Montastraea cavernosa. Zooxanthellae-free DNA extracts of coral samples (N = 105) were analyzed from four depths, shallow (3–10 m), medium (15–25 m), deep (30–50 m) and very deep (60–90 m) from Little Cayman Island (LCI), Lee Stocking Island (LSI), Bahamas and San Salvador (SS), Bahamas which range in distance from 170 to 1,600 km apart. Using AMOVA analysis there were significant differences in ΦST values in pair wise comparisons between LCI and LSI. Among depths at LCI, there was significant genetic differentiation between shallow and medium versus deep and very deep depths in contrast there were no significant differences in ΦST values among depths at LSI. The assignment program AFLPOP, however, correctly assigned 95.7% of the LCI and LSI samples to the depths from which they were collected, differentiating among populations as little as 10 to 20 m in depth from one another. Discriminant function analysis of the data showed significant differentiation among samples when categorized by collection site as well as collection depth. FST outlier analyses identified 2 loci under positive selection and 3 under balancing selection at LCI. At LSI 2 loci were identified, both showing balancing selection. This data shows that adult populations of M. cavernosa separated by depths of tens of meters exhibits significant genetic structure, indicative of low population connectivity among and within sites and are not supplying successful recruits to adjacent coral reefs less than 30 m in depth.Mesophotic coral reefs (30–150 m) have recently received increased attention as a potential source of larvae  to repopulate a select subset of the shallow water (<30 m) coral fauna. To test the refugia hypothesis we used highly polymorphic Amplified Fragment Length Polymorphism (AFLP) markers as a means to assess small-scale genetic heterogeneity between geographic locations and across depth clines in the Caribbean coral,  The effective design and management of MPAs requires an understanding of the nature and extent of the movement of individuals and their successful establishment within and among protected areas. While most assessments of the genetic connectivity between populations of corals are conducted on reefs leass than 30 m in depth Given the widespread damage common to most coral reefs Using genetic data we can gain important insight into overall population connectivity Nm and STF. These indirect genetic indices of connectivity have been problematic An assessment of gene flow among populations has traditionally employed population genetic methodologies based upon comparisons of allele frequency distributions among populations to estimate classic parameters like Montastraea annularis complex AFLPs have been used widely in plants, bacteria, and fungi though AFLPs have been underutilized as a population genetic marker in animals Montastraea cavernosa.AFLPs also have limitations; as dominant markers, each band has at maximum half the information content compared to microsatellites A total of 105 coral samples were analyzed from three locations, LCI, LSI, Bahamas and SS. For the analyses described below coral samples were binned into shallow (3–10 m), medium (15–25 m), deep (30–50 m) and very deep (60–90 m) categories for all locations. The binning was done prior to analysis in order to minimize sample size differences among groups . SamplesST of 0.088. Samples from SS were not included in this AMOVA analyses due to small sample sizes. The component of variance between groups (LCI and LSI) was 7.8% compared to 1.0% among samples within groups , that is, the assignment of an individual back to the population from which it was “collected” . In contTo assess genetic similarities potentially due to depth we repeated the AFLPOP analysis of the entire data set  categorized by depth alone. There was significant similarity among the samples  as measu2 of the variable, compared with all other variables in the model) <0.01 were excluded. The DFA of the randomized data set failed to identify any markers that would significantly differentiate among groups. In the first analysis using the actual data set, 16 of the 86 markers were necessary to build a model that significantly differentiated among the three populations  without respect to depth  a step-wise analysis method was used to build a model using the markers that contributed significantly to the discriminant function. All markers were evaluated and added one at a time to the model beginning with the marker that contributed most to the discrimination between groups. Additional variables were added until the respective F-values for the variable were >1.0. In order to avoid the addition of redundant markers to the model, markers with tolerance-values  than expected based upon simulations suggesting selection on these markers or genes closely linked to them. Similarly, in this study the markers identified as contributing significantly to the discriminant function may reveal loci under local selection. Interestingly, the FST outlier analysis did not reveal any markers that were common to both sites. Rather, the analysis indicated that the two sites (LCI and LSI) may be experiencing different selection regimes. At LCI two of the markers identified show greater differentiation than would be expected of neutral loci suggesting positive selection. For both markers the differences appear to be greatest in comparing the shallower sites (S and M) with the deeper sites  of the populations sampled within each site and the reproductive strategy of M. cavernosa as a broadcast spawner. Given that “adult” corals were sampled during this study and that the genetic structure of the populations is likely the result of multiple years' of recruitment, some sort of “sweepstakes recruitment” event is also unsatisfactory as an explanation for the observed results. Selection remains a possible explanation and is supported by the FST outlier analysis for at least LCI. Bongaerts et alSeriatpora hystrix on the Great Barrier Reef over a 30 m depth range and later provided evidence that divergent selection probably led to local adaptation for these populations of S. hystrixEunicea flexuosa, may be due to weak selection operating over the long pre-reproductive period of this species (15+ years) even in the face of what is presumed to be wide dispersal as evidenced by the little geographic differentiation among populations across the Caribbean. In this regard the genetic differentiation observed in this study could also be the result of strong selection on specific loci simultaneously with high levels of larval dispersal and gene flow that is unable to homogenize the effects of selection resulting in local adaptation Symbiodinium sp. phylotypes from the same colonies of M. cavernosa used in the AFLP analyses from LSI populations The cause of genetic heterogeneity among depths for Despite the small sample sizes used here the utility of AFLPs to reveal genetic differences among individuals of this broadcast spawning coral on this very small geographic scale suggests that this marker system warrants strong reconsideration as a tool in population genomic analysis, particularly when sampling is constrained. The great numbers of polymorphic markers that can be assayed combined with the universality of the protocol are definitive strengths as a tool in assessing the genetic connectedness of coral reef organisms , LSI  and SS . The distance between SS and LSI is ∼170 km. Distances between LCI and LSI and SS are 1640 and 1350 km respectively. Collection and CITES export permits for all coral samples were obtained from The Department of the Environment, Cayman Islands and The Department of Fisheries, Ministry of Agriculture and Marine Resources, Bahamas. Depth collections at these sites occurred at 3, 10, 15, 25, 30 50, 75, 60 and 90 m ±1 m. Samples were collected using both open and closed circuit mixed gas technical diving (LCI and LSI) as well as by using the Johnson-Sea-Link submersible (SS).High molecular weight genomic DNA was isolated using the Wizard SV Genomic DNA Purification System, as per manufacturer's protocol  for animal tissues. Prior to DNA isolation, samples were macerated lightly in saturated EDTA-DMSO saline (SED) buffer and spun at 16,000× g for 5 min to pellet the zooxanthellae and debris from the homogenate. All DNA isolations were checked for zooxanthellae DNA contamination using stringent zooxanthellae specific PCR AFLPs, like other multi-locus techniques, generate many bands, some of which are sensitive to PCR reaction conditions. Here, we have processed samples from DNA isolation through the final selective PCR in large, random lots containing samples from all sites to distribute any experimental error that may have been introduced by reaction conditions in an unbiased fashion. In addition, all PCR reactions were done using one machine and the same thermal cycle profile. Finally, the final selective PCR step was repeated three times for each sample. A band was scored as present only if appeared in all three replicates.et al.EcorRI adapter: 5′-CTC GTA GAC TGC GTA CC-3′, 3′5′-CAT CTG ACG CAT GGT TAA-; MseI adapter: 5′-GAC GAT GAG TCC TGA G-3′, 3′-TA CTC AGG ACT CAT-5′) at 16°C overnight with 1 U of MseI (New England Biolabs), 5 U EcoRI (Promega Corp), and 1 WeissU T4 DNA Ligase in 1X ligase buffer (0.1 mM ATP) with 0.5 M NaCl. Digested/ligated DNA fragments were diluted twenty-fold for the first pre-selective PCR amplification. Primers used in the “pre-selective amplification” were complementary to the adapters, with the addition of a single nucleotide - an “A” for the EcoRI adapters, and a “C” for MseI adapters. Five µl of the diluted restriction-ligation reaction was added to 15 µl of PCR mix . The pre-selective amplification program consisted of an initial cycle of 72°C for 2 min (to complete the ligation of the synthetic adapters), followed by 20 cycles of 94°C for 20 s, 56°C for 30 s, and 72°C for 2 min, with a final extension of 72°C for 20 min. The pre-selective PCR products were diluted ten-fold for use in the final “selective amplifications”. Primers used in the selective PCR had the same sequences as the pre-selective primers, with the addition of two additional nucleotides at the 3′ and a FAM tag on the 5′ end. Five µl of the diluted pre-selective PCR reaction products were added to 15 µl of the PCR mix . The selective amplification program consisted of an initial cycle of 94°C for 2 min, 94°C for 20 s, 66°C for 30 s, and 72°C for 2 min. This was followed by 9 cycles of 94°C for 20 s, 66°C for 30 s (decreasing 1°C/cycle), and 72°C for 2 min and another 20 cycles of 94°C for 20 s, 56°C for 30 s, and 72°C for 2 min, finishing with 72°C for 20 min. Products for the selective PCR were run on an Amersham MegaBACE 1000 96 capillary sequencer at the University of Florida's Interdisciplinary Center for Biotechnology Research. Resulting electropherograms were analyzed using SoftGenetics GeneMarker® (ver 1.51) for bands ranging from 50 to 400 bp in size in 5 bp increments. AFLP markers were scored as present for an individual sample only if a band appeared in all three replicates runs. A total of 213 marker size classes were assessed (3 markers ×71 size classes from 50 to 400 bp at 5 bp increments). Of the 213 marker size classes only those markers with a minimum frequency of “band presence” greater than 5%  were used in the final analysis.AFLP analysis was performed following protocols based upon Vos ST. In addition a population assignment technique was used to assess the genetic structure of the samples collected from LSI and LCI. The program, AFLPOP, examines the AFLP banding patterns –presence/absence data – and calculates log-likelihood values for any individual's membership in a population. Each individual is allocated to the population showing the highest likelihood for that genotype Overall levels of genetic differentiation among sampled populations were assessed using a nested Analysis of Molecular Variance  using all loci was then conducted using the computed value for neutral FST. Loci falling outside the 95% confidence intervals and with a false discovery rate (FDR) of 0.01 were considered putative candidates for loci under selection.To assess the contributions of specific markers to the observed patterns two techniques were employed. Multiple discriminant function analysis  was performed to build a discriminant function model to assess the utility of AFLP data to differentiate among populations. A forward stepwise analysis was used to build a model that included only those markers that significantly contributed discrimination among groups. Discriminant canonical function scores can be visualized by plotting the individual scores by group membership. Finally, to identify markers that display unusually high of levels of genetic differentiation and therefore may be subject to selection in the LCI and LSI populations F


The presence of a southeast to northwest gradient across Europe in human genetic diversity is a well-established observation and has recently been confirmed by genome-wide single nucleotide polymorphism (SNP) data. This pattern is traditionally explained by major prehistoric human migration events in Palaeolithic and Neolithic times. Here, we investigate whether (similar) spatial patterns in human genomic diversity also occur on a micro-geographic scale within Europe, such as in the Netherlands, and if so, whether these patterns could also be explained by more recent demographic events, such as those that occurred in Dutch population history.We newly collected data on a total of 999 Dutch individuals sampled at 54 sites across the country at 443,816 autosomal SNPs using the Genome-Wide Human SNP Array 5.0 (Affymetrix). We studied the individual genetic relationships by means of classical multidimensional scaling (MDS) using different genetic distance matrices, spatial ancestry analysis (SPA), and ADMIXTURE software. We further performed dedicated analyses to search for spatial patterns in the genomic variation and conducted simulations (SPLATCHE2) to provide a historical interpretation of the observed spatial patterns.We detected a subtle but clearly noticeable genomic population substructure in the Dutch population, allowing differentiation of a north-eastern, central-western, central-northern and a southern group. Furthermore, we observed a statistically significant southeast to northwest cline in the distribution of genomic diversity across the Netherlands, similar to earlier findings from across Europe. Simulation analyses indicate that this genomic gradient could similarly be caused by ancient as well as by the more recent events in Dutch history.Considering the strong archaeological evidence for genetic discontinuity in the Netherlands, we interpret the observed clinal pattern of genomic diversity as being caused by recent rather than ancient events in Dutch population history. We therefore suggest that future human population genetic studies pay more attention to recent demographic history in interpreting genetic clines. Furthermore, our study demonstrates that genetic population substructure is detectable on a small geographic scale in Europe despite recent demographic events, a finding we consider potentially relevant for future epidemiological and forensic studies. The presence of genetic gradients across Europe has been described and discussed for more than 30 years. In the case of autosomal markers, a southeast to northwest gradual change in the distribution of the genetic diversity has been reported using principal component analysis (PCA) ,2. InitiWe chose the Dutch population as an example because results from geological, archaeological, and historical studies strongly indicate that during several prolonged periods of time different factors and events resulted in discontinuities of human populations on the current territory of the Netherlands. A summary of the population history of the Netherlands is provided in the supplementary material [see Additional file http://www.affymetrix.com/estore/) from which 443,816 quality-controlled genome-wide autosomal SNPs were used in various spatial, cluster, and simulation analyses.Given the archaeological, geological and historical evidence for genetic discontinuity in Dutch population history, one might expect that the ancient genetic signatures from Palaeolithic and Neolithic times, such as the southeast to northwest cline seen across Europe, would not be detectable in the contemporary Dutch gene pool. To test this hypothesis via studying the spatial distribution of the Dutch genomic diversity, including computer simulations, and to investigate the overall genomic-geographic substructure of the Dutch population, we sampled 999 individuals at 54 sites across the Netherlands following a grid-like scheme. DNA samples were genotyped with the Genome-Wide Human SNP Array 5.0  and genotypes were inferred with the Bayesian Robust Linear Model with Mahalanobis distance classifier (BRLMM) algorithm. Sampling sites were not considered in the microarray genotyping procedure to avoid batch effects. Individual data cleaning was performed using the Tukey’s approach as applied in Lao et al. . In brieet al.  using thet al. ), where et al. .et al. [An IBS distance matrix between pairs of individuals was computed and plotted by means of classical multidimensional scaling (MDS) as implemented in the cmdscale routine of R software . Identicet al. :ij is a value ranging from 0  to 1  between individuals i and j. Wtot is the maximum value of sharing that can be obtained:where WF(t) is the normalized length of an interval between two SNPs, and it weighs the length of the fragment by the number of individuals sharing the segment:and ij for all the pairs. The distance matrix was plotted by means of MDS after adding a constant to the matrix in order to make all the eigenvalues positive [A distance measure was obtained by setting 1-Wpositive . The meapositive  as impleK) to 1 to 6. A pie chart map was constructed for K = 5 on ADMIXTURE consensus results  of indicij between i and j was computed as:The covariance in formula 13 of .P value of the autocorrelogram.The covariance matrix was used to perform a genetic based spatial autocorrelation analysis . We conshttp://genetics.cs.ucla.edu/spa/). This method attempts to model the allele frequency of each marker as a function of geographic positioning, and then places the individuals in this defined space. SPA was conducted on all SNPs in order to identify genomic regions showing steep allele frequency gradients. Genomic regions showing an excess of large scores for selection signal detection were detected by means of local Moran’s I statistic [To model the genotypes of each individual in two dimensions, we performed a spatial structure analysis (SPA)  with SPAtatistic . Local Mi is the marker of interest, n is the number of markers that are within a distance <50 kb of the marker of interest, Zi is the computed SPA value of the marker i and wij is the weight between marker i and j . Local Moran’s I statistic takes positive values  if the value of one SNP is extreme compared to the rest of the genome and it is surrounded by SNPs with values of similar magnitude. A P value was computed by reshuffling the value of the score 1,000 times at random, then computing local Moran’s I statistic for each marker and comparing it with the observed one. A Manhattan plot of the Local Moran’s I statistic value for these markers with a P value <5e-04 was computed using mhtplot function of the gap R package [where  package .We computed Weir and Cockerham’s combined Fst  between et al. [We performed two different SPLATCHE2  simulatiet al.  in Europet al.  with a cet al. ). Migratet al. , and negThe locations of the 54 Dutch geographic subpopulations from which the 999 individuals were sampled are shown in Figure P value = 1.455e-14) and somewhat less strongly with longitude . The second dimension  tends to differentiate individuals from the central-east region of the Netherlands  from the rest and correlates with longitude, albeit not very strongly . When considering both dimensions at the same time, the correlation in a symmetric Procrustes rotation [P value after 1,000 simulations <0.0005), suggesting that the proposed genetic map of the Netherlands fits the geographic map of sampling locations well.First, we performed a classical multidimensional scaling (MDS) analysis on an identical-by-state (IBS) distance matrix between all pairs of 969 individuals . By applying Mclust  to the frotation  between P value: 0.0001348) while the second dimension correlates strongly with latitude . Both MDS dimensions together correlate with the geographic coordinates . Furthermore, we observed a strong correlation between the MDS based on IBS and the one based on IBD . Third, we carried out two genetic clustering analyses of the genomic data. In the first analysis, we used ADMIXTURE [K = 1 to 6 [see Additional file K = 1 the cross-validation error was smallest indicating the most sensible model and that this error increased until K = 4. At K = 5, however, the cross-validation error decayed compared to K = 4 and K = 6 [see Additional file K = 5 could also be regarded as a sensible model. We therefore focused on the results of K = 5 in Figure K = 1 to 6 are available from [Additional file K = 5, the consensus plot of ancestries . In the second analysis, we used FRAPPE [K = 5  distance matrix between pairs of individuals  d FRAPPE , which panalysis ) using iP value = 0.001 after 1,000 replications). Latitude seems to be more influenced by the second dimension of SPA , while both SPA dimensions seem to contribute to longitude . We also found several regions dispersed throughout the genome with individual SNPs displaying stronger frequency gradients  across the Netherlands  . In order to infer the slope of this cline, we computed a pairwise Fst matrix between pairs of subpopulations and performed a Bearing correlogram analysis [P value after 999 permutations = 0.001). This indicates a southeast to northwest orientation of the genomic cline within the Netherlands for the increase of genetic differentiation between the 54 Dutch subpopulations analyzed. A Mantel test between the geographic distance matrix and the Fst subpopulation pairwise matrix, without considering angle information, revealed a correlation of r = 0.165 .Fourth, and to further explore the geographic distribution of the genome-wide diversity across the Netherlands, we performed two different spatial analyses of the genomic data. In the first analysis, we carried out a spatial ancestry analysis (SPA)  on the ss Europe . One exps Europe . The obtanalysis . The angP = 0.99707 after 1,000 iterations) of the total genetic variance observed. This result indicates that dialects are unlikely to have influenced our genomic findings including the spatial distribution of genomic diversity across the Netherlands.We additionally explored whether geographically restricted dialects of the Dutch language, which also show north–south gradients as reported elsewhere , could bet al. [Anno Domini (AD)  in the Netherlands, when most of the country close to the sea remained uninhabitable by humans . Furthermore, we observed that the outcomes of both model simulations are statistically significant in their correlation with each other . The Bearing correlogram analysis using the Fst distance matrix obtained with the model of genetic discontinuity is highly similar to the one produced by considering genetic continuity , which suggests that the genetic gradient produced by both models is virtually indistinguishable. This finding, together with the rich archaeological evidence for human genetic discontinuity on Dutch territory led us to propose that the observed genomic gradient across the Netherlands was not caused by ancient but rather by recent events in Dutch history.The genome-wide southeast to northwest cline in the distribution of the genomic diversity across the Netherlands observed here via different analyses could be interpreted as fitting the southeast to northwest genetic cline previously found for the whole of Europe ,4. Withoet al.  , described as elite-dominance, but by movements of several populations from adjacent areas of similar latitudes in a way that the northern parts of the Netherlands received immigrants from northern/northeastern neighboring regions, southern parts from southern/southeastern neighboring regions, and central parts from eastern neighboring regions. Also, the mainly south–north geographic orientation of the Dutch territory provides a suitable prerequisite for such a scenario given the south–north genomic cline observed across Europe. However, there is no clear evidence provided by the archaeological records that would support such a scenario. The observation that subpopulations from the central-east of the Netherlands appeared more diverse (within and between groups) on the genome-wide level compared to all other Dutch subpopulations tested, could be indicative of recent admixture with other genetically diverse subpopulations not analyzed in our study. It would require, however, more detailed archaeological and/or historical research in addition to similarly detailed genetic information from regions outside the current Dutch political borders to disentangle the exact demographic events that shaped the current genetic variation of the Dutch population.Besides evolutionary implications, our findings of small but detectable genomic substructure in the Dutch population, particularly the detection of geographic groups of Dutch subpopulations that can be differentiated using genome-wide data, also is of relevance for epidemiology and forensics. For future epidemiological studies, this knowledge may be relevant for (disease) gene mapping on Dutch individuals to avoid confounding effects that in principle can reveal false-positive findings. For future forensic genetic studies, the implications are two-fold. First, the detected population substructure may be considered as a correction factor when estimating match probabilities of STR profiles obtained from crime scene and suspect materials in the Netherlands. Second, our data provide evidence that in case the large number of SNPs used here can be derived from a forensic DNA sample, inferring the subregion of biogeographic ancestry within the Netherlands of an unknown may be feasible, which can provide useful investigative information to find unknown perpetrators.We have shown that despite the genetic differentiation between Dutch individuals and subpopulations sampled systematically across the country being very small, the overall genome-wide diversity tends to correlate statistically significantly with geography and that the genomic map of the Netherlands resembles the geographic map of sampling locations in all dedicated analyses we performed. Furthermore, we identified a significant southeast to northwest cline in the distribution of genomic diversity across the Netherlands, similar to earlier findings from across Europe. For the Netherlands however, the classical interpretation of the observed genetic gradient by Paleolithic-Neolithic processes is challenged by the geological, archaeological and historical evidence pointing towards population discontinuity on the Dutch territory through the ages. Our demographic simulations revealed that the expected Paleolithic-Neolithic pattern in autochthonous populations would be similar to the one produced by a recent colonization of a region from neighboring areas. Considering the evidence for population discontinuity we therefore believe that the genomic patterns we observe are caused by recent rather than ancient events in the Dutch population history. On a wider picture, our results indicate that local and more recent demographic events can produce genetic patterns strongly resembling those traditionally explained by the major prehistoric migrations. We therefore suggest that future studies pay more attention to local and more recent demographic events when explaining clinal distributions of genetic diversity. Ultimately, ancient DNA analysis of past populations in comparison with DNA analysis of contemporary populations from the same region should be used to elucidate the contribution of ancient versus recent populations to the current gene pool of the Netherlands.Anno Domini; BRLMM: Bayesian robust linear model with Mahalanobis distance classifier algorithm; FLDO: Forensic Laboratory for DNA Research of the Leiden University Medical Center; HWE: Hardy-Weinberg equilibrium; IBD: Identical-by-descendent; IBS: Identical-by-state; LD: Linkage disequilibrium; MAF: Minimum allele frequency; MDS: Multidimensional scaling; PCA: Principal component analysis; SNPs: Single nucleotide polymorphisms; SPA: Spatial ancestry analysis.AD: The authors declare that they have no competing interests.MK designed the study with contributions from PdK and OL, and provided resources. CB, SB, TK and PN performed experimental analyses. OL performed most statistical data analyses. MvO performed some data analyses. EA provided archaeological and historical information. PdK provided samples. OL, EA, PdK and MK wrote the manuscript. All authors read and approved the final manuscript.is a document containing a supplementary note about the demographic history of The Netherlands. It also contains Supplementary Figures 1 to 4, and a table listing the geological and cultural periods with corresponding dates and population size estimates for the Dutch area.Click here for filelisting the SNPs identified by SPA analysis with strong geographic gradients in The Netherlands.Click here for file


Microdipodops megacephalus) and pallid  kangaroo mice are ecological specialists found within the Great Basin Desert and are potentially ideal organisms for assessing ecosystem health and inferring the biogeographic history of this vulnerable region. Herein, newly acquired nuclear-encoded microsatellite loci were utilized to assess patterns of variation within and among spatially discrete groups of kangaroo mice and to evaluate gene flow, demographic trends, and genetic integrity. Results confirm that there are at least three genetically distinct units within M. megacephalus and two such units within M. pallidus. The three units of M. megacephalus appear to have different demographic histories, with effectively no gene flow among them since their divergence. Similarly, the two units of M. pallidus also appear to have experienced different demographic histories, with effectively no gene exchange. Contemporary effective population sizes of all groups within Microdipodops appear to be low (<500), suggesting that each genetic lineage may have difficulty coping with changing environmental pressures and hence may be at risk of extirpation. Results of this study indicate that each Microdipodops group should be recognized, and therefore managed, as a separate unit in an effort to conserve these highly specialized taxa that contribute to the diversity of the Great Basin Desert ecosystem.The Great Basin Desert of western North America has experienced frequent habitat alterations due to a complex biogeographic history and recent anthropogenic impacts, with the more recent alterations likely resulting in the decline of native fauna and flora. Dark  and pallid  kangaroo mouse, and to evaluate gene flow, demographic trends, and genetic integrity. Results of this study indicate that each Microdipodops group should be recognized, and therefore managed, as a separate unit in an effort to conserve these highly specialized taxa that contribute to the diversity of the Great Basin Desert ecosystem (photo credit J. C. Hafner).The Great Basin Desert of western North America has experienced frequent habitat alterations due to a complex biogeographic history and recent anthropogenic impacts, with the more recent alterations likely resulting in the decline of native fauna and flora. Herein, newly acquired nuclear-encoded microsatellite loci were utilized to assess patterns of variation within and among spatially discrete groups of the dark ( Microdipodops  have become increasingly rare members of the Great Basin Desert community  and the pallid kangaroo mouse . Both species are sand-obligate endemics to the Great Basin Desert and, as such, are highly specialized to survive in an extreme environment  and are not protected  gene regions to elucidate the biogeographic history of the Great Basin Desert  with two subunits , the central geographic unit (n = 69) with two subunits , the western geographic unit (n = 62) with one subunit , and the Idaho geographic unit (n = 4). Two geographic units were recognized within M. pallidus  with two subunits , and the western geographic unit (n = 63) with one subunit .For many of the analyses, populations were defined by grouping specimens together within each species based on geography. These geographic units correspond to previously identified mtDNA clades  and subcMicrodipodops by Lance et al.  labeled tail sequence (defined above), and an unlabeled reverse primer. Amplified DNA from each PCR reaction was combined with a 400 HD Rox size-standard DNA ladder (Applied Biosystems) and electrophoresed on 6% polyacrylamide gels using an ABI PRISM 377 DNA Sequencer (Applied Biosystems). Sizes of microsatellite fragments were visualized in GENESCAN v. 3.1.2 (Applied Biosystems) and assessed using GENOTYPER v. 2.5 (Applied Biosystems).DNA extracts were available from previous studies  for each locus were calculated with Fstat v. 2.9.3.2 (Goudet Each microsatellite locus was tested for conformance to the expectations of Hardy–Weinberg equilibrium (HWE) using 2 Goudet .Arlequin v. 3.5.1.2 . The population admixture model was used with 10 replicate runs from K = 1 to K = 10, where K is a user-defined number of clusters. Each run consisted of a burn-in of 10,000 steps followed by 100,000 additional steps. To evaluate the most likely K value, Structure Harvester (Earl and vonHoldt K (change in ln Prob (Data) between successive K values) as suggested by Evanno et al.  and M  among geographic units within each species using Bayesian inference. Due to the small sample size of the M. megacephalus Idaho geographic unit (n = 4), it was excluded from the analysis. Theta was estimated to detect if there were significant differences in NeLT among geographic units, while M was used to quantify average, long-term gene flow between geographic units. Preliminary runs were performed to estimate priors for M and θ for final runs. Final runs were executed in replicate at different starting points and parameter estimates were examined to ensure chain mixing and convergence. For M. megacephalus, runs consisted of three long chains, geometric heating, and a burn-in of 100,000 steps followed by 1,000,000 steps with a tree recorded every 100 steps, resulting in 10,000 trees sampled. For M. pallidus, runs consisted of one long chain and a burn-in of 10,000 steps followed by 100,000 steps with a tree recorded every 100 steps, resulting in 1000 trees. In all analyses, effective sample sizes were >50.Ma , where a positive t value would indicate divergence and a value that peaked at or very close to zero would reveal no divergence  and a burn-in of at least 1,000,000 generations followed by at least 90,000 generations (resulting in effective sample sizes >50). Final runs were executed in replicate with different starting seeds to ensure convergence, and the R package BOA (Smith IOA Smith  was usedM from Migrate-N and IMa. Preliminary pilot runs (each at a length of 2000 steps) were executed to provide a rough estimation of starting points for final runs. Replicate runs consisted of a burn-in of 20,000 iterations, followed by an additional 100,000 and 60,000 iterations for M. megacephalus and M. pallidus, respectively. The R package BOA  via the modified linkage disequilibrium approach . MSVAR v. 1.3 . One locus in M. megacephalus was monomorphic in the western and Idaho geographic units, but polymorphic in the eastern and central geographic units. After correction for multiple tests, genotypes at two loci (Mime11 and Mime32) in M. pallidus from the western geographic unit deviated significantly from the expectations of HWE. This was due to the isolated population from Deep Springs  were run including and excluding the two deviated loci, and there was no difference in the results. Therefore, results reported in this study included all loci that amplified successfully.Of the 17 initial polymorphic loci screened, 11 and 10 loci amplified successfully and were used in the population genetic analyses of  Springs  where hoRST values were consistently significantly greater than FST values, indicating that FST may be underestimating actual values of genetic structure  to 0.61 , and the RST estimate between M. pallidus geographic units was 0.89 (P < 0.0001). AMOVA analyses focusing on specific geographic units showed a significant component of variation among subunits in the M. megacephalus western geographic unit  and the M. pallidus western geographic unit . The remaining subunit analyses resulted in nonsignificant variation, possibly due to small sample sizes or lack of physical isolation.Allele size permutation tests indicated that Structure analyses revealed that K = 3 was the most likely number of clusters for both M. megacephalus and M. pallidus (when plotting ln Prob (Data); M. megacephalus corresponded to the (1) eastern, (2) central, and (3) western/Idaho geographic units. The 3 clusters of M. pallidus corresponded to the (1) eastern geographic unit, (2) Deep Springs subunit, and (3) the remainder of the western geographic unit for M. pallidus. Additional bar plots with increasing number of clusters also were analyzed in case discrete populations could be distinguished. However, population structure became less resolved with K > 3. Results from the ΔK metric suggested by Evanno et al. (K = 2 was the most strongly supported (Δ ln Prob (Data) = 388.02 and 551.32 for M. megacephalus and M. pallidus, respectively), while K = 3 was the next most strongly supported (Δ ln Prob (Data) = 106.02 and 24.86 for M. megacephalus and M. pallidus, respectively). The 2 clusters were an eastern/central and western/Idaho group for M. megacephalus and an eastern and western group for M. pallidus. When individual geographic units were analyzed separately, K = 1 was the most likely number of clusters of nuclear variation for all units with the exception of the M. pallidus western geographic unit, where K = 2 was most likely number of clusters (corresponding to Deep Springs and the rest of the western geographic unit).o et al.  indicateMigrate-N for M. megacephalus were low, with modal values for M ranging from 0  to 0.05 , the 2.5% and 97.5% bounds were 0.01 and 0.92, respectively. With such a large confidence interval it was therefore unclear how much long-term gene exchange was occurring between these two geographic units. Estimates of θ were not statistically different among geographic units  and 0.01  did not include zero for the eastern, central, and western geographic units, indicating divergence from an ancestral, panmictic population. Estimates for t were quite low  with large confidence intervals . Estimated long-term gene flow (M) from IMa was very small, with a lower confidence interval and mode for all three groups of 0.01 , with a rather large confidence interval . Estimated rates of gene flow for eastern → western and western → eastern were 0.09 and 0.04, respectively , for M. megacephalus and M. pallidus are presented in Ne , which may serve as a conservative estimate for wildlife management .Point estimates of contemporary −4, 2.75 × 10−4, 2.45 × 10−4, 3.23 × 10−4, and 3.89 × 10−4 for the M. megacephalus eastern, central, and western geographic units, and the M. pallidus eastern and western geographic units, respectively. EBSP results showed that the three M. megacephalus geographic units might have undergone a recent population expansion, whereas the M. pallidus eastern unit remained fairly constant and the western unit underwent a recent population contraction (Ne while LDNe is generating estimates of contemporary Ne. The two estimates may therefore differ because (a) the time periods to which the two effective size estimates apply are not necessarily concordant . We could, however, reject a constant population size in the M. megacephalus western geographic unit (population size changes ranged from 1 to 3).Mutation rates estimated by MSVAR averaged 2.40 × 10traction . None oftraction . While tt Waples  and (b) t Waples . One addM. megacephalus and M. pallidus are comprised of multiple genetically distinct units within the Great Basin Desert. Primary population genetic analyses (AMOVA and pairwise RST) support genetic heterogeneity within each species and Structure analyses revealed that K = 3 was the most likely number of clusters for both M. megacephalus and M. pallidus . Increasing the number of individuals from the Idaho clade in future studies would probably tease it apart from the western cluster , effective population sizes, and demographic history. Results of this study therefore provide information that can be used for management strategies and conservation efforts specific to each evolutionarily significant unit within the Great Basin Desert.Microsatellite markers reveal that pallidus . AlthougM. megacephalus supports the perspective that kangaroo mice found in the eastern, central, and western units are, at minimum, distinct populations . In agreement with previous phylogenetic analyses, population genetic analyses of microsatellite data reveal a close affinity between the eastern and central populations (Table A4) and a clearly more differentiated western population  suggests that the populations have been isolated for a sufficiently long period of time such that mutation has played a relatively important role in genetic differentiation. The lack of significant differences among our historical θ values, and t parameters significantly larger than zero, suggest that each lineage may have diverged from a single ancestral population  diverged outside of the Great Basin Desert and two independent lineages invaded the region at the beginning of the Pleistocene . The series of mountain chains that currently serve as a physiographic baffle between the eastern and western populations, likely prevented historic gene flow between these two lineages allowing for further divergence. Demographic analyses also suggest the M. pallidus western population has undergone a recent population contraction while the eastern population has remained constant, or has possibly undergone a population expansion, indicating that these two populations have historically been demographically independent from each other. Demographic results based on EBSP, however, should be interpreted cautiously (see above).Similarly, the eastern and western Ne of both the eastern and western populations of M. pallidus are well below 500 . Furthermore, repeated efforts to collect M. pallidus in once fruitful areas have either proven to be increasingly difficult or completely unsuccessful (Hafner et al. Microdipodops likely serve as indicator species of a healthy sandy desert ecosystem (Light et al. Microdipodops abundance may signal deterioration of the habitat, and further reduction in their abundance may prove detrimental to the survival of individual populations. The genus Microdipodops is a rare and highly specialized endemic of the Great Basin Desert, and this study provides further support that management and conservation efforts should be applied to each population in an effort to conserve these valuable taxa and the imperiled habitats of the Great Basin Desert.The amount of available habitat within the Great Basin Desert is decreasing as a result of a variety of anthropogenic alterations, and future climate change is predicted to reduce available habitat even further. Chaplin et al.  ranked t


Zostera noltii, in order to explore their respective influences on the resistance of the experimental population to stress. Thus far intra-specific diversity was seldom taken into account in management plans, and restoration actions showed very low success. Information is therefore needed to understand the factors affecting resistance and resilience of populations.The relationship between species diversity and components of ecosystem stability has been extensively studied, whilst the influence of the genetic component of biodiversity remains poorly understood. Here we manipulated both genotypic and allelic richness of the seagrass Our results show a positive influence of both allelic and genotypic richness on the resistance of meadows to environmental perturbations. They also show that at the low genotypic  richness levels used in prior experimental approaches, the effects of genotypic and allelic richness could not be disentangled and allelic richness was a likely hidden treatment explaining at least part of the effects hitherto attributed to genotypic richness.Altogether, these results emphasize the need to acknowledge and take into account the interdependency of both genotypic and allelic richness in experimental designs attempting to estimate their importance alone or in combination. A positive influence of allelic richness on resistance to perturbations, and of allelic richness combined with genotypic richness on the recovery (resilience) of the experimental populations is supported by differential mortality. These results, on the key species structuring of one of the most threatened coastal ecosystem worldwide, seagrass meadows, support the need to better take into account the distinct compartments of clonal and genetic diversity in management strategies, and in possible restoration plans in the future. The relationship between diversity and ecosystem stability has been explored for a long time in biology, as the number and type of species was expected to determine the specific traits of the ecosystem . Yet theResolving the relationship between biodiversity and ecosystem function is not simple because several components of biodiversity can affect ecosystem functioning, even when considering only diversity at the species level. These components include species richness (the number of species), species evenness (their relative abundance), species composition  and non-additive effects (their interactions) as well as the spatial and temporal variations of those patterns . StabiliIn ecosystems where the physical habitat is shaped by one key-species, the genetic diversity within populations of this structural species may have similar importance as species richness does on ecosystem resilience and resistance. Genetic diversity is one of the three main components of biodiversity recognized by the Convention for Biodiversity as a priority target for conservation measures, yet it is still largely neglected in management plans . GeneticEcosystems dominated by one or a few species, such as seagrass meadows or algae stands, are particularly vulnerable, because the loss of genetic diversity resulting from population decline or fragmentation in key-species  may haveZostera marina[sensu stricto , that can also be designated as genomic diversity [per se. However, the associated level of allelic richness, a genetic diversity parameter more sensitive to demographic events [Posidonia oceanica[sensu stricto on the stability of seagrass meadows, should attempt to clarify the respective effects of both components by dissociating them. In this study, we tested experimentally the relationship between genetic diversity and the stability of experimental assemblages of Zostera noltii, a key-species structuring the intertidal ecosystem of Ria Formosa, to i) test for the level of interdependency of genotypic and allelic richness and ii) test for their respective or combined influence on the resistance and potential for recovery (resilience) of experimental populations.Recent experimental studies on the seagrass species ra marina,51,57 sura marina, on the ra marina. Yet theiversity  was not c events ,62 and mc events ,63, was c events . In factc events . Besides oceanica,64 suggeGenotyping of the 376 collected clones returned a total of 343 individuals, fully-genotyped at all loci, of which 164 were distinct MLGs. Allelic richness in one thousand possible combinations of 3, 6 and 9 genotypes ranged from 16 to 31 at G = 3, 24 to 42 at G = 6, and 31 to 48 at G = 9  and increasing A . When analysing the influence of genotypic richness while ignoring the parallel increasing levels of allelic richness , results supported a significant effect  which show that a correlation between allelic and genotypic richnesses also becomes at best marginal above levels of genotypic richness lower or similar as those commonly observed in natural meadows. It appears therefore essential to discriminate their respective effects in order to advance towards unravelling the underlying mechanisms responsible for differential survival, and not to erroneously interpret GR as a good proxy for AR (or the other way round) as their entanglement does not appear systematic in the wild.This study makes a first attempt to test for the effect and interaction of allelic richness, a component of genetic diversity that has not been manipulated in previous experimental studies. The first important result is the relationship between genotypic and allelic richness at the low levels of genotypic richness typically used thus far in similar experiments Figure . Indeed,riments  than genotypic diversity . Experimental results reported here rather favour a positive effect of allelic richness on resistance of populations, while no clear trend or effect of genotypic richness could be inferred. Later after recovery, the interaction of allelic and genotypic richness seems to influence resilience of populations according to stepwise regression (but not path) analysis.Our simulation analysis indicates that it is possible to dissociate effects of allelic and genotypic richness at much higher levels of genotypic richness than those typically used in experimental assessments. Indeed, the levels of genotypic richness that must be manipulated to allow separation of these effects are close to the levels recorded in natural meadows (up to 90% of sampling units for this species ). These ra marina,57,68 bu oceanica and Zostproxy for genetic richness may need be reassessed to test for the likelihood that allelic richness was a hidden treatment. In addition to accurately assigning effects to genotypic versus allelic richness, as done here using path analysis, one may bear in mind that allelic richness is an estimate of the number of genetic variants per locus meaningful for all organisms, whereas genotypic richness that reflects the proportion of genetically distinct individuals is only relevant for organisms capable of clonal propagation. The predominant influence of one or the other has therefore different interpretations in evolutionary terms, and major implications for management plans. Whether the decisive parameter for resistance or resilience is the number of distinct clonal lineages or the number of different genetic variants  they bear, or both, may lead to drastically different priorities and plans to protect, manage or even restore populations of clonal organisms. Future experiments should therefore systematically report both these components to avoid confounding effects, and should ideally attempt to separate both levels of allelic and genotypic richness, as also recently emphasized by Avolio et al. [Hence, we submit that previous studies testing the role of genetic diversity on seagrass resistance ,66 or reo et al. .This study supports a positive effect of intra-specific diversity on shoot survival immediately after perturbation. Algal blooms are increasingly reported and forecasted to intensify worldwide -72, ofteThe significance of the relationship between survival, measured 40 days after the bloom, and allelic richness supports, at least on seagrass experimental populations, two classical hypotheses underlying conservation genetics studies: i) the positive effect of the genetic component of diversity as estimated through allelic richness on resistance of populations, and ii) that a large enough set of neutral markers delivers reliable estimates of the level of polymorphism for the whole genome, including those genes potentially involved in a variety of responses to selective pressure ,89. No sZ. noltii in the Ria Formosa, may therefore be particularly dependent on the genetic diversity of these structural species.Biodiversity has been shown to enhance the ecosystem’s ability to cope with stress  but ecosEcosystems may contain functional redundancy whenever species are capable of replacing each other -13,91. WHypotheses based on interaction among species, such as synergy or facilitation effects, can be similarly transposed to genotypic and allelic richness. This is particularly so for those based on observation of the importance of phylogenetic diversity in species assemblages, suggesting a higher contribution of distantly related species -13. MechZ. marina, suggesting a higher survival of assemblages composed of more genetically related genotypes [At the spatial and diversity scales explored in this and previous experiments, the effect of specific genotypes and of their interaction in a homogeneously impacted environment is expected to be favoured. At this scale, both genotypic and genetic diversity as estimated through allelic richness may lead to higher redundancy, as well as increased complementarity, synergistic or sampling effects. The results obtained here indeed suggest that assemblages showing higher A, and therefore bearing genotypes with more divergent genetic backgrounds are showing higher survival rates. Such observation at intra specific level also recalls the reports on the positive influence of phylogenetic divergence in species assemblages ,13, suggenotypes . Alternaenotypes ). FinallIn terms of implications for conservation genetics, our results support the correlation between intra specific diversity and resistance or resilience by previous studies ,57,66. YThis study reports the first experimental manipulation of both genotypic and allelic richness in a structural species, in an attempt to dissociate the effect of both parameters on its demographic response to stress. Our study shows that the two effects can hardly be disentangled at low levels of genotypic richness and that allelic richness may have been a hidden treatment in previous experiments reporting the positive effect of genotypic diversity on resistance and resilience of experimental populations. Results are in agreement with a positive effect of allelic richness on the resistance of populations to environmental stress, and suggest a positive effect of the interaction between genotypic and allelic richnesses on their resilience. This study underlines the potential importance of genetic diversity for the persistence of populations, an issue of particularly great concern when affecting key-species of an ecosystem such as seagrasses, and strongly indicates the importance of taking this parameter into account in management strategies.Zostera noltii shoots were setup in order to test for the existence of a relationship between allelic and/or genotypic richness and the resistance or resilience to stress conditions. Nine experimental treatments (= 9 subplots) were planned, each replicated 4 times  assessed with the 9 microsatellite markers following . The mul3). In order to make experimental conditions as close as possible from field ones, the tanks were filled with running seawater pumped from the Ria Formosa and a system was installed to mimic tide effects at a timing comparable to the natural periodicity.Each subplot was set up in an individual small vase approx. 2.5 L) with sediment from the Ria Formosa with a standardized initial density of 27 shoots per vase, comprising 9 sets of 3 connected shoots each. In order to respect a perfectly standardized setup in terms of number of shoots and connections, subplots with a genotypic richness of 3 MLGs had 3 sets of 3 shoots from each one of the 3 genets; subplots with a genotypic richness of 6 MLGs had a mixture of 3 shoots from 3 genets and 6 shoots from the other 3 genets split in two fragment of 3 shoots; and subplots with a genotypic richness with 9 MLGs had sets of 3 shoots from each one of the 9 different genets .Algal bloom was the stress treatment in this work, although it had been initially designed for a temperature shock. The temperature shock was not applied because the tank where all the experimental plots were set suffered a diatom bloom two weeks after the assemblage of the combinations, leading to significant seagrass mortality. The diatoms formed an epiphytic layer over the leaves. Mortality reached a sub-lethal level in this tank, whereas no comparable loss was recorded in two other neighbouring tanks where other unrelated experiments with The effect of genotypic richness was initially meant to be assessed by two-way ANOVA Figure a. Yet thIn order to illustrate results expected when controlling and measuring only one of those two parameters and leaving the other as a hidden treatment, regressions were also performed on the three levels of genotypic richness ignoring allelic richness differences, and the effect of allelic richness was assessed by regression for each genotypic richness treatment.In order to attempt discriminating the effect of each parameter, we proceeded as follows.t-test where performed to compare only two means of density, for A = 25  and at the last count taken eleven months later (i.e. resilience). This level of allelic richness was found in plots exhibiting the maximum levels of Â richness for 3 MLG, medium for 6 and minimum for 9 . A correction for multiple tests described here above and compiled in Table SIM conducted the experimental work, the data analyses and drafting of the manuscript. CMP helped with the sampling, genotyping and shoot density monitoring. CMD participated in the discussion of the findings and reviewed the manuscript. EAS and SAH were involved in the conception and design of this study, participated in the data analysis and reviewed the manuscript for important intellectual content. All authors read and approved the final manuscript.Sónia Massa is a PhD student interested in molecular ecology and gene expression, with special emphasis on the diversity-stability debate. Cristina Paulino is a MSc student interested in the ecology of marine organisms. Ester Serrão leads a research group that is primarily interested in marine ecology, adaptation and population genetics. Carlos M. Duarte leads a team studying marine biodiversity from the genetic, species and habitat level to global biogeochemical cycles. Sophie Arnaud-Haond is an evolutionary ecologist interested in the factors driving the evolution of marine populations, with a special emphasis on the mating system and connectivity patterns.


S. cerevisiae. Treating acetylation of every nucleosome as a quantitative trait allowed the identification of hundreds of genetic loci underlying epigenomic divergence either locally (in cis) or distantly (in trans), and this genetic control overlapped only partially with the genetic control of gene expression. We also estimated the stability of epigenetic differences across environmentally induced reprogramming. Strikingly, ‘labile’ and ‘persistent’ nucleosomal variations were associated with poor and strong genetic control, respectively. The study reveals the dual nature of natural chromatin epi-polymorphisms (DNA-encoded vs. not), providing a basis for the development of population epigenomics. Supported by grant SiGHT nº281359 from the E.U.Advances in epigenomics are opening the exciting perspective to associate chromatin variation to the variation of physiological traits in natural populations. Yet, inter-individual epigenomic differences may or may not result from regulatory variation encoded in the DNA. If they do, association then resumes to mapping underlying genetic variants, as for any genetic study. If they don’t, association requires a framework where the epigenetic nature of the variation is taken into account, in particular its potential reversibility. The development of population epigenomics therefore requires an assessment of the nature of epigenomic variation. As a model experimental system, we have traced quantitative variation of acetylation at Lysine 14 of Histone H3 at all nucleosomes in a segregating population of


Ammodytes personatus. A total of 429 individuals sampled from 17 populations through the species' range are sequenced. Two distinct lineages are detected, which might have been divergent in the Sea of Japan and Pacific costal waters of Japanese Island, during the low sea level. Significant genetic structure is revealed between the Kuroshio and Oyashio Currents. However, significant genetic structure is also detected in the Sea of Japan, contracting expected homogenization hypothesis in Tsushima Current. The haplotype frequency of lineages in both sides of Japanese Island and significant genetic structure between north and south groups revealed that the distribution of lineage B and north group were highly limited by the annual sea temperature. The lack of lineage B in Qingdao population with low sea temperature reflects the sea temperature barrier. Lack of genetic structure in the south group and north group populations indicated that ocean currents within groups facilitated the dispersal of A. personatus.To assess the role of historical process and contemporary factors in shaping population structures in Northwestern Pacific, mitochondrial control region sequences were analyzed to characterize the phylogeography and population structure of the Japanese sand lance  Geographical patterns of genetic variation reflect both historical process and present gene flow attributable to the biological characteristics of the organism under study Previous studies have demonstrated that the genetic variation of marine species in Northwestern Pacific have been heavily influenced by Pleistocene glaciations. Comparative phylogeography studies have revealed the Sea of Japan as glacial refugium for marine species in northwestern Pacific Except historical process, in marine environments the geographic structure of populations may be also influenced by contemporary factors. One way to obtain further insight into the factors shaping the genetic structure of populations is to combine genetic data with information on landscape characteristics, referred to as landscape genetics Surface sea temperature in coastal waters of Japanese Island is heavily influenced by the Kuroshio Current and Oyashio Current in the Pacific Ocean side and the Tsushima Current in the Sea of Japan side. In the northwest Pacific Ocean, two western boundary currents, the subtropical warm and saline Kuroshio Current and the subarctic cold and less saline Oyashio Current, meet each other off the east coast of the Japanese Islands. The Kuroshio Current flows along the south coast of the Japanese Islands and toward the east off central Japan as a part of the wind-driven subtropical gyre circulation cell. This current transports tremendous amounts of heat from the low latitudes to the high latitudes, similar to the Gulf Stream. The Oyashio Current is the western component of the Kamchatka–Alaskan Current, and carries cold and high-nutrient surface water to Northwestern Pacific. These two currents produce the mixed water mass region between the Kuroshio and Oyashio Fronts, where the most pronounced latitudinal gradients of these surface temperature (SST) and sea surface salinity (SSS) are observed in the North Pacific Ammodytes personatus is a common and commercially important species in Northwestern Pacific. Populations of A. personatus are distributed along the both sides of coast around Japan and the Yellow Sea. It is a cold-water species and abundant in shallow near shore areas ranging in depth to 100 meters. It rises to the sea surface by night, and buries itself in sand when water temperature increases to 17–20°C during summer in southern and central Japan populations A. personatus, a cold-water fish, to live at the southern “environmental edge” for this species A. personatus are able to survive for long periods without food after hatching. When being kept without food, time to 50 percent mortality was about 11, 16, and 21 days after hatching at 15.5, 10.5, and 6.5°C, respectively. Horizontal distribution, and possible abundance of larvae, is strongly influenced by tidal currents, oceanography, and wind conditions A. personatus is high. If larvae could travel on the currents, the connectivity should be high among populations within the current area. No long migration but a micro geographic migration was observed in adults of A. personatusA. personatus exhibits considerable genetic, morphological and ecological variation among populations. Okamoto et al performed the genetic study on A. personatus using horizontal starch gel electrophoresis to analyze genetic structure of species along the Pacific coast of Japan A. personatus. A. personatus around the coastal waters of Japan would therefore seem a well-suited system for testing hypothesis about correlation between genetic differentiation and local environmental variable. In the present study, we assess and compare the phylogeographical pattern of A. personatus in different ocean current system by mitochondrial DNA (mtDNA) control region sequences and evaluate the role of historical and present environment factors in shaping the genetic structures of marine species.The Japanese sand lance pro) was sequenced in 429 specimens from 17 populations revealing 402 haplotypes. All haplotype sequences were deposited in GenBank, with accession nos JQ913643-JQ914044. Sequence comparisons revealed 140 polymorphic sites (96 parsimony informative) with 105 transitions, 55 transversions and 26 indels. Fifteen haplotypes were shared among populations and three were found in more than one individual, but only in one population. The number of pairwise differences between haplotypes ranged from 1 to 36. The gamma distribution shape parameter was 0.677, indicating moderate mutation rate heterogeneity among sites in A. personatus.A 532-bp segment of the 5′end of the control region  in either analysis of NJ tree and haplotype network ; Fig. 2.A. personatus but the frequency declined steadily along the Pacific coast and Sea of Japan coast from south to north. Lineage B was sympatric with lineage A in ten populations, included five populations in south group and all populations in north group, and the frequency of lineage B declined along the coast from Cape Soya to Kashima. The annual sea temperature in lineage A and lineage B varied from 11.26°C to 19.99°C and 11.26°C to 16.41°C, respectively  shared by all populations , Table 3P<0.001). This significant differentiation between groups was consistent with the plot figure based on the frequency of lineage B and annual sea temperature. A very small (0.50%) but significant (P = 0.001) amount of genetic diversity was found among populations within groups. The AMOVA analysis also showed that a large and significant genetic differentiation  was found between Kuroshio Current (including transition zone) and Oyashio Current, a small and non-significant amount of genetic variation  was found among populations within the current area. No significant genetic structure was found between the Pacific Ocean and Sea of Japan groups , but a high and significant amount of genetic variation was detected among populations within the two sea groups . Populations within the south group showed no significant structuring . Populations within the north group also showed no significant structuring . Pairwise FST values between 17 populations ranged from −0.020 to 0.513, and most of pairwise FST values between north and south groups were significant after sequential Bonferroni correction except 6 comparisons between groups , with a broad zone of admixture between the two genotypes in intermediate latitudes. The genetic variation between northern and southern of F. heteroclitus was regarded as a means of adapting to a changing environment. This genetic pattern of F. heteroclitus was similar to A. personatus. A. personatus is a cold-water species and is sensitive to sea temperature. It starts aestivation, when the water temperature exceeds the tolerance limit of A. personatus. The duration of aestivation declined from south to north along the Japan coast. For example, aestivation duration in Sendai Bay (August–November) is shorter than that in Ise Bay (May-November) . No aestivation was observed in Cape Soya The differences in seawater temperature, strong correlation of these differences with distribution of lineage and groups suggest an adaptation to local thermal regimes. Santos et al. (2006) states that water temperature is one of the main factors that affect the distribution of marine fish  migrated south along the costal waters off the Japan Islands with the low sea temperature and weak Kuroshio Current, and finally entered the southern coast of Japanese Island; the A. personatus populations isolated in Sea of Japan (Lineage B) might benefit the low sea temperature and the Sea of Japan had been colonized by this lineage B. After the termination of glaciation, the communication between Sea of Japan and Pacific Ocean was created with the rising sea level. The lineage A obtained the aestivation to adapt to the rising sea temperature in southern Japanese Island. The lineage A also expanded its distribution northward to second contact with the lineage B with the strengthen Kuroshio Current in Pacific Ocean and Tsushima Current in the Sea of Japan The isolation of Sea of Japan and Pacific Ocean resulted in two lineages of A. personatus. The process of secondary contact between two lineages and the distribution of north group and south group were heavily influenced by sea temperature. The ocean current reinforces and weakens the sea temperature barrier. This gives us a lesson about the cold-water species how respond with the ocean current and sea temperature. This also provides information about the mechanism of ocean current on population structure in marine species. Our results suggested that annual sea temperature could serve as an indicator for the group identification. The fishery management of A. personatus can benefit from our result.Our results illuminate the combination of historical process and contemporary factors to shape present population structure in All experimental procedures involving fish were approved by the Institutional Animal Care and Use Committee of Zhejiang Ocean University under 2012-01.No specific permits were required in sampling. No specific permissions were required for these locations/activities in this study. The sampling location is not privately-owned or protected in any way. This study did not involve endangered or protected species.www.jodc.go.jp). Muscle was obtained and preserved in 95% ethanol or frozen for DNA extraction. Genomic DNA was isolated from muscle tissue by proteinase K digestion followed by a standard phenol–chloroform method. DNA was subsequently resuspended in 100 μL of TE buffer. PCR primers specific to A. personatus were designed from the universal primers to amplify the first hypervariable segment of the mtDNA control region 5′-CCC ACC ACT AAC TCC CAA AGC-3′ (forward) and DL-R, 5′-CTG GAA AGA ACG CCC GGC ATG-3′ (reverse). PCR was carried out in 50 μL volumes containing 1.25 U Taq DNA polymerase , 20 ng template DNA, 200 nmol/L forward and reverse primers, 200 μmolL/L of each dNTPs, 10 mmol/L Tris, pH 8.3, 50 mmol/L KCl, 1.5 mmol/L MgCl2. The PCR amplification was carried out in a Biometra thermal cycler under the following conditions: 3 min initial denaturation at 94°C, and 40 cycles of 45 s at 94°C for denaturation, 45 s at 50°C for annealing, and 45 s at 72°C for extension, and a final extension at 72°C for 10 min. All sets of PCR included a negative control reaction tube in which all reagents were included, except template DNA. PCR product was purified with Gel Extraction Mini Kit . The purified product was used as the template DNA for cycle sequencing reactions performed using BigDye Terminator Cycle Sequencing Kit , and sequencing was conducted on an ABI Prism 3730 (Applied Biosystems) automatic sequencer with both forward and reverse primers. The primers used for sequencing were the same as those for PCR amplification.The sample size consists of 429 individuals from 17 samples across its natural distribution, from the southern extreme of its range off the coast of Kyushu and its northern limit Cape Soya. Sampling information and sampling locations are listed in h), nucleotide diversity (π) and the mean number of pairwise differences (k), and their corresponding variances were calculated following Nei (1987) as implemented in ARLEQUIN Sequences were edited and aligned using Dnastar software . Molecular diversity indices such as number of haplotypes, polymorphic sites, transitions, transversions, and indels were obtained using the program ARLEQUIN (ver. 2.000) A. personatus used in the phylogenetic analysis were representatives of transversion haplotypes. In addition, genealogical relationships were examined based on reduced datasets that contained only the transversions, by constructing haplotype networks using median-network approach Genetic distances were generated for phylogenetic reconstruction with MEGA4.0 using the model of Tamura and Nei FST). The significance of the FST was tested by 10,000 permutations in ARLEQUIN. To further examine hierarchical population structure as well as the geographical pattern of population subdivision, we used analysis of molecular variance (AMOVA). We conducted AMOVA analysis with two groups representing the north group and south group. The division of groups is shown in A. personatus was portioned into two groups, representing the Sea of Japan and Pacific Ocean. When multiple comparisons were performed, P values were adjusted using the sequential Bonferroni procedure Genetic differentiation between pairs of population samples was evaluated by pairwise fixation index , which is based on three parameters: θ0, θ1 (θ before and after the population growth) and τ  P>0.05) from the expectation of the sudden expansion model, the parameter of the demographic expansion was τ estimated by a generalized nonlinear least square approach, and confidence intervals was computed using a parametric bootstrap approach. The values of τ were transformed to estimates of real time since expansion with the equation τ = 2ut, where u is the mutation rate for the whole sequence under study and t is the time measured in years since expansion.The historical demographic expansions were examined by two different approaches. First the A. personatus with short generation time, high metabolic rate and small body size like Japanese anchovy might have a rapid molecular clock. In the present study, sequence divergence rate of 8%/Myr was applied for the control region sequences of A. personatus.The molecular clock for the control region seems to vary among major taxonomic groups of marine fishes (reviewed in


Introgression of Sahel livestock genes southwards in West Africa may be favoured by human activity and the increase of the duration of the dry seasons since the 1970’s. The aim of this study is to assess the gene flow patterns in Burkina Faso goat and to ascertain the most likely factors influencing geographic patterns of genetic variation in the Burkina Faso goat population.http://dx.doi.org/10.5061/dryad.41h46j37. Although overall differentiation is poor (FST = 0.067 ± 0.003), the goat population of Burkina Faso is far from being homogeneous. Barrier analysis pointed out the existence of: a) genetic discontinuities in the Central and Southeast Burkina Faso; and b) genetic differences within the goat sampled in the Sahel or the Sudan areas of Burkina Faso. Principal component analysis and admixture proportion scores were computed for each population sampled and used to construct interpolation maps. Furthermore, Population Graph analysis revealed that the Sahel and the Sudan environmental areas of Burkina Faso were connected through a significant number of extended edges, which would be consistent with the hypothesis of long-distance dispersal. Genetic variation of Burkina Faso goat followed a geographic-related pattern. This pattern of variation is likely to be related to the presence of vectors of African animal trypanosomosis. Partial Mantel test identified the present Northern limit of trypanosome vectors as the most significant landscape boundary influencing the genetic variability of Burkina Faso goat (p = 0.008). The contribution of Sahel goat genes to the goat populations in the Northern and Eastern parts of the Sudan-Sahel area of Burkina Faso was substantial. The presence of perennial streams explains the existence of trypanosome vectors. The South half of the Nakambé river (Southern Ouagadougou) and the Mouhoun river loop determined, respectively, the Eastern and Northern limits for the expansion of Sahelian goat genes. Furthermore, results from partial Mantel test suggest that the introgression of Sahelian goat genes into Djallonké goat using human-influenced genetic corridors has a limited influence when compared to the biological boundary defined by the northern limits for the distribution of the tsetse fly. However, the genetic differences found between the goat sampled in Bobo Dioulasso and the other populations located in the Sudan area of Burkina Faso may be explained by the broad goat trade favoured by the main road of the country.A total of 520 goat were sampled in 23 different locations of Burkina Faso and genotyped for a set of 19 microsatellites. Data deposited in the Dryad repository: The current analysis clearly suggests that genetic variation in Burkina Faso goat: a) follows a North to South clinal; and b) is affected by the distribution of the tsetse fly that imposes a limit to the Sahelian goat expansion due to their trypanosusceptibility. Here we show how extensive surveys on livestock populations can be useful to indirectly assess the consequences of climate change and human action in developing countries. Desertification limits the presence of vectors of trypanosomosis and, therefore, favours the introgression of Sahelian genes into southern trypanotolerant livestock populations in West Africa -3. This A detailed description of the geography, environmental areas and goat populations of Burkina Faso is given as Supplementary Background. Burkina Faso is a flat country with no clear geographic barriers limiting the spreading of animal populations. Only the Mouhoun river (formerly known as Black Volta) has perennial streams  the Each of the environmental areas of Burkina Faso is assumed to be the habitat of a different goat population ,10: the Glossina spp.) 25] LEA. http://www.esri.com/software/arcview/. The Inverse Distance Weighted (IDW) option with a power of two was selected for the interpolation of the surface. IDW assumes that each input point has a local influence that diminishes with distance. The area of sampling of each population was used as geographic coordinates, and the six nearest neighbors were used for the calculation. Interpolation surfaces were divided into seven equal classes.The PCA, LEADMIX and LEA scores obtained for each population were used to construct interpolation maps drawn using the Spatial Analyst Extension of ArcView, available at: Population Graph analysis ,44 was pi) the present limit of the Sahel area; ii) the present limit of the Sudan area; iii) the present Northern tsetse limit in Burkina Faso [iv) like boundary iii but including in the tsetse free area those populations sampled on the road from Ouagadougou to Bobo Dioulasso . These four analyses are expected to characterise if differentiation between the Sahelian, Mossi and Djallonké goat has genetic support (matrices i and ii), the genetic differentiation due to the presence or absence of trypanosome vectors (matrix iii) and the combined effect of the presence of trypanosome vectors and the human action (matrix iv). In all cases, p-values were obtained using 10,000 permutations. Statistical significance level of the four hypothesis tested on our dataset was assessed at α = 0.05 after Bonferroni correction.Partial Mantel test ,47 was cina Faso ; and iv)This work results from the collaboration between INERA and SERIDA, starting in 2007, on the characterisation of the Burkina Faso livestock populations and the assessment of the impact of the progressive desertization of the country. AT, HHT and FG conceived the project; AT, YZ, AK, GO-S, and HHT planned and performed the sampling. AT and YZ extracted DNA; IA and AT performed most laboratory analyses; LP-P contributed to laboratory analyses; IF and FG made statistical analyses; AT, IF and FG wrote the paper. All authors read and approved the final manuscript.Supplementary Background. Detailed description of the geography, environmental areas and goat populations of Burkina Faso.Click here for fileTable S1. Between-populations molecular coancestry and Reynolds’ distance matrices.Click here for fileFigure S1. Bidimensional scaling plots constructed using genetic distance matrices computed after removal of the five smallest populations .Click here for fileTable S2. Dummy matrices created to characterise the presence of landscape boundaries in the Burkina Faso territory.Click here for file