcluster λ, aged 400 ± 100 years old, is specific for Hazara from various countries and can be distinguished within the cluster.The modal haplotype 3 Table  and 189 The modal haplotype 4 Table  and 97 rThus, three out of four signals of expansion in Transoxiana are connected to Mongol populations and likely reflect the migration to Transoxiana from Mongolia or neighboring regions which was followed by rapid growth of the migrants’ descendants (Supplementary Text). Notably, such successful demic expansion was not accompanied by cultural expansion (language change) - most populations of present-day Transoxiana speak not Mongolian, but Turkic languages. The factor that unifies not just most, but all, populations of Transoxiana is Islam. However, our analysis has not revealed any signs of significant demic expansions linked to the Arabs. In a more direct attempt to uncover signs of such expansions, we have analyzed the Y-chromosomes of nomadic Islamic clergy.39 was based on haplogroup frequencies, in this study we have analyzed STR haplotypes of the Kozha and Sunak clans.The spread of Islam was one of the most powerful cultural expansions of West, South and Central Asia. There are ethnographic and genealogical findings identifying the demic traces of Arabs in Transoxiana. In particular, the clans Kozha and Sunak are traditionally considered as descendants of the Prophet Muhammad’s close paternal-line relatives (Supplementary Text). These clans have maintained a privileged position among the nomadic populations of Transoxiana with a status similar to Genghis Khan’s relatives, who were treated as nobility. While our previous study of the Kozha clan40, was not found among the Kozha-Sunak.Y-chromosomal haplotypes of the Kozha-Sunak tribe are shown in Fig. 33. This suggests that the origin of this mini-cluster is local and has not migrated with the Arab expansion.In order to trace the origin of the haplotype mini-clusters identified Fig. , we searet al.41, who reported that they are genetically closer to Arabs than to the surrounding populations of Pakistan and India, but did not find a founder effect. Furthermore, the Pakistani Sayeds are quite different genetically from the Transoxianan Sayeds. Thus, despite traditionally attributing their paternal ancestry to one common root among Arab missionaries, the pronounced Y-chromosomal variation suggests that Transoxiana’s lineages have descended from several unrelated local ancestors.Sayeds (lineage within the Kozha and Sunak clans) are known as descendants of the Prophet Muhammad on the paternal side, and reside beyond Transoxiana as well. Sayeds of Pakistan, for example, were analyzed by Belle silsila, a spiritual legacy passed from teacher to disciple, rather than a biological relationship. In Central Asia, Islam was spread by the Sufi orders Yasawiyya, Naqshbandi and Bektashi. In these orders, the leadership was based on silsila, a sequence of teachers who taught the succeeding leader of the Sufi order. However, even in agriculturalists spiritual succession was sometimes passed from father to son42 and when occurring within a nomadic patronymic tradition could become a patrilineal biological legacy. The age of mini-cluster ε1 (600 ± 200 years old) corresponds to the time when the Golden Horde adopted Islam as the official religion, and the rise of the Kozha-Sunak tribal-clan group in social status. This may have facilitated the transition from spiritual silsila to biological genealogy in order to maintain the privileged social status within the tribal-clan group. This conclusion coincides with the supposition of Heyer et al.43 that cultural transmission of reproductive success could play an important role in shaping genetic diversity in Central Asia.A possible explanation is that nomadic clergy genealogy was based on We have analyzed human Y-chromosomal variation in ten populations from Transoxiana, a historical region covering Uzbekistan, western Tajikistan, western Kyrgyzstan, northwestern Turkmenistan and southern Kazakhstan. Considering the peculiar features of the geographical landscape of the region, abrupt shifts of cultural landscapes in the course of its history, and presence of patrilineal tribal-clan groups, we jointly analyzed the patrilineal genetic variation, patrilineal genealogies and historical data. We identified three features of the genetic landscape of Transoxiana and its connection to geographical and cultural landscapes.First, cultural and demic expansions of Transoxiana were not closely connected with each other. Arab cultural expansion introduced Islam to the region but did not leave a significant mark on the Y-chromosomal pool. The Mongol expansion, in contrast, had enormous demic success, but did not impact on cultural elements like language and religion.Second, the geographic landscape of Transoxiana, despite its peculiarity and diversity  had no strong influence on the genetic landscape. The main factor structuring the Y-chromosomal variation was the mode of subsistence: settled agriculture or nomadic pastoralism.Third, the genealogy of Muslim missionaries within the settled agricultural community was based on spiritual succession passed from teacher to disciple, rather than on biological relationship. However, among nomads, spiritual and biological succession merged, leading to the formation of haplotype mini-clusters among nomadic clergy.44.Blood samples were collected in 2009–2012 by the expeditions of the Laboratory of Human Population Genetics of the Research Centre for Medical Genetics, Genome Geography Laboratory of the Vavilov Institute of General Genetics, The National Laboratory Astana of Nazarbayev University, Forensic science centre of the Ministry of Justice of the Republic of Kazakhstan (Astana), Center of High Technologies and Institute of Bioorganic Chemistry (Tashkent), partially under the auspices of the Genographic Project. All expeditions were supervised by Elena Balanovska and followed the same sampling rules10 populations of 5 ethnic groups were studied , followed by normalization of DNA to a concentration of 2 ng/µl.DNA was extracted from white blood cells of peripheral blood using standard methods57  using the TaqMan probes on the 7900HT instrument (Applied Biosystems) according to the manufacturer’s protocol. Haplogroups were classified according to ISOGG-2016 17 Y-STR loci were genotyped in all samples  using the Yfiler PCR Amplification Kit (Applied Biosystems) on the ABI 3130xl genetic analyzer (Applied Biosystems). The results were analyzed using the GeneMapper Software v. 4.1 (Applied Biosystems).58. Multidimensional scaling, cluster analysis (Ward’s method), and principal component analysis were conducted using the Statistica v.7.1 software59. Genetic differentiation within and among groups of populations (AMOVA) was performed in Arlequin 3.5.1.3 software60.Genetic distances between 79 populations were calculated using Nei’s method and DJ software  with reduction limit = 1. The resulting phylogenetic networks were then edited using Network Publisher63 (http://www.fluxus-engineering.com/nwpub.htm).15-STR-haplotypes were analyzed . Haplotype variability statistics were calculated using the Arlequin 3.5.1.3 software. The search for related haplotypes was conducted using the Haplomatch software64. Briefly, we first looked for a zone in the network carrying mostly haplotypes from a single population; then the most recent common ancestral haplotype for all haplotypes in the zone was identified in the network; finally, all haplotypes downstream to this ancestral haplotype were attributed to the cluster, though some very distant haplotypes were ignored. This procedure transforms the initial arbitrary “zone” to a monophyletic – to the best of the network’s performance – clade. In previous studies64 we selected the ancestral haplotype as cluster’s founder, but65 found that using the modal haplotype works much better, so here we selected modal haplotypes as founders. Cluster age was determined using the rho-statistic67 and, because rho was shown to introduce a systematic bias68, we also used the average squared distance (ASD) estimator70. Rho was calculated using Network 5, and ASD was calculated by Y TMRCA Calculator (http://ehelix.pythonanywhere.com/init/default/about), which is a derivative of Matlab/Octave program Ytime71. To convert the number of mutations into number of generations, the “genealogical” mutation rate of 2.1 × 10−3 mutations per STR per generation was used73 as the analysis in Karmin74 indicated that for clusters younger than 30,000 years, this rate is consistent with full Y-chromosomal sequence data. When converting the number of generations into an age in years, the male generation time was set to 30 years75. To determine whether or not most haplotypes in the same genealogical lineage originated from a common ancestor, we used phylogenetic networks, haplotype variability statistics and the mean number of pairwise differences within each lineage .In distinguishing the clusters on the networks, we followed the procedure described earlierSupplementary DatasetSupplementary information


Operophtera brumata), we genotyped 699 individual winter moths collected from 15 Eurasian countries with 24 polymorphic microsatellite loci. We find strong evidence for the presence of two major genetic clusters that diverged ~18 to ~22 ka, with evidence that secondary contact  resumed ~ 5 ka along a well‐established hybrid zone in Central Europe. This pattern supports the hypothesis that contemporary populations descend from populations that resided in distinct glacial refugia. However, unlike many previous studies of postglacial recolonization, we found no evidence for the “southern richness, northern purity” paradigm. We also find evidence for ongoing gene flow between populations in adjacent Eurasian countries, suggesting that long‐distance dispersal plays an important part in shaping winter moth genetic diversity. In addition, we find that this gene flow is predominantly in a west‐to‐east direction, suggesting that recently debated reports of cyclical outbreaks of winter moth spreading from east to west across Europe are not the result of dispersal.Changes in climate conditions, particularly during the Quaternary climatic oscillations, have long been recognized to be important for shaping patterns of species diversity. For species residing in the western Palearctic, two commonly observed genetic patterns resulting from these cycles are as follows: (1) that the numbers and distributions of genetic lineages correspond with the use of geographically distinct glacial refugia and (2) that southern populations are generally more diverse than northern populations . To determine whether these patterns hold true for the widespread pest species the winter moth ( This species has a broad range of woody host plants including, but not limited to, oak (Quercus), maple (Acer), and birch (Betula) trees in Europe  following the manufacturer's instructions. Prior to DNA extraction, the tip of the abdomen  and wings were removed and stored as vouchers for most of the samples. Vouchers were deposited at the Yale Peabody Museum of Natural History, New Haven Connecticut, USA. Twenty‐four polymorphic microsatellites were then amplified from each individual using primers and protocols presented in Havill et al. . Genotyp2.3After genotyping, individuals from which ≥20 microsatellite loci were successfully amplified were included in subsequent analyses. Genotype data for each included individual is provided as a single‐line formatted file titled Appendix .2.3.1structure v.2.3.2  that an individual could be classified as belonging to one of these two clusters or to hybrid categories , using the software program newhybrids v.1.1.b3 .Previous postglacial recolonization studies have found that the Quaternary climatic cycles have played an important role in shaping genetic diversity. This diversity is evident by the presence of two to three distinct genetic lineages in Eurasia Hewitt, , with we2.3.2n), the average number of alleles per locus (Na), the effective number of alleles (Eff_Na), the average observed heterozygosity across loci (Ho), the average inbreeding coefficient (GIS), and the presence of deviation from Hardy–Weinberg Equilibrium (HWA) using genodive v.2.0b27  among populations was calculated  in freeNA, and Jost's estimator of actual differentiation (Dest) was calculated using smogd v.1.2.5 , calculated in genepop, versus the linear distance between populations (km) in R v. 3.1.3  within diyABC indicated that these changes in the mutation rate and the Mean and Individual locus coefficient P's improved the shape of the cloud of simulated datasets. In addition, visualization of preliminary results based on PCAs indicated that the removal of four loci  also improved the shape of the cloud of simulated datasets. These four loci each had allelic ranges ≥120 repeat units . Given that lepidopteran genomes have high rates of duplication events  with the software program 2.3.4Na, Eff_Na, Ho, and GIS) were correlated with latitude, we constructed generalized additive models (GAM) using the R package “mgcv”  did not include 0 are reported as significant. Average rates of gene flow between eastern  and western  European countries were then calculated. The difference between these rates was used as the basis for simulations to determine if one or the other populations would eventually become ubiquitous and if so in how many generations. Simulations were carried out using the software program popG v. 4.03 (© University of Washington and Joseph Felsenstein), under a simple two allele model with equal starting frequencies and the observed difference in migration rates. Ten independent simulations were conducted; each with random starting seeds, and the number of generations to fixation of one of the alleles averaged across runs was recorded.The rates of contemporary gene flow among populations in Eurasia were examined by estimating the proportion of migrant individuals in each population using the software program 33.13.1.1newhybrids is presented in Table Z ≥ 0.8, and 22 individuals with moderate probabilities of assignment 0.5 ≤ Z < 0.8). Four hundred and thirty‐four individuals were assigned to Cluster 2 Cluster 2 (386 with high probability of assignment and 47 with moderate probability of assignment). In addition, 78 individuals were identified as hybrids (13 with high probability of assignment and 65 with moderate probabilities of assignment). All hybrids were assigned to the F2 hybrid class. Thirteen individuals were classified as “unassigned” due to low probabilities of assignment (Z < 0.5 to any category). In general, assignments showed a clear distinction between eastern and western Europe, with moths from Georgia, Poland, and Serbia being primarily assigned to Cluster 1 , and individuals from Spain, Scotland, and England being primarily assigned to Cluster 2 . In addition, while the overall number of hybrids was low, the greatest proportions of hybrid individuals were found in Austria, Sweden, and the Czech Republic , roughly corresponding with a previously identified hybrid zone in central Europe  deviations from HWE, while only 13 of the 276 pair‐wise comparisons showed significant (p ≤ .05) evidence of LD between loci after using the Benjamini–Hochberg false‐discovery method  was 0.060 and for Dest was 0.091  among sampled populations. Network analysis indicated that most populations could be divided into two large subnetworks  and latitude. However, Eff_Na and latitude showed a marginally significant relationship , England (n = 48), France (n = 124), Georgia (n = 27), Germany (n = 108), Italy (n = 15), Norway (n = 30), Poland (n = 29), Scotland (n = 40), Serbia (n = 52), Slovakia (n = 20), Spain (n = 20), Sweden (n = 21), and Switzerland (n = 52). Visual inspection of the four bayesass runs using tracer indicated that after 10 million generations all runs had converged on similar estimates for migration rates. After summarizing across runs, 15 of 205 possible migration routes showed evidence of significant gene flow  between populations .Included in the gene flow analyses were all individuals from Austria (n = 64), Czech Republic n = 9, Englan44.1We find that populations of winter moth can be broadly assigned to two geographically and genetically distinct populations Figure , which dFST = 0.060). This level of divergence is similar to that which has been observed for other species of Lepidoptera , but less than that which has been observed for species with recognized subspecies . Beginning ~20 ka the glaciers began to retreat (Clark et al., For winter moth, our findings suggest that populations became reproductively isolated in glacial refugia located both in the Iberian and Balkan peninsulas and in the Caucasus region, as a result of the expansion of the glaciers to their glacial maximums ~ 26 ka (Clark et al., Thaumetopoea pityocampa (Rousselet et al., Following the northward recolonization of the European continent, populations in Spain and Georgia—regions that acted as glacial refugia during the last glacial cycle—likely became reproductively isolated in these regions as evident by the long branches connecting these populations to other European populations Figure . For theGenetic distance analyses also revealed that populations from two countries, Norway and Scotland, that were recolonized after the retreat of the glaciers showed additional evidence of genetic differentiation Figure . In nort4.1.1One common finding for postglacial recolonization studies is that populations in geographic regions that acted as glacial refugia during the LGM are more genetically diverse than populations in recolonized regions (Babik et al., 4.2Another interesting finding from our study involves the level and direction of gene flow among contemporary European populations. Recent studies have explored whether outbreaks of winter moth occur on a cyclical basis, suggesting a cycle length of approximately 10 years during which winter moths move across Europe predominantly from east to west (Tenow et al., structure format. See Appendix Microsatellite genotype scores for each individual are provided as a tab‐delimited file in single line JSE coordinated the collection of samples and directed the research. AC provided laboratory access and oversaw the analyses. JCA and NPH collected the molecular data. JCA analyzed the dataset and oversaw manuscript preparation. All authors contributed to the project design and in preparing the manuscript.The authors declare no conflict of interests. Click here for additional data file. Click here for additional data file.


Human populations are often dichotomized into “isolated” and “open” categories using cultural and/or geographical barriers to gene flow as differential criteria. Although widespread, the use of these alternative categories could obscure further heterogeneity due to inter-population differences in effective size, growth rate, and timing or amount of gene flow. We compared intra and inter-population variation measures combining novel and literature data relative to 87,818 autosomal SNPs in 14 open populations and 10 geographic and/or linguistic European isolates. Patterns of intra-population diversity were found to vary considerably more among isolates, probably due to differential levels of drift and inbreeding. The relatively large effective size estimated for some population isolates challenges the generalized view that they originate from small founding groups. Principal component scores based on measures of intra-population variation of isolated and open populations were found to be distributed along a continuum, with an area of intersection between the two groups. Patterns of inter-population diversity were even closer, as we were able to detect some differences between population groups only for a few multidimensional scaling dimensions. Therefore, different lines of evidence suggest that dichotomizing human populations into open and isolated groups fails to capture the actual relations among their genomic features. In fact, when applying such a distinction to genetics based on environmental and socio-cultural factors, we implicitly assume that it is not confounded by inter-population differences in effective size, growth rate and timing or extent of gene flow reduction. Unfortunately, current knowledge on human isolates cannot help us understand whether this is consistent with the patterning of genetic diversity.Human groups that have been subject to geographical and/or socio-cultural barriers  to inward gene flow during their evolutionary history are commonly referred to as isolated or closed populations (hereafter “isolates/isolated”). In current genetic literature, they are often opposed to open or outbred populations - exempt from known limitations to admixture - since a higher level of inbreeding and drift and a lower efficiency of recombination in redistributing variation across individuals are to be expected under isolation12A number of studies has investigated the effects of isolation in human populations by exploiting the high sensitivity to drift of unilinear markers of mtDNA and the non-recombining portion of the Y chromosome45678With the introduction of SNP microarrays, which enable the simultaneous analysis of hundreds of thousands of loci distributed across the genome, it is now possible to investigate the genetic structure of human populations on a fine scale23111213151617181217182122Here we present a study of 24 European populations, nine of which were newly genotyped using the GenoChip 2.0 arrayIn this study, we analysed a dataset including ten linguistically and/or geographically isolated populations, which differ substantially in their census sizes, as well as 14 open populations see . The isoRegarding the selection of open populations, we considered the following three criteria: (i) geographic proximity with the isolated population dataset; (ii) geographic coverage of the European continent; (iii) sample size of at least 15 individuals.In order to better understand how genomic diversity is structured in open and isolated populations, we first analysed the distribution of seven intra-population measures. Three of them are based on variation at a single nucleotide: homozygosity, inter-locus variance between all pairs of loci and intra-population pairwise identities by state (IBS pairwise identities). The remaining four are based on haplotype variation: average number and total length of runs of homozygosity (RoHs), average intra-population sharing of blocks identical by descent between individuals (IBD blocks) and average length of blocks in linkage disequilibrium between pairs of individuals (LD blocks). As expected, the median values of these measures were significantly higher in isolates , the length of LD blocks being the only exception . These r2728Moving on from groups to single populations, we observed the strongest signals of isolation in Sauris and Sappada . Due to We combined all the measures of intra-population variation using a PCA in order to rank populations according to their degree of isolation . All varAmong isolated populations, the estimated values of effective population size (Ne) range between 209  and 3739  able to produce the inbreeding coefficients observed in four isolated populations , with an indication of time since their foundation as suggested from historical and genetic sources Finally, we explored patterns of population split and mixture using Treemix. The best fit with the data was obtained for the tree model with the maximum number of migration events tested, i.e. five (f = 0.961). As shown in All measures of intra-population genomic variation and the multivariate analysis  highlighted a relative homogeneity among European open populations, a finding which is in accordance with previous investigations2729Firstly, the principal component analysis helped disentangle the effects of the different forces that have shaped the genome of isolates. In fact, the analysis seems to indicate that most of their heterogeneity reflects variable intensities of drift and inbreeding, rather than their size or the time since isolation as suggested by historical sources. Taking the score of the first principal component as a means to rank populations according to their degree of isolation, Sauris, Sappada and Basques were found to be the most isolated, while Orkney, Carloforte and Sulcis Iglesiente were the least, with Timau, Benetutti and North Sardinia in between.Secondly, the fact we found populations with low scores for both the first and the second principal component - which means a combination of signatures of isolation with a relatively large effective size - calls into question the widespread view that human genetic isolates originate from a small group of founders1233637Our analysis highlights a continuous pattern of genomic variation among populations that has been categorized as open and isolated. Looking at the first principal component, it is possible to identify a denser area, which corresponds to the high homogeneity of open populations, and another sparser zone, which reflects the greater diversity among isolates. In the contact zone, we found Cimbrians, who cluster along the first principal component with the open populations, while Carloforte and Sulcis Iglesiente are borderline. The behaviour of these three populations, all of which have been subject to both geographic and linguistic barriers in the course of their recent history, does not mean that their genomic structure is only marginally different from that of open populations. Rather, it points to the lack of any clear discontinuities between the two groups when multiple indicators of isolation are used simultaneously. In fact, these three populations show signatures of past inbreeding which were undetected in the open ones. This is effectively evidenced by their long upper tails of pairwise intra-population IBS and IBD block sharing  and, in 44At inter-population level, the picture obtained by using the genetic distance matrix directly does not discriminate between open and isolated populations. Previous studies revealed that diversity among European populations complies with an isolation by distance model on a continental2147The overall picture provided by our study contrasts with previous observations based on unilinear markers. Using a dataset including all the isolates studied here, with the Orkney Islands being the only exception, we showed that most of them behave as outliers with both mtDNA (hypervariable region 1) and Y chromosome markers (six microsatellite loci)Through our study, we gain new insights into the genomic diversity of European populations that have been subject to linguistic and geographic barriers to gene flow. We were able to shed more light on their heterogeneity, challenge the generalized view of isolates as units that originated from small founding groups, and reveal that genomic patterns of intra-population variation in open and isolated populations are distributed along a continuum. We believe that there are two possible avenues to follow up these first results. Firstly, a comparison of the structure of open and isolated populations using whole genome sequences would provide a complete representation of their genomic diversity. Secondly, extending comparisons to geographical contexts other than Europe will help us understand to what extent the observed patterns may be appropriate to isolates in other continental or regional scenarios. Waiting for further investigations, we hope this first study can reach its own target: in making us more aware of the value of human population isolates to understand how the interplay of environmental, socio-cultural and demographic factors, has shaped human genomic diversity.We assembled the genome-wide SNP chip data of 561 healthy unrelated adult individuals from 24 European populations. New genotype data were obtained for 211 subjects from three areas: (i) Sardinia ; (ii) German-speaking linguistic islands of the eastern Alps ; (iii) the Aosta province, in the Val d’Aosta region . Only individuals with grandparents born in the same geographic area of sampling were enrolled in the study. Informed consent was obtained for all subjects. All methods were carried out in accordance with Italian Law . All experimental protocols were approved by the Bioethic Committee of the Azienda Ospedaliera Universitaria Pisana .www.genographic.com) SNP microarray known as the GenoChipAll samples were genotyped using the Geno 2.0 DNA Ancestry Kit (https://www.cog-genomics.org/plink2) under default settings . Each SNP was considered to be part of a homozygous segment when the proportion of overlapping homozygous windows was above 5%. RoHs were defined as stretches of at least 0.5 Mb with at least 25 homozygous SNPsMclust from the R package mclust V3Runs of homozygosity (RoHs) were estimated using PLINK v1.9 (–homozyg option) . By default, this option produces a lower-triangular tab-delimited text file with pairwise IBS between all individuals in the dataset. From this matrix, we extracted the values calculated between pairs of individuals belonging to the same population in order to obtain the intra-population pairwise IBS.Blocks in linkage disequilibrium were calculated by using –blocks option (default settings) in PLINK v1.9. We used the –hardy option in PLINK v1.9 to obtain the average observed heterozygosity (het) per population and the inter-locus variance between all pairs of individuals, calculated as the square root of the standard deviation. Homozygosity was calculated as hom = (1 − het).The Levene test for the equality of variances was performed with the R software package CarPrincipal component analysisMultiple regression analysis was performed with R software using the scores of the first and second principal component as dependent variables and the inbreeding coefficient, drift parameter , the effective population size point estimates and the time since isolation as independent variables. The inbreeding coefficient was calculated as the proportion of the autosomal genome in runs of homozygosity, excluding the centromeres−4 between iterations and the quasi-Newton convergence acceleration method with q = 3 secant conditions). We applied unsupervised clustering analysis to the whole sample set, exploring the hypothesis of K = 1 to 10 clusters. We assessed cross-validation errors for each value of K using the ADMIXTURE’s Cross Validation procedure.Maximum likelihood estimation of individual ancestries was performed using ADMIXTURE v1.23MDS analysis was performed using PLINK v1.9 (–distance-matrix option). The information carried by each dimension was assessed by calculating the ratio of their respective eigenvalues compared to the sum of all eigenvalues.Genetic structure and gene flow were investigated using TreeMix v1.1http://biodiversityinformatics.amnh.org/open_source/gdmg). The geographical coordinates of the sampling areas were downloaded from the http://maps.cga.harvard.edu/gpf/. When the exact locations of sampling were unknown, we used the coordinates of the centroid of the nation as reported in http://gothos.info/resources/. In order to control for the effects of geographical proximity, we calculated the deviation of any observed genetic distance from the one predicted by the regression line obtained for geographic and genetic distances of open populations.The geographic distance matrix was calculated using the Geographic Distance Matrix Generator (e with 95% (parametric) confidence intervals.Effective population size for all populations was estimated using the LD methodtThe number of generations since isolation and the relative values of effective population size under a model of demographic stationarity were calculated from the formulaF stands for the difference between the inbreeding coefficient estimated for each population and its hypothetical value at the time of population split. The latter parameter was assumed to range between the highest and lowest inbreeding coefficients observed among open populations.where ΔAccession codes: All data are available at the Zenodo Database (https://zenodo.org/) with accession number: 10.5281/zenodo.50114.How to cite this article: Anagnostou, P. et al. Overcoming the dichotomy between open and isolated populations using genomic data from a large European dataset. Sci. Rep.7, 41614; doi: 10.1038/srep41614 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.