Nannoperca obscura, is a poor disperser found in small, isolated populations in wetlands and streams of southeastern Australia. Modifications to natural flow regimes in anthropogenically-impacted river systems have recently reduced the amount of habitat for this species and likely further limited its opportunity to disperse. We employed highly resolving microsatellite DNA markers to assess genetic variation, population structure and the spatial scale that dispersal takes place across the distribution of this freshwater fish and used this information to identify conservation units for management. The levels of genetic variation found for N. obscura are amongst the lowest reported for a fish species . We identified very strong population genetic structure, nil to little evidence of recent migration among demes and a minimum of 11 units for conservation management, hierarchically nested within four major genetic lineages. A combination of spatial analytical methods revealed hierarchical genetic structure corresponding with catchment boundaries and also demonstrated significant isolation by riverine distance. Our findings have implications for the national recovery plan of this species by demonstrating that N. obscura populations should be managed at a catchment level and highlighting the need to restore habitat and avoid further alteration of the natural hydrology.Habitat fragmentation caused by human activities alters metapopulation dynamics and decreases biological connectivity through reduced migration and gene flow, leading to lowered levels of population genetic diversity and to local extinctions. The threatened Yarra pygmy perch,  Human activities such as land development, agriculture, and exploitation of natural resources have long been acknowledged as driving processes behind habitat fragmentation and degradation Conservation genetics is the application of evolutionary principles and molecular methods to species and biodiversity conservation Nannoperca obscura (Yarra pygmy perch), found across two biogeographic provinces N. obscura  is a small  freshwater fish endemic to coastal drainages of southeastern Australia Nannoperca obscura has declined since European settlement, and is currently listed as vulnerable by the IUCN N. obscura in the MDB and southeastern South Australia was recently exacerbated by extreme drought, which resulted in the extirpation of all populations in the lower Murray River N. obscura, Hammer et al. N. obscura. In order to address issues of relatedness between isolated river systems, we treat each river with an independent connection to the ocean as a separate unit, herein defined as a catchment. Firstly, we used these data to test the proposed ESUs of Hammer et al. N. obscura are poor dispersers and should therefore display very low connectivity between catchments and low connectivity between sites within catchments, with many populations representing different MUs within highly structured ESUs. For this hypothesis, we use a combination of frequency-based and genotypic-based statistical methods to assess the number and spatial distribution of MUs within inferred ESUs. Finally, we explore factors that have shaped the spatial distribution of genetic diversity in N. obscura and highlight the direct implications of our findings for conservation management.Here we employed recently developed microsatellite DNA markers to provide finer resolution of recent genetic divergences. This, combined with greatly increased sampling, allows a more detailed assessment of population structure and genetic diversity over multiple spatial scales (i.e. within and between catchments) across the entire distribution of Permission to undertake field work and collect specimens was obtained under the following permits: Victorian Fisheries research permits RP 581 and RP 945, Victorian Flora and Fauna permits 10002072 and 10004939, Victorian National Parks permit 10004939, South Australian Primary Industries and Resources - Section 59 and 115 Exemptions. Specimens were obtained under Arizona State University Institutional Animal Care and Use Committee (IACUC) approval 09-1018R, Brigham Young University IACUC approval 070403, University of Adelaide Animal Ethics Committee approval S-32-2002 and Flinders University Animal Welfare Committee approval E313.N. obscura (N. obscura were amplified in two polymerase chain reaction (PCR) multiplexes of six and eight loci, respectively 2O, sized with GS500LIZ size standard and analysed using an automated ABI 3130 capillary electrophoresis system (Applied Biosystems) with one run per multiplex PCR. Genotypes were binned and scored visually with GeneMapper 4.0 (Applied Biosystems). Genotypes were checked for scoring errors putatively related to null alleles, stuttering, and large allele drop-out using Micro-Checker 2.2.3 A total of 541 individuals were sampled from 27 locations (n = 5–40 per site) across the entire extant range of  obscura . DNA wasA), expected (HE) and observed (HO) heterozygosity, and inbreeding coefficient (FIS) were calculated in GenoDive 2.0 R) was estimated using the rarefaction procedure in HP-RARE Fisher's exact test of linkage disequilibrium and tests for departures from Hardy-Weinberg equilibrium (HWE) were conducted using GENEPOP 4.1.4 STSTST and RST was also performed between years at 12 sites where samples were collected on multiple occasions. In order to determine if either FST or RST was more appropriate for this study, the relative contribution of genetic drift and mutation to population differentiation was assessed ST with permutated RST (pRST) values. Arlequin was used to perform an analysis of molecular variance (AMOVA) with 1000 permutations based on FSTPopulation genetic structure was assessed at multiple spatial scales, both across the entire distribution and within each of the proposed ESUs, using a combination of several frequency-based and genotype-based statistical methods. Pairwise FA Bayesian clustering analysis of individual genotypes using STRUCTURE 2.3.4 Principal coordinates analysis (PCA) was also employed to allow visual examination of the genetic affinities of individuals across the entire distribution and to clusters identified within each lineage. Pairwise genetic distances m) is the proportion of each population having migrant ancestry. First generation migrants, or the offspring of at least one first generation migrant, are considered as having migrant ancestry. The software was run for 10 million iterations with a 1 million iteration burn in. Mixing parameters for allele frequencies, inbreeding coefficients and migration rate were adjusted to achieve optimum acceptance rates of 20–40% We used BayesAss 3.0 ST and geographic distance. Euclidean distance was calculated because most sites occur in isolated catchments and are not connected by continuous stream lengths.Spatial genetic structure was assessed at both the population and individual level. At the population level isolation by distance (IBD) was assessed using Mantel tests. These tests were applied to each of the three lineages that displayed evidence of population structure to determine the association between pairwise population FST and geographic distance while controlling for hierarchical population structure by using a binary model matrix describing whether comparisons were made either between or within the identified clusters Results of a test for IBD can be difficult to interpret when population structure is strongly influenced by sharply divided spatial groups r) was plotted as a function of discrete distance classes, partitioned so as to achieve a similar number of pairwise comparisons for each class r value indicates the presence of IBD and the x intercept can provide an estimate of the extent of IBD for each lineage r was calculated using multiple distance class analysis, also in GenAlEx 6.5. This method plots r as a function of increasing distance class sizes r is expected to decrease with the increasing size of each distance class. The last distance class for which r is significant is considered the limit of detectable IBD r by bootstrapping 1000 pairwise comparisons for each distance class To further evaluate spatial dimensions of genetic structure at an individual level within each lineage we used spatial autocorrelation N. obscura, with mean observed heterozygosity of 0.318 and mean allelic richness of 1.92  was found to deviate significantly from expectations of Hardy-Weinberg equilibrium . The 14  of 1.92 . No majoClustering analysis in STRUCTURE demonstrated high levels of differentiation, identifying four major clusters . These gN. obscura (341 pairwise comparisons were significant out of 351 tests), with FST ranging from 0–0.84 (mean FST = 0.45), and RST ranging from 0–0.95 (mean RST = 0.49) . This indicates that, for this dataset, genetic drift contributes more to genetic diversity than mutation. Therefore, FST was used as the measure of population differentiation As predicted by the STRUCTURE analyses, significant and very high levels of genetic structure were evident between most demes of  = 0.49) . Assessm = 0.49) . The comST, AMOVA calculated across all sites attributed 34% of the variation to differences among proposed primary lineages (P<0.001), 18% to variation between sites within lineages (P<0.001), and just 2.6% among individuals within sites (P<0.001) (Based on FP<0.001) . When caP<0.001) . No signP<0.001) .The PCA results were also generally concordant with the other analyses of geographic population structure. The four primary genetic lineages were well supported in the initial analysis of all individuals , while tm were within the 95% credible interval and there was no evidence for asymmetric gene flow between populations. These results validate the delineation of genetically and demographically isolated clusters within the four lineages, and are in line with expectations given the high level of population structure indicated by the other analyses.Estimates of recent gene flow in BayesAss demonstrate extremely low exchange of migrants both among lineages (0.2–0.7%)  and betwN. obscura. Mantel tests demonstrated a strong, significant association between population FST and geographic distance   and Central  but were significant for the pure Eastern lineage   demonstrate the pattern of IBD also exists at a smaller scale for N. obscura  , indicatd 0.833) . The res= 0.005) . Althoug obscura .x-axes at 81 km, 84 km, and 115 km for the pure Eastern, Merri/Curdies, and Central correlograms, respectively , which intercepted the ectively . This stlineages  and at 3lineages . SignifiN. obscura. Remarkably strong population genetic structure that corresponds with catchment boundaries was detected in a pattern that broadly validates the four ESUs previously proposed for this species N. obscura. Several MUs were identified within each of the pure Eastern (3 MUs), Merri/Curdies (3 MUs), and Central (4 MUs) lineages  Our fine-scale analysis of genetic structure identified 11 hierarchical clusters nested within the four ESUs ; Fig. 2BST estimates of differentiation were mostly significant and high between sites  frequently coexists with N. obscura, shares many similar life history traits, including restricted dispersal, and is also threatened by habitat fragmentation G. pusilla is low and broadly similar to N. obscura, and population structure for both species follow similar patterns of distribution among catchments. Macquaria australasica (Macquarie perch) are a larger percichthyid that although disperses more readily than N. obscura, today is restricted to isolated headwaters and shows reduced connectivity associated with human-induced habitat fragmentation Maccullochella peelii (Murray cod) inhabits the main river channels and larger tributaries, tends to encounter fewer natural barriers to dispersal and is therefore less affected by habitat fragmentation Connectivity is a fundamental ecological and evolutionary process shaping the spatial distribution of genetic variation of species N. obscura. At a regional level there is very strong genetic structure with significant divergence between ESUs  and O. mykiss (rainbow trout). The increase in hybridisation observed for N. obscura provides further evidence of the negative effects of habitat fragmentation and degradation in this region.At a smaller spatial scale however, hydrology has been subjected to significant anthropogenic modification. For example, an extensive network of flood mitigation drains have been constructed throughout the Millicent Coast region N. obscura. Using a combination of spatial and non-spatial, and both individual and population based analyses, we identified 11 MUs confined mostly to individual catchments, within which individuals are largely genetically and demographically independent. Conservation of these units should therefore be managed separately in order to maintain the genetic integrity of populations. The MDB lineage appears to be one highly connected population, indicating that family groups for the breeding program can be formed using any combination of fish from different MDB sites. Also, offspring from the breeding program can therefore be released into any MDB site regardless of the specific MDB site of their parents' origin. This permits greater flexibility in selecting the best habitat for release sites. However, populations from other ESUs should not be mixed with the MDB ESU as, due to their significant genetic divergence, this could lead to a reduction in overall fitness because of issues related to local adaptation, and perhaps to outbreeding depression The findings presented here have several direct implications for conservation management of in situN. obscura might occur should this be considered as a conservation management option in the future. Translocations have however generally had a low success rate in the past Cottus cognatus (slimy sculpin) exhibited reduced fitness in a study of the consequences of freshwater fish translocations in southeast Minnesota In addition to providing information for management of the conservation breeding program, there is potential for the same principles to be used to directly manage fragmented wild populations in other ESUs N. obscura across its range. Modifications to natural flow regimes have reduced both the amount of available habitat and population connectivity. Given the patchy distribution and low abundance that characterises N. obscura, the low genetic diversity and highly differentiated populations uncovered in this study are not surprising. The limited opportunities for dispersal appear dependent upon intermittent flooding that occasionally connects isolated habitat patches. It is therefore critical for the conservation of N. obscura that no further modifications to the natural hydrology of this region are undertaken. Furthermore, it is vital that habitat is protected wherever extant populations persist and, where possible, connectivity between populations within MUs is restored to allow natural evolutionary processes to continue.Habitat fragmentation has clearly had a major influence on the decline of Table S1Nannoperca obscura.Microsatellite markers  amplified for (DOCX)Click here for additional data file.Table S2Nannoperca obscura.Geneclass2% probability of individual population assignment of (DOCX)Click here for additional data file.Table S3ST and RST for Nannoperca obscura.Pairwise population F(DOCX)Click here for additional data file.Table S4ST and RST for sites where Nannoperca obscura samples were collected in multiple years.Pairwise population F(DOCX)Click here for additional data file.Table S5m) between Evolutionarily Significant Units (ESUs) and 95% credible intervals (CI) calculated with BayesAss.Estimated migration rates ((DOCX)Click here for additional data file.Table S6m) between Management Units (MUs) and 95% credible intervals (CI) calculated with BayesAss.Estimated migration rates ((DOCX)Click here for additional data file.Figure S1Principal coordinates analysis based on 14 microsatellite loci for Nannoperca obscura individuals from each genetic lineage. A) pure Eastern. Eigen values for the first and second axes have been plotted, which explain 38% and 23% of the variance, respectively. E1, E2 and E3 refer to genetic clusters identified within this lineage; B) Merri/Curdies. Eigen values for the first and second axes explain 40% and 26% of the variance, respectively. M1, M2, and M3 refer to genetic clusters identified within this lineage; C) Central. Eigen values for the first and second axes explain 34% and 26% of the variance, respectively. C1, C2, C3, and C4 refer to genetic clusters identified within this lineage; D) Murray-Darling basin. Eigen values for the first and second axes explain 24% and 22% of the variance, respectively. No genetic structure was apparent within this ESU.(DOCX)Click here for additional data file.Figure S2Correlograms showing the autocorrelation coefficient r as a function of increasing distance classes for A) pure Eastern, and B) Merri/Curdies ESUs. Distances are the maximum for each class, grey bars indicate 95% CI about the null hypothesis of no genetic structure, and error bars about r indicate 95% CI as determined by bootstrapping.(DOCX)Click here for additional data file.Figure S3Correlogram showing the autocorrelation coefficient r as a function of increasing distance classes for Central ESU. Distances are the maximum for each class, grey bars indicate 95% CI about the null hypothesis of no genetic structure and error bars about r indicate 95% CI as determined by bootstrapping.(DOCX)Click here for additional data file.


A. c. talifuensis–concinnus) with the combined analysis of phylogeography and Ecological Niche Models (ENMs). We sequenced three mitochondrial DNA markers from 32 populations  and used phylogenetic inferences to reconstruct the intra-specific relationships among haplotypes. Population genetic analyses were undertaken to gain insight into the demographic history of these populations. We used ENMs to predict the distribution of target species during three periods; last inter-glacial (LIG), last glacial maximum (LGM) and present. We found three highly supported, monophyletic MtDNA lineages and different historical demography among lineages in A. c. talifuensis–concinnus. These lineages formed a narrowly circumscribed intra-specific contact zone. The estimated times of lineage divergences were about 2.4 Ma and 0.32 Ma respectively. ENMs predictions were similar between present and LGM but substantially reduced during LIG. ENMs reconstructions and molecular dating suggest that Pleistocene climate changes had triggered and shaped the genetic structure of black-throated tit. Interestingly, in contrast to profound impacts of other glacial cycles, ENMs and phylogeographic analysis suggest that LGM had limited effect on these two subspecies. ENMs also suggest that Pleistocene climatic oscillations enabled the formation of the contact zone and thus support the refuge theory.Although a number of studies have assessed the effects of geological and climatic changes on species distributions in East Asian, we still have limited knowledge of how these changes have impacted avian species in south-western and southern China. Here, we aim to study paleo-climatic effects on an East Asian bird, two subspecies of black-throated tit ( Phylogeographic analysis is a powerful way of gaining insights into the historical processes that have shaped current species distribution and genetic variation South-western and southern China is one of the most species-rich regions in the world. This region ranges from an elevation of more than 2000 m on the Yunnan-Guizhou (Yungui) plateau to the medium and low mountains (lower than 1800 m) areas of East China. Many theories have been proposed to explain its extraordinary number of species. For instance, this area may have served as a refugium during the Pleistocene glaciations  is a wide-spread and small passerine bird that lives in pine and open broadleaf forest, generally at middle altitudes. Its range extends from the foothills of the Himalayas in Pakistan and northern India through large areas of southern China to northern Burma, Vietnam and Taiwan. Currently, six subspecies are recognized; A. c. concinnus, A. c. talifuensis, A. c. iredalei, A. c. manipurensis, A. c. pulchellus and A. c. annamensis, some of which differ markedly in plumage pattern and coloration. For instance, A. c. pulchellus and A. c. annamensis are gray-headed but the other subspecies are red-headed. Studies have discovered significant genetic divergence among these red crowned subspecies A. c. iredalei to 5.4–5.8% between A. c. iredalei and A. c. manipurensis, and up to 5.3–5.8% between the latter two subspecies and subspecies A. c. talifuensis–concinnusA. concinnus complex has not been resolved, the monophyly of A. c. talifuensis–concinnus was strongly supported by the nuclear and MtDNA makers (A. c. concinnus). However, in our previous survey of the phylogenetic relationships of the long-tailed tits, we discovered substantial genetic differentiation between four black-throated tit specimens, including two A. c. concinnus and two A. c. talifuensisThe black-throated tit A. c. talifuensis–concinnus. MtDNA sequences served as our genetic data because its patterns can be robust indicators of patterns of population In this paper, we aim to improve the understanding of glacial impacts on East Asian birds by coupling phylogeographic analysis and ecological niche models to investigate the population structure and demography of the intensive sampling specimens of A. c. talifuensis–concinnus and the historical demography of any identified lineages.Describe the geographic structure of genetic variation within A. c. talifuensis–concinnus.Illustrate the effects of climate changes on the genetic divergences of Determine if last glacial maximum (LGM) had an impact on this species.Identify the mechanism for the formation of the narrowly circumscribed contact zone.All used samples are unprotected bird specimens from the specimen collection of the National Zoological Museum, Institute of Zoology, Chinese Academy of Sciences . The collection was under the permit from Forestry Department and conformed to the National Wildlife Conservation Law in China. No living animal experiments were conducted in the current research.A. c. talifuensis–concinnus (cyt b) and NADH dehydrogenase subunit 2 (ND2) genes. As described in We examined a total of 203 individuals from 32 localities within the breeding range of oncinnus . Total gcyt b and No. HQ605492 - HQ605694 for ND2.Each round of PCR reactions also included one negative control to check for contamination. The PCR products were purified using a QIAquick PCR purification kit (QIAGEN) and sequenced on a Perkin-Elmer 377 semiautomated DNA sequencer (Applied BioSystems) using a Perkin-Elmer Prism terminator cycle sequencing kit (Applied BioSystems) with Ampli Taq FS polymerase and BigDye terminators. Both strands of each PCR product were sequenced. Complete sequences were assembled using Seqman II (DNASTAR). Sequences were compared visually to the original chromatograms to avoid reading errors. All sequences are accessible at GenBank under the accession No. HQ605086 - HQ605288 for COI, No. HQ605289 - HQ605491 for cyt b and ND2 were aligned using ClustalW as implemented in MEGA 4 cyt b from nucleotide 1228–2211 and ND2 from nucleotide 2212–3192. The number of segregating sites, haplotype diversity and nucleotide diversity for each population and all populations combined were estimated using DnaSP 5.0 Sequences for COI, (Parus major) and long-tailed tit  were used as outgroups. The substitution models for each of the three gene sequence and the concatenated data sets were selected using MrMODELTEST 2.2 http://genedrift.org/mtgui.php) based on the Akaike information criterion Maximum likelihood (ML) and Bayesian methods (BI) were used to identify major lineages and evaluate relationships among haplotypes. An individual from the great tit ML trees were constructed using PhyML version 3 A. c. talifuensis–concinnus because many of the underlying assumptions of traditional tree-building methods  are often violated in intraspecific studies FST. Genetic distance and pairwise number of nucleotide differences were used to calculate the pairwise FST. A permutation test was used to calculate the probability that two populations had no genetic differentiation. For both tests, the significant level was set to 0.01.We used unrooted networks to explore the phylogeographic structure of divT = t/2eN), the migration rate between populations since divergence (M = 2emN), and the population parameter theta . The program was first run using default search settings and priors (for theta and divergence time). Following this data exploration, we set our prior for T and M to 5. Analyses were run for 5 million generations following a burn-in period of 500,000 generations, and repeated three times to ensure convergence upon the same posterior distributions for each of the parameter estimates. Because direct rate estimates for the concentrated sequence were not available, estimates of T were converted to real time assuming a neutral mutation rate of 1.05×10−8 substitutions/site/year according to the recent studies We used the coalescent-based program MDIV A. c. talifuensis–concinnus. First, statistical tests designed to assess whether nucleotide polymorphisms deviated from expectations under neutral theory [Tajima's D SF] were carried out in ARLEQUIN 3.1.2. Significance of D and SF values were determined using 1000 simulated samples to produce an expected distribution under selective neutrality and population equilibrium. The cut-off level for statistical significance was 0.05. For Fu's SF, significance at the 0.05 level was indicated when P values were <0.02 Three commonly used methods were applied to explore the demographical history of τ) of the mismatch distribution expressed in units of mutational time as t = τ/2ut is the expansion time in number of generations and u is the mutation rate per generation for the whole sequence.Second, the program ARLEQUIN 3.1.2 was used to compare the observed frequency distribution of pairwise nucleotide differences among haplotypes with that expected from a population under expansion . For populations which have undergone historical expansions, plots of the distribution of pairwise differences among haplotypes are expected to be unimodal, whereas populations in equilibrium generally show multimodal distributions Third, past population dynamics were examined using Bayesian skyline plots (BSP) in the program BEAST 1.4.8 http://www.worldclim.org/) Rhinolophus ferrumequinumWe used algorithms of MAXENT For MAXENT, we used the quadratic features and set the maximum iterations to 2000, replicates to 10. Eighty percent of the localities were randomly selected to train the model and the remaining 20% to test the model performance. The fade-by-clamp option was selected to remove heavily clamped pixels from the final prediction. Other parameters were used default values. For GARP, We changed the total run to 100, max generations to 900 and training proportion to 80. The training data was divided into true training data (for model rule development) and intrinsic testing data (for rule-testing intrinsic to GARP processing), and the remaining 20% data was set aside for an independent test of model quality. The ‘best subset’ was generated according to the soft omission threshold of 20% and the commission threshold of 50%.The value of AUC (Area under curves), sensitivity and omission error were used to assess the model's performance The combined length of the obtained sequences (3192 bp) contained 336 polymorphic sites, 235 of which were parsimony informative. These polymorphic sites defined 139 unique haplotypes, 112 of which were observed in a single bird. Two widespread haplotypes were observed and shared by 15 individuals from different sampling sites. For sample sizes larger than four, haplotype diversity values were nearly maximal: from 0.8333 to 1; nucleotide diversity values were between 0.052% and 0.378% . PhylogeThe Maximum likelihood and Bayesian phylogenetic inferences both divided our data into three major clades with strong support for both the lineages themselves and the relationships between them . The priClade A: A lineage comprising sampling areas spanning Yunnan Province.Clade B: This lineage is restricted to the region between northern Yunnan Province (Lijiang) and southwest Sichuan Province .Clade C: The largest lineage which is distributed from the western border of Guizhou and Guangxi, across the wide areas of central China, to the eastern seaboard.A. c. talifuensis includes lineages A and B, whereas lineage C, with the exception of samples collected at site PZH which includes individuals of both lineages B and C, exhibits strong concordance between the geographical mtDNA lineage and the currently described nominate subspecies, A. c. concinnus.All three lineages are strongly supported by posterior probabilities (PP>95%) and bootstrap values (BV>70). The relationships among these lineages are also supported at greater than 95% PP and 70 BV. Lineage B and C are inferred to be sister taxa, whereas lineage A is a sister to B and C. Several well supported clades (PP>95%) are apparent within lineage C. In terms of the current taxonomy, subspecies mN<1.The constructed major-joining network revealed three major clusters which were congruent with the three clades in the phylogenetic tree . Within The estimated divergence time between lineage A and lineage B–C was 1.79 (95% CI: 0.84 – 2.68) coalescence units. Using our estimated mutation rate to convert the MDIV estimate to real time, we estimate a divergence time of approximately 2.4 Ma (95% CI: 4.29 – 0.88 Ma). Lineages B and C were estimated to diverge at 0.29 (95% CI: 0.25 – 0.56) coalescent units, which converted to real time corresponds to 0.32 Ma (95% CI: 0.76 – 0.21 Ma). The migration rate between lineage A and lineage B–C is near zero (95% CI: 0 – 0.44). Estimation of the migration rate between lineage B and lineage C suggests a rather restricted gene flow M = 0.12 (95% CI: 0.03 – 0.91).Fs statistics were negative for all three lineages. However, only the statistics values for lineages A and C were significant. For mismatch analyses, lineages A and B showed a multimodal distribution profile, but the statistical mismatch distribution test statistics (SSD and rg) were not significant. Plots of the distribution of pairwise differences for lineage C are unimodal . However, GARP model reconstructions fail to project Taiwan as a suitable habit, possibly because of a lack of data. The ENMs suggest that the black-throated tit had distribution similar to the present during LGM . Furthermore, analysis of molecular variance partitioned data into three phylogroups with clearly defined geographical population structures. We noticed that the sampling sites of specimens for A. c. talifuensis analyzed by the previous studies were located in the area of lineage C. Thus, we compared the genetic distance  for a specimen of lineage A  with other two subspecies specimens  which are available at GenBank . The genetic distance was 4.9% between A. c. talifuensis and A. c. manipurensis and 5.1% between A. c. talifuensis and A. c. iredalei. These results suggest that the black-throated tit displays high genetic differentiation and zones of lineage endemism even within the Chinese part of its range.Previous studies suggested that there was no genetic divergence within Fs statistical tests and the unimodal mismatch distribution suggest that lineage C has undergone a past population expansion. For lineage A and lineage B, however, values of statistical tests (significantly negative or negative) and mismatch distributions  were not consistent with each other well. The coalescent-based method, which incorporates information from the genealogical tree structure of DNA sequences, seemed to be a more effective way of estimating demographic processes. Bayesian skyline plots indicate different population dynamics among lineages; a relatively constant population size in lineage A, a past population reduction and recent slight increase in lineage B, and successive population expansions in lineage C. Although BSP confidence intervals for lineage B were large, they are all supported by the ENMs. ENMs indicate that the amount of suitable habitat available was constant for lineage A, very limited range for lineage B during both the LIG and LGM, and a reduced distribution for lineage C during LIG.The significant values for Tajima's D and Fu's The genetic distances  between lineages (of 4.8% between lineages A and B and 4.9% between lineages A and C) indicate relatively clear genetic separation between these lineages. However, the reasons for this are not clear and there is no obvious geographic barrier to dispersal in the current distribution. Marked genetic divergence within continuous habitat generally could be due to a cryptic barrier, or be a relic of a now absent historical barrier. Recently, the uplift of the Tibetan plateau has been hypothesized to induce phylogeographic breaks within or among species both on and outside the plateau in Southeast Asia Alternatively, our molecular dating and ENMs reconstructions strongly suggest that Pleistocene climatic oscillations were more likely the reasons for the divergence between lineage A and lineages B–C. Our ENMs reconstructed for LIG and LGM, especially for LIG, show that the present contact zone among those lineages was relatively unsuitable. The predictive fragmented habitats would indicate the major refuges for the ancestors of those extant lineages. Therefore, climatic oscillations had real impact on the distribution of the black-throated tit. As the starting point of diversification is given right at the Pliocene/Pleistocene with 2.4 Ma, this separation was very likely under some Pleistocene impact at least with respect to the first cooling event. Given the fact that the ENMs simulations represent just the final phase of a series of repeated glacial cycles and fragmentation events, there were surely multiple effects of population decrease and expansion during the total time frame of lineage divergence. Thus, it was likely that climatic oscillations had caused the divergence between lineages A and B–C.Sasia ochracea) to mountainous refugia resulting in increased genetic differentiation Southeast Asia had experienced several climatic oscillations during the last 0.4 million years A. c. talifuensis–concinnus. This conclusion is supported by both the ENMs and phylogeographic analyses. For instance, the ENMs suggest that there has been suitable environment for lineage A in Yunnan since the LIG. This result, combined with the constant population suggested by Bayesian skyline analyses, indicates that LGM may have had little impacts on this lineage. For lineage C, Bayesian skyline and mismatch analyses detect a strong signal of a recent population expansion across most of this lineage's current range. However, because the ENMs suggest that a similar geographic range as the present during the LGM and much of its LGM habitat was uninhabitable during the LIG, and the genetic data suggest that the range of this lineage expanded before the LGM (about 0.07 Ma), we think that LGM effects were limited. This is also true for lineage B, as our result showed that this lineage has a more restricted distribution during LGM than it does at present but more suitable habitat than it had during LIG. However, the significant genetic divergence between the western (PZH and JZS) and other populations within lineage C suggests that small refugia may have existed. Similar results were found in a phylogeographic study of the canopy tree Eurycorymbus cavalerieiLast Glacial Maximum of Pleistocene climatic oscillations (0.021−0.018 Ma) had a profound impact on species' distribution and population sizes, e.g. in the heavy ice sheets covering northern regions of Europe and North America In fact, our reconstructions of LGM distribution are much congruent with previous phylogeographic studies Contact zones generally refer to the sympatric areas of closely related species which have their own separated primary distributions. Contact zones are important to understand the process of speciation, dispersal and vicariance. It is known that many closely related species pairs of birds are distributed in parapatry or allopatry with narrow contact zones Seicercus affinisCettia flavolivaceaCerthia discolorPhylloscopus cantator and P. rickettiCerthia familiaris bianchii and C. f. khamemsisA plethora of hypotheses have been suggested to explain the cause of contact zones, such as the refuge, river refuge and disturbance-vicariance hypotheses Table S1Coordinates, haplotype diversity and nucleotide diversity of each sampled population.(DOC)Click here for additional data file.Figure S1Ecological niche models predicted distributions using Maxent for the current (a), LGM (b) and LIG (c).(TIF)Click here for additional data file.


Hordeum vulgare L.) landraces from the central highlands of Ethiopia, we have examined the molecular variation at seven nuclear microsatellite loci.Among the cereal crops, barley is the species with the greatest adaptability to a wide range of environments. To determine the level and structure of genetic diversity in barley , across three districts , and within each district along an altitudinal gradient . Overall, although significant, the divergence (e.g. FST) is very low between seasons and geographical districts, while it is high between different classes of altitude. Selection for adaptation to different altitudes appears to be the main factor that has determined the observed clinal variation, along with population-size effects.A total of 106 landrace populations were sampled in the two growing seasons  that have large within-population diversity. These landraces are also shown to be locally adapted, with the major driving force that has shaped their population structure being consistent with selection for adaptation along an altitudinal gradient. Overall, our study highlights the potential of such landraces as a source of useful alleles. Furthermore, these landraces also represent an ideal system to study the processes of adaptation and for the identification of genes and genomic regions that have adaptive roles in crop species. Among the cereal crops, barley is the species that presents the highest adaptability to a wide range of environments. It is cultivated from arctic latitudes to tropical areas, and it is grown at the highest altitudes. In Tibet, Nepal, Ethiopia and the Andes, farmers cultivate barley on mountain slopes at altitudes higher than any other cereals ,2.Ethiopia is probably the region of barley cultivation that presents the highest variability for climatic and edaphic conditions. It is cultivated from 1,400 m above sea level (a.s.l.) to over 4,000 m a.s.l., and it has adapted to specific sets of agro-ecological and microclimatic regimes throughout the country . LandracKnowledge of the population structure of Ethiopian barley landraces, together with a deeper understanding of the nature and extent of their variation, is an important prerequisite for efficient conservation and use of the existing plant materials. Several studies have reported a high level of genetic diversity in barley populations from Ethiopia, such as those based on morphological traits -16, and Numerous studies on Ethiopian barley have detected correlations between altitude and the frequency of morphological -16,28, aMeher and Belg growing seasons [The present study is based on a large collection of Ethiopian barley landraces  that was established in 2005 in the North Shewa zone , from farmers who have used their own seed for generations . The col seasons  (see [16Belg and Meher), the three districts  and the three altitude classes . The same statistics were also computed for each locus, and are given in Additional file an) ranging from four (HVM20 and HVM67) to 23 (Bmac0156), with an average of 9.4 alleles per locus.Seven mapped nuclear SSRs were used to examine the levels and patterns of genetic variation of the barley landraces collected in North Shewa, in the central highlands of Ethiopia . Table 1RS = 8.22) and Tarmaber districts  and the allelic richness (RS) of the low altitude class  were lower than those of the intermediate altitude class  and of the high altitude class . The genetic diversity (He) of the intermediate altitude class (He = 0.65) was significantly higher  than that of the high altitude class (He = 0.57).The variation between seasons was not significant across any of the statistics considered (Wilcoxon signed-paired rank test), while the only significant difference among the districts  was for the allelic richness between the Ankober  out of the 66 SSR alleles, with each of these showing a frequency lower than 5%. The three districts showed a total of 15 private alleles (23%), three of which had a frequency higher than 5%. Finally, compared to seasons and districts, the altitude classes had a higher number of private alleles , four of which  had a frequency higher than 5%. Moreover, the average frequency of private alleles that showed a frequency higher than 5% was 0.00 for seasons, 0.063 for districts, and 0.085 for altitude classes.He, [na .The overall level of observed heterozygosity was very low (0.003). Only three individuals, all located at high altitude, showed one or two heterozygous loci. Overall, the average genetic diversity  in the na = 62, not considering the HVM20 locus) than seen for both the Nepalese barley landraces (na = 44) [na = 41) [Although it is important to consider the differences in the sample sizes, the Ethiopian barley landraces showed a higher number of alleles (na = 44)  and the na = 41)  (Table 2FST and RST) estimates between the seasons, districts, and altitude classes are given in Table FST (0.02) and RST (0.01), even if in contrast to RST, the FST was significantly different from zero (P < 0.001). A slightly higher level of differentiation was seen for districts . Among the altitude classes, the levels of differentiation were higher compared to seasons and districts, both for FST  and RST . The divergence (FST and RST) estimates between seasons, districts and altitude classes for each of the SSR loci analysed are given in Additional file FST,) between seasons was significant for four loci , even if the values were low, and varied from 0.02 to 0.05  than among districts or altitude classes  among the three districts within the same altitude classes . The differentiation among the low altitude classes of the three districts was higher  than that among the three districts within the intermediate  and high  altitude classes.Table et al. [Recently, various studies  haveet al.  non-spatet al. , and theet al. .ln likelihood values over 100 runs for K values ranging from 1 to 10 shows that the ln likelihood estimates increase progressively as K increases. Thus, we used the Evanno et al. [K) that characterised our sample. The highest ΔK value was found at K = 2, although high values of ΔK were found also for K = 3, 4 and 10. To minimise the effects of outlier runs, we also computed the ΔK value considering five random re-samplings of 80 of the 100 runs for each K value. In all cases, the highest ΔK corresponded to K = 2. Thus, hereafter, we will refer to cluster S1 and cluster S2 for the K = 2 populations identified by STRUCTURE.The plot of the average o et al.  method tq), or inferred group ancestries, of the individuals in each of the two clusters were computed. One hundred and ninety-seven individuals (93%) had a q higher than 0.70, in 192 individuals (91%), q was higher than 0.80, and in 183 (86%), it was higher than 0.90. Considering the threshold for membership of q ≥ 0.70, cluster S1 included 110 individuals, and cluster S2, 87 individuals.The percentages of membership  and the Belg  seasons  ; this parameter weights the relative importance given to spatial connectivities. We tested three different Ψ: 0.0 , and 0.6 and 1.0 . For each Ψ value considered, we performed 100 runs for K from 2 to 10. Then the DIC values of the 10 runs with the smallest DIC were averaged. In plotting the average DIC values versus K (from 2 to 10), we obtained an inflection point at K = 6. This was the same for all of the different interaction parameter (Ψ) values considered. Thus the best model was obtained for K = 6 and the lowest DIC value corresponded to Ψ = 0.6, indicating a moderate spatial correlation between individuals. The average over 10 runs with the smallest DIC value q was computed. We defined the six TESS clusters as T1, T2, T3, T4, T5 and T6. One hundred and twenty-five individuals (59%) were assigned to one of the six clusters with a q higher than 0.60, with 104 (49%) higher than 0.70, and 70 (33%) higher than 0.80. Figure q for the six TESS clusters for the three altitude classes, we observed that the previous cline of variation along altitude identified by the STRUCTURE assignment was also maintained for TESS; indeed, at low altitudes, the T1 and T2 clusters were mainly present, and these tended to be substituted by the T5 and T6 clusters at high altitudes  at low altitudes , and decreases at intermediate (35%) and high (45%) altitudes.The second approach used for the population structure analysis was the algorithm implemented in the TESS software that allows for prior spatial information when identifying clusters of individuals. This TESS software allows the testing of different values of the spatial dependent parameter (2 = 0.84), and T4, T5 and T6 are the major contributors for cluster S2 (R2 = 0.84)  for the TESS clusters T1, T2, T5 and T6 , significant (P = 0.05) for T3, but not significant for T4 (P = 0.08).The correlation analysis using Spearman's rho (ρ) for non-parametric tests showed that cluster S1 identified by STRUCTURE was significantly correlated with TESS clusters T1 and T2, and cluster S2 with T5 and T6 . No inference was carried out for TESS cluster T3 because no genotypes were assigned to this group according to this threshold. The analysis was conducted considering the seven morphological traits: kernel-row number, spike density, lemma awn barbs, glume colour, lemma type, length of rachilla hair, and lemma colour [To investigate the correspondence between the morphological and molecular characterisations, a contingency analysis was performed. The genetic groups defined by TESS analysis were considered, and only the genotypes that were assigned to one of the groups with a a colour .2) varied from 36.6% (length of rachilla hairs) to 13.0% (lemma type), with an average of 22.5%. The types and frequencies of each of these seven qualitative traits in each of the TESS clusters  are given in Additional file The results show that all of the traits except lemma awn barbs P = 0.066), were significantly associated with the TESS genetic clusters. The association was higher for length of rachilla hairs, glume colour and spike density (P < 0.0001), followed by lemma colour (P < 0.001), row number (P = 0.003) and lemma type (P = 0.011). The proportion of variance of each morphological trait explained by genetic groups  . However, the correlation between molecular and geographical distance was about 2-fold lower than that between the molecular distances and differences in altitude , and also lower than that between geography and altitude .Mantel tests between the genetic and geographical distance matrices were significant  were more genetically similar (i.e. related) than expected from a random distribution  were compared, they tended to be genetically more similar than for a random distribution ; and for the x axis, the mean altitude over all of the 20 populations. Thus, the diversity and LD estimates were computed along the altitudinal cline. This analysis was carried out separately for each of the two seasons. The Belg season was not represented at low altitudes, while the Meher season was characterised by the whole altitude range considered.Figure Belg: B1, B2 and B3 , and four for Meher: M1, M2, M3, M4 . The significance was tested between adjacent altitude ranges, using the Wilcoxon signed-paired rank test and the Bonferroni correction. The choice of the altitude ranges was designed to have the average altitudes more uniform between the Belg  and the corresponding Meher  classes . The number of individuals per altitude range varied from 34 to 40 for Belg, and from 16 to 36 for Meher.To test the significance of the trends seen, we grouped the individuals into different and non-overlapping altitude ranges: three for Meher season, the diversity (He) showed a significant increase according to altitude, to about 2,500 m a.s.l.. Indeed, M2 showed a higher level of diversity than M1 ; although no other comparisons were significant in the Meher season, a reduction at high altitudes was seen. A significant reduction of He at high altitudes was seen for the Belg season (B2-B3: P = 0.046)  Figure .RS, showed a marked increase from low to intermediate altitudes in the Meher season , and reached a plateau at intermediate and high altitudes   and a decrease at high altitudes .The allelic richness, ) Figure . For theBelg season reached non-significant levels in the interval between 2,685 and 3,010 m a.s.l.  with no private alleles (p < 0.05) between the two different planting seasons. Moreover, the two seasons show very similar levels of diversity. These data are very consistent to those for the morphological traits on the same populations [Overall, the population structure appears to be very low between seasons and geographical districts, while it is highly associated with altitude differences. Indeed, we show significant, but very low, divergence  to make inferences on the population structure of our sample. STRUCTURE identified two main clusters: rather than a subdivision among seasons or districts, these two clusters highlighted a clear altitudinal cline, and this tendency was maintained both within seasons and districts. Even though the analysis conducted with TESS identified more genetic groups (six), these were in line with the STRUCTURE results. Indeed, five TESS clusters were highly associated to the two clusters identified by STRUCTURE, with only one exception: TESS cluster T4. This indicates that the data obtained with the two different approaches are not completely redundant, with a more detailed and refined picture being provided by TESS. However, independent of a clear or a less clear definition of the "real" numbers of the populations that characterised our sample of genotypes, it is important to stress that both of these tools were very useful and that they were in agreement in the defining of the major aspects of the genetic structure of these barley landrace populations. For instance, as for the STRUCTURE clusters, the TESS groups reflect the altitudinal cline of variation, with five of them significantly correlated with altitude. The same effect of altitude was also seen for all of the SSR loci used in the present study. Moreover, most (84%) of the character-state frequencies obtained from Tanto Hadado et al.  . Furthermore, the Moran I values became positive only at high geographical distances, when comparisons were mainly made between genotypes at similar altitudes, but in different districts. These data confirm the hypothesis that selection for adaptation at different altitudes is the main factor that determines the clinal variation seen. This divergence between different districts but within the same altitude class is consistent with the selection hypothesis, and it is suggestive of different selection intensities at low and high altitudes. Indeed, when we compared individuals at low altitude across the three districts, there was a significant and relatively high FST , while there were very low and non-significant FST values between individuals at intermediate (0.04) and high (0.01) altitudes from different districts. These data are confirmed by a separate spatial autocorrelation at different altitude classes, which shows a correlation between geographical distances and genetic distances only at low altitudes. This suggests that the homogeneous selection for adaptation to high altitudes is the major factor that explains these data, along with isolation by distance (IBD) at low altitudes. Alternatively, we should consider the hypothesis of different migration patterns at low vs high altitudes: a higher level of seed flow between districts at high altitudes than at low altitudes.Two possible evolutionary scenarios can be considered to explain the altitudinal cline of variation that we have detected here: a clinal variation might have originated by various patterns of migration, or by different selections along the geographical or environmental gradients . The resgradient . Howevergradient . The hypIn support of the role of adaptive selection in shaping the allele frequencies of our collection, we found that the two morphological traits analysed that relate to colour (kernel and glume colour) are significantly associated with altitude. With their colour linked to the presence of anthocyanins, the coloured types in particular were positively related to altitude, while the white types showed a negative relationship with altitude. These anthocyanins are a subgroup of the flavonoids, for which an important role is well documented in responses to both biotic and abiotic stresses  and by the higher population sizes (larger plots), as shown by Tanto Hadado et al. [However, even though there is a large amount of coherent evidence that suggests that selection is a crucial factor in shaping the diversity of Ethiopian barley landrace populations, particularly at high elevations, selection alone is not enough to explain the diversity patterns seen. Indeed, the diversity significantly increases from low to moderate altitudes , both in its richness and its expected heterozygosity. This occurred only for the o et al. . At low o et al. . The maxo et al. . Thus, tBelg season, while at the extreme altitudes, the multilocus LD increased slightly. The reduction in the LD might also be explained by both the greater population size, which would increase the effective recombinations, and the selection, which will favour recombination that will produce novel multilocus genotypes.Our interpretation is further supported by the reduction in the multilocus LD that was seen from low to high altitudes, which was even non-significant in the The level of observed heterozygosity was very low (0.003), in agreement with the strict selfing nature of barley where the level of outcrossing is lower than 1-2% -50. Onlyin-situ conservation of landraces can provide a number of advantages, including the potential for adaptation to environmental changes because of an ongoing evolutionary process. Nevertheless, few studies have described the population structures of landraces or have analysed the roles of different evolutionary forces in the shaping of their genetic diversity. Here, we show that barley landraces from Ethiopia have highly variable local populations (farmer's fields) with large within-population diversity. These landraces also appear to be locally adapted, and the major driving force that has shaped their population structure is selection for adaptation along an altitudinal gradient. Moreover, our data suggest that the two-season system (which characterises barley cultivation in Ethiopia) and its effects on landrace population size have important roles in counterbalancing the homogenising effects of selection. Our study highlights the potential of barley landraces from Ethiopia as a source of useful alleles. They are also an ideal system to study the processes of adaptation and for identification of genes and genomic regions that have adaptive roles in a crop species, which can be achieved through association mapping and scanning for signatures of selection for molecular-diversity structure.Landraces are a key component of agro-biodiversity, and they represent a crucial reservoir of genetic diversity for plant breeding. Moreover, Meher) and the short (Belg) growing seasons. Within each farm visited, and according to the information from the farmers, all of the different landraces of barley that were grown and kept separated by the farmers during the seed selection process were collected separately from different fields and considered as different landrace populations (including those sampled in different seasons from the same farmer). In most cases (80 farmers), only one landrace was grown, while 10 of the farmers grew two landraces, and only two grew three landraces. For this reason, per farmer, more than one landrace population was sampled . Overall, the collection includes 106 barley landrace populations (fields), and Additional file The plant materials were derived from a barley landrace collection that was carried out in 2005, in the North Shewa zone in the central highlands of Ethiopia . This coFrom this collection, we randomly sampled two individual spikes for each population (farmer's field) collected from different individual plants. From each spike, a single seed was grown to the three-leaf stage and used for DNA extraction. Overall we analysed 212 genotypes from 106 barley landrace populations.et al. [Seven SSR markers , as one marker per chromosome , the average number of observed alleles per locus (no), the effective number of alleles per locus  and the Nei's [He) estimates based on allele frequencies, were calculated for each SSR locus. These were analysed as averages for each of the seasons (Belg and Meher), the districts , and the three altitude classes , and for the whole sample, using the POPGENE software, version 1.31 [RS, [na, ne, He and RS) were tested using the Wilcoxon signed-ranks non-parametric test for two groups, arranged for paired observations (i.e. one pair of estimates for each locus) [The number of alleles , the Lecus , using .31 [RS, , a metho.31 [RS, . The difh locus) .FST [RST . Similarly, the pairwise FST estimates between districts within the same altitude classes were computed.The partitioning of the genetic diversity was obtained using an analysis of molecular variance framework . This AFST  and RST tistics, ) estimat on SSRs ; indeed et al. [et al. [et al. [Hordeum vulgare.All of the seven SSRs used were the same as those for the study of Russel et al. , who wor [et al. , who wor [et al. , who woret al. [K) from 1 to 10, following the admixture ancestry model. The run length was 100,000 MCMC repetitions and 100,000 burn-in periods, with 100 independent replicates for each K, to achieve consistent results. An ad-hoc statistic  of the individuals in each of the inferred K clusters were computed by an additional run using 1,000,000 MCMC repetitions and 1,000,000 burn-in periods.To further investigate the population structure of our sample, a Bayesian-model-based approach was used, as proposed by Pritchard et al.  and implet al. , to assitic  was useK populations. The HMRF represents spatial connectivities as links in a network of individuals. Furthermore, it incorporates decay of the membership coefficient (q) correlation with distance, a property similar to isolation-by-distance. The network was automatically generated by the TESS programme using a Dirichlet tessellation obtained from the spatial coordinates of the samples. Considering the spatial distribution of our samples, we modified the network by removing several links, to account for potential geographical barriers. Runs were based on a burn-in period of 20,000 cycles followed by 30,000 iterations. One hundred replicates were performed for K values from 2 to 10, and for all of the runs the admixture model was used. In the analysis, we considered three values of the spatial dependence parameter (Ψ), 0.0, 0.6 and 1.0. This parameter weights the relative importance given to the spatial connectivities . For each run, the programme computed the Deviance Information Criterion (DIC), which is a model-complexity-penalised measure of how well the model fits the data. The lower DIC values represent the better fits for the data. Thus, we selected the 10 runs for each K that corresponded to the 10 lowest values of the DIC, and we averaged these values to determine the 'true' K number.An additional cluster analysis was performed with TESS, version 2.0 , a progrK [q over the 10 runs with the smallest values of the DIC for the K value identified. We used the software CLUMPP, version 1.1 [Greedy algorithm, to allow for label switching and to decide which of the clusters of each run corresponded to a specific label.The TESS algorithm incorporates an additional regularisation feature that generally leads to a less ambiguous determination of K ,68. Moresion 1.1 , which iK coloured segments, which represent the individually estimated membership fractions in the K clusters.The results of both of the STRUCTURE and TESS Bayesian clustering programmes were visualised using DISTRUCT, version 1.1 . The outq for the STRUCTURE and TESS clusters. The q values for STRUCTURE and TESS were used to investigate the relationships between the clusters identified by these two different programmes. The analysis was performed using Spearman's rho (ρ) for non-parametric correlation. Moreover, a step-wise multiple regression model was also performed, with the STRUCTURE clusters as dependent variables and the TESS clusters as independent variables. These analyses were carried out using the JMP 7 software .A simple linear regression model was used to test the relationships between the altitude and the 2) was also calculated. For this analysis, we considered the TESS genetic groups and only the genotypes assigned to one of the groups with a q higher than 0.70. No naked (hulless) barley was present among the 212 individuals analysed, thus the analysis was conducted considering seven morphological traits: kernel-row number, spike density, lemma awn barbs, glume colour, lemma type, length of rachilla hair, and lemma colour [To investigate the associations between the genetic and morphological characterisations, a contingency analysis was performed with the likelihood ratio chi-squared test, using the JMP 7 software. The proportion of the total uncertainty attributed to the model fit . The 99% confidence envelopes were estimated.Spatial autocorrelation between spatial  and genetic distances were computed separately using Spatial Genetic Software (SGS), version 1.0 d . These cI ,73 for sI correlograms over all of the seven SSRs and for each locus. Moreover, we also designed the distogram between geographical distance and altitude difference by considering the former as a quantitative trait and the latter as a linear geographical distance, and using the city-block distance.We separately obtain the Moran's The AIS software  was usedet al. [To test for correlation between the SSR alleles and altitude, an analysis of variance (ANOVA) was performed with the Wilcoxon non-parametric test. Moreover, using the morphological data of the Tanto Hadado et al.  study (tHe), the allelic richness (RS), and the linkage disequilibrium (LD) along the altitudinal cline.To obtain a representation of the total genetic structure of the Ethiopian barley landraces from North Shewa that is as accurate as possible, we performed a sliding windows analysis. This approach is commonly used in ecology ,78, but,We reconstructed populations of 20 landraces (fields), which corresponded to 40 individuals (windows); each window differed by only one landrace population , that represented the step size, moving forward from the lowest to the highest 20 landrace populations along the altitudinal gradient. Thus, considering the step size of one population, the windows partially overlapped, sharing 38 individuals (19 landrace populations). This analysis was carried out separately for the two seasons. The altitudes for each window were obtained by averaging the altitudes of the populations (farmers' fields) included in the window. Thus the differences in altitudes between the windows varied along the altitudinal cline.d, was calculated [To examine the LD  between our unlinked loci, a multilocus summary statistic of association between loci, rlculated . The siglculated , which pHe and RS, we grouped the individuals following three altitudinal ranges for Belg, and four for Meher. The differences were tested between adjacent altitudinal ranges within each season using the Wilcoxon signed-ranks non-parametric test [To test the significance of the trends observed for ric test .RP conceived, designed and coordinated the study; TTH and DR carried out the barley landrace collection in Ethiopia; EB performed the DNA extraction and molecular analysis; TTH DR EB and RP analysed the data and contributed to the drafting and writing of the manuscript. All of the authors have read and approved this version of the manuscript.This study is a component of the PhD thesis of Tesema Tanto Hadado, which focused on an analysis of phenotypic and molecular diversity in barley landraces from the central highlands of Ethiopia. All of the contributing authors are broadly interested in molecular evolution and ecological genetics, with particular interests in the conservation and use of biodiversity and crop genetic resources.Summary statistics computed for each locus considering the two seasons, three districts, and three altitude classes, and for the whole sample.Click here for fileFST and RST) estimates for each of the SSR loci analysed, computed considering the seasons, districts, and altitude classesDivergence (.Click here for fileNon-parametric correlation (Spearman's rho) between TESS and STRUCTURE clusters.Click here for fileStep-wise multiple regression analysis for STRUCTURE and TESS. The model was performed considering the STRUCTURE clusters as dependent variables and the TESS clusters as independent variables, from the data illustrated in Figures Click here for fileRelationships between the molecular data and altitude.Click here for fileq) higher than 0.70  between the TESS clusters for their morphological traits .Click here for fileSpatial autocorrelation analysis between the geographical and genetic distances, performed separately for the three altitude classes. See legend to Figure 7 for colour key.Click here for fileCollection site coordinates of the barley landraces analysed.Click here for fileList of SSRs used in the present study.Click here for file


Uyghurs are one of the many populations of Central Eurasia that is considered to be genetically related to Eastern and Western Eurasian populations. However, there are some different opinions on the relative importance of the degree of Eastern and Western Eurasian genetic influence. In addition, the genetic diversity of the Uyghur in different geographic locations has not been clearly studied.In this study, we are the first to report on the DNA polymorphism of cytochrome B in the Uyghur population located in Xinjiang in northwest China. We observed a total of 102 mutant sites in the 240 samples that were studied. The average number of mutated nucleotides in the samples was 5.126. A total of 93 different haplotypes were observed. The gene diversity and discrimination power were 0.9480 and 0.9440, respectively. There were founder and bottleneck haplotypes observed in Xinjiang Uyghurs. Xinjiang Uyghurs are more genetically related to Chinese population in genetics than to Caucasians. Moreover, there was genetic diversity between Uyghurs from the southern and northern regions. There was significance in genetic distance between the southern Xinjiang Uyghurs and Chinese population, but not between the northern Xinjiang Uyghurs and Chinese. The European vs. East Asian contribution to the ten regional Uyghur groups varies among the groups and the European contribution to the Uyghur increases from north to south geographically.This study is the first report on DNA polymorphisms of cytochrome B in the Uyghur population. The study also further confirms that there are significant genetic differences among the Uyghurs in different geographical locations. Uyghur is one of the populations living in central Asian where people have undergone unceasing migration and interacted with other populations in prehistoric and historic times. As a consequence, Uyghurs share anthropometric and genetic traits with Eastern and Western Eurasian. They are genetically considered to be an admixture of Eastern and Western Eurasian populations, demonstrated by archaeological, anthropologic and genetic studies -6. The UCYTB is located in the coding region of mtDNA (positions 14576–16047), spanning the 1140 bp fragment. CYTB has undergone changes during evolution resulting in the occurrence of multiple single nucleotide polymorphic sites ,9. CompaIn this study, we for the first time report on the gene diversity of CYTB in the Uyghur of Xinjiang by completely sequencing their CYTB gene. We demonstrate that there was a genetic variance of CYTB occurring in the different regional Uyghurs and CYTB is a very useful genetic marker for the study of genetic differentiation of Uyghurs in Xinjiang. In particular, we showed the founder/ bottleneck event in the Xinjiang Uyghur.1190 bp PCR fragments were amplified in all studied samples and 1140 bp of DNA fragment encoding CYTB (positions 14747–15886) were sequenced. Compared with the standard reference sequence (rCRS), a total of 102 mutant sites were observed in the studied 240 samples  was predominantly found in the Aksu with a frequency of 20% and in the Kashgar with a frequency of 14.93%. Haplotype 13  existed most frequently in the Turpan (27.78%), Atush (16.67%) and Korla (16%), but was not in the Bortala nor Kumul. Haplotype 11  appeared frequently in the Korla (32%), Sanji (30%), Turpan (16.67%), and Atush (12.5%). Haplotype 83  was only observed in the Kumul group with a frequency of 30%, but not in other groups thereby showing region-specificity. Haplotype 89  was observed only in the Turpan with a frequency of 11.11%, while haplotype 59  and haplotype 66  were only found in the Kashgar group with a frequency of 2.99%, respectively.In order to investigate the genetic diversity of CYTB in ten regional Xinjiang Uyghurs, MDS analysis was first conducted within ten regional Xinjiang Uyghurs and groups were made based on their geography. As shown in Figure Tianshan and the ancient Silk road are a geographic dividing line separating Xinjiang into two parts: Southern Xinjiang (Nanjiang) and Northern Xinjiang (Beijiang). Nanjiang consists of Kashgar, Aksu, Hotan, Atush and Korla, while Kumul, Turpan, Sanji, Gulja and Bortala belong to Beijiang. Accordingly we divided ten regional Xinjiang Uyghurs into two subgroups: Southern Uyghur (groups living in Nanjiang) and Northern Uyghur groups (living in Beijiang). We then performed MDS analysis with the Southern and Northern Uyghur and other populations -18 in orFurthermore, we noticed that Gulja Uyghurs who geographically reside in northern Xinjiang appeared closely related to the southern subgroups  in the MDS plot, while geographic southern Uyghur subgroups Korla and Atush were close to the northern subgroups  as shown in Figure ST distances as seen in Table To further explore the genetic linkage, the genetic distances between each of the regional groups were determined Table . GeneticWith the same strategies in MDS analysis, we regrouped samples and calculated genetic distance Tables  and 5 anIn this study we first reported the genetic polymorphisms of cytochrome B in Uyghur population of Xinjiang. We demonstrated that cytochrome B is a very useful and informative marker for matrilineal identification and population studies, second to the mtDNA control region. There are population specific and regional population specific nucleotide positions in the cytochrome B sequence, which can help identify DNA sample's region and population information. The results of the present study conclude that: 1) there are founder/ bottle neck events in Xinjiang Uyghur; 2) the Uyghur is closer to Chinese, rather than to Caucasian in genetic distance; 3) There are genetic difference between the Southern and Northern Uyghur; 4) Gene influence from Asian (Chinese) is stronger on the Northern Uyghur, than the Southern Uyghur; and 5) CYTB is a good genetic marker for differentiation of subgroups.Xinjiang is located in Central Asia which is an intermediate region of the Eurasian continent. There were 36 nations existing in Xinjiang during prehistorical and historical times. Some nations were defeated by others and destroyed, overrun or forced to migrate to other places. Unceasing wars propelled endless migration and coalition, leading to genetic mixture and gene drafting. Additionally, there were two silk roads extending from east to west through Xinjiang, resulting in extensive exchange of culture and marriage between different populations. Our data confirm that Uyghur is an admixture population with contributions from both Eastern and Western Eurasian ancestries, which is consistent with the results from mtDNA control region sequences . Moreover, there were intermarriages in the Uyghur living in the Atush region. This might explain why the gene pattern of cytochrome B in the Atush Uyghurs was between that of the southern and northern Uyghur group, different from the other three southern group samples. As for Gulja, there were two large migrations of the southern Uyghur from south to north Gulja in history. Over time, the migrated southern Uyghurs expanded in Gulja and became the largest minority in Gulja . As a result, the Gulja Uyghur was and still is a branch of southern Uyghur, having little relationship with the northern Uyghur, which is in line with our data that the Uyghur from Gulja shares similar genetic characters with the Southern Uyghur. Although Korla is located in the south of Tianshan belonging to Nanjiang ( southern of Xinjiang), it is also adjacent to Turpan and Sanji. Korla is located in Bayingolin Mongol Autonomous Prefecture where the current major population is Chinese Han. As early as 94 CE, the Chinese government and military started to administrate this area and forced different populations to exchange and mix. The results from CYTB genotyping are consistent with that from the control region of mtDNA (unpublished data). Our data showed that the Uyghurs from Korla in genetics are different to the south Xinjiang Uyghurs.Cytochrome B is a very useful DNA marker with high discriminate power for matrilineal identification, as well as deduction of the region and population of people. The polymorphisms of CYTB were significantly different between different geographical Uyghur (between south and north). The influences of East Asian and European genetics in Uyghur varies between different geographic locations (particularly south and north) of Uyghur.A total 240 healthy unrelated samples were collected from ten different Uyghur concentrated locations/communities in Xinjiang, China Figure : Aksu . PCR was conducted in a PCR thermal cycler  with the following thermal cycling conditions: pre-denaturation at 95°C for 5 min; followed by 35 cycles at 95°C 30 s; 55°C 30 s; 72°C 60 s; and final extension at 72°C for 10 min.The primers used for PCR amplification of the CYTB gene listed in Table PCR products were visualized on a 1.5% agarose gel and purified using a Biomiga kit . Sequencing reactions were conducted in a PE-9600 thermocycler , using a BigDye terminator v3.1 Cycle sequencing kit (Applied Biosystems) with the following conditions: 25 cycles of 95°C for 30 s; 50°C for 30 s; and 60°C for 4 min. The primers L14724, H15149, L15283 and H15363 were used for sequencing listed in Table http://www.megasoftware.net/mega.php). The haplotypes of CYTB were designed using DnaSP5.0. The genetic distances between Xinjiang regional Uyghur populations and other populations were determined by the analysis of molecular variance using ARLEQUIN3.5 (http://cmpg.unibe.ch/software/arlequin3). A difference with a P-value < 0.05 was considered statistically significant. Multidimensional scaling (MDS) plot of mtDNA was created by SPSS 19 for studying the genetic relationship between populations. Phylogenetic trees were constructed by the neighbor-joining method using a Phylip 3.69 program (http://en.bio-soft.net/tree/Phylip.html). Median networks were constructed using NETWORK 4.6.1.0 software (http://www.Fluxus-engineering.com/sharenet.htm).Sequences were aligned using MEGA5.0 and compared with the standard reference sequence (rCRS) (The authors declare that they have no competing interests.AA carried out data collection and analysis. WQ carried out data analysis. WS participated in data analysis. WW, participated in data collection. FL participated in data discussion and manuscript preparation. KL participated in manuscript preparation. CZ participated in discussion of the study. FZ participated in the design of the study and coordination. ZM participated in the design of the study and sample collection. XZ participated in the design of the study, coordination, data analysis and manuscript preparation. All authors read and approved the final manuscript.Nucleotide mutation in the Uyghur.Click here for fileFrequency of haplotype of MTCYB.Click here for file


Of all countries in the Western Hemisphere, Brazil has the highest economic losses caused by dengue fever. We evaluated the cost-effectiveness of a novel system of vector surveillance and control, Monitoramento Inteligente da Dengue (Intelligent Dengue Monitoring System [MID]), which was implemented in 21 cities in Minas Gerais, Brazil. Traps for adult female mosquitoes were spaced at 300-m intervals throughout each city. In cities that used MID, vector control was conducted specifically at high-risk sites (indicated through daily updates by MID). In control cities, vector control proceeded according to guidelines of the Brazilian government. We estimated that MID prevented 27,191 cases of dengue fever and saved an average of $227 (median $58) per case prevented, which saved approximately $364,517 in direct costs  and $7,138,940 in lost wages  annually. MID was more effective in cities with stronger economies and more cost-effective in cities with higher levels of mosquito infestation. Dengue viruses cause ≈50 million infections annually worldwide, and ≈1% of these infections require hospitalization because of dengue hemorrhagic fever   have been developed to reduce personnel costs and directly measure adult female mosquito abundance in Brazil , a publicly available database of the Health Ministry of Brazil. Dengue cases were expressed as incidence per 100,000 inhabitants on the basis of the Brazilian Institute of Geography and Statistics  2010 population census.MID was implemented in 21 cities in Minas Gerais during April 2009–June 2011. These cities are dispersed throughout the state in areas that included a range of population sizes and incidences . Cities Dengue incidence was strongly seasonal, and outbreak probability varied substantially between cities , panel Ad = RIcontrol – RIMID). Under the null hypothesis that MID had no effect at decreasing RI, the median of the 1,000 differences would be 0. We tested this hypothesis using a sign test. The alternative hypothesis was that the median of the 1,000 differences would be significantly >0 if MID decreased the RI of treatment cities.To compare this large sample size in a case–control format to only 21 MID cities, we generated 1,000 random sets of 21 control cities with the same population distribution as the MID cities. Next, we calculated the relative difference in incidence (RI) for the same period before and after the start of surveillance for each treatment city . Likewise, for each set of the control cities, we calculated RI using the distribution of time frames in each group of treatment cities , panel CWe identified factors that affected the effectiveness and cost-effectiveness of MID by using a generalized linear model  with either RI or US dollars/prevented case as response variables. Factors considered were population size (PS); distance to 3 large populations (D3L); distance to 3 high-incidence populations; a ranking system for the effectiveness of using MID (PED); a measure of average mosquito infestation during the dengue season in 2011 (IMFA); population density; income per capita; and an index between 0 and 1 that included employment, income, education, and health, all with equal weight. Distances were the sum of Euclidian distance to 3 cities with population size (D3L) or density in the 90th percentile. We fit each variable individually and fit all possible linear combinations of the 8 variables.We chose between competing models by using delta Akaike Information Criterion (ΔAIC = AIC of intercept only model – AIC of target model). A higher ΔAIC indicates a better model of the data. When comparing nested models that differed by only 1 factor, 2 AIC points is considered a significant difference (α = 0.05). Statistics for all single-variable models, model selection results, and fits of the best multivariable and full models are shown in of E] – observed annual cases [O]). We calculated E by using a logistic model according to the equation iE = iOid(1 – iO/iK), where K is the maximum number of possible cases in city i. The logic is that the number of cases prevented depends on the estimated growth coefficient (control – RIMIDd = RI) and the observed cases (O) but is capped by a theoretical maximum on the number of possible new cases (K). In the main text, we assumed that K was equal to 30% of the population in city i, which has been observed   .All costs were in US dollars. Costs per dengue case were taken from the report of Sheppard et al. . Similarly, the estimated costs of dengue in the absence of MID were divided into ambulatory and hospitalized case-patients by multiplying the sum of the number of observed cases plus the number of prevented cases by the proportion of observed cases in persons who were ambulatory or hospitalized. The dollars saved annually were calculated by subtracting the cost of dengue in MID cities from the predicted cost of dengue if MID were not implemented. Underreporting was not accounted for because we had no city-specific data to inform estimates. Costs were not discounted because we considered all cases to be nonfatal and our study period was only 2.5 years.z = −31.59, p<0.0001)  , panel DThe most parsimonious generalized linear model of RI in MID cities included PS and IDFM . PS and Under the assumption that dengue could affect 30% of a population, we estimated that the number of cases prevented by MID annually in the largest cities  was 2,300–3,900 . In the Accurate estimates of dengue incidence and its economic effects are more limited (r = 0.53). The single-variable model results suggest that MID may be more effective in larger populations (The trend of increased effectiveness in larger populations might not be significant in the multivariable model (which includes IDFM) because PS and IDFM showed a positive correlation  to predict the number of cases that would be prevented. Although 30% is not unreasonable based on previous studies  per case prevented in Minas Gerais, the average value increased to $616 in 6 moderately sized cities  that did not show any savings in direct costs . Three oFurthermore, cities in which MID was implemented had historically high dengue incidences relative to control cities (mean ± SD 2007 and 2008 were 549.1 ± 592 in MID cities and 240.4 ± 567.6 in control cities). Thus, average estimates of cost-effectiveness may be high in cities in which MID was implemented. However, factors determining incidence patterns in a given city, such as population immunity, infrastructure, or human behavior, may not be static over time because high population immunity is not protective against novel serotypes (or genotypes with high forces of infection) and human behavior and infrastructure are continually changing. A predictive model of serotype dynamics across cities formulated on the basis of serologic data would be useful for decisions on which cities should implement MID so that the most cost-effective strategy can be achieved statewide.<$58. Furthermore, these numbers are underestimates because our study did not account for underreporting or additional costs from deaths. Investing more effort into integrating MID strategies and costs with vector control operations, and standardizing the MID-based control system across cities, should help to increase MID cost-effectiveness.Our study showed that MID is generally effective for decreasing case loads and suggested that an MID strategy is theoretically better than other strategies. Although MID cost-effectiveness varied between cities, implementation of MID saved hundreds of thousands of dollars on health care and ≈7 million dollars in lost wages statewide, and half the cities had cost-effectiveness values Monitoramento Inteligente da Dengue (Intelligent Dengue Monitoring System), Minas Gerais, Brazil.Monitoramento Inteligente da Dengue (Intelligent Dengue Monitoring System), Minas Gerais, Brazil. Table 1, Factors that affected effectiveness and cost-effectiveness; Table 2, Top 5 multivariate models; Table 3, Fit of multivariable models; Table 4, Cost-effectiveness and savings for each city.