AbstractUrticadioica L.) is co-associated with willows  in riparian habitats across Europe. We sampled the widespread nettle psyllid, Triozaurticae , from Urtica in willow habitats on a megatransect of Europe from the Aegean to the Arctic Ocean. The aim of this study was to use an unusually widespread insect to assess the influence of geographic distances and natural geographic barriers on patterns of genetic variation and haplotype distribution.The common nettle . These groups are supported by both phylogenetic analysis  and network analysis (four major haplotype groups). The boundary between southern and central groups corresponds to the Carpathian Mountains and the boundary between the central and northern groups corresponds to the Gulf of Finland. Overall these groups form a latitudinal cline in genetic diversity, which decreases with increasing latitude.Phylogeographic analysis using DNA sequences of two mtDNA regions, COI and cytB, shows that  Salix (willow) habitats from the Aegean to the Arctic ocean has already been described [Urticadioica L.) is associated with the eutrophic riparian habitats favoured by willows, and variation in Urtica samples along this transect has also been described [Salix and Urtica allowed sampling of insects on both plant groups [e.g., Triozaurticae  , which is found at nearly every site along the transect (32 out of 42 sites). Members of the hemipteran superfamily Psylloidea are small, phloem-feeding, oligophagous insects [Triozaurticae is one of the most common European psyllid species with a wide distribution across the Palaearctic [Triozaurticae is a host-specific herbivore that feeds on Urticadioica, the common stinging nettle, and a few related species [A transect of Europe that sampled escribed . The comescribed . The co-ps e.g., , includi insects , general insects . Triozaaearctic . Trioza species .T.urticae populations.The aim of this study is to take advantage of a widely dispersed pan-European insect, together with a megatransect approach to sampling to assess population structuring across Europe, particularly in relation to latitude. We wish in particular to determine the effect of biogeographic barriers across Europe on genetic structuring of Salix transect of Europe [Triozaurticae collected from common stinging nettle, Urticadioica [T.urticae were selected from each site along the transect  and cytochrome B (cytB) were amplified using standard PCR procedures as described previously . The ampBactericera (family Triozidae): B.cockerelli  and B.albiventris .To determine regional clade structure for the 82 transect samples, a NJ analysis (p-distance) with 1,000 bootstrap replicates was performed in PAUP*, and a maximum likelihood (ML) analysis was conducted with 100 rapid bootstrap replicates using RAxML (version 8.2.4)  and run ST and phi-ct: φCT) [To analyse haplotype variation across the latitudinal gradient, the DNA sequences were used to create a haplotype median-joining network  and a hact: φCT)  as a mtDLatitudinal clines of genetic variation were assessed based on the concept of “isolation by distance” (IBD) . A pairwThe 857bp matrix of all 96 samples contains 37 parsimony informative characters, 36 of which are found among the transect samples, and the addition of the nine western European individuals adds only one additional informative character. Total sequence divergence (p-distances) for all 96 samples was 2.8%. Each of the phylogenetic analyses are able to separate populations into regional groups. The consensus analysis using MP with all 96 samples recovers three of the four regional groups Fig. , Fig. 2,In total, 48 haplotypes were found using 82 samples along the transect. The sites with the most sequence divergence among individuals (>1.5%) are all southern sites: site 11 (1.9%), site 14 (1.8%), and site 8 (1.6%). This is due to some samples from these sites also clustering within the central region clade. Among the other regions the most diverse site is a central region site, 26 (1.1%), which has some individuals clustering in the northern region clade Figs . InteresST, which is a measure of variance among populations relative to total variance, shows significant differentiation among the populations across the transect . This confirms that there is substantial population structure in the data even though the overall mtDNA sequence divergence is less than 3 percent. Furthermore, φCT, which is a measure of variance among regional groups relative to total variance, also shows highly significant differentiation among the regional groups of the transect .The simple AMOVA found 70.4 percent variation between sites, and 29.6 percent variation within sites. The nested AMOVA found 61.3 percent variation between regional groups, 14.9 percent variation between sites within groups, and 23.81 percent within sites within groups. The φThe haplotype median-joining network reflects the four regional clusters found in the phylogenetic analyses Figs , 6. TherA Mantel test of the correlation between genetic distance  and geographic distance data  exhibited high significant positive results . This indicates that genetic similarity declines with increasing geographic distance.Four regional groups were discovered using genetic analysis and a latitudinal cline of the genetic diversity of these groups is evident with lower genetic diversity at high latitudes. The arctic region has lowest genetic diversity consistent with it being a marginal area of occurrence for the species. Variation of genetic diversity with latitude has been pointed out before  and thisCT indicates that four regional groups across the transect are highly structured with limited gene flow occurring among these groups. Although adult psyllids are winged allowing active migration, biogeographical barriers along the transect possibly restrict gene flow among these four groups. The barrier between southern and central groups (between sites 14 to 17) is coincident with the Carpathian Mountains, a major biogeographic boundary in Europe. The boundary between the central and northern groups (sites 29 to 30) is coincident with the Gulf of Finland, and approximately to the temperate to boreal transition in Europe. Consistent with the general assumption that genetic differentiation increases with increasing geographic distance [High value of φdistance , positivUrticadioica has been referred to as an invertebrate “super-host” as this plant provides food and shelter for large numbers of specialist and generalist insects, notably in the Lepidoptera, Coleoptera and Hemiptera [Aglaisurticae L.), found high levels of gene flow and little or no population or geographic structure across the Palaearctic [Triozaurticae compared to other psyllid systems [emiptera  as well aearctic , which t systems  suggests systems  and therTriozaurticae, overall genetic divergence, particularly considering the extremely large geographic range, is relatively small (<3%) and is well within intraspecific divergence reported for other psyllid taxa [Triozaurticae to be typical and not suggestive of reproductive isolation indicative of incipient species divergence. However, local adaptation may be contributing to the maintenance of clinal variation and further study of the morphotypic variation associated with genetic variation and local environmental variables will be the subject of a following study.Despite distinct regional structure in the genetic variation of lid taxa . We ther


Salvelinus namaycush that considers variation as a cline rather than discriminatory among ecotypes. Genetic and phenotypic traits organized along common ecological gradients of water depth and geographic distance provide important insights into diversification processes in a lake with high levels of human disturbance from over-fishing.Adaptive radiation involving a colonizing phenotype that rapidly evolves into at least one other ecological variant, or ecotype, has been observed in a variety of freshwater fishes in post-glacial environments. However, few studies consider how phenotypic traits vary with regard to neutral genetic partitioning along ecological gradients. Here, we present the first detailed investigation of lake trout Pst) and neutral genetic variation (Fst) revealed that morphological traits related to swimming performance  departed more strongly from neutral expectations along a depth gradient than craniofacial feeding traits. Elevated phenotypic variance with increasing water depth in pelvic fin length indicated possible ongoing character release and diversification. Finally, differences in early growth rate and asymptotic fish length across depth strata may be associated with limiting factors attributable to cold deep-water environments.Four putative lake trout ecotypes could not be distinguished using population genetic methods, despite morphological differences. Neutral genetic partitioning in lake trout was stronger along a gradient of water depth, than by locality or ecotype. Contemporary genetic migration patterns were consistent with isolation-by-depth. Historical gene flow patterns indicated colonization from shallow to deep water. Comparison of phenotypic  contains supplementary material, which is available to authorized users. Coregonus spp. ).Pst-Fst method  cannot hod , 39Pst-Fhod  and deep  environments. Deep water lake trout tend to mature at smaller sizes and grow slower than shallow water ecotypes , 52, 97.iluviana . Therefoiluviana –101.Salvelinus alpinus, where cranioskeletal genes were expressed differentially when exposed to different food types and were less diverse, and craniofacial traits were less responsive to novel environments than those associated with lipid levels and body shape [Fst). Thus, perhaps prey- or water depth-based selection or phenotypic plasticity has occurred or is still occurring to a degree. Nonetheless, head shape varied less than body shape at Isle Royale. By contrast, at Great Bear Lake , head shape varied more than body shape among piscivorous and invertivorous lake trout ecotypes collected from shallow water <30 m [Variation in lake trout craniofacial traits  at Isle Royale did not exceed expectations based on the variation at neutral loci, and phenotypic variance was low within . Hoer <30 m , 30, 104er <30 m , 106.Gasterosteus aculeatus [Coregonus sp.; [Oncorhynchus mykiss [Salmo trutta [Salvelinus alpinus [Our results suggest that hard-wiring at adaptive genes in the presence of high gene flow occurred differentially among traits . Similarly, genetically differentiated intraspecific groups vary in the relative proportions of genetic correlations with different traits in three-spine stickleback culeatus –109, whinus sp.; ), rainbos mykiss , brown to trutta , and Arc alpinus . Each tr alpinus ; also se alpinus , 114.2) deep cold water lakes [Consistent with a species at an early stage of speciation, we found that four visually distinct lake trout ecotypes could not be distinguished from each other genetically using various population genetic methods for neutral genetic variation. PCOA and Bayesian clustering results on neutral microsatellite loci implied a single panmictic lake trout population , which er lakes . Althouger lakes .Lake trout fisheries have targeted the shallow water lean ecotype, whereas humper, siscowet, and redfin typically located farther offshore were fished less. Intensive stocking after the lake trout fishery collapse of the 1950s focussed solely on re-introductions of the lean ecotype during the 1960s through 1990s , 119. LiLake trout reproductive boundaries are not completely isolated in Lake Superior, and their evolutionary potential may be especially vulnerable to future changing environmental conditions, and to expansions and reductions in ecological opportunity. Lake trout of Lake Superior appear to have collapsed genetically and morphologically, concurrent with dramatic overall depletions in allelic richness since the 1940s , 41. To We provide evidence of reductions in gene flow and divergent natural selection associated with water depth in Lake Superior. These patterns in genetic variation among depth strata persist despite a lack of significant genetic structure when the data set is divided by ecotype. Thus, water depth appears to be a strong ecological axis of divergence important to the maintenance of organization of lake trout genetic and morphological diversity. Lake Superior is the largest freshwater lake in the world, and lake trout have recently lost considerable genetic diversity . Therefo


A25 = 1.396 and 1.341, respectively) indicated that the Austrian germplasm contained a higher genetic diversity than the Belgian pool. The expected heterozygosity (EH) values of the Austrian and Belgian pools were 0.411 and 0.375, respectively. Moreover, the values of the polymorphic information content (PIC) of the Austrian and Belgian pools were 0.337 and 0.298, respectively. Neighbour-joining tree divided each of the Austrian and Belgian germplasm pools into two genetically distinct groups. The structure analyses of the Austrian and Belgian pools were in a complete concordance with their neighbour-joining trees. Furthermore, the 95 cultivars were compared to 618 wheat genotypes from nine European countries based on a total of 141 common DArT markers in order to place the Austrian and Belgian wheat germplasm in a wider European context. The rarefacted allelic richness (A10) varied from 1.224 (Denmark) to 1.397 (Austria). Cluster and principal coordinates (PCoA) analyses divided the wheat genotypes of the nine European countries into two main clusters. The first cluster comprised the Northern and Western European wheat genotypes, whereas the second included the Central European cultivars. The structure analysis of the 618 European wheat genotypes was in a complete concordance with the results of cluster and PCoA analyses. Interestingly, a highly significant difference was recorded between regions (26.53%). In conclusion, this is the first study to reveal the high diversity levels and structure of the uncharacterised Austrian and Belgian wheat germplasm maintained at the CGN as well as place them in a wider European context. The results should help plant breeders to utilise the most promising wheat genotypes of this study in future breeding programmes for enhancing wheat cultivars.Analysis of crop genetic diversity and structure provides valuable information needed to broaden the narrow genetic base as well as to enhance the breeding and conservation strategies of crops. In this study, 95 Austrian and Belgian wheat cultivars maintained at the Centre for Genetic Resources (CGN) in the Netherlands were characterised using 1052 diversity array technology (DArT) markers to evaluate their genetic diversity, relationships and population structure. The rarefacted allelic richness recorded in the Austrian and Belgian breeding pools ( Triticum aestivum L.) is an important allohexaploid cereal crop that originates from three diploid species: Triticum urartu (AA), a progenitor related to Aegilops speltoides (BB), and Aegilops tauschii (DD) [Wheat (hii (DD) . Wheat ihii (DD) ,3. Domeshii (DD) . The narhii (DD) . Introgrhii (DD) . Hundredhii (DD) ,7.Characterisation of the genetic diversity and phylogenetic relationships provides valuable information needed to broaden the narrow genetic base as well as enhance breeding and conservation strategies for crops ,9,10,11.DNA markers such as inter-simple sequence repeats (ISSR), amplified fragment length polymorphism (AFLP), restriction fragment length polymorphism (RFLP), microsatellites, and single nucleotide polymorphisms (SNPs) have been utilised to assess the relationships and genetic diversity levels in wheat germplasm ,24,25,26The main objectives of the current research were to utilise DArT markers to (i) evaluate the genetic diversity of Austrian and Belgian wheat breeding pools maintained, without appropriate genetic characterization, at the Centre for Genetic Resources (CGN) in the Netherlands; (ii) analyse the population structure of the Austrian and Belgian genetic pools, and reveal new information regarding their breeding and genetic structure; and (iii) place the Austrian and Belgian germplasm in a wider European context through accessing and exploiting a combined set of available DArT genotypes.T. aestivum L.), maintained at the CGN, was obtained and analysed in the present study  The TriticeaeGenome panel comprising 376 wheat cultivars from the UK, Germany and France ; (ii) ThGenomic DNA (gDNA) was isolated from the young leaf tissue of each of the Austrian and Belgian wheat accessions using DNeasy Plant Mini Kit  and was subjected to DArT analysis according to the general procedures previously described . Out of n = 25 (A25) using FSTAT version 2.9.3 [P), effective number of alleles (EN), expected heterozygosity (EH), and polymorphic information content (PIC) were also calculated using GenAlex v. 6.41 [DArT-based diversity analysis of the Austrian and Belgian wheat cultivars was assessed using different diversity indices. Since the Austrian and Belgian breeding pools are different in sample size, rarefaction method was used to calculate the rarefacted allelic richness of the two pools, in a standardised sample size equivalent to the smallest population size of on 2.9.3 . The pol v. 6.41  and Powe v. 6.41 .k) varied from 1 to 15, with 100,000 burn-in run iteration, followed by 100,000 Markov chain Monte Carlo replicates. Ten independent iterations were run. Structure Harvester [k [k), generated by Structure analysis, of each of the Austrian and Belgian wheat breeding pools using GenAlex v. 6.41 [A Jaccard dissimilarity matrix-based neighbour-joining tree with 1000 bootstraps was conducted for each of the Austrian and Belgian breeding pools, separately. The population structure analysis was conducted for each of the Austrian and Belgian breeding pools using Structure 2.4.1 software . The hyparvester  was thenester [k . Analysi v. 6.41 .P, EH, EN, PIC). Rarefaction was used to calculate the rarefacted allelic richness of the nine European populations in a standardized sample size equivalent to the smallest population size of n = 10 (A10) using FSTAT version 2.9.3 [Based on the 141 common DArT markers, the genetic diversity of the European wheat varieties from nine countries  has been estimated using the same diversity indices mentioned above (%on 2.9.3 .STF) values were calculated between pairs of countries and were used to construct the cluster analysis among the nine countries using the Unweighted pair group method with arithmetic mean (UPGMA) method. To reveal the relationships among the 618 wheat accessions representing the nine European countries, principal coordinate analysis (PCoA) was conducted using GenAlEx 6.41 software [Analysis of molecular variance analysis was performed to partition the molecular diversity between regions , within countries, and among countries within regions. The Northern and Western European region contained Belgium, Germany, Denmark, Sweden, France, and UK. The Central European countries included Austria, Croatia, and Hungary. The genetic differentiation (software . The genA25) recorded in the Austrian and Belgian breeding pools were 1.396 and 1.341, respectively, indicating that the Austrian breeding pool contained a slightly higher diversity than the Belgian pool. Furthermore, the percentage of polymorphic markers recorded in the Austrian and Belgian breeding pools was 93.79% and 91.46%, respectively (EN) found in the Austrian and Belgian breeding pools were 1.698 and 1.602, respectively. The EH values of the Austrian and Belgian pools were 0.411 and 0.375, respectively. Furthermore, the values of PIC of the Austrian and Belgian pools were 0.337 and 0.298, respectively.In this study, a total of 1052 DArT markers were used to assess the genetic diversity and structure of 95 Austrian and Belgian wheat cultivars. ectively . The aveK value was recorded at K = 2 (315.68) followed by that at K = 6 (92.65) , in a complete concordance with the neighbour-joining tree results  followed by that at K = 3 (92.78)  . At K =  results . Further results . The fir (92.78) . At K =  results .K = 2), generated by Structure analysis, of each of the Austrian and Belgian wheat breeding pools  to 1.397 (Austria) (EH ranged from 0.250 (Denmark) to 0.372 (Austria). The PIC varied from 0.202 (Denmark) to 0.301 (Austria). The average value of the effective EN varied from 1.332 (Denmark) to 1.588 (Austria)  . MoreoveAustria) . These rp < 0.0001), which accounted for 26.53% of the total variance, whereas the difference recorded among countries within regions represented 5.64%  , indicatPrincipal coordinate analysis also revealed the relationships among the 618 wheat cultivars from the nine European countries . The firK value was recorded at K = 2 (4156.47) (K = 2). The majority of wheat genotypes from the Northern and Western European countries were assigned to Population A, while the great majority of genotypes belonging to the Central European countries were assigned to population B  . Table 5A25 = 1.396 and 1.341, respectively) indicated that the Austrian breeding pool contained a slightly higher diversity than the Belgian pool. The neighbour-joining tree divided the Austrian germplasm into two main clusters, which are in a complete concordance with the structure analysis results. These results suggest that the breeders used different genetic material as parental resources, which resulted in genetically diverse cultivars. Furthermore, the neighbour-joining tree divided the Belgian germplasm into two main clusters. The first cluster contained 11 cultivars, whereas the second comprised the majority of cultivars, in a complete concordance with the structure analysis. This study also revealed that the majority of the diversity was attributed to differences among varieties within populations. These clustering and AMOVA results suggest that the crossing among inter-cluster cultivars may develop cultivars with promising agronomic traits.Evaluation of the genetic diversity, population structure and relationships provide valuable information needed to broaden the narrow genetic base and to enhance breeding and conservation strategies of crops. DArT markers showed their efficiency in assessing the genetic diversity of different crops. Raman et al.  assessedA10 varied from 1.224 (Denmark) to 1.397 (Austria). The results revealed that the Central European countries  contained higher level of genetic diversity than that of the Northern and Western European countries . This result is in accordance with that reported by Novoselović et al. [Combining the DArT genotypes from three wheat panels previously published ,34,35 wić et al. ,43.Analysis of molecular variance revealed that the majority of genetic variation was attributed to the differences among varieties within countries 67.83%). This is in agreement with previous studies %. This i. White eUnweighted pair group method with arithmetic mean cluster analysis showed that wheat genotypes of the Northern and Western European countries were clustered separately from the genotypes of the Central European countries. Within the Northern and Western European wheat cluster, the German and Swedish genotypes were separated in a distinct subcluster, in a complete accordance with the findings of Novoselović et al. . PrincipIn conclusion, the present study confirmed that the use of combined datasets, regional contribution made by plant breeders, and genotyping approaches could provide a promising opportunity for unlocking the genetic potential and improving conservation strategies of wheat. It is considered the first study that successfully revealed the high levels of genetic diversity and structure of the uncharacterised Austrian and Belgian wheat germplasm maintained at the CGN as well as facilitated placing them in a wider European context using efficient DArT markers. In addition, current results would help plant breeders to utilise and maintain the high levels of diversity recorded as well as selecting and using the most promising wheat genotypes in future breeding programs for enhancing and developing wheat cultivars of highly valuable agronomic traits.


It is thus important to identify and conserve genetically connected populations, as well as to maintain connectivity within them. We collected samples from wild tigers (Panthera tigris tigris) across India and used genome-wide SNPs to infer genetic connectivity. We genotyped 10,184 SNPs from 38 individuals across 17 protected areas and identified three genetically distinct clusters . The northwest cluster was isolated with low variation and high relatedness. The geographically large central cluster included tigers from central, northeastern and northern India, and had the highest variation. Most genetic diversity (62%) was shared among clusters, while unique variation was highest in the central cluster (8.5%) and lowest in the northwestern one (2%). We did not detect signatures of differential selection or local adaptation. We highlight that the northwest population requires conservation attention to ensure persistence of these tigers.Tigers have lost 93% of their historical range worldwide. India plays a vital role in the conservation of tigers since nearly 60% of all wild tigers are currently found here. However, as protected areas are small (<300 km Such information is particularly important for endangered species, where conservation strategy and management planning depends on accurate detection of species status, population structure and connectivity, inbreeding, adaptive variation, admixture, and population demographic history2.Genetic variation and its partitioning across populations helps inform evolutionary history, population connectivity, and demographic fluctuations3. Rapid development of next generation sequencing (NGS) has facilitated survey of genome-wide variation. Such data has helped identify African savannah (Loxodonta africana) and forest elephants (Loxodonta cyclotis) as separate species4, detect cryptic population structure in chimpanzees (Pan troglodytes)5, reveal local adaptation in giant panda (Ailuropoda melanoleuca) populations6 and quantify genetic diversity in Tasmanian devils7 (Sarcophilus harrisii). Genome-wide Single Nucleotide Polymorhphisms (SNPs) have been developed for monitoring populations in the wild, including bears8 (Ursus arctos), wolves9 (Canis lupus) and river otters10 (Lontra canadensis). Several thousands of SNPs provide higher statistical power in comparison to examining a limited number of microsatellites, resulting in more robust population genetic inference12.Until recently, highly polymorphic microsatellites  were the markers of choice for conservation genetic studies13 by the IUCN redlist. A majority of them have suffered range reduction and fragmentation, with surviving individuals restricted to small isolated populations that tend to have low genetic variation and a high probability of extinction14. Identifying such populations can help devise conservation strategies to avoid inbreeding depression, aid in population recovery and plan assisted gene flow for genetic rescue. Such strategies have been implemented effectively in carnivores like the Florida panther15 (Puma concolor coryi). However, genetic rescue could be counter-productive if populations are locally adapted16. Conservation strategy must consider the relative risks of inbreeding and outbreeding depression before genetic rescue is implemented17.Many large carnivores are currently threatened, with around 61% of them designated as ‘threatened’18. Of the nine tiger subspecies, four are extinct . The remaining five subspecies have experienced intense population bottlenecks (e.g. 90% decline in the Indo-Chinese tigers)20 in historical times and prolonged reduction in distribution21. Tiger conservation and recovery is a global priority as exemplified by several international efforts . Recent surveys suggest an increase in tiger numbers22 indicating the success of such initiatives.Tigers have lost an estimated 93% of their historical range23. Securing the future of Indian tigers is critical for survival of the species. From a time when tigers were widespread24, today tigers survive in small 25, often isolated populations, many of which may not be viable on their own. Tiger monitoring is conducted at the scale of protected areas or geographically defined landscapes  with clustered protected areas. Data on historical occupancy of Indian tigers24 reveals relatively continuous distributions. This implies that tiger genetic clusters may have wider geographical extents than currently monitored landscape units, as supported by microsatellite data20. However, tiger monitoring surveys indicate that tiger populations within some geographic landscapes have become isolated and habitat connectivity between others is tenuous25. Additionally, these landscapes represent different biogeographic zones, such as the Semi-Arid or the Western Ghats, with distinctive habitats26 such as grasslands, subtropical moist forests, mangroves and tropical dry forests27. Given their wide distribution, tigers may be categorized as generalist species. However, tiger populations living in different habitats may potentially be locally adapted; in such cases genetic rescue may not be considered a valid conservation strategy17. Therefore, understanding the spatial extent of genetic clusters as determined by genetic connectivity and local adaptation is essential for conservation.The Indian subcontinent is home to more than 60% of all tigers and harbors over half the global genetic diversity of the speciesIn this paper, we collect genome-wide SNP data from wild tigers to better understand how protected area-based populations in India are segregated into genetic clusters. We investigate the following questions: (i) what are the genetic clusters for tigers in India? (ii) how is genetic variation distributed among these genetic clusters? (iii) are there any signatures of differential local adaptation between these clusters?28). After alignment, the program Stacks was used to assess the depth at unique loci obtained per individual. The number of unique loci  obtained per sample (Table 29) had 1,527,595 loci. This vcf file was passed through several filters, as mentioned in the methods. In the final dataset, only individuals with less than 25% missing data  and SNPs with less than 5% missing data were retained. Details of samples removed are presented in Table The total number of retained reads per sample varied from 4,869 to 12,882,482, with an average of 4,118,660 , ‘NE’  and ‘CI’ , supported to varying degrees by different analyses. The phylogenetic network Fig.  showed tTheta (H) was highest for C (0.48), intermediate for SI (0.35) and lowest for NW (0.22) Table . Mean heST, estimated assuming three clusters , was highest for the NW–SI comparison  of the total variation observed was due to variation within individuals . Variation contributed by differences among groups (Va), although not statistically significant , was much lower (9.26%). FIS was higher in CI and NE clusters  and geographic distances . This positive relationship was observed only at short distances Fig. . The ManFor almost all sample sizes, NW had the lowest genetic diversity and C had the highest Fig. . Much ofThe PCA including all points across India showed a gradient of separation of tiger reserves along PC2 – mainly driven by precipitation Figs  and S14.The Bayescan analysis did not identify any outlier loci Fig.  at a falIn this study, we investigated population differentiation and genetic diversity of Indian tigers using 10,184 SNPs typed for 38 wild individuals from 17 protected areas (PAs). Bayesian clustering, PCA plots and isolation by distance tests establish genetic differentiation within the Indian subcontinent. Our data and analyses reveal that tiger populations in India cluster into three genetic groups that broadly map onto geographic tiger landscapes and represent groups of proximate protected areas. A large proportion of genetic diversity is shared between clusters, while population specific diversity is variable but low. Importantly, from a conservation perspective, our study reveals an isolated tiger population with low genetic diversity.STs 33. Together, this makes Ranthambore the western-most extant population of wild tigers today.Our analyses reveal that tigers from the NW cluster (Ranthambore) are genetically isolated. Such genetic isolation is supported by the fact that they form a distinct cluster in structure analysis Fig.  and haveTs Table  with othTs Table , 0.16. G14. Deleterious mutations also accumulate in small populations (e.g. Woolly mammoths (Mammuthus primigenius)34). While we point out that our data contain some related individuals  = 0.21), possibly due to small effective size and/or isolation. Low genetic variation contributes to higher extinction risk in wild populationsST). This is in contrast to a previous microsatellite-based study20 that found no evidence for differentiation within peninsular India, but consistent with a report on tiger monitoring in India25. Our genome-wide SNP data was able to reveal this cryptic population structure36. We caution that our final dataset did not include the only large PA in the Eastern Ghats , an independent cluster (phylogenetic network) and part of a North – northeast cluster . Further, tigers from this region also have high genetic diversity  that show greater diversity in centrally located populations, including in endangered species, such as the Cross River gorilla38 (Gorilla gorilla diehli) and violets39 .The central genetic cluster harbors the highest diversity. High effective size and/or gene flow with neighbouring clusters could explain this. Our results are also consistent with theoretical40). On the other hand, a genetic study found support for even higher dispersal distances (~650 km41). Our results support ecological theory, with isolation by distance operating at the scale of 300–400 km  is maintained between PAs within the identified (or between) clusters, further genetic differentiation can be avoided.Despite relatively continuous historical tiger distributions, we observe a signal of genetic isolation by distance. Ecological theory predicts that large-bodied carnivores like tigers may move far  occurs over varied environments and reveals signatures of local adaption despite high gene flow44. Tigers occur across a range of vegetation types and climatic gradients across India. However, while we do find high genetic differentiation among the clusters, we do not find evidence of adaptive divergence (based on outlier tests). In summary, the clusters identified do not appear to merit the status of conservation units45. Our analyses suggest that assisted gene flow between genetic clusters could potentially be a viable conservation strategy if genetic rescue is required. We caution that methods such as ddRAD usually sample only about 1–10% of the genome46 and data from whole genomes may be better able to identify signatures of selection.The widely distributed North American grey wolf  and planning species recovery45.Population structure can arise through the interplay of multiple factors including population history, selection and connectivity. Disentangling the effects of each of these is often challenging. However, understanding their relative contributions can have important consequences for species survival. For instance, while it is generally understood that small populations are at greater risk of inbreeding and extinction, the duration of time for which the population has been small may have important consequences. Recently bottlenecked populations may be of greater conservation concern than populations that have remained small for a long time23, making it difficult to accurately infer demographic history48. In summary, we cannot explain which parameters  are responsible for the northwestern differentiation we observe. Whole genome sequencing and higher sample sizes may allow better detection of low frequency alleles and more accurate reconstruction of demographic history in the future.In the case of Indian tigers, we find evidence for population structure. While the southern Indian cluster includes several populations, the northwestern cluster is restricted to a single tiger population, Ranathambore. The observed genetic differentiation between Ranathambore and the central cluster could signify the lack of gene flow relatively recently and/or older population divergence. Additionally, differential population size changes through historical time could impact the magnitude of differentiation. We attempted to understand the demographic history of the genetic clusters we identified using a site frequency spectrum-based approach. However, the estimated site frequency spectrum had very few singletons and none of the models fit our data well 54), to set up breeding programs (e.g. Tasmanian devils (Sarchophilus harrisii)7) and to designate management/conservation units55. Our population genomic study of wild Indian tigers finds evidence for a small and isolated population of tigers in northwest India. The long-term persistence of this population may require connectivity with neighboring populations, such as with central India. In summary, our study reveals how genome-wide data and analyses can flag populations that may require urgent conservation attention (such as Ranthambore) and identify strongholds of variation . Such inference has significant impacts for on-ground conservation practice.Population genomics of wild species is being increasingly used to inform conservation practice, including to make recommendations on reintroduction . The list of samples is provided in supplementary Table et al.56. A combination of two frequent cutters, Nla III and MluC I (NEB), was used to maximize coverage by getting more fragments. DNA concentration of all extracts was measured using Qubit (Invitrogen) and the initial quantity for library preparation was standardized to 200 ng. DNA quality was not uniformly high, especially in samples that had been preserved for a long time. Some samples were processed despite poor quality if they were single representatives of their population and were usually included in the library twice. Extended ligation was carried out for 13 hours, as standardized by Chattopadhyay et al.51. Library quality was assessed based on the bioanalyzer profile. A total of ten libraries was prepared and sent for paired-end sequencing on three lanes of an Illumina HiSeq. 1000 machine. The low diversity issue encountered with ddRAD samples during sequencing was circumvented by the addition of 30% phiX genome into the sequencing run (as suggested by Illumina).Double-digested RAD libraries were prepared as outlined in Peterson http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). The data was demultiplexed into individual samples using the program Stacks28. Reads with one mismatch in the index were rescued. Demultiplexed data were filtered to remove reads with poor quality (option –q) and the presence of uncalled bases (option –c), and were also truncated to 80 base pairs (option –t). The read numbers retained per sample varied considerably. The quality-filtered reads were aligned to the reference genome57 using Bowtie258. Both paired and unpaired reads were aligned with default criteria. Technical replicates were merged and Samtools59 was used to process the aligned data. Read group information was added using Picard tools (http://picard.sourceforge.net) to reflect the unique sample identity. Reads were realigned to account for indels using the GATK package60. Since data from samples that had been sequenced twice were combined after alignment, the number of unique loci/regions identified in each sample (instead of raw reads per sample) was used to compare data obtained per sample. Depth and coverage stats were calculated using Stacks and Bedtools62.The sequencing quality of reads was initially assessed using the program FastQC  were retained. To filter loci with low data, we calculated the missing data per sample using vcftools63. To optimize the tradeoff between the number of samples retained and the number of loci with low missing data, the dataset was filtered multiple times. The vcf file obtained was subjected to filtering based on missing data (<5% per SNP), mapping quality (>= 30) and minor allele frequency (>= 0.05). Thinning of loci within 500 bp of each other was done to remove SNPs that might be in linkage disequilibrium. SNPs that were not in Hardy-Weinberg equilibrium (p < 0.01) were filtered out. Additionally, SNPs were also filtered based on mean depth of coverage across samples. However, as the results obtained with this set of loci (7741 SNPs) were qualitatively similar to those observed with the larger dataset (comparison of datasets based on FST and Admixture results), the smaller dataset was not utilized in the final analysis (data not shown).Unique regions were identified in the program Stacks for each sample. Samples that had fewer than 100,000 loci were eliminated from further analysis. Our data had low average depth  was performed using an R package66. Additionally, a network-based approach was also used to infer hierarchical clustering as implemented in NetStruct67. The program constructs a network by connecting individuals above a genetic similarity threshold. This is followed by community detection to identify groups of individuals much more closely related to each other than to those in other groups – a process analogous to detecting genetic clusters. Based on the observed range of genetic distance, the network was plotted at multiple thresholds, followed by community detection using a fast-greedy algorithm. A permutation test was done to test the significance of the observed network.Multiple methods were used to infer tiger genetic clusters. A phylogenetic network was constructed in the program SplitsTree4ST was calculated between clusters using the Weir and Cockerham estimator in Arlequin68. Summary statistics, including heterozygosity (He) values and theta (H), were computed in Arlequin68. Pair-wise relatedness (p^) and individual inbreeding coefficients (f) were estimated in Plink69. AMOVA68 was used to test for hierarchical structure. Theta (H) was estimated as a proxy for effective size (Ne) of each genetic cluster.Pair-wise F66. Genetic  and geographic distance were calculated for all pairs of individuals. To test for significance of the observed Mantel’s R a randomization was done with 999 replicates. A Mantel’s correlogram (package vegan) was used to identify the distance classes at which the correlation was significant.A Mantel test was performed to check for isolation by distance in R70 and plotted. To visualize sharing of diversity, the results were plotted as a Venn diagram by calculating the proportion for total diversity in each (or combination of) cluster(s).To see how genetic diversity is distributed across the populations, private allele richness was estimated for each cluster, as well as for combinations of clusters. Since diversity measures can be influenced by sample size, these were standardized across clusters using rarefaction. Private allele richness was estimated for each standardized sample size using the program ADZEhttp://www.fao.org/nr/aboutnr/nrl/en/) and land cover classification (GlobCover Land Cover v2.2 - http://geodata.grid.unep.ch/options.php?selectedID = 2054&selectedDatasettype = 16). Regular points were placed on an outline of the India map (grid spacing 0.1) in QGis (v2.0.1). Multiple climate layers (http://www.worldclim.org/bioclim), aridity and vegetation (GlobCover) were used to define the environmental space for the sampled points. The ‘point sampling tool’ was used to extract data for the previously generated points. Points falling within tiger reserves were marked in the attribute table. This table was exported to the software R (v3.3.2) and after centering and scaling the data, a PCA was performed with all points. To closely examine the separation between the tiger reserves, a PCA of only points falling in tiger reserves was performed.In order to visually inspect whether the available habitats in tiger reserves were differentiated based on climate and/or vegetation, a PCA was run for 19 bioclimatic variables (bioclim), aridity  when using thousands of markers.A Bayesian approach was used to test for SNPs that might be under selection among the different genetic clusters identified using the software tool BayescanThe sequences are available on GenBank and can be obtained from the SRA database with the accession number SRP114885.Our study does not involve any experiments with live animals. Further, while we use tissue samples, none of these were directly obtained for the purpose of this study. We only used samples from tissue previously collected for other studies/purposes. Therefore, ethical clearance regarding sample collection is not applicable to our study.Supplementary Materials: Conservation priorities for endangered Indian tigers through a genomic lens


Rhizophora mangle, a dominant mangrove plant in the Western Hemisphere. Additionally, we aimed to study the relationships between R. mangle, R. racemosa, and R. × harrisonii and to test for evidence of hybridization and introgression. Our results confirmed the north‐south genetic structure pattern in R. mangle and revealed a less abrupt genetic break in the northern population than those observed in Avicennia species, another dominant and widespread mangrove genus in the Western Hemisphere. These results are consistent with the role of oceanic currents influencing sea‐dispersed plants and differences between Avicennia and Rhizophora propagules in longevity and establishment time. We also observed that introgression and hybridization are relevant biological processes in the northeastern coast of South America and that they are likely asymmetric toward R. mangle, suggesting that adaptation might be a process maintaining this hybrid zone.Mangrove plants comprise plants with similar ecological features that have enabled them to adapt to life between the sea and the land. Within a geographic region, different mangrove species share not only similar adaptations but also similar genetic structure patterns. Along the eastern coast of South America, there is a subdivision between the populations north and south of the continent's northeastern extremity. Here, we aimed to test for this north‐south genetic structure in  Rob. (Rhizophoraceae)  W. Theob.  Lamk. , an area rich in mangrove species in the Eastern Hemisphere , a biogeographic region with lower mangrove species diversity than the IWP, a similar phenomenon was observed at a comparable geographic scale. Along the eastern coast of South America, there is a pattern of genetic subdivision between populations north and south of the northeastern extremity of the South America continent (NEESA). This divergence pattern is shared by the sea‐dispersed plants R. mangle, a widespread, dominant mangrove species in the AEP biogeographic region, as the biological system. To assess the repeatability of previous results  are sympatric, although the distributions of the last two species are disjunct  and Embrapa Amazônia Oriental (IAN) herbaria, which are both in Brazil. For genetic analyses, we sampled leaves from flowering trees that were at least 20 m from any other sampled trees and maintained the leaves in resealable zipper plastic bags containing silica gel. The leaf material was lyophilized and stored at −20°C prior to DNA isolation.2.2R. mangle and R. racemosa. Using the DNeasy® Plant Mini Kit  according to the manufacturer's instructions, we isolated the genomic DNA from one individual of each species sampled in northern Brazil . We constructed microsatellite‐enriched libraries for both species following a magnetic bead‐based method , respectively , 0.2 mM dNTPs, 0.19 mg/ml bovine serum albumin (BSA), 0.15 mM primer and 1 U Taq DNA polymerase. PCR was performed according to a touchdown thermocycling program: 94°C for 2 min; 2× ; 18 cycles of 94°C for 1 min, 55°C for 1 min and 72°C for 2 min; and 72°C for 5 min. The amplified samples were genotyped by vertical electrophoresis using 6% denaturing polyacrylamide gels, and the bands were visualized using silver nitrate  considered, which ranged from 1 to 10, we performed 30 independent Markov Chain Monte Carlo (MCMC) runs with 500,000 iterations, following a burn‐in period of 100,000 steps. We determined the K value that best explained our data at the uppermost hierarchical level, considering the log likelihood of each K (lnL)  to identify meaningful Ks that summarized our data. Additionally, we employed the optim.a.score function to balance discrimination power and overfitting.We used the Bayesian method implemented in STRUCTURE 2.3.4, assuming a model with correlated allele frequencies and admixture  higher than 0.80 for the typical R. mangle inferred cluster for the most likely scenario, K = 2 . Microsatellite alleles were combined into classes of alleles with frequency differentials between the nonadmixed groups  equal to those obtained when each allele was considered separately. This approach reduces data complexity without distorting the genetic similarity between species or losing information  and a significant departure from a random distribution obtained after 10,000 permutations.Finally, due to the complex pattern of hybridization and introgression among these roup see  as well 2.4After describing the genetic structure at the interspecific level, we performed intraspecific genetic structure analyses of each inferred group see . As we uHO), unbiased expected heterozygosity (HE), the number of private alleles (A), and the fixation index (f) using GenAlex 6.502 , which yielded six and three new polymorphic microsatellites considering intra‐ or interspecific levels for R. mangle and R. racemosa, respectively . The posterior probabilities for trees being classified as F1, F2 or backcrosses with any parental species were close to zero , we estimated hybrid index values for individuals that met our criteria and were identified in the field as R. × harrisonii. Using a multinomial regression implemented in the INTROGRESS package to estimate individual hybrid indices, we observed a wide range of hybrid index values, that is, ranging from zero to one. Greater variation was observed for R. mangle , whereas all the R. × harrisonii individuals presented hybrid index = 1  and three a posteriori hypotheses . This model yielded slightly better performance than model A, explaining 67.5% of the total genetic variability. We created models C and D based on model B to contemplate the expected and observed geographic structure of R. mangle. We considered previous expectations supported by the genetic subdivision previously described for mangrove species for the same geographic range evaluated herein  that despite its location within the northeastern coast of southern South America was more genetically similar to samples from the southern populations (see below). Models C and D explained 61.8% and 65.6% of total genetic variation, respectively. Thus, according to our criteria, despite the slight differences among models, the hierarchical AMOVA analysis supports the grouping described by two groups: One composed of individuals identified as R. mangle and one composed of individuals with R. racemosa and R. × harrisonii morphological traits  but some global structure (p < .001); furthermore, when we plotted the sPCA first positive eigenvalues on a map, the pattern supported the westward gradient, we observed in the northern populations based on the STRUCTURE results , and the NEWHYBRIDS classification, wherein R. mangle was assigned to one parental species and R. racemosa and R. × harrisonii were assigned to the alternative parental group. Because “hybrids” comprise a heterogenous biological categorization, we further explored introgressive and hybridization processes in this Rhizophora species complex by estimating hybrid indexes for admixed individuals and comparing these values with two‐generation simulated hybrids . We observed no evidence of first‐generation hybrids between nonadmixed plants, whereas admixed individuals are likely outcomes of second or higher generation hybrids. This result suggests that hybrids are fertile at least toward non‐admixed parental species and that introgression is likely asymmetrical (see below). Moreover, all R. harrisonii individuals we studied presented hybrid indexes equal to the nonadmixed R. racemosa, which supports DAPC, NEWHYBRIDS, and STRUCTURE (K = 3 and K = 4) results from all samples or only considering Northern populations (Figure R. mangle, R. racemosa, and R. × harrisonii, and model D, which accounted for the previously discussed R. mangle geographic subdivision (Table The genetic differences among R. racemosa and R. × harrisonii, there are two main clusters that correspond to the morphologically identified individuals and two intermediate clusters (Figure At a finer scale, regarding the genetic structure within the group composed of s Figure . This cls Figure , the sysRhizophora species from the AEP biogeographic region because of their nonreciprocal distinctiveness based on morphological and genetic information. As interspecific hybridization and introgression may interfere with species integrity, and because these processes are pervasive in the studied species complex, we advocate for caution when using these morphological traits for species identification due to the extensive admixture between the R. racemosa and R. × harrisonii groups and considering the entire species complex. Therefore, a synthetic reevaluation of current AEP‐region Rhizophora taxonomy would be beneficial, preferably simultaneously considering morphology and genetic data and additional lines of evidence to corroborate or reject the current classification.Under the “unified species concept,” which allows for decoupling of species conceptualization and species delimitation (De Queiroz, Rhizophora species complex, it is intrinsically difficult to identify and record individuals using an objective strategy. Thus, we recommend that different scenarios of classification, namely individual morphological “species,” hybrids, groups of species and the entire species complex, should be used as units for applied purposes (Fitzpatrick, Ryan, Johnson, Corush, & Carter, R. mangle, R. racemosa, and R. × harrisonii as independent entities. Acknowledging that introgression and hybridization are relevant biological processes in this genus would be a useful feature for consideration by conservationists and managers for efficient long‐term conservation plans. Using each grouping may present desirable and unsatisfactory consequences depending on the geographic and temporal scales considered, and thus a case‐by‐case approach is advisable based on conservationists and managers’ objectives.The incongruence between species boundaries and morphological traits and its associated taxonomic uncertainty lead to conservation and management issues. In groups without clear delimitations, as it is the case for the AEP Rhizophora species complex in the Atlantic basin of South America. When we considered the K = 2 scenario of the STRUCTURE results, admixture was evident between R. mangle and the other two species, as was more clearly shown by the INTROGRESS results (Figure R. mangle, supporting the adaptive evolution hypothesis of this species complex based on physiological traits (Cerón‐Souza et al., R. mangle and F1 hybrids presented higher salinity tolerance than did R. racemosa, suggesting that introgression and hybridization could be maintained by adaptive evolution (Cerón‐Souza et al., R. racemosa and R. × harrisonii are more geographically restricted than R. mangle, such that these species are not recorded south to the NEESA, and they are not homogenously distributed in the northern coast of Brazil, where they are found only in estuaries with substantial freshwater input (Menezes et al., Our results highlight that hybridization and introgression are major biological processes shaping the genetic diversity of the s Figure . This fiA. germinans (Nettel et al., Rhizophora species complex studied herein, interspecific gene flow, or more specifically, asymmetrical hybridization and introgression toward R. mangle, could increase the genetic diversity of the R. mangle northern populations. Indeed, we observed higher levels of genetic diversity in these populations (Table R. mangle (Pil et al., R. mangle populations and altered the distribution of genetic variation between and within populations, possibly creating a bias in R. mangle demographic analyses that do not account for these evolutionary processes.In addition to blurring species boundaries, a consequence of ancient and ongoing hybridization and introgression could be an increased genetic diversity in populations where the parental species coexist. For example, in East Pacific Central America, ancient interspecific introgression associated with intraspecific population divergence following secondary contact likely increased the genetic diversity of ns Table , supportns Table . Thus, aRhizophora mangle north‐south genetic structure along the northeastern coast of South America. This subdivision is shared among R. mangle (Pil et al., A. germinans, A. schaueriana (Mori, Zucchi, & Souza, H. pernambucensis (Takayama et al., R. mangle. Finally, we found that past hybridization and introgression processes play important roles in the Rhizophora species complex along the northeastern coast of South America. This interspecies gene flow is likely asymmetric, suggesting that adaptation may play a role in maintaining this hybrid zone.In this study, we recovered and refined None declared.Conceived and designed the experiments: PMF, GMM,and APS. Performed the experiments: PMF and FMA. Analyzed the data: GMM. Contributed reagents/materials/analysis tools: APS. Wrote the article: GMM. All authors revised, edited and approved the final version of the article. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Acipenser gueldenstaedtii), Persian (A. persicus) and Siberian (A. baerii) sturgeons are closely related ‘Ponto-Caspian’ species. Investigation of their population structure is an important problem, the solution of which determines measures for conservation of these species. According to previous studies, ‘baerii-like’ mitochondrial genotypes were found in the Caspian Sea among 35% of Russian sturgeon specimens, but were not found in Persian sturgeons. This confirms genetic isolation of the Persian sturgeon from the Russian sturgeon in the Caspian Sea. However, in order to clarify the relationships of these species it is necessary to analyze nuclear DNA markers. The amplified fragment length polymorphism (method) allows estimating interpopulation and interspecific genetic distances using nuclear DNA markers. In the present study, four samples were compared: Persian sturgeons from the South Caspian Sea, Russian sturgeons from the Caspian Sea and the Sea of Azov, and Siberian sturgeons from the Ob’ River, which are close to the latter two species, but are also clearly morphologically and genetically distinct from them. For the amplified fragment length polymorphism (AFLP) method, eight pairs of selective primers were used. The analysis revealed that the Siberian sturgeon has formed a separate branch from the overall Persian-Russian sturgeons cluster, which was an expected result. In addition, the results showed that the Caspian Russian sturgeon is closer to the Persian sturgeon from the Caspian Sea than to the Russian Sturgeon from the Sea of Azov. The present DNA marker data confirm that despite the genetic isolation of the Persian sturgeon from the Russian sturgeon in the Caspian Sea, the Persian sturgeon is a young species.Russian ( Acipenser gueldenstaedtii), Persian (A. persicus), and Siberian (A. baerii) sturgeons belong to a polychromosomal group of sturgeon species  sturgeons  method is also applicable, as the AFLP technique allows to obtain a high number of dominant nuclear DNA markers , 24 specimens ofA. gueldenstaedtii from the Caspian Sea , 24 specimens ofA. persicus from the Southern Caspian Sea  and 24 specimens ofA. baerii from the Ob’ River .For this research, sturgeon tissue samples (ethanol fixed fin fragments) were obtained from the Russian Federal Reference Collection of Genetic Materials . The sample included 24 specimens ofDNA was extracted and purified with the Wizard SV Genomic DNA Purification System (Promega). For genetic analysis, the AFLP method was used :1) EcoFAM_AAG - Msp_pr_AAC, 2) EcoFAM_ATT - Msp_pr_AAG, 3) EcoFAM_ACA - Msp_pr_AAT, 4) EcoFAM_AAG - Msp_pr_ACA, 5) EcoFAM_ACA - Msp_pr_ACC, 6) EcoFAM_ATT - Msp_pr_ACC, 7) EcoFAM_AAG - Msp_pr_ACT, 8) Eco-FAM_AAG - Msp_pr_ATC.Pre-selective PCR was performed for 20 cycles with the following cycle profile: a 30 sec DNA denaturation step at 94°C, a 1 min annealing step at 56°C, and a 1 min extension step at 72°C. Selective PCR was performed for 36 cycles with the following cycle profile: a 30 sec DNA denaturation step at 94°C, a 30 sec annealing step, and a 1 min extension step at 72°C. The annealing temperature in the first cycle was 65°C, was subsequently reduced each cycle by 0.7°C for the next 12 cycles, and was continued at 56°C for the remaining 23 cycles. All steps were carried out with the PTC-225 Peltier Thermal Cycler (MJ Research). Capillary electrophoresis was carried out with the ABI Prism Genetic Analyzer 3100 (Applied Biosystems).Analysis of the obtained AFLP-profiles was performed using Phoretix 1D Advanced v. 5.20 software (Nonlinear Dynamics). The resulting binary matrix was created for further statistical analysis with the program Tools for Population Genetic Analysis v 1.3 (TFPGA). To estimate the allele frequencies of the dominant markers, we used the approach ofA. baerii. The differentiation between Russian and Persian sturgeons was observed only in the marker frequencies.Using eight combinations of primers, we obtained AFLP profiles  with 588A. gueldenstaedtii from the Caspian Sea; (2)A. gueldenstaedtii from the Azov Sea; (3)A. baerii from the Ob’ River; and (4)A. persicus from the Southern Caspian Sea  DOI,10.5281/zenodo.167463 .Dataset 2 includes AFLP profiles for Phoretix 1D Advanced v. 5.20 software (Nonlinear Dynamics).Dataset 3 includes TFPGA files  with genetic distances and trees. I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Acipenser gueldenstaedtii Brandt,Acipenser persicus Borodin, andAcipenser baerii Brandt” by Alexey Sergeev describes the relationship between mitochondrial genotypes of four closely related sturgeon species by using the amplified fragment length polymorphism (AFPL) method. As a result, an analog of a phylogenetic tree is constructed, one in which the species paradoxically cluster by the habitat (A. gueldenstaedtii from the Caspian Sea is more closely related toA. persicus from the Southern Caspian Sea rather than toA. gueldenstaedtii from the Azov Sea).The manuscript entitled “Evolutionary relations and population differentiation of Generally, the experiment is carried out at a good level and the findings are sufficiently novel. My main criticism is that it is not quite clear to what extent the AFPL method reflects the actual phylogenetic distance between species and that only the mitochondrial genome was interrogated. Generally, the reader has to get an idea of the evolutionary similarity between species by looking at the similarity of patterns in the AFPL profiles. I think the paper can be accepted for indexing if the author explains the caveats and limitations related to this method somewhere in the introduction. Otherwise the readership might be mislead by the dendrogram and the phylogeny reported by the author. Currently I have very cosmetic comments which are outlined below. Abstract: According to previous studies, ‘baerii-like’ mitotypes => mitochondrial genotypes -- the readership might be unfamiliar with this term The amplified fragment length polymorphism (method) =>  The amplified fragment length polymorphism method (AFLP) -- the abbreviation must cited in the abstract next to full size sentence, since it appears later without reference Persian sturgeons from the South Caspian Sea, Russian sturgeons from the Caspian Sea and the Sea of Azov, and Siberian sturgeons from the Ob’ River, which are close to these - which these? Change 'these' to 'the latter two' - two species, but are also clearly morphologically and genetically distinct from them. Main text has to be checked carefully with a native English speaker for word usage.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. In this study the mitochondrial genome was not investigated at all.  It will be mentioned in the article. In fact, mitochondrial DNA of A. gueldenstaedtii has only two restriction sites which EcoRI endonuclease recognizes and about fourty sites matched to MspI. In our research we analyze those fragments of AFLP pattern which meet the following conditions: 1) the AFLP band must have lenghth between 100 – 400 bp, 2) the amplified fragment must be produced with EcoRI selective primer (due to that only EcoRI primer has fluorescent dye), 3) the bases that followed by restriction site should be complementary to the selective bases of primers at final step of amplification. This mtDNA has only two restriction fragments which could be produced but they do not meet these conditions. Previous studies showed weak ability of mitochondrial DNA markers to perform exact species identification of individuals from this sturgeon species group and to clarify their phylogenetic relations. The main goal of this work was to analize nuclear markers as much more informative ones. The AFLP method allows obtaining a large set of anonymous nuclear marker of the genome.AFLP profiles reveal patterns of nuclear DNA markers obtained from the whole genome and reflect its polymorphism. Interrogating these profiles we can estimate similarity of the sample genomes, and statistically verify significance of  AFLP pattern difference which reflect similarity of nuclear genomes. By examining the differences between the populations and computing genetic distances, and taking into account former geological events, we can make suggestions on approximate time of the population and species separation .Obviously, this method has some limitations. Dominant markers are applicable for polyploid genome study but less informative than co-dominant markers . We obtained large marker set from nuclear DNA but these markers are anonymous. We can’t distinguish which of them are selectively neutral and more informative. We work with them in complex which can somewhat distort the whole picture. Therefore, it’s not correct to make the ultimate phylogenetic conclusions based only on this data. However, these results can be very useful in comparison with the data obtained from other approaches. It will also be mentioned in the article.  We appreciate a lot such a detailed analysis of the article and very useful comments of Dr. Pervuchine. All suggestions will be applied to the edited version of the article. Some clarifications are needed.  Many thanks for very useful comments. They will be very helpful for improving the manuscript. 1. Guillot G.∗ and Carpentier-Skandalis  А. 2010. On the Informativeness of Dominant and Co-Dominant Genetic Markers for Bayesian Supervised Clustering.  The Open Statistics & Probability Journal 3(1) · December 2011 with 160 ReadsDOI: 10.2174/1876527001103010007 2. Nei, M. (1972). "Genetic distance between populations". Am. Nat. 106: 283–292. doi:10.1086/282771 1-3. Molecular genetic methods  open a possibility to solve some principal and controversial questions of sturgeon species systematic relationships.In this article, Sergeev addressed the problem having long and complex history of discussion It is of particular interest also, because the problem has strong practical  dimension, connected with commercial use, conservation and restoration of sturgeons. In spite of significant amount of literature on the subject, there is still a deficit of studies using genomic markers.Acipenser gueldenstaedtii) from the Caspian Sea and the Sea of Azov, previously revealed by using other methods4. It should be specially mentioned, that Sergeev's study supports differentiation between Russian sturgeons (Acipenser spp. I think results obtained by Sergeev are important for resolving relationships betweenI have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. We appreciate the time and effort of Dr. Ponomareva and would like to thank her for detailed analysis of the investigated theme and high appreciation of this work.


CPE was highest for the Khoisan Tuu ethnolinguistic group and the Northeast District at 0.9999582029 and 0.9999922652 respectively. CMP ranged from 6.28 × 10−19 (Khoisan Tuu) to 1,02 × 10−18 (Northwest district). Using pairwise genetic distances (FST), analysis of molecular variance (AMOVA), factorial correspondence analysis (FCA), and the unsupervised Bayesian clustering method found in STRUCTURE and TESS, ethno-linguistics were found to have a greater influence on population structure than geography. FCA showed clustering between Bantu and Khoisan, and within the Bantu. This Bantu sub-structuring was not seen with STRUCTURE and TESS, which detected clustering only between Bantu and Khoisan. The patterns of population structure revealed highlight the need for regional reference databases that include ethno-linguistic and geographic location information. These markers have important potential for bio-anthropological studies as well as for forensic applications.Population structure was investigated in 990 Botswana individuals according to ethno-linguistics, Bantu and Khoisan, and geography (the nine administrative districts) using the Identifiler autosomal microsatellite markers. Genetic diversity and forensic parameters were calculated for the overall population, and according to ethno-linguistics and geography. The overall combined power of exclusion (CPE) was 0.9999965412 and the combined match probability 6,28 × 10 The Niger-Congo language phylum is widespread through-out sub-Saharan Africa; with languages belonging to its Bantoid branch collectively referred to as Bantu2. Botswana Bantu languages are members of the Central-K (Subiya and Mbukushu), Central-R (Yeyi), and Central-S  Bantu sub-groups2. Khoisan languages have distinctive click consonants and are widely used by hunter-gatherer (San) and pastoralist (Khoi) populations of southern Africa3. The Khoe-Kwadi, Kx’a, and Tuu Khoisan languages in Botswana are members of the Southern African Khoisan (SAK) family6. In this study, the terms “Bantu” and “Khoisan” are applied as they relate to linguistics and ethnicity (ethno-linguistics).Botswana is a landlocked country in Southern Africa. It has 25 languages belonging to the Niger-Congo and Khoisan African language phyla7. The Khoi pastoralists appear in the archaeological record of southern Africa around 2000–1,200 years ago having migrated from eastern Africa9. Bantu speakers originated around the Cameroon/Nigeria border region about 5000–3000 years ago, and only arrived in southern Africa not earlier than the Khoi pastoralists8. The Kgalagadi, Tswana and Kalanga of the Central-S Bantu ethno-linguistic sub-group entered present day Botswana between the 15th and the 17th century, the Kalanga from the north (present day Zimbabwe), and the Kgalagadi and Tswana from the south-east (present day South Africa)7. The Yeyi  and Mbukushu  moved towards the Okavango Delta (Northwest district) in the 18th century from southwest Zambia, the Caprivi Strip and the Kwando and Linyanti Rivers; while the Subiya  fled into Botswana from the Caprivi Strip in 187510.Khoisan speakers were the first inhabitants of Botswana15. As the Bantu farmers entered new areas and intermarried with autochthonous populations, the local languages, cultures and genetic composition were influenced or even replaced by the Bantu18. There is strong evidence of sex-biased gene flow from autochthonous populations to the Bantu20, and admixture and assimilation partly involving now extinct autochthonous populations20. Besides anthropological evidence, linguistically, click consonants (distinctive to Khoisan languages) have been borrowed into southern African Bantu languages such as the Central-S Bantu Nguni (found in South Africa), and the Central-K Mbukushu and Central-R Yeyi from Botswana22.There is little information about the genetic and demographic processes that influenced the genetic landscape of Botswana population groups today. In southern Africa, the admixture dynamics between the Bantu farmers and foraging San and pastoralist Khoi played an important role in shaping the current patterns of genetic diversity2 20. Evidence of genetic structure between the Bantu and Khoisan has been detected using over 1, 000 nuclear microsatellites in 121 African populations24; 1 million autosomal markers in 103 Bantu and Khoisan southern African populations14; 900 complete mitochondrial DNA sequences in southern African populations25; however this has not been investigated with forensic markers.The official country census makes no reference to ethnicity because at independence from the British in 1966 the government attempted to create an ethnic-blind state by placing all the Bantu ethno-linguistic groups found in Botswana under the umbrella of the Tswana27. Results from these few studies indicate high levels of polymorphism detected with the forensic kit AmpFLSTR Profiler Plus PCR Amplification Kit (Applied Biosystems)27 indicating their potential for forensic and bio-anthropological studies. Analysis with Y-filer markers also revealed a lack of geographic, regional and ethnic variation in Botswana. The Botswana police has adopted the AmpFlSTR Identifiler PCR Amplification Kit (Identifiler) for forensic investigations. Identifiler is a commonly used commercial multiplex PCR kit designed to amplify the 13 core STR loci from the FBI Combined DNA Index System (CODIS), and two additional markers (D2S1338 and D19S433), plus a homologous region of the Amelogenin gene on the X and Y chromosomes. It has been used to generate relevant reference data on various worldwide and African populations43 as well as elucidating the population structure of samples from Rwanda28, Namibia34, Sudan38, and South Africa43.Forensic markers have been poorly investigated in BotswanaIn this work, the widely utilized forensic kit Identifiler was used to investigate the population structure in Botswana, according to ethno-linguistics and geography. For this, we applied summary statistics, multivariate analysis, and model-based Bayesian clustering methods. These markers were further investigated for their ability to provide ancestry information of random individuals. These analyses of the patterns and distribution of genetic diversity are discussed in a forensic and bio-anthropological context.44. A total of 15 ‘rare variant’ alleles and 9 ‘off ladder’ alleles were found in this study 35 and Angola37.Deviations from HWE were observed for CSF1PO, D19S433, and FGA in the Ghanzi district, and for CSF1PO and D18S51 in the Kgalagadi and Southeast districts respectively. Locus D19S433 is the only locus in which deviations from HWE were found in both ethno-linguistic and geographic population groups. This observation can be linked to the high concentration of Khoisan in the Ghanzi district. Schlebusch 48 revealed that silent alleles at frequencies normally reported in literature are unlikely to introduce serious biases in average exclusion probabilities. However, they can introduce errors that may lead to false exclusions of maternity or paternity in individual assessment. Amorim and Carneiro49 reported that the presence of silent alleles may lower paternity index (PI) ratios in trios. Therefore, the presence of silent alleles should be taken into account in all forensic analysis. The vWA locus was reported to show high frequencies of silent alleles in South African Bantu and Coloured populations50. Therefore, in absence of trio genotypes, we investigated the possibility of silent alleles applying the maximum likelihood method incorporated in ML-NullFreq51 . Supplementary Table FST results comparing the language subgroups of the Bantu  and Khoisan . There were significant differences between all the ethno-linguistic subgroups with the exception of Central-K and Central-R Bantus. The FST analysis between the administrative districts revealed significant differences between the Ghanzi district and all other districts except the Kgalagadi and Kgatleng districts; between the Southern and Northwest district; and between the Northwest and the Kweneng and Central districts  Fig.  Bantu CeFST, AMOVA, and FCA show evidence of the greater influence of ethno-linguistics on population structure than geography. The partition of genetic variation within Botswana is due to ethno-linguistics which consequently influences the geographic distribution of genetic variation due to the geographic pattern of genetic distribution as evidenced by FST, AMOVA and FCA data.K method52 identified three populations as having the highest level of genetic structuring. Three genetic clusters were also identified by InP(D | K) as the most likely K using the Evanno method53. TESS results indicate that there are two district genetic clusters in the Botswana population as seen in the plateau seen in the deviance information criterion (DIC) plot from the no-admixture model implemented in TESS. The ethno-linguistic population groups Bantu and Khoisan are distinguishable at K = 2 up until K = 6. Clustering analysis indicates that the largest concentration of the Khoisan ethno-linguistic group is in the Ghanzi district  of the Botswana individuals to the two major ethno-linguistic groups was evaluated using two strategies: log likelihoods using WHICHRUN and proportions of ancestral components from STRUCTURE Table . The ranIn = 0.154), with D7S820 (In = 0.036) being the least informative (Table In across the markers (and standard deviation) In was 0.078 (0.035). This is lower than the average In found for the Sudanese populations at 0.167 (0.070)38. These low In values indicate that individually these markers are not best suited for the inference of ancestry.The most informative locus for the inference of ancestry was locus CSF1PO 38 and the South African40 populations. It was lower than the southern African Namibia (Ovambo)34 and Angola30, and the east African Uganda (Buganda41 and Karamoja)35 populations.Forensic parameters for each locus were estimated overall Botswana populations, and according to ethno-linguistics, and geography (Supplementary Table −19. CMP ranged from 6.28 × 10−19 (Khoisan Tuu) to 5.91 × 10−14  when tested according to ethno-linguistics; and ranged from 1,02 × 10−18 (Northwest district) to 5,12 × 10−15 (Kweneng district) when calculated according to geography. The highest CPM was higher than that detected in the South African40, Sudanese38, and Ugandan (Buganda41 and Karamoja)35 populations, but lower than that found for the Equatorial Guinea population29. Forensic summary statistics results are comparable to other African populations41.The probability of obtaining a random match between individuals, the combined match probability (CMP), for the overall Botswana population as a whole was 6,28 × 10This study shows the possibility of investigating bio-anthropological processes, admixture, gene flow and major ethnic affiliation of individuals using Identifiler. These markers a suitable in the identification of individuals. This is a step towards closing the gap in understanding the amount and distribution of genetic diversity in Botswana and understand their contributing factors in order to provide recommendations for the application of these markers for the Botswana police. The population structure found in Botswana illustrates the need for regional reference databases that includes ethno-linguistic and geographic location instead of a single national reference database of voluntary donors. This is a much needed step towards creating a regional or national reference DNA database.A total of 990 unrelated voluntary donors  kit, containing the loci D8S1179, D21S11, D7S820, CSF1P0, D3S1358, TH01, D13S317, D16S539, D2S1338, D19S433, vWA, TPOX, D18S51, D5S818, FGA, and the Amelogenin locus for sex typing following the user’s manual recommendations. Fragment sizes were detected using the Applied Biosystems ABI 3100 genetic analyser and sized with GeneScan500-LIZ internal size standard  following manufacturer’s protocols. Allele calling was performed using GeneMapper ID v 1.1 software .64. Departures from Hardy-Weinberg equilibrium (HWE), observed (Ho) and expected (He) heterozygosity were calculated using Arlequin vs 3.5.1.365. P-values for HWE were executed with 106 steps in the Markov chain and 106 dememorization steps. The Bonferroni adjustment (α = 0.05/15 = 0.00333 for 15 loci) was applied to the probability of HWE to minimize possible type I errors66. Heterozygote deficiency or excess as a cause of deviation from HWE was investigated using Genepop. Silent allele frequencies were estimated using a maximum likelihood approach as implemented in ML-NullFreq51.Genetic diversity parameters such as allele frequency were estimated using Genepop vs 4.2.2FST) and AMOVA were calculated with the program Arlequin vs 3.5.1.365. FSTP-values were estimated at a significance of 0.01 using 10,000 permutations and applied Bonferroni adjustment. AMOVA was run using a FST-like distance matrix at 10,000 permutations. Factorial correspondence analysis (FCA) was conducted using the program GENETIX vs 4.0567.Population structure was evaluated using different approaches, summary statistics, analysis of molecular variance AMOVA, multivariate methods, and unsupervised model-based Bayesian clustering. Pairwise genetic distances  and geographic subdivision (administrative districts). Non-hierarchical AMOVA was used to test for ethno-linguistic heterogeneity amongst the self-declared ethnolinguistic groups: (1) Central-K Bantu, (2) Central-R Bantu, (3) Central-S Bantu, (4) Khoe-Kwadi Khoisan, (5) Kx’a Khoisan, (6) Tuu Khoisan (This test was limited to n = 970 because there was no self-declared ethno-linguistic sub-language classifications for twenty donors); and geographic heterogeneity in the nine administrative districts: (1) Central, (2) Ghanzi, (3) Kgalagadi, (4) Kgatleng, (5) Kweneng, (6) Northeast, (7) Northwest, (8) Southern, (9) Southeast (n = 990). Hierarchical AMOVA grouping strategy tested for variation between the self-declared Bantu and Khoisan ethnolinguistic sub-population groups: (1) Bantu- Central-K + Central-R + Central-S; (2) Khoisan- Khoe-Kwadi + Kx’a + Tuu; and geographic variation between the north and south regions of the country: (1) North: Northwest + Ghanzi + Central + Northeast and (2) South: Kgalagadi + Kweneng + Kgatleng + Southern + Southeast.68 and TESS vs 2.3.169. These two programs were used to take advantage of their different sensitivities to population structure and admixture. STRUCTURE is advantageous in that it is able to explore the number of populations in a dataset by optimizing Hardy-Weinberg equilibrium within putative groups, while as an addition TESS uses geographical information in assigning membership.Population structure was further evaluated using the unsupervised Bayesian clustering methods in STRUCTURE vs 2.3.4admixture and correlated allele frequencies model for a burn-in period of 1,000,000, followed by 100,000 iterations. Ten replicates were run for each K = 1 to K = 6. Summary reports were generated using the STRUCTURE HARVERSTER tool52, an ad hoc statistic-based approach which estimates the true number of populations K by estimating ΔK. Individual ancestral components were visualized using CLUMPAK (http://clumpak.tau.ac.il/index.html).STRUCTURE was run using the parameters no-admixture model to estimate the upper bound on the number of distinct genetic clusters as recommended in the user manual. The spatial interaction parameter (ψ) was set to 0.6 as recommended69. The model was run for 1,000,000 iterations with a burn-in period of 100,000 iterations for K = 2 to K = 10 with ten replicates for each k. The ideal number of clusters (Kmax) was chosen based on when the deviance information criterion (DIC) values reached a plateau . Using the resulting Kmax, 10 replicates were run using the conditional auto-regressive (CAR) admixture model using the same parameters mentioned above. The average of each individual’s proportions of ancestral components was calculated using the program CLUMPP70 over the 10 replicates.The Bayesian clustering algorithm in TESS was applied using geographical information as an additional parameter in the model. Initially TESS was run with the 71. These maps provide useful information in addition to the standard unidimensional bar chart representation showing the predictions of admixture proportions for individuals at their geographic locations. The R (http://www.R-project.org) 3.0.372 package spatial 7.3.973 and maps 2.3.974 were used to map the extent of genetic clusters and identify barriers between clusters using ordinary kriging surface interpolation of admixture proportions. Each of these maps  extrapolates the admixture proportions (proportion of genotype belonging to that particular cluster) across the study area. Areas with low values in the combined map were considered boundary regions between genetic clusters.The CAR model uses a hidden regression approach which allows the possibility to display posterior predictive maps of admixture coefficientset al.61 and Ristow et al.43. Group (1) consisted of individuals who had Bantu ethno-linguistic population group proportions ≥0.7, Group (2) individuals with Khoisan ethno-linguistic population group proportions ≥0.7, and Group (3) individuals whose membership probability proportions were below 0.7 for either the Bantu or Khoisan ethno-linguistic group and therefore admixed. Using proportions of ancestral components, the ability of Identifiler to determine ancestry was evaluated. Those individuals with a K ≥ 0.7 in a group that corresponded to their self-declared ethno-linguistic population group (Bantu or Khoisan) were “correctly assigned”. “Wrongly assigned” meant individuals whose self-declared ethno-linguistic population group showed ancestral proportion ≤0.7. Those whose ancestral components were below 0.7 for both Bantu and Khoisan were considered “admixed”.Using the results of STRUCTURE at K = 2, three groups were identified according to whether their ancestral proportions at a cut-off of 0.7 for ancestral membership proportions for either the Bantu or Khoisan ethno-linguistic groups following criteria similar to Phillips 75. The genotypes of Group (1)-Bantu and Group (2)-Khoisan were used as a baseline (training set) in WHICHRUN using the critical populations method to determine the log likelihood (log(L)) of the population probabilities for all 990 samples used in the study. The individual genotypes probability ratio limit of 3 (log103 = 0.477) was chosen for population assignment to exclude false positives. An individual was considered “correctly assigned” when assigned to the self-declared population group. An individual was considered “wrongly assigned” when assignment corresponded to other than the self-declared population group. The proportion of wrongly assigned individuals is the error rate. Those individuals that were not assigned to a group were classified as “not assigned” and therefore considered admixed.The efficiency of assignment of the Identifiler markers was also evaluated using the program WHICHRUN vs 4.1In) implemented in the program Infocalc76. In ranges from zero (no information) to the natural logarithm of the number of populations (maximum information). The highest theoretically attainable In per locus for this study was 0.693 (log102).The ability of the markers in the inference of ancestry was evaluated in Group (1)-Bantu and Groups (2)-Khoisan using the loci informativeness for assignment (77: Power of Exclusion (PE = h2(1–2hH2) where h and H are the number of heterozygotes and homozygotes respectively, and the Combined i) were PEi is the specific exclusion probability of the ith genetic marker and π(1 − PEi) means (1 − PE1) × (1 − PE2) × (1 − PE3) × … × (1 − PEn) from locus i = 1 to the nth locus, Match Probability , the combined PM for more than one locus is the product of the individual PM at each locus assuming that they are not linked, Power of Discrimination (PD = 1 − MP), and Typical Paternity Index 78.Standard summary statistics estimating forensic parametershttp://www.ebi.ac.uk/ega/), which is hosted by the European Bioinformatics Institute (EBI), under accession number EGAS00001002380.All data used in this study has been deposited into the European Genome-Phenome Archive (EGA) (Supplementary Information