Anthropologists have proposed another probable explanation: contribution to the genetic structure of West Siberian indigenous populations by ancient human groups, which separated from western and eastern Eurasian populations before the final formation of their phenotypic and genetic features and evolved independently in the region over a long period of time. This hypothesis remains untested. From the genetic point of view, it could be confirmed by the presence in the gene pool of indigenous populations of autochthonous components that evolved in the region over long time periods. The detection of such components, particularly in the mtDNA gene pool, is crucial for further clarification of early regional genetic history.The craniometric specificity of the indigenous West Siberian human populations cannot be completely explained by the genetic interactions of the western and eastern Eurasian groups recorded in the archaeology of the area from the beginning of the 2We present the results of analysis of mtDNA samples (n = 10) belonging to the A10 haplogroup, from Bronze Age populations of West Siberian forest-steppe (V—I millennium BC), that were identified in a screening study of a large diachronic sample (n = 96). A10 lineages, which are very rare in modern Eurasian populations, were found in all the Bronze Age groups under study. Data on the A10 lineages’ phylogeny and phylogeography in ancient West Siberian and modern Eurasian populations suggest that A10 haplogroup underwent a long-term evolution in West Siberia or arose there autochthonously; thus, the presence of A10 lineages indicates the possible contribution of early autochthonous human groups to the genetic specificity of modern populations, in addition to contributions of later interactions of western and eastern Eurasian populations. The West Siberian Plain covers a vast area within northern Eurasia: from the Ural Mountains in the west to the Yenisei River in the east. The region, along with Central Asia, has traditionally been considered to form part of a contact area between western and eastern Eurasian human populations , 2, 3, 4From the paleoanthropological point of view, the ancient substrate could be associated with human groups belonging to the so-called ‘northern Eurasian anthropological formation’ that arose in the region by the Neolithic period , 6. But Paleogenetic approaches allow the direct study of the gene pool of regional ancestral populations, and facilitate the search for potentially indigenous genetic features. Ancient DNA studies also allow us to investigate ancient genetic components, the role of which in the gene pool was subsequently strongly modified by genetic drift , 8. Ancith to the beginning of 1st millennium BC)  belonging to the A10 haplogroup, from West Siberian forest-steppe zone human populations dating from the Early Metal Age to the Late Bronze Age  to the Late Middle Ages. The most representative materials, including collections of paleoanthropological remains, were obtained for all Baraba populations from the Bronze Age , 13. TheAll specimens analyzed in this study were obtained from Repository of paleoanthropological collections of Institute of Archaeology and Ethnography SB RAS . All specimens are available for possible future studies under its whole description, including the name of archaeological site , number of burial . Whole descriptions of the specimens under study are given in Climatic conditions in the Baraba region are favorable for the preservation of skeletal material and the DNA therein. All skeletal remains investigated were macroscopically well-preserved. For each individual, at least two samples from the same or different post-cranial bones, or several teeth, were collected and studied separately . Most saAncient DNA (aDNA) experiments were carried out in the Laboratory of molecular paleogenetics in the Institute of Cytology and Genetics SB RAS .DNA was extracted as previously described , 16. In Four overlapping fragments of mtDNA hyper-variable region (HVR) I and a coding-region fragment containing the A663G polymorphism which is characteristic for haplogroup A were amplified and sequenced using primers from , 17 S1 . The PCRhttp://www.phylotree.org) [www.fluxus-enginering.com). Phylogeographic analysis was conducted by using a database of mtDNA variability including more than 30,000 samples from modern Eurasian populations collected from published sources   by usingree.org) . A phylorces see .Nine autosomal STRs and the sex-determining marker amelogenin were simultaneously co-amplified by using the AmpFlSTR Profiler Plus Kit  according to the manufacturer`s instructions. Results were analyzed on an ABI Prism 3130XL Genetic Analyzer  at the SB RAS Genomics Core Facility .Work with the ancient material was carried out in specially equipped, isolated clean rooms using special clothes. All work surfaces and instruments were routinely cleaned with a 5% solution of bleach and irradiated by UV light. Blank controls were run in parallel with samples in all extraction and amplification procedures, to identify possible contamination. MtDNA HVRI sequences were determined for all staff working with the ancient DNA, as well as for anthropologists and archaeologists who may have had contact with the study material.Sequences of the mtDNA HVR I fragment (15997–16409) were obtained for all the individuals under study. Differences between each ancient mtDNA sample and the rCRS are shown in Phylogenetic analyses of A10 mtDNA lineages published in the scientific literature to date (and discussed in detail below) reveal that most of them can be grouped into a subcluster within haplogroup A10, with common haplotype 16223T, 16227C, 16230G, 16290T, 16311C, 16319A. We provisionally named this subcluster A10a, in accordance with the rules of nomenclature of mtDNA phylogenetic clusters. In the remainder of this paper we will refer to A10 mtDNA lineages without the A16230G transition as A10*. The precise phylogenetic substructure of the A10 haplogroup can be fully elucidated only by complete mtDNA genome sequencing. The overall frequency of A10 in the West Siberian Bronze Age population that we studied was at least ~8.5%  were only one component of A10 variability in West Siberian Bronze Age populations, and were relatively rare.The modern indigenous populations of Eurasia differ from ancient West Siberian human groups in the distribution and phylogenetic structure of A10 lineages in their gene pools. Generally, the mtDNA haplogroup A10 is a rare component of the gene pool of current Eurasian human populations see . We founModern Eurasian populations differ strongly in the phylogenetic structure of their haplogroup A10 lineages from West Siberian Bronze Age human groups. We identified only seven different A10 haplotypes in contemporary Eurasians. All lineages found within the main current area of the A10 haplogroup  share motifs 16223T, 16227C, 16230G, 16290T, 16311C, 16319A, and therefore belong to the subcluster A10a . The rarFurthermore, some phylogenetic differences in A10 lineages are common in the western and eastern parts of the whole area. In the north-eastern part of the area (including eastern and northern parts of West Siberia and East Siberia), haplogroup A10 is represented almost solely by the A10a subhaplotype 16223T, 16227C, 16230G, 16256T, 16290T, 16311C, 16319A, and, in only one case, by a haplotype derived from the latter by an apparent reverse substitution (back mutations) at position 16227 . The othWe employed the experimental standards and measures against intra-laboratory contamination widely accepted by ancient DNA researchers –29 see . Direct The following circumstances indicate that ancient samples were not cross-contaminated: experiments with haplogroup A10 samples dated to different periods of the Bronze Age were performed at different times with long time intervals between them; all series of samples include a large variety of lineages belonging to different East and West Eurasian haplogroups, in addition to the lineages of haplogroup A10 described in this paper  . The resIn conclusion, we confirm the authenticity of the ancient human mtDNA sequences presented here.Our data on ancient mtDNA are particularly significant for the reconstruction of the development of the genetic and phenotypic specificity of West Siberian indigenous human populations. Based on data from physical anthropology, several modern indigenous populations from West Siberia were attributed to the Uralic (or West Siberian) anthropological type , 5, whicHowever, it was argued that the craniometric specificity of modern West Siberian populations could not be completely explained by such genetic interactions , 2. It wFrom the genetic point of view, this hypothesis can be confirmed by the presence, in the gene pool of indigenous West Siberian populations, of components with autochthonous origin or/and long-term evolution in the region. These components may be represented by some subclusters of Y-chromosome N1b haplogroup that show signs of autochthonous origin in the ancestral gene pool of Uralic-speaking human groups .To our knowledge, no published study to date includes evidence for indigenous components at the clusters or subclusters level in the mtDNA gene pool of modern West Siberian aboriginal populations, including Ugric, Samoyed and Turkic-speaking populations, but with the exception of a few lineages within U4 haplogroup .In the light of our ancient DNA data, we suggest that the mtDNA haplogroup A10 is an autochthonous component of the gene pool of West Siberian indigenous populations, and that this component underwent a long evolution in West Siberia before the arrival in the region of genetically contrasting western Eurasian and eastern Eurasian groups.Considering the specificity of human population history in the region, a genetic component potentially indicating the contribution of the ancient autochthonous population to the gene pool structure of modern West Siberian populations must meet the following criteria:It should be distributed in the ancient population within a territory that corresponds to a potential region of development of the population under consideration. The area of its distribution must be large enough to have an impact on the development of populations in the larger territory.It must have been present in the gene pool of West Siberian populations for a long time, at least since before large-scale migrations to the region and genetic interactions with newly arrived groups.The cluster must have undergone several stages of its evolution in the region.It must show a specific distribution in modern populations evolved in the region for a long time. The variability and frequency in the gene pool of the component is highly likely to be, but not necessarily, different in the modern and in the ancient populations, as a result of recent influence of genetically contrasting groups that migrated to the region or owing to an effect of random genetic drift.In the context of this research, it is of particular significance that the supposed genesis center of the phenotypic specificity of West Siberian populations  anthropological type genesis) is placed by anthropologists in the southern taiga and the forest-steppe zones of West Siberia , 5, inclMoreover, the earliest human groups of the West Siberian forest-steppe for which paleoanthropological materials are available are dated to the Neolithic period, and Baraba populations of the subsequent Early Metal Age and Bronze Age  already showed features of West Siberian anthropological type , 11. TheArchaeological and anthropological data show that the local ancient Baraba populations under study were not strongly isolated, either culturally or genetically from the populations of neighboring forest-steppe, southern taiga and steppe areas . The indThe presence of several HVRI haplotypes among the samples and the significant differences in chronology of the populations under study suggest that close relationships between the ancient individuals studied are unlikely to have influenced the results . The A10 lineage identified in two Ust-Tartas samples differs from the A10 root haplotype by substitution at the 16148 position, so we can conclude not only that mtDNA haplogroup A10 was present in the mtDNA gene pool of the population of the West Siberian forest-steppe zone, but also that it had already passed the initial stage of diversification, resulting in the appearance of A10 lineages diverged from the root haplotype at least ~5500–6000 years ago. Clearly, this haplogroup originated and the initial stages of its diversification occurred in earlier periods: in the Neolithic , or even in the Late Pleistocene. This assumption is indirectly confirmed by the high degree of continuity between the Neolithic population of the region (~8000–7000 years of age) and the populations of the Early Metal Age and the Early and Middle Bronze Age (6000–4000 years of age), revealed by physical anthropological methods .The earliest archaeological materials (from a few sites with scant traces of stone tool use) found so far in the well-studied Baraba forest-steppe are only 13,000–14,000 years old, i.e., they date from the final Pleistocene  period   preceded the first significant migrations to the region and the interaction there of genetically contrasting human groups .Phylogeographic analyses to study early population processes have been conducted for several other subclusters of mtDNA haplogroup A: the evolution of A2 cluster is related to early settlement of North-East Asia in the Late Paleolithic, and the subsequent settlement of America , 40; theThe origin and initial stages of haplogroup A10 diversification were probably associated with early groups that infiltrated West Siberia in the final Pleistocene (post-LGM) or early Holocene, and then evolved independently in the region for a long period, allowing the formation of early features of regional population`s gene pool. Under the influence of genetically contrasting groups from western and eastern Eurasia, intensively migrated into the region from the first half of the II millennium BC , Turkic, Mongolian, etc.), the role in the gene pool of the ancient indigenous components must have been greatly reduced, or they disappeared completely.The specificity of the A10 mtDNA haplogroup’s distribution in ancient and modern Eurasian populations is in good agreement with this scenario. The overall distribution of the A10 haplogroup in modern populations of Eurasia corresponds with its origin and/or evolution in West Siberia. A10 lineages are present in gene pools of modern Ugric, Samoyed and Turkic-speaking groups from the central region of northern Eurasia . The souTherefore, haplogroup A10 has all the features expected for the indigenous component of the mtDNA gene pool of the aboriginal populations of West Siberia. It is highly probable that West Siberia is the place of origin of A10 or, at any rate, the place of its long-term evolution and diversification; thus the mtDNA haplogroup A10 could be one of the genetic markers of the ancient autochthonous population that has contributed to the early stages of specificity formation of West Siberian indigenous populations, independently of the subsequent influence of genetically contrasting groups.We expect that there were other autochthonous mtDNA markers in the regional population’s gene pool. They may be present as rare variants or be almost absent in modern populations of the region under study. Our study demonstrates the effectiveness of our approach to research on assessing the significance of such elements in the gene pool of the West Siberian populations. It is necessary to continue studying the ancient populations of West Siberia and adjacent areas from the Neolithic period to the Middle Ages to obtain an objective reconstruction of the genetic history of this region. We have already begun to implement such a large-scale project.S1 Table(DOC)Click here for additional data file.S2 Table(PPT)Click here for additional data file.S3 Table(DOC)Click here for additional data file.S1 File(DOC)Click here for additional data file.S2 File(DOC)Click here for additional data file.


Rheum tanguticum (Dahuang in Chinese) has becoming endangered in China. This study aims to examine the genetic structure and genetic diversity of R. tanguticum within species, and the genetic differentiation within and among populations in China.Wild R. tanguticum were investigated by their chloroplast DNA matK sequences. The genetic diversity index was calculated by Dnasp, PERMUT, and Arlequin 3.0 software, and a neighbor-joining (NJ)-tree was constructed by MEGA 5.0 software.The variability and structure of 19 populations of matK sequence analysis. The mean genetic diversity within species was 0.894, and the genetic variability among populations (67.6%) was relatively higher than that within populations (13.88%) according to the AMOVA and PERMUT analyses. The NJ-tree and a pairwise difference analysis indicated geographical isolation of R. tanguticum. The gene flow among populations was 0.05, indicating a genetic drift among some populations, which was also confirmed by the NJ-tree and haplotype distributions. Furthermore, a mismatch distribution analysis revealed the molecular evolution of R. tanguticum.Fifteen haplotypes were obtained based on the R. tanguticum in China was demonstrated.Genetic diversity among and within populations of  Rheum tanguticum Maxim. ex Balf belongs to the family Polygonaceae, and grows mainly in high-altitude areas in the southwest and northwest of China, such as Sichuan, Gansu, and Qinghai provinces [R. tanguticum (Dahuang in Chinese) are used in Chinese medicine for unloading the tapping product, clearing re (heat), purging huo (fire), removing pathogenic huo from the xue (blood), stimulating menstrual flow, and promoting diuresis and detoxification [R. tanguticum has caused excessive consumption in China [R. tanguticum is low and environment-dependent, and the wild resources of R. tanguticum are becoming endangered [rovinces , 2. The fication –7. The hin China –11. The dangered .R. tanguticum. Chen et al.[R. tanguticum by evaluating an SSR marker. These findings were in accordance with those of Wang et al.[et al.[R. tanguticum using an ISSR marker. These studies of R. tanguticum genetic diversity involved limited materials, and their results were contradictory. Therefore, large samples and new molecular markers were required to reveal the real state of R. tanguticum genetic diversity.Genetic diversity involves organism complexity , ecosystn et al. discoverg et al. based on[et al. demonstrmatK gene (1500 bp) is a molecular marker for plant molecular systematics and evolution, and is located within the intron of the chloroplast gene trnK on the large single-copy section adjacent to the inverted repeat [matK gene sequence avoided any interference of heterozygosity and its evolutionary rate was relatively fast [matK gene has been employed as an important and powerful tool for examining intergenus and intragenus genetic diversity because of its high substitution rate [The d repeat . Among vely fast , 23. Theion rate , 25.R. tanguticum within species, and the genetic differentiation within and among populations in China. The genetic diversity of R. tanguticum at the species level and within and among populations was investigated using the matK gene sequences, and the population structure of R. tanguticum was clarified.This study aims to examine the genetic structure and genetic diversity of R. tanguticum individuals were collected from 19 populations in Sichuan, Gansu, and Qinghai provinces of China  and trnK2R (5′-AACTAGTCGGATGGAGTAG-3′), the second primer pair was matkK592F (5′-TCCTACCGTGTGTGAATGCG-3′) and matK8R (5′-AAAGTTCTAGCACAAGAAAGTCGA-3′), and the third primer pair was Pt-trnK692F (5′-GACTGTATCGCACTATGTATC-3′) and trnK1544R (5′-GGATAACCCCAGAATGCTTAG-3′). All primers were synthesized by Shanghai Shenggong Company (China). Each PCR amplification was performed in a 50-μL reaction mixture by a Cycler™ Thermal Cycler  PCR procedure as follows: 94°C for 5 min; 35 cycles of 94°C for 45 s, annealing at 51°C for 1 min, and extension at 72°C for 1 min; final extension at 72°C for 10 min. A 1/10 volume of each PCR product was examined by electrophoresis in a 1.0% (w/v) agarose gel, and the remaining part was sequenced for correction.Total DNA was extracted from the silica gel-dried leaves using the CTAB method . The matSequences were aligned by ClustalX  and manumatK sequence was obtained from 18 populations. The only exception was the TZ population from Gansu province, which produced a 1524-bp matK sequence with a ‘TAAACC’ insertion at the 1022-bp site. A total of 21 segregated sites were found in the matK sequence of R. tanguticum, and 15 haplotypes were determined . The changes in Pi showed a similar trend toward haplotype diversity, and the only difference was that the highest Pi was found in population QL (Pi = 0.00276), rather than population ZN (Pi = 0.0022). The Hd and Pi values within the species were 0.894 and 0.00308, respectively, demonstrating a relatively high level of genetic diversity.The genetic diversity of the ns Table . The higR. tanguticum.The AMOVA results showed high variability among the populations Table . The genP < 0.05). The SPSS analysis demonstrated a significant positive relationship between genetic difference and geographic distance .R. tanguticum  demonstrated the presence of gene exchange among R. tanguticum populations. The average number of migrants (Nm) between populations calculated by AMOVA and Dnasp was 0.05 for both analyses.A mismatch distribution analysis based on Dnasp was performed, and multi-peak traces were obtained to explain the gene exchange present among different populations of m Figure . Tajima’R. tanguticum, and the genetic diversity among populations was higher than that within populations. Endangered species often showed a relatively low level of genetic diversity [R. tanguticum is a herbaceous perennial with a long living history [i.e., different environmental and climate conditions, thereby enhancing gene exchange and leading to high genetic diversity [In this study, a relatively high genetic diversity was found in iversity –38, whiciversity . R. tang history  and self history , and itsiversity –43.e.g., by mountains and rivers, was noted among different populations of R. tanguticum, and explained why the genetic diversity differed among populations. In this study, the geographic distance between populations JZ and BM was close, but the difference in their haplotypes was significant.The distribution of the 15 haplotypes and the SPSS analysis results demonstrated a significant positive relationship between genetic difference and geographic distance. On the NJ-tree, the samples from the same population were clustered together, and the samples from different populations were clustered into different subgroups. Geographic isolation, R. tanguticum mainly occurred among different populations. The multi-peak traces and Tajima’s test results  demonstrated that the evolution of R. tanguticum was consistent with the neutral theory [Haplotypes TH1–TH5 were present in different populations at the same time. However, in two populations, JZ and TK, many different haplotypes were simultaneously observed. Although the geographic distances between populations ZS and JZ, DR and QL, and TB and LQ were significant, they had the same genotypes, respectively. On the NJ-tree, some samples from the same population did not cluster into the same subgroup, such as the samples from populations JZ and TK. The genetic differentiation of l theory , indicatl theory . As our R. tanguticum in China was demonstrated.Genetic diversity among and within populations of


Leuciscus idus). The ide is a stenohaline freshwater fish that primarily inhabits rivers, with frequent anadromous behavior when sea salinity does not exceed 15%. Unlike most other anadromous Baltic Sea fish species, the ide has yet to be subjected to large‐scale stocking programs, and thus provides an excellent opportunity for studying the natural population structure across the current salinity gradient in the Danish Belts. To explore this, we used Genotyping‐by‐Sequencing to determine genomic population structure of both freshwater resident and anadromous ide populations in the western Baltic Sea region, and relate the results to the current salinity gradient and the demographic history of ide in the region. The sample sites separate into four clusters, with all anadromous populations in one cluster and the freshwater resident populations in the remaining three. Results demonstrate high level of differentiation between sites hosting freshwater resident populations, but little differentiation among anadromous populations. Thus ide exhibit the genomic population structure of both a typical freshwater species, and a typical anadromous species. In addition to providing a first insight into the population structure of north‐western European ide, our data also (1) provide indications of a single illegal introduction by man; (2) suggest limited genetic effects of heavy pollution in the past; and (3) indicate possible historical anadromous behavior in a now isolated freshwater population.Climate change experts largely agree that future climate change and associated rises in oceanic water levels over the upcoming decades, will affect marine salinity levels. The subsequent effects on fish communities in estuarine ecosystems however, are less clear. One species that is likely to become increasingly affected by changes in salinity is the ide ( Esox lucius, perch Perca fluviatilis, roach Rutilus rutilus, and ide Leuciscus idus) .DNA was extracted using Qiagen's Blood and Tissue kit  according to the manufacturer's protocol, although with minor modifications to ensure DNA‐yields and concentrations needed for subsequent analyses. Specifically, after adding Buffer AL, samples were incubated at 57°C, for 30 min, with 20 sec vortex every 10 min to ensure complete dissolution of the samples. In the final step only 100 Population genomic data were generated using the Genotyping‐by‐Sequencing (GBS) approach  as reference for the Tassel 4.3 pipeline , the expected frequency of heterozygotes within sites (HS), also known as gene diversity, and the expected frequency of heterozygotes over all populations (HT). Other general statistics included were number of alleles, number of effective alleles, fixation index (FST) and deviations from Hardy‐Weinberg equilibrium described as inbreeding coefficients (GIS) , with positive results meaning heterozygote deficiency, and negative results meaning excess of heterozygotes. Isolation by distance was assessed for FST values and Euclidean geographic distances, as well as FST values and waterway distances (Table S1) for all sample sites, using the Isolation‐By‐Distance Web Service (IBDWS)  error, were summed up for each sample site and plotted on a geographic map . Also displayed on the map was 10 × 10 km squares from which ide have been observed since 1995. This data were provided by the Natural History Museum of Denmark's extensive national fish atlas database . Also included were coastal areas of Sweden where ide have been reported . Large differences were observed in DNA yield between sample sites with POL, TRY and KOG having no samples reaching the desired 125 μg/mL concentration whereas KRO had no samples with less than 125 μg/mL of DNA .DNA extractions yielded high molecular DNA, as envisioned by TA gel staining of bands >10,000 bp, and concentrations between 16.5 and 568 Between 1,286,964 and 3,060,226 reads were produced for each ide GBS library . Of these, sequences containing tags ranged from 234,277 to 430,808  . Sequencing depth (reads/tag) per SNP ranged from 5.49 to 7.10, with an average of 6.50 . Mean number of reads, mean number of tags and mean reads/tag in the sample sites did not seem to be affected by the varying DNA concentrations in the extracts .R2 = 0.991). The final dataset allowing only 5% missing data had 12,359 SNPs. In the filtered dataset, each individual had missing data in 64 (0.52%) to 534 (4.32%) SNPs.Data filtering demonstrated that prior to filtering the total dataset encompassed 40,429 SNPs, with all individuals exhibiting 32.5–45.0% missing data. It also revealed a linear relation between allowed missing data for each SNP and the number of SNPs kept in the dataset (T) level was 0.231 and for the sample sites the expected heterozygosity (HS) (gene diversity) ranged from 0.197 in VID to 0.259 in TRY. The observed heterozygosity (HO) for the sample sites was between 0.197 (VID) and 0.259 (TRY). Inbreeding coefficient (GIS) of the total dataset was −0.002, but not statistically significant. The inbreeding coefficients for the sample sites were between −0.042 and 0.033, but only the positive values were statistically significant, with localities KRO, OND, SUS, KOG, and LOD showing a deficit of heterozygotes. For the individual sample sites the inbreeding coefficients (GIS) were significant (P < 0.05) for 4.4–8.5% of SNPs within the sample sites, with an average of 6.1% (data not shown).The effective number of alleles in the total dataset was 1.319  than freshwater resident populations, all of which had FST values between 0.135 and 0.289 (95% CI: 0.178–0.254), except for the KRO‐GUD comparison that had an FST of 0.070. All pairwise FST comparisons among sites were significantly different (P > 0.001), except KOG and TRY that had a very low FST value of 0.001 (P = 0.192).FK = 2) shows that there was a basic east‐west split between either sides of the marine channel known as ‘The Great Belt’ (Storebælt), that separates the two principal Danish islands of Funen (Fyn) and Zealand (Sjælland) , OND is the first site to separate from the other western sample sites. The most likely number of ancestral population was four (K = 4) (CV error rate = 0.48697)  with the sample sites divided into (1) a western cluster consisting of KRO in the Netherlands and the two populations VID and GUD on the Jutland peninsula; (2) a cluster consisting of the OND sample site; (3) a cluster consisting of the POL site; and (4) the four eastern low‐salinity sample sites SUS, KOG, TRY, and LOD all clustering together  and KRO and GUD stay clustered together. This was consistent throughout plots for 5–8 ancestral populations despite VID being geographically in between KRO and GUD. In the eastern cluster sample sites SUS and LOD separate from the other eastern sample sites (KOG and TRY) in the plots for 6 (K = 6) and 7 (K = 7) ancestral populations respectively. Sample sites KOG and TRY, which are 3 km apart, cluster together for K = 2 through K = 8.The cluster analysis implemented in Admixture showed that all individuals sampled from the same site have similar ancestral fractions Fig. . The ploThe plot of eigenvectors show that the eastern sample sites LOD, SUS, KOG, and TRY cluster on top of each other Figs. , 5. SampFST with both waterway distances  and Euclidean geographic distances  . However, both the correlation of genetic similarity with waterway distances  and Euclidean geographic distances  were significant when not including the KRO sample site.Isolation by distance was statistically insignificant for the correlation of Perca fluviatilis, spined loach Cobitis taenia, and bullhead Cottus)  results reveal that geographic distance cannot explain all the genetic variation among sample sites. Rather, as suggested in studies of other freshwater species    with possible interpopulation gene flow in the Baltic Sea  LOD, KOG, TRY, and SUS; 2) KRO, VID, and GUD; (3) OND; and (4) POL. Anadromous ide populations are also characterized by lower levels of genetic differentiation than freshwater resident ones. This observation is in accordance with previous published comparisons of genetic differentiation between anadromous and freshwater species   Carl . The KROis) Carl , supportA last point of note relating to the ide in this study, are those from the Odense Å (OND) stream. Archaeological excavations have indicated that this population has been present since at least the Iron Age  error rate of admixture analysis of ide.Click here for additional data file.Table S1. Geographic distance (km) between ide Leuciscus idus sample sites. Euclidean distance (top) and waterway distance (below).Click here for additional data file.


Zosterops (white‐eyes) from this region as our model system. Species of the genus occur in contrasting distribution settings, with geographical mountain isolation driving diversification, and savannah interconnectivity preventing differentiation. We analyze (1) patterns of phenotypic and genetic differentiation in high‐ and lowland species (different distribution settings), (2) investigate the potential effects of natural selection and temporal and spatial isolation (evolutionary drivers), and (3) critically review the taxonomy of this species complex. We found strong phenotypic and genetic differentiation among and within the three focal species, both in the highland species complex and in the lowland taxa. Altitude was a stronger predictor of phenotypic patterns than the current taxonomic classification. We found longitudinal and latitudinal phenotypic gradients for all three species. Furthermore, wing length and body weight were significantly correlated with altitude and habitat type in the highland species Z. poliogaster. Genetic and phenotypic divergence showed contrasting inter‐ and intraspecific structures. We suggest that the evolution of phenotypic characters is mainly driven by natural selection due to differences in the two macro‐habitats, cloud forest and savannah. In contrast, patterns of neutral genetic variation appear to be rather driven by geographical isolation of the respective mountain massifs. Populations of the Z. poliogaster complex, as well as Z. senegalensis and Z. abyssinicus, are not monophyletic based on microsatellite data and have higher levels of intraspecific differentiation compared to the currently accepted species.The moist and cool cloud forests of East Africa represent a network of isolated habitats that are separated by dry and warm lowland savannah, offering an opportunity to investigate how strikingly different selective regimes affect species diversification. Here, we used the passerine genus  Oceanic islands have been extensively used as model systems to understand genetic and morphological effects of long‐term isolation and divergent selection pressures. The strong isolation and selection in these settings have often led to population differentiation, allopatric speciation, and even species radiations documented for a variety of archipelagos and organisms Emerson , 2015a. In both types of islands , the level of geographic isolation, geological age, and diverging ecological conditions may lead to differentiation on inter‐ and intraspecific levels  occurs exclusively at higher elevations, which has resulted in many geographically isolated populations  have led to diverging inter‐ and intraspecific signatures; andMorphological and molecular characters exhibit diverging differentiation patterns.In this study, we use a combination of phenotypic and molecular markers to understand differentiation processes in the East African sky island system and use the bird genus Zosterops has been much debated. In the study region, the genus comprises 14 morphologically distinct taxonomic groups, which have been conversely split into three  species complex occurs exclusively in the highland forests of East Africa and is found at altitudes between 1500 and 3400 m , Z. p. kaffensis (SW Ethiopia), Z. p. kulalensis , Z. p. kikuyuensis , Z. p. silvanus (Taita Hills of SW Kenya), Z. p. mbuluensis (S Kenya and N Tanzania), Z. p. winifredae (South Pare Mts. of N Tanzania), and Z. p. eurycricotus (N Tanzania). According to the recent IOC World Bird List (http://www.worldbirdnames.org), two of the eight subspecies of Z. poliogaster have been given species rank: Z. kikuyuensis  and Z. silvanus (Taita Hills). Zosterops senegalensis has a broad lowland range in sub‐Saharan Africa, but within our study area, the species is restricted to highland forests, forest edges, and afroalpine scrubland from 1100 to 3400 m , Z. s. stuhlmanni (NW Tanzania and W Kenya), and Z. s. stierlingi (Usambara Mts. of Tanzania). Zosterops abyssinicus is a lowland species, occurring in savannahs, in woodland, and at forest edges below 1500 m, with occasional records up to 2300 m  and Z. a. flavilateralis (N Kenya to N Tanzania). In our subsequent analyses, we assign individuals into the following three main groups: Z. poliogaster, Z. senegalensis, and Z. abyssinicus. However, we consider the complex taxonomic relationships and the potential species status of some populations , following the manufacturer's protocols for tissue, blood, and feather samples . Biometric data were integrated with genetic data using discriminant function and principal coordinates analyses (Bray–Curtis dissimilarity). Individual‐based genetic distances were calculated in genalex v. 6.4.1 , AR  , and locus‐specific allele frequencies were calculated with fstat v. 3.1 (Goudet Ho) and expected heterozygosity (He), tests of HWE (Hardy–Weinberg equilibrium), and LD (linkage disequilibrium) were calculated with arlequin v. 3.5.1.3  based on our microsatellite data set. We then used this genetic matrix to generate ordinary least squares and equal angle representation with the program splittree v. 4.11.3.  and tarsus length  were strongly positively correlated with body weight, irrespective of species membership. Eye‐ring perimeter did not significantly depend on body weight , but was moderately positively correlated with head size . Two‐way PERMANOVA revealed significant differences in morphology among study sites  and the three species . Highly significant species × site interaction terms  also pointed to intraspecific morphological differentiation. Principal coordinates analysis revealed that morphological characters separated Z. poliogaster and Z. abyssinicus  clustered within Z. poliogaster, while other Z. senegalensis populations were intermediate between Z. poliogaster and Z. abyssinicus  between morphological Bray–Curtis and geographical Euclidean distances. Finally, we observed a significant increase in wing length  and body weight  with the altitude, while eye‐ring perimeter was not significantly related to altitude . Morphometric raw data are provided in Appendix S1.Regressions of wing length Fig. A and B aude Fig. A and B. ast Fig. C. The hiEffects from null alleles and large allele dropout were detected for the loci Zl44, Zl54, Zl35, and Mme12. No linkage disequilibrium was detected between any pair of loci after Bonferroni correction. Deviations from HWE were detected for loci Cu28, ZL41, and ZL45. As only few loci and populations were out of equilibrium, all data were included in subsequent analyses.Mme12, Zl54) and 17 (Zl22) alleles, with a mean of 8.9 (±4.5 SD). Among the 133 alleles across all loci, 38 were restricted to single mountain massifs or populations. Allele frequencies for all loci and populations are given as Appendix S2. Genetic diversity , AR, and percentage of expected He (heterozygosity) and observed Ho (heterozygosity) showed higher values for the two populations analyzed for Z. abyssinicus in comparison to populations assigned to Z. poliogaster ; however, no further significant differences among the other taxa were detected. Values of Genetic diversity measures and mean values of morphological characters are given in Appendix S3.The number of alleles per locus ranged between 4 , dividing the populations from the Taita Hills from all other populations analyzed . Under this model, the populations were divided into the following five clusters: (1) populations from the Taita Hills together with Mt. Kasigau, (2) the Chyulu Hills, (3) all locations from the central Kenyan highlands including Mt. Kulal, (4) the lowland species Z. abyssinicus, clustering together with the Z. senegalensis population from Kakamega Forest, and (5) populations from the Ethiopian Highlands with individuals of Z. senegalensis sampled at Mt. Marsabit. The populations of Z. poliogaster from Tanzania  were genetically not clearly assigned to any specific cluster   strongly intermix and do not form distinct branches. Results are given in Appendix S5.Structure provided the highest support for zed Fig.  suggest RST = 0.3694, P < 0.0001). The intraspecific differentiation was highest within Z. poliogaster explaining 69.6% of the variance  and lowest for Z. senegalensis . We found no significant differentiation between the populations of Z. abyssinicus . Dest values ranged between 0.001 and 0.315, with highest divergence between the populations from Ethiopia and Mt. Kasigau. Pairwise Dest values are given in Table AMOVAs  suggested strong genetic differentiation among all species and populations explaining 78.8% of the molecular variance (P < 0.0001). This ordered loss of alleles occurred along a gradient from northwest to southeast . Only the Pare Mts., Mt. Kasigau, Bura (Taita Hills), and Simba Valley (Chyulu Hills) differed from this nested appearance having negative nestedness contributions and an idiosyncratic number of allele occurrences. Accordingly, allele turnover among sites was lower than expected by chance .Allele occurrences among sites were significantly nested . Nevertheless, the morphological and genetic analyses suggested strong differentiation among the study regions  of the populations and their respective genetic dissimilarity  evolve under the regime of different evolutionary drivers. In the following, we will discuss our findings against the background of potential effects from different distribution settings (disjunction versus panmixis) and contrasting evolutionary drivers ; finally, we make some taxonomic recommendations for this bird species complex.Our data showed strong phenotypic and genetic splits among the three taxa, but even stronger splits among local populations within single species. Specifically, strong intraspecific differentiation was found between isolated mountain populations of Z. poliogaster, with a main split found between populations from the Eastern Arc Mountains  and all other studied populations. We further detected shallower splits among the other mountain populations in line with previous studies  our results also suggest differences in morphological structure among habitat types. Correlations between altitude and the expression of morphological characteristics in white‐eye bird species have been previously detected Moreau . Such clOur data suggest that the divergence of the studied phenotypic traits may be mainly driven by selection and not stochastic processes, which drive the divergence of neutral traits, in our case microsatellites  Zosterops silvanus including the populations from the Taita Hills and Mt. Kasigau, supported by mtDNA sequences  supported by mtDNA and microsatellite data, but also by morphological data , tarsus length (mm), body weight (g) and eye ring length and height (mm).Click here for additional data file.Appendix S2. Allele frequencies of all populations and microsatellite loci analysed. Site numbers coincide with other supplementary material, figures and tables of the article.Click here for additional data file.Appendix S3. Summary data of sample sites, with species, subspecies, exact geographical location, altitude, and mean values of all genetic and morphological characters analysed: Total number of alleles (A), allelic richness (AR), number of private alleles restricted to single mountain massifs (AP), percentage of expected heterozygosity (He) and observed heterozygosity (Ho). Site numbers match with those of Fig. Click here for additional data file.Appendix S4. Mean probability values with standard deviations calculated based on ten runs each for K = 1–10, and respective ∆K‐values.Click here for additional data file.Appendix S5. Neighbornet generated with the program Splitstree.Click here for additional data file.


Environment Protection and Biodiversity Conservation Act 1999. Historical accounts indicate this species occurred naturally in two adjacent river systems in Australia, the Burnett and Mary. Current day populations in other rivers are thought to have arisen by translocation from these source populations. Early genetic work detected very little variation and so had limited power to answer questions relevant for management including how genetic variation is partitioned within and among sub-populations. In this study, we use newly developed microsatellite markers to examine samples from the Burnett and Mary Rivers, as well as from two populations thought to be of translocated origin, Brisbane and North Pine. We test whether there is significant genetic structure among and within river drainages; assign putatively translocated populations to potential source populations; and estimate effective population sizes. Eleven polymorphic microsatellite loci genotyped in 218 individuals gave an average within-population heterozygosity of 0.39 which is low relative to other threatened taxa and for freshwater fishes in general. Based on FST values (average over loci = 0.11) and STRUCTURE analyses, we identify three distinct populations in the natural range, one in the Burnett and two distinct populations in the Mary. These analyses also support the hypothesis that the Mary River is the likely source of translocated populations in the Brisbane and North Pine rivers, which agrees with historical published records of a translocation event giving rise to these populations. We were unable to obtain bounded estimates of effective population size, as we have too few genotype combinations, although point estimates were low, ranging from 29 - 129. We recommend that, in order to preserve any local adaptation in the three distinct populations that they be managed separately.The Australian lungfish is a unique living representative of an ancient dipnoan lineage, listed as ‘vulnerable’ to extinction under Australia’s  Ne) and thus maintaining levels of genetic diversity has been considered critically important [For species of conservation concern (i.e. endangered or vulnerable), increasing or maintaining effective population size  because the translocations occurred within the last 150 years and the generation time for the lungfish is very long, thus slowing the possibility of genetic divergence since translocation. We also use the genetic data to attempt to estimate effective population sizes, as previous work has suggested that these may be very low and thus represent a risk for the future of the species.Tissue and scale samples were collected from 218 individuals from seven localities in the Burnett, Mary, Brisbane and North Pine River catchments . Two locAll field and experimental protocols carried out in this study were approved by the Griffith University Animal Ethics Committee. All procedures were carried out according to Australian Ethics Committee protocol numbers CA2011/10/551 (Seqwater) and ENV/17/14/AEC (Griffith University).N. forsteri, 115 successfully amplified a product of expected size and of these, 94 were monomorphic and 21 were variable [et al. [et al. [et al. [A standard phenol-chloroform extraction method  was usedvariable . Further [et al. , showed  [et al. . PCR amp [et al. . MicrosaHO) and expected (HE) heterozygosity using ARLEQUIN v3.5.1.2 [AR) and private allelic richness (ARpriv) were calculated using HP-Rare [FIS) was estimated over all loci for each population using the Analysis of Molecular Variance (AMOVA) framework in ARLEQUIN and tested for deviation from the random expectation by 10,000 permutations of alleles among individuals within populations.Genetic diversity averaged across eleven loci within each of the seven population samples was quantified from observed  was made for each of the seven population samples using the single-sample linkage disequilibrium method [An estimate of effective population size  was corrected for multiple tests using the BY False Discovery Rate method (BY-FDR) which controls experiment-wide Type I error without the loss of power associated with the Bonferroni adjustment [Genetic structure among the seven population samples was quantified by estimating pair-wise and global justment . A hieraK) using STRUCTURE 2.3.1 [6 iterations after a burn-in of 2×105 iterations. The most likely number of clusters was assessed first by plotting the mean probability of data for successive values of K to find a plateau in probability values and then choosing the smallest of these values [K or the second-order rate of change L˝ (K) following Evanno et al. [We tested for the existence of distinct genetic groups within the pooled set of 218 individual multi-locus genotypes using model-based Bayesian clustering. The probability of an admixture model was tested for up to eight clusters  not showing a significant deficit . In contn sample .FST value across 11 loci was 0.111 and was highly significant (P<0.001). Each of the 11 locus-by-locus FST values were also significant at P<0.05 and ranged from 0.019 for locus 2LF032 to 0.243 for locus 1LF005 , the FSC value was 0.085 (P<0.001), indicating significant differences within at least one of the catchments. This result was possibly caused by the highly significant differences between Tinana Creek and the other two Mary River sites.The overall s 1LF005 . Most ofnificant  and rangK values from one to eight showed the mean likelihood score (Ln(K)) plateaued at K = 4 while the Delta K method indicated K = 2, although the latter method did not show relatively strong support for any value  Burnett River (pooled Burnett upper and Burnett lower samples); 2) Mary river (pooled Mary upper and Mary lower samples); 3) Tinana Creek. The data from putatively translocated samples (North Pine and Brisbane) were treated as having unknown affinity and assigned to the three genetic clusters from the native range. Twenty replicate runs of STRUCTURE at K = 3, showed all individuals sampled from putatively translocated populations (North Pine and Brisbane) had a close affinity to the Mary River genetic cluster (Assignment of putatively translocated samples (North Pine and Brisbane) to potential source populations in the native range of lungfish  using STRUCTURE was complicated by strong genetic structure within the Mary catchment where the sample from Tinana Creek comprised a distinct genetic group. For this reason we set potential source populations in the native range of lungfish to  cluster .et al. [Monachus schauinslandi of 5% from a survey of 154 loci [Arapaima gigas [Huso huso [Maccullochella peelii [HE of 0.18 was recorded in marble trout populations subject to periodic catastrophic floods causing genetic bottlenecks [The level of genetic variation in this long-lived vulnerable species is indeed extremely low. This had already been reported for allozymes by Frentiu et al. , who fouet al. ,40. Whil154 loci . This vama gigas , the beluso huso  and Murra peelii . Howevertlenecks .et al. [We surveyed a range of populations from the species’ native range as well as putatively translocated populations but found very similar levels of diversity in terms of heterozygosity and allelic richness across all sites. Therefore low genetic diversity does not appear to be a consequence of recent human activities as in the case of the monk seal. The Australian lungfish has either experienced a number of bottlenecks in the past, possibly due to Pleistocene range contraction as suggested by Frentiu et al.  and Kempet al. , or has et al.  and micret al. [There is also some genetic evidence of inbreeding in the natural populations, as all but one of them had significant heterozygote deficits. This was also observed in the two allozyme loci analysed by Frentiu et al. , so it iet al.  reportedNe. The linkage disequilibrium method works on the number of allelic combinations and so when variability is low, precision is severely affected. Waples et al. [While our microsatellite markers were sufficiently variable for an analysis of population structure, they were not sufficiently variable for good estimates of s et al.  showed tWe had hypothesized that samples taken from populations believed to be natural would be differentiated between the two river systems, because the species is restricted to freshwater and highly intolerant of elevated salinities. This prediction was partly supported in that samples from the Burnett and the main stem of the Mary River were differentiated and appeared as distinct populations in the STRUCTURE analysis. However, we had not expected that the sample from Tinana Creek, a tributary of the Mary River, would also be distinct. Clearly there is a barrier separating Tinana Creek from the rest of the Mary River system despite their close proximity to one another . Tinana Maccullochella mariensis [Melanotaenia dublayi [Cherax dispar [Interestingly, a number of other freshwater species show genetic differentiation between Tinana Creek and the rest of the Mary River. These include the threatened Mary River cod ariensis , the rai dublayi  and a frx dispar . The facet al. [et al. [The other unexpected finding was that the sample from the North Pine River was differentiated from all the samples taken from natural populations and from the Brisbane River population, particularly in the initial STRUCTURE analysis . This diet al.  showed tet al. . However [et al.  also fouFST values were lower than with other samples.Our results for the Brisbane River sample support the hypothesis that this population has come from the Mary River proper. It clearly has not come from Tinana Creek, or from the Burnett. This finding is consistent with historical records that the source population of various translocations made by O’Connor in the late nineteenth century was the Mary River . Some liEvidence for genetic distinction between Mary and Brisbane river lungfish populations was found using randomly amplified DNA fingerprinting (RAF) markers in an unpublished report by Lissone . This daWe conclude that there are at least three distinct natural populations of the Australian lungfish, the Mary River proper, Tinana Creek and the Burnett River. Our evidence suggests that the North Pine River and Brisbane River populations have been sourced from the Mary River proper. While numbers of lungfish may be quite high in some of these sites, the extremely low levels of genetic diversity at neutral loci might be cause for concern, as they may also reflect low diversity in coding genes that are responsible for the long-term survival of the populations. If the effective population sizes are indeed very low, then this diversity could be further eroded over the next few generations. Given that these microsatellite loci did not have sufficient power to estimate effective population size with any precision, we propose to use a RAD-sequencing approach  to sampl‘Genetic rescue’ has sometimes been proposed as a strategy to increase genetic diversity in small and/or threatened populations . HoweverS1 Microsatellite DatasetData for each individual in rows, data for each locus in columns. Data for each allele of each locus is recorded in a separate column with the header row giving the name of the locus. Missing data marked by “?”. First column is individual sample code; second column is population code corresponding to population groupings analysed in the manuscript.(XLSX)Click here for additional data file.


Lycaeides melissa) onto an exotic host plant . We examine variation among seven alfalfa populations that differed in terms of colonization by L. melissa; specifically, we examined variation in phytochemistry, foliar protein, and plant population genetic structure, as well as responses of caterpillars and adult butterflies to foliage from the same populations. Regional patterns of alfalfa colonization by L. melissa were well predicted by phytochemical variation, and colonized patches of alfalfa showed a similar level of inter-individual phytochemical diversity. However, phytochemical variation was a poor predictor of larval performance, despite the fact that survival and weight gain differed dramatically among caterpillars reared on plants from different alfalfa populations. Moreover, we observed a mismatch between alfalfa supporting the best larval performance and alfalfa favored by ovipositing females. Thus, the axes of plant variation that mediate interactions with L. melissa depend upon herbivore life history stage, which raises important issues for our understanding of adaptation to novel resources by an organism with a complex life history.From the perspective of an herbivorous insect, conspecific host plants are not identical, and intraspecific variation in host nutritional quality or defensive capacity might mediate spatially variable outcomes in plant-insect interactions. Here we explore this possibility in the context of an ongoing host breadth expansion of a native butterfly (the Melissa blue,  Dietary niche breadth, or the number and type of resources consumed by an organism, drives numerous ecological and evolutionary processes, from mediating the coexistence of competitors , 2 to prLycaeides melissa and its introduced host, alfalfa (Medicago sativa).Dietary niche breadth of herbivorous insects is necessarily dependent on host plant variation, the importance of which has typically been explored in a comparative fashion across plant taxa. This comparative work has laid the foundations of plant defense theory and has generated many hypotheses for how and why insects use a particular plant species . Within the last 200 years, L. melissa has expanded its host range to include the non-native legume alfalfa ) and the variance in PC 1 and 2 scores. Genome-average pairwise and global Fst values were then estimated from the sample allele frequencies to assess the extent of population genetic structure. Finally, we tested for a positive correlation between genome-average Fst and the geographic distance between pairs of populations to determine whether alfalfa showed signs of isolation-by-distance .We used several methods to quantify and summarize patterns of genetic variation within and among populations. We generated a genetic covariance matrix where each element in the matrix measured the genetic similarity (covariance in genotypes) for a pair of individuals. We then used principal components analysis (PCA) to visualize patterns of genetic similarity based on this matrix. Next, to quantify genetic variation within populations, we calculated the average genetic variance  and females associated in the wild with alfalfa (from VUH) with plants from either VUH and AWFS, or from AWFS and APLL, the three alfalfa sources used in extended larval rearing see . The beh2 = 17.7, df = 6, p < 0.01; a rank-based test was used because of non-normality of data)  . Plants of data) . Neitherof data) .L. melissa, and two occurred only in uncolonized populations. Consequently, phytochemical α-diversity was very similar across populations  has been well characterized for intra-specific variation in numerous traits but the plant had previously been treated as a single entity (i.e. without accounting for inter-population differences). We found inter-population differences with respect to three axes of plant variation  that affect L. melissa. The plant traits of largest effect to L. melissa were not the same for adults and juveniles, which adds complexity to the task of predicting the outcome of this novel plant-insect interaction.For any pair of interacting organisms, the strength, and even the occurrence, of the interaction tends to be variable across the geographic ranges of the interacting species , 67. UndL. melissa, which has been suggested previously in a survey of preference in ten L. melissa populations [Not only did plant effects differ between adults and larvae, but there was also a reversal of relative host quality when comparing early instar survival with survival to adulthood. Alfalfa obtained from VUH supported higher larval survival for the first two weeks , thus we consider it unlikely that L. melissa herbivory per se drives phytochemical diversity in colonized patches through induction of host defenses. Finally, the inference of plasticity should be tempered for now by the resolution of our genetic analyses. Although we assayed thousands of genetic regions , analyses focused on overall similarity and thus could obscure important genetic variants that influence phytochemistry.While we discovered dramatic differences in the extent to which different alfalfa sources support larval development or elicit oviposition, linking population-specific variation in alfalfa nutrition, chemistry, or genetic variation to cupation , suggest another , yet ten another . Althoughenotype , 77, 78,henotype . With reL. melissa population persistence . Future studies should also, of course, attempt to measure other factors relevant to population colonization and persistence, including geographic barriers to L. melissa dispersal, the role of natural enemies, and the age of alfalfa populations.Beyond the overall differences between occupied and unoccupied locations , other aL. melissa larval performance and oviposition preference. This result provides an important foundation for our understanding of metapopulation dynamics in this system. In a population genomic survey of hundreds of L. melissa individuals throughout the Great Basin, the majority could be assigned to their population of origin based on their genotypes [L. melissa may be further constrained because not all emigrating L. melissa adults will encounter alfalfa patches that can support larval development. These ecological filters suggest a mechanism explaining why novel host use by L. melissa in the Great Basin is characterized by the founder bottlenecks uncovered by [In summary, we found dramatic consequences of intraspecific variation among alfalfa populations for enotypes . Thus, mvered by .L. melissa, [In a study of the genetic architecture of host use by melissa,  found a melissa,  hypothesS1 Appendix(DOCX)Click here for additional data file.S1 FigL. melissa larvae reared on alfalfa sourced from APLL and VUH . Days to eclosion reflects time elapsed from hatching of first instar through to eclosion of adult butterflies. Sample sizes shown reflect those butterflies that survived out of the initial ~60 larvae reared on each host population).Days to eclosion significantly differed between (DOCX)Click here for additional data file.S2 FigDiversity equivalencies calculated using phytochemistry data from each alfalfa population. Equivalencies were calculated using increasing values of the q parameter to explore how they might change as the more abundant compounds were more heavily weighted in calculations. α-diversity reflects chemical richness within an individual plant, β-diversity the distinct number of chemical phenotypes among plants or populations (chemotypes), and γ-diversity total phytochemical diversity within a population. Standard errors were calculated using 1,000 randomizations.(DOCX)Click here for additional data file.S3 FigNon-metric multidimensional scaling (NMDS) ordination of phytochemical data obtained from seven populations of alfalfa (see (DOCX)Click here for additional data file.S4 FigL. melissa colonization. The top row depicts results of linear discriminant analysis. In the top left panel all 49 compounds characterized via HPLC were analyzed, while in the top right panel only those compounds occurring at all alfalfa populations were analyzed. Points are individual plants, triangles are used for alfalfa populations uncolonized by L. melissa, circles for colonized populations. The bottom left panel shows differences in compound concentration between colonized and uncolonized alfalfa populations. Differences in means shown reflect differences in mean peak intensity for a given compound. This frequency distribution only includes those compounds that occurred at all alfalfa populations analyzed.Phytochemical separation between alfalfa populations differing in (DOCX)Click here for additional data file.S5 FigL. melissa, circles for colonized populations. Average values output by discriminant functions for a population are plotted by larval survival (row 2) and larval mass (row 3) to determine if the compounds that best differentiate alfalfa populations can predict the differences in larval performance, or colonization status, observed between those same populations. Discriminant functions proved poor predictors of larval performance.Phytochemistry of surveyed alfalfa populations. The top row depicts results of linear discriminant analysis of 49 compounds characterized via HPLC (each combination of axes is a different combination of discriminant functions). Points are individual plants, triangles are used for alfalfa populations uncolonized by (DOCX)Click here for additional data file.S1 TableMedicago sativa) populations utilized for this study.Location and elevation of alfalfa ((DOCX)Click here for additional data file.S2 TableL. melissa butterfly with alfalfa from two populations and a negative control (Lotus nevadensis). All oviposition assays were conducted for 48 hours outdoors at ambient temperature. The two dates for Silver Lake were because inclement weather caused few eggs to be laid during the first assay of SLA females’ preference between AWFS and APPL. Consequently, two days later this assay was performed again using fresh females and data from the two assays were pooled.Collection and sample size information for each oviposition assay performed. Each assay consisted of challenging a single female (DOCX)Click here for additional data file.S3 TableMedicago sativa) individuals sourced from five populations (see main text for locations). Protein data was generated via a Bradford assay (absorbance/divided by mass). Phytochemistry data consisted of a matrix of peak intensity for 49 compounds (HPLC data again standardized by dry weight); and, genetic data consisted of a pairwise genetic covariance matrix . All data were converted to distance matrices using a Euclidean distance measure, then analyzed with a multiple mantel test . Correlation coefficients using both Pearson’s product-moment correlation and Spearman’s rank correlation are given along with corresponding p values.Results from multiple mantel tests correlating a distance matrix of protein content with both a phytochemical distance matrix, and a genetic covariance matrix generated from alfalfa ((DOCX)Click here for additional data file.S4 TableMedicago sativa) individuals sourced from five populations (see main text for locations). Phytochemistry data consisted of a matrix of peak intensity for 49 compounds (HPLC data standardized by dry weight); and, genetic data consisted of a pairwise genetic covariance matrix . Both matrices were converted to distance matrices using a Euclidean distance measure, then analyzed with a multiple mantel test . Correlation coefficients using both Pearson’s product-moment correlation and Spearman’s rank correlation are given along with corresponding p values.Results from multiple Mantel test correlating phytochemical and genetic distance matrices generated from alfalfa ((DOCX)Click here for additional data file.S5 TableMedicago sativa) populations examined. Populations prefixed by “A” were not colonized by L. melissa See main text for analytical and sequencing details.Pairwise Fst values for alfalfa ((DOCX)Click here for additional data file.


Gentiana lutea L. (G. lutea L.) is an endangered plant, patchily distributed along the mountains of Central and Southern Europe. In this study, inter-simple sequence repeat (ISSR) markers were used to investigate the genetic variation in this species within and among populations of G. lutea L. var. aurantiaca of the Cantabrian Mountains (Northwest Iberian Peninsula). Samples of G. lutea L. collected at different locations of the Pyrenees and samples of G. lutea L. subsp. vardjanii of the Dolomites Alps were also analyzed for comparison. Using nine ISSR primers, 106 bands were generated, and 89.6% of those were polymorphic. The populations from the Northwest Iberian Peninsula were clustered in three different groups, with a significant correlation between genetic and geographic distances. Gentiana lutea L. var. aurantiaca showed 19.8% private loci and demonstrated a remarkable level of genetic variation, both among populations and within populations; those populations with the highest level of isolation show the lowest genetic variation within populations. The low number of individuals, as well as the observed genetic structure of the analyzed populations makes it necessary to protect them to ensure their survival before they are too small to persist naturally. Gentiana lutea L. (Gentianaceae) is an herbaceous perennial plant native to the mountains of Central and Southern Europe, where their typical habitat is cattle grazing pastures [Gentiana lutea is self-incompatible and, thus, depends on pollination by insects to produce any seeds, being pollinated mainly by Hymenoptera and Diptera, although neither has special features to facilitate dispersal [pastures . In summispersal .Gentian roots are widely used in bitter beverages, in food products and also in traditional medicine to stimulate the appetite and improve digestion . Such usGentiana lutea L. includes G. lutea var. aurantiaca (M. Lainz), G. lutea L. subsp. vardjanii Wraber and G. lutea L. subsp. montserratii (Vivant ex Greuter) Romo. The populations of the Iberian Peninsula correspond to G. lutea, except for small areas of the Pre-Pyrenees and Central Pyrenees, where G. lutea L. subsp. montserratii is endemic. However, most of the populations from the Northwest Iberian Peninsula have flowers ranging in color from orange to almost red, as compared to the yellow flowers of G. lutea. These populations have been classified as Gentiana lutea L. var. aurantiaca [rantiaca .The Iberian Peninsula is the northwestern region of the Mediterranean Basin hotspot, one of the world’s biodiversity hotspots, with a high diversity in endemic vascular plant species. As Europe’s vacation destination, populations of threatened species are increasingly fragmented and isolated to make way for resort development and infrastructure.G. lutea located in northwestern León province (Spain), in the Cantabrian Mountains. In this region, gentian has been collected historically, for sale and use in medicinal remedies, as in other European regions. Due to the slow growth of this species, its populations have been decimated, and now, they are close to their disappearance and/or are difficult to recover. The main economic sources in the area include coal mining (ceasing activity), ranching and tourism, which all also directly affect gentian populations. People in this area have begun to collect gentian again in a furtive way for extra income due to the current economic crisis. Besides, the local tradition of burning the mountain hillsides to create new pastures for free-range cattle farming can affect the gentian populations, because even though mature plants tolerate fire, undeveloped young plants die because of burning or drying.This study mainly focuses on the wild populations of G. lutea, the genetic variability at population levels remains unknown for this species to date. Nowadays, it is possible to use several molecular methods to analyze the genetic variability in plant species. One of them, inter-simple sequence repeat polymorphism (ISSR), has been successfully used for genetic analysis in the case of medicinal plants, requiring no prior knowledge of the DNA sequence and being universally applicable as dominant markers [Understanding the level of genetic diversity and the population’s genetic structure is important for endangered plant species, because this allows the establishment of effective and efficient conservation practices and can guide choices for their genetic management. Although there are different botanical ,6 and ch markers  for rapi markers ,10,11,12G. lutea L. var. aurantiaca from the northwest of the Iberian Peninsula using ISSR markers and to compare their relationship and variation with other populations of G. lutea and G. lutea L. subs. vardjanii collected in the Pyrenees and Dolomite mountains, respectively. The knowledge of the genetic diversity and variation within and among the populations not only enhances our understanding of population dynamics, adaptation and evolution, but also provides useful information for biological conservation of these endangered species.The aim of this study was to investigate for the first time the level of genetic diversity, within and among populations of G. lutea L. var. aurantiaca), Pyrenees (G. lutea) and Dolomite Mountains (G. lutea L. subsp. vardjanii), respectively. Among populations of the variety, aurantiaca, the wild populations, CMV, CVJ and CTL, and the cultivated population, FCP, showed just one, two, one and three private loci, respectively. AMOVA analysis revealed significant (p < 0.001) genetic differences among the three taxonomical units analyzed (p = 0.05 according to the Student’s t-test), although the different number of samples analyzed for each population may be responsible for such results. The average gene diversity was estimated to be 0.0900 at the population level and 0.2168 at the species level. The CVJ population showed the highest level of genetic diversity (0.1329), while the DBT population exhibited the lowest (0.0546). The average Shannon’s indices showed a strong correlation (Pearson’s r = 0.999) with gene diversity, and they were 0.1342 at the population level and 0.3415 at the species level, respectively. The values of gene diversity and Shannon’s index showed a similar trend to the percentages of polymorphic loci.The amplification of the ISSR fragments in the 123 individuals, analyzed with nine primers, yielded 106 unambiguous and reproducible electrophoretic bands ranging from five to 17 bands for each of the primers, with an average of 11.7 bands per primer. Ninety-five bands (89.6%) were polymorphic when comparing all the samples ; of the analyzed . Of the Qxy, was used  to 0.3998 (between the CTR and DBN populations), with a mean of 0.1734. The neighbor joining cladogram based on these genetic distances clustered the populations according to their geographical regions of origin A. A nearwas used B. Two claurantiaca, from the Cantabrian Mountains, showing a clearly separated cluster of individuals from the CTL and CTR populations, with the first two coordinates explaining 29.21% and 22.69% of the variation, respectively. All the individuals from eastern populations  were also grouped, but without a clear boundary that separates the rest of the individuals  analysis using Jaccard’s genetic distance matrix for the 123 samples, the first two coordinates that accounted, respectively, for 40.33% and 24.95% of the total genetic variability clearly discriminate the three geographical groups of individuals A. PCoA aividuals B.K = 5 . K = 5. All the individuals from the Pyrenees and the Dolomite Mountains are clearly separated into two independent clusters, respectively. The 88 individuals from the Cantabrian Mountain populations were assigned to the three remaining clusters: (i) individuals from CTL and CTR (southwestern populations); (ii) individuals from CPN, CVJ  and CTN (eastern populations); (iii) individuals from the CMV, CLU, CTB, CSN, FCP and CLT (northwestern populations). Individuals showing probabilities of assignment to more than one cluster were observed in all of the Cantabrian Mountains populations, revealing that there has been some gene flow between clusters. The proportions of individuals that have at least a 5% of probability of assignment to another cluster were 33.3% for the northwestern population group, 37.5% for the eastern population group and 12.5% for the southwestern population group. The Z-test  comparing such proportions resulted in a non-statistically significant difference.The analysis of individual multilocus genotypes of the 123 samples using the Structure algorithm showed the best clustering solution for p < 0.001) differences among the clusters and populations of G. lutea var. aurantiaca. Of the total genetic diversity, 20% was attributable to among the above clusters of populations, 14% among the populations and the remainder (67%) to within population diversity  between Nei’s distance and geographical distance for the 16 populations (samples from the Pyrenees were grouped as a single population located at PBQ coordinates) and also for the 11 populations from the Cantabrian Mountains  when analyzed independently. No significant correlation  was observed when the four populations from the Dolomite Mountains were analyzed alone. The Mantel test using Jaccard’s distances calculated for the samples from the Pyrenees Mountains did not detect a significant correlation with the geographical distances  between those populations.The Mantel test detected a significant correlation , we observed clear genetic differences between samples collected in the Dolomite Alps (G. lutea L. subsp. vardjanii) and those sampled in the Iberian Peninsula (G. lutea L. and G. lutea L. var. aurantiaca), as expected, due to their taxonomical classification. Furthermore, the individuals from the Pyrenees and Cantabrian Mountains are also clearly differentiated in our analysis, giving support to previous taxonomic classifications for the red flowered populations from the Cantabrian Mountains, such as the subspecies, aurantiaca [G. aurantiaca [Applying different statistics and analysis , and therefore, he proposed their classification as a variety. However, we also reported a large number of ISSRs bands  detected only in the red flowered plants from the Cantabrian Mountains populations. This percentage is about twice as much as the percentages obtained when the gentian species, G. atuntsiensis (8.5%) and G. striolata (11.3%), were compared using 129 ISSR bands [Renobales  indicateaurantiaca, all along the Cantabrian Mountains, but the Ventana Pass  was set as the easternmost point from which their populations are less common, and the plants show this distinguishing feature attenuated. Anchisi [G. lutea L. var. aurantiaca, located outside of the growing areas described by Renobales [Renobales  stated t Anchisi  also desGentiana lutea L. is a self-incompatible species and, thus, depends on pollination by insects to produce any seeds [Gentiana species, such as G. lutea L. [G. cruciata L. [G. lutea. The small and light seeds can be blown by the wind, and gene flow by seeds can occur among close populations. Therefore, this potential for dispersal by both pollen and seeds may help to explain gene flow between neighboring populations and genetic isolation between widely separated populations.The analysis of the Cantabrian Mountain populations revealed that they are clustered in three different groups with a significant correlation between genetic and geographic distances. The analysis of genetic distances using neighbor joining trees and PCoA methods showed a well-defined cluster, including the two southwestern populations, CTL and CTR. The Bayesian analysis of individual genotypes also separated CTL and CTR from the rest of the populations and grouped the eastern populations  in a different cluster. Besides, AMOVA analysis assigned 20% of the total variance to differences among the three clusters. This population structure can be explained by the lack of continuity in the typical mountainous habitat where gentian grows. This discontinuity might have generated the partial isolation of the CTL and CTR populations. ny seeds ,20. Outclutea L.  or G. crciata L. . Their let al. [G. striolata (percentage of polymorphic loci (PPL) = 80.5% at the species level, with a range from 40.42% to 52.57% at the population level) and G. atuntsiensis . Several endangered Chinese gentians  showed lower levels of ISSR diversity at the species level, with a range of PPL = 43.75%–67.71% [G. lutea L. var. aurantiaca demonstrated a remarkable level of genetic variation among populations  = 0.1480) and low-moderate genetic variation within populations when compared with G. striolata . The most threatened taxa have lower genetic diversity than closely related, non-threatened taxa, indicating reduced reproductive fitness and elevated extinction risks [Gentiana lutea L. var. aurantiaca is a perennial herb with a patchy distribution. The populations are often located in distant mountains and are isolated from each other by plateaus or valleys. Southwestern populations  show the lowest level of genetic diversity , due to the higher genetic isolation of these two populations.Zhang et al.  reported%–67.71% . The eston risks . GentianG. lutea, that are, to a greater extent, affected by the effects of habitat fragmentation [et al. [G. lutea. Reproduction was strongly reduced in small populations, where plants produced fewer seeds per fruit and per plant. Reproduction was most strongly depressed in populations consisting of fewer than 500 plants, which occurs in the studied gentian populations (with less than 150 plants per population). These small populations may face an increased short-term risk of extinction, because of reduced reproduction, and an increased long-term risk, because they are less able to respond to environmental changes. There is controversy about the impact of genetic factors on the risk of extinction for threatened species and populations in nature [Rare species are typically considered to be genetically less variable than common and widespread species . This inentation . Kery et [et al.  studied n nature . Populatn nature .Gentiana lutea L. var. aurantiaca from the Cantabrian Mountains implies that as many populations of this variety as possible should be considered for conservation practice.The genetic structure of plant populations reflects the interactions of various evolutionary processes, including the long-term evolutionary history, such as shifts in distribution, habitat fragmentation and population isolation, mutation, genetic drift, mating system, gene flow and selection . A high G. lutea L. var. aurantiaca, in the Cantabrian Mountains (northwest of Iberian Peninsula); one sample of G. lutea L. from fifteen locations along the Pyrenees, and five individuals from each of four populations of G. lutea subsp. vardjanii from the Dolomite Alps, near Trento . All wild populations studied are endangered due to their small size.Root samples of 123 individuals were collected from three regions ; Table 3Gentiana lutea L. var. aurantiaca have also been analyzed. This population was cultivated in an experimental field located in the Cantabrian Mountains where natural conditions allow gentian also to be found growing wild in the area.Eight individuals from a cultivated population of Populations were sampled randomly; individuals 5–10 m from each another were chosen to avoid collecting the same plant, since, due to their root development, it is possible that several shoots correspond to the same individual. The altitude of the sampled populations ranged from 1170 to 1904 m. The collected fresh roots were dried in a ventilated oven at 38 °C for 72 h to avoid degradation of their chemical compounds. Dried roots were stored at room temperature with silica gel, since these roots are very hydrophilic.Genomic DNA was extracted from 0.2 g of dried root ground using a hammer mill with a 1-mm diameter sieve. The extraction protocol was based on the CTAB (Cetyl Trimethyl Ammonium Bromide) method . DNA con2, 0.02 mM dNTP mix, 5 µmol of primers, 0.25 units of DreamTaq Green DNA Polymerase (Thermo Scientific) and sterile distilled water. Sixteen primers from UBC primer set No.9 , were selected based on previous studies on other Gentianaceae species [G. lutea were chosen for the analysis. For the amplification of ISSR fragments, the following program was used: initial denaturation at 95 °C for 1 min; followed by 45 cycles of 94 °C for 30 s, 50 °C for 30 s, 72 °C for 2 min; and a final synthesis at 72 °C for 10 min.Polymerase chain reaction (PCR) was performed in 15 µL of reaction volume containing 100 ng DNA, 10× DreamTaq Green Buffer , 2.5 mM MgCl species ,32,33 anw/v) ethidium bromide-stained agarose gel in 1× TBE (Tris–Borate–EDTA) buffer with xylencyanol loading buffer. PCR products were separated for 2 or 3 h (depending on the primer used) at 100 V, avoiding distortions caused by higher voltages. The amplified DNA fragments were documented by using image analysis software Total Lab 1.2 . The ISSR amplification protocol was reproduced at least twice for each DNA sample.The PCR products were applied on a 1.5% (Na), the effective number of alleles (Ne), the gene diversity (He) [I) [Only bands that could be unambiguously scored were used in the analysis. Owing to the dominant character of ISSR markers, each ISSR band was treated as a binary character and was scored as present (1) or absent (0), and it was assumed that each observed band represented the phenotype at a single biallelic locus. Popgene version 1.32  was usedity (He)  and Shan(He) [I) . The fifQxy [Qxy) as above. The significance of the branch order for both cladograms was examined independently using 100 bootstraps across the set of loci to generate neighbor joining trees. A majority rule consensus tree was constructed by using the CONSENSE module in PHYLIP.The population structure was studied using different methods. First, we obtained an unrooted neighbor joining cladogram  based onQxy , matrix Grouping of the samples was also carried out by PCoA analysis based on Jaccard’s genetic distance matrix of the individuals, and implemented in the software, Genetic Analysis in Excel (GenAIEx) version 6.5 .K), multiple values were tested (from 1 to 7) using a length of burning period of 10,000 steps and 10 repetitions. The results were analyzed using the on-line tool, Structure Harvester [The third approach used the program, Structure version 2.3.4 , which iarvester , which iarvester  to detecAnalysis of molecular variance (AMOVA) was also used to describe the genetic structure and variability among and within populations using GenAIEx version 6.5 . The varIn order to test the correlation between genetic and geographic distances among populations, the Mantel test was performed using GenAIEx (version 6.5), computing 999 permutations.Gentiana lutea L. var. aurantiaca showed a remarkable level of genetic variation, both among populations and within populations; those populations with the highest level of isolation show the lowest genetic variation within populations. The low number of individuals, as well as the observed genetic structure of the analyzed populations makes it necessary to protect them to ensure their survival before they are too small to persist naturally.The gentian populations from the Northwest Iberian Peninsula were clustered in three different groups, with a significant correlation between genetic and geographic distances.