Thunnus thynnus ] population structure constitutes a challenging task of great importance. Most of the unique challenges stem from its biology, as well as the attributes of the marine realm in which it disperses. Accurate information is urgently needed for stock assessment, and the identification of critical features to the persistence and adaptation of populations in order to formulate and adopt effective strategies for ABFT conservation and management. Conclusions of a great number of ABFT genetic studies on the Mediterranean Sea stock structure are rather controversial and not yet conclusive. In this study, ABFT genomic diversity was investigated in the Mediterranean Sea, which is the most important area for the species’ reproduction.Elucidating the patterns of the Atlantic Bluefin tuna [ABFT, Analyzing genome-wide SNPs and microsatellites from ABFT samples collected throughout the Mediterranean Sea did not provide strong evidence of genetic structure, pointing towards the existence of a single panmictic unit. An alternative view would recognize a failure to reject the null hypothesis of a panmictic unit as an effect of the study’s sampling design, the type of markers used, and the effectiveness/suitability of analysis methods in respect to the species biological characteristics or any combination of the above.Unravelling the drivers of ABFT population diversity would require the consideration of important aspects of the species spawning behavior for the determination of the appropriate sampling design. Novel approaches and methods of analysis that will bring together experts in genetics/-omics, ecology and oceanography are deemed necessary. Analyzing ABFT genetic data under the discipline of seascape genetics could provide the analysis framework under which major abiotic and biotic forces controlling ABFT recruitment could be identified, elucidating the complicated population dynamics of the species, while multiple and continuous fisheries monitoring should in all cases be considered as a prerequisite in order to achieve efficient and long-term ABFT conservation. Describing the patterns of population subdivision in continuously distributed populations has always been a challenge in population genetic studies. Some of the most important aspects regarding genetic data analysis include the model’s assumptions, which, when met, lead to insightful realizations. However, given that assumptions are tailored to certain study systems, their appropriateness is depended upon the pattern of population subdivision. As a result, they are rarely met in real studies, while basic biological processes with evolutionary implications  are often not taken into consideration , 2. FurtUnderstanding and quantifying dispersal processes in marine settings and the impact of spatial factors to genetic changes over both space and time is an extremely difficult task with most of the unique challenges stemming from the biology of marine taxa and the fluid medium in which they disperse , 5. The Thunnus thynnus ] constitutes such a challenging case, since it represents an animal with a wide geographic distribution, high potential for dispersal and interesting life history traits (i.e. spawning fidelity). These attributes necessitate the employment of methodologies that could provide more accurate information needed for stock assessment, and the identification of critical features to the persistence and adaptation of populations, based on those effective strategies for its conservation and resource management that could be formulated . Furthermore, 14 samples were obtained from the eastern Atlantic Ocean (off the coast of Morocco). In total 67 samples from the western, 265 from the central and 96 from the eastern Mediterranean Sea were analyzed together with 14 from the eastern Atlantic, covering the majority of the species’ spawning areas within the Mediterranean Sea . PCR amplification conditions consisted of: 1.5 mM MgCl2, 0.15 mM dNTPs, 0.125 μM of each primmer, 0.5 U Taq  in a total volume of 20 μl. Reactions were carried out using an initial step at 94 °C for 2 min, followed by 35 cycles of denaturation at 94 °C for 60 s, annealing at different temperatures depending on the multiplex PCR scheme  for 60 s and extension at 72 °C for 60 s, and a final extension at 72 °C for 10 min. Alleles were resolved by electrophoresis on an ABI Prism 3700 DNA Analyzer . Genotypes were determined using the software STRand (http://www.vgl.ucdavis.edu/STRand). In order to minimize microsatellite alleles miscalling, the binning of alleles was accomplished using FLEXIBIN 2 [All samples were genotyped for 16 microsatellite loci: 4, Tth38 . MicrosaEXIBIN 2  the outpTth207, Tth208, and Tth38). Furthermore, samples that yielded genotypes for less than nine loci were also excluded from further analyses providing a final dataset of 428 samples representative of all studied sites.We used MICROCHECKER v. 2.2.3  on the cSbfI (restriction site 5′-CCTGCAGG-3′) as a rare cutter and MspI (restriction site 5′-CCGG-3′) as a common cutter in a single reaction in accordance to the guidelines of the manufacturer . Fragments were purified with Agencourt AMPure XP beads  in all steps of the library preparation. The oligonucleotide sequences used for barcoding and Illumina indexes during library preparation are provided in Peterson et al. [Forty-eight ABFT samples yielded the required DNA both in terms of quality and quantity, enabling their use in the next generation sequencing protocol. Those samples were representative of all studied sites  and MUSCLE v.3.8.31 [Raw Illumina reads were processed using the program pyRAD v 3.0.5 . Samplesv.3.8.31 , establiv.3.8.31  and a fiSamples with low loci recovery were removed from the dataset . This step allowed the inclusion of 44 (out of 48) samples in our SNPs dataset for further analyses.The final ddRAD-seq loci were assembled by adjusting the minimum individual value , (1) to 40 , and (2) to 44 where missing data were not allowed .(http://genepop.curtin.edu.au/). Comparative measures of genetic diversity and the FST index, assessed by the estimator θ [The ABFT specimens were grouped into six predefined populations based on their geographic origin, namely, one from eastern Mediterranean Sea (off the coasts of Syria), three from central Mediterranean Sea , one from western Mediterranean Sea (Spain) and one from Morocco Fig. . Hardy–Wimator θ , employeimator θ .In an attempt to acquire insight into the demographic and evolutionary processes that have shaped the genetic patterns of ABFT in the Mediterranean Sea, for both types of datasets (microsatellites and SNPs), three statistical methods were employed exhibiting different strengths and limitations. Those methods cover a wide range of levels of population structure and patterns of genetic diversity generated by different evolutionary processes i.e. two Bayesian clustering methods, principal-component analysis (PCA) and a method for the estimation of effective migration surfaces (EEMS). Clustering methods are better suited in cases with a medium to strong signal of population structure , where sampling localities may or may not be in use, where admixture events are recent and there is no isolation by distance. PCA are multivariate descriptive methods that unlike Bayesian clustering methods, do not rely on explicit population genetics models, and they are preferable when many loci are available and the structure is subtle , 37. ThePatterns of population structure were investigated using two Bayesian clustering approaches implemented in STRUCTURE v. 2.3.4  and GENE6 iterations for each run, including a burn-in of 10,000 iterations, and a sampling frequency of 1000.GENELAND was employed as a better definition of spatial genetic units by integrating spatial coordinates of samples. An explicit model is employed which describes the fact that differentiated populations tend to be spatially structured occupying distinct areas, and maps of population ranges are being generated. It incorporates a non-admixture model assuming that each individual originates purely from one of the defined genetic clusters . MoreoveThe PCA analysis was performed with the R  package7 iterations, with a burn-in of 106. Results were averaged across all the independent realizations.The EEMS analysis was run for all generated datasets . Furthermore, in order to detect any bias that might be due to the present study’s sampling scheme, EEMS analysis was employed to the seven microsatellite loci dataset of Riccioni et al.  which caTth16-2, Tth226, Tth4 and Tth8) displayed heterozygote deficit, and two (Tth14 and Tth34) excess of heterozygosity, when α = 0.05. All loci exhibited a high number of alleles ranging from eight (Tth157) to 30 (Tth4).Deviation from Hardy–Weinberg (HW) equilibrium was detected (highly significant probability test) when considering ABFT individuals as a single population. Four loci .Measures of genetic diversity of the six predefined populations are depicted in Table The clustering analysis of STRUCTURE based on the admixture model with no use of the sampling locations of individuals, could not be performed given that the estimate of α  varied greatly throughout the run (i.e. >0.2). The problem was not fixed even after the increase of ALPHPROPSD parameter that was performed in an attempt to improve mixing . Such a The PCA analysis indicated that the genetic similarity among the predefined populations of ABFT at 13 microsatellite loci was high and did not reveal any population structure reflecting the geographic origin of the samples. The results of the analysis are presented graphically along the first and second axes in line with eigenvalues in Fig. 2 = 0.248. On the contrary, within demes differences are better predicted with R2 = 0.82 (data not shown). When plotting the observed between demes dissimilarities in respect to their great circle distances, the bad fit of the model is also evident, where the coefficient of determination is equal to R2 = 0.053  was 856. Of these 856 loci, almost half of them (415 loci) did not contain any variable site, 262 contained one SNP, 119 loci two SNPs, 39 loci three SNPs, 15 loci four SNPs and 6 loci contained from five to eight SNPs. The total number of variable sites was equal to 714, while the sampled unlinked SNPs were 441 (SNPs dataset 1).The number of homologous loci for all samples (0% missing data) was 336. Of these, 179 loci did not contain any variable site, 119 contained one SNP, 51 loci two SNPs, 10 loci three SNPs and 7 loci contained from four to seven SNPs. The total number of variable sites was equal to 283, while the sampled unlinked SNPs were 187 (SNPs dataset 2).ST parameter estimates could not be performed or trusted due to the insufficient information contained in the data to compute estimates and/or confidence intervals  to 0.0398 . Similar values were observed for SNPs dataset 2 ranging from 0.0221  to 0.0396 . Hardy–Weinberg equilibrium tests and F sample, ).The clustering analysis of both STRUCTURE and GENELAND indicated no signal of population structure with the most probable number of clusters being K = 1.The PCA analysis implied that the genetic similarity among the predefined populations of ABFT at 441 unlinked SNPs was high and did not reveal any population structuring, coinciding with microsatellite data results of this study. A two-dimensional plot based on the top two PCs is shown in Fig. The averaged, over different grids, effective migration and diversity contour plots across the Mediterranean estimated by EEMS based on 441 SNPs (SNPs dataset 1) are depicted in Fig. In respect to the effective migration plots, ABFT migration in the Mediterranean is estimated to be uniform except in the area of the Alboran and the Balearic Seas where it is slightly higher. On the contrary, it is slightly lower in the South Adriatic and in the Canary Islands. However, none of the deviations is statistically significant (posterior probabilities <0.90) indicating no deviations from exact isolation by distance.2 = 0.298. The within demes differences are better predicted with R2 = 0.995 (data not shown). When plotting the observed between demes dissimilarities in respect to their great circle distances, the coefficient of determination is equal to R2 = 0.264  yielded similar results (not shown) indicating uniform migration rates that were slightly higher only at the Alboran and Balearic Seas though without any statistical support. Furthermore, the genetic diversity was higher at the areas off Morocco coasts, south Tyrrhenian, and near the Libyan coasts, while it was lower at the Alboran Sea, Malta and the Levantive with only the last one being statistically supported (posterior probability ≥ 0.95). The diagnostic scatterplots of between demes pairwise genetic differences indicate that the EEMS model is not a good fit to the data with a coefficient of determination equal to RST values being statistically different from zero, except those associated to the Italian ABFT specimens, a fact that could be attributed to the low number of studied samples from that area. The SNPs were characterized by the absence of population structure and low levels of heterozygosity, coinciding with the lowest SNP heterozygosities observed for the species and its congenerics [Analysis of genome-wide SNPs and microsatellites of ABFT samples from throughout the Mediterranean Sea did not provide strong evidence of genetic structure, pointing towards the existence of a single panmictic unit. Microsatellite genetic diversity was high with most Fgenerics . These rGadus morhua), Atlantic herring (Clupea harengus harengus), and pollock (Pollachius virens), are characterized by spawning aggregations that occur regularly in the same geographic area every year . A q. A qGadur . Thr  indicated that the studied ABFT specimens could constitute a single panmictic population that assorts randomly to spawn in different areas within the Mediterranean Sea. Microsatellite allelic richness was high throughout the study area, a fact that could be indicative of the population’s long-term potential for adaptability and persistence, but also of the importance of the Mediterranean Sea for the species. An alternative view would recognize a failure to reject the null hypothesis (under which Mediterranean ABFT constitutes a panmictic unit) as an effect of the study’s sampling design , the type of markers used (i.e. low information content), and the effectiveness/suitability of the analysis methods in respect to the species’ biological characteristics or any combination thereof. Deciding which is the case and identifying the ‘real’ causes is crucial for ABFT sustainability and conservation.When investigating the population structure of a particular species, the limitations of the given study should always be considered. Furthermore, identification of the population structure is not always straightforward and there are cases where detection of genetic heterogeneity fails, despite its presence .Given that ABFT in the Mediterranean regulates under panmixia, one might assume that the depletion of one local unit/population would be offset by the regular immigration or ongoing larval recruitment from another. However, this has not always been the case for ABFT indicating the existence of a population structure . BesidesTo date, the genetic studies demonstrating population structuring of ABFT in the Mediterranean basin used both temporal and spatial sampling and/or more than one genetic marker –21, 61. Thunnus alalunga [An important aspect when analyzing microsatellite loci in fish with large populations and high gene flow, is that underestimation of genetic differentiation due to the confounding allele size homoplasy is quite common . On the alalunga , 63).In marine populations it is quite common to have large effective population sizes and relatively high rates of gene flow, resulting in a great difficulty to assess population structure, due to low or no genetic differentiation between populations , 64. TheIn respect to the model-based methods employed in this study, either their performance was hampered by violations in model assumptions  or provided clues of the existence of a single population. However, it has been documented that at levels of genetic differentiation similar to our study (<0.02) STRUCTURE models fail to perform . The infThe EEMS was not a good descriptor of the migration and diversity of ABFT in the Mediterranean Sea. This could be due to the sampling scheme used in this study or lack of signal in the data, leading the estimation of migration rates being driven only by the prior (i.e. no heterogeneity in migration rates), or due to the violation of the equilibrium in time assumption of the model or a combination of the above.Given that EEMS analysis of the dataset of Riccioni et al. , with anΑlthough several types of investigations have improved our knowledge of ABFT life history and stock structure, significant gaps still exist and must be addressed to ensure sustainability of the species. The dynamic fluid medium of seas and oceans in combination with the species traits, necessitates novel approaches and methods of analysis that will bring together experts in genetics/-omics, ecology and oceanography.ST) in combination to the analysis of multiple independent genetic loci [The study of samples of a certain age class like the young of the year would undoubtedly assist the research of ABFT population dynamics. Collecting a larger sample size from each ‘population’, could probably ameliorate the bias of estimates of interest  of interest, the temporal stability of those spatial factors, and the dispersal behavior of the species . It coulThe degree of complexity of the ABFT population structure coupled with the potential impact of environmental changes on the spatial and temporal distribution of the spawning areas , 18, 23 Concluding, unravelling the drivers of ABFT population diversity would require the consideration of important aspects of the species spawning behavior for the determination of the appropriate sampling design. Plasticity in the selection of spawning sites is influenced by the spatial and temporal variability in the location of major oceanographic features and environmental conditions, such as salinity and sea-surface temperatures , 76. Sin


By encompassing the whole continuum between allopatric and sympatric scenarios, parapatric speciation includes many potential scenarios for the evolution of new species. Here, we investigate how a genetic barrier to gene flow, that relies on a single postzygotic genetic incompatibility, may further evolve under ongoing migration. We consider a continent island model with three loci involved in pairwise Dobzhansky–Muller incompatibilities (DMIs). Using an analytic approach, we derive the conditions for invasion of a new mutation and its consequences for the strength and stability of the initial genetic barrier. Our results show that the accumulation of genetic incompatibilities in the presence of gene flow is under strong selective constraints. In particular, preexisting incompatibilities do not always facilitate the invasion of further barrier genes. If new mutations do invade, they will often weaken or destroy the barrier rather than strengthening it. We conclude that migration is highly effective at disrupting the so-called “snowball effect”, the accelerated accumulation of DMIs that has been described for allopatric populations en route to reproductive isolation. UNDER what conditions can geographically separated populations that are connected by migration build up a genetic barrier to gene flow? When and how can this barrier be strengthened and eventually lead to speciation? Following the increasing awareness that gene flow and hybridization between related (incipient) species is ubiquitous in both plants and animals . In contFollowing previous work , we studi.e., local adaptation) and selection against hybrids  are nonexclusive . Lower case letters indicate the ancestral state, upper case letters are derived alleles. We study both haploid and diploid populations. We always assume that the continent is fixed for a unique genotype; substitutions on the continent can occur, but they are instantaneous and do not lead to a persistent polymorphism. We focus on the migration–selection dynamics on the island, where all three loci can be polymorphic.To study the accumulation of incompatibilities in the presence of gene flow, we use a migration–selection model in continuous time with three loci. We consider two panmictic populations, one on a continent and the other on an island, each of sufficient size such that we can ignore the effects of genetic drift. There is unidirectional migration from the continental population to the island population at rate abc, with Malthusian (or log-) fitness normalized to 0. We have three parameters for single-locus fitness effects, α, β, and γ. Three parameters, There are eles see . RestricA is always an island adaptation . As a consequence, α is always strictly positive. In contrast, allele B is always a continental adaptation . There is no constraint on its selective advantage, β, on the island: both negative and positive values are investigated. We always assume that A and B are incompatible, i.e., C can appear either on the island or on the continent. There is no constraint on its selective advantage, γ. C can interact positively or negatively with the other derived alleles. To keep our model tractable, we only allow for epistasis between island and continental adaptations. In other words, if C appears on the island, it only interacts with the continental adaptation B . This gives rise to five different linkage architectures: C appearing on the island or on the continent respectively, leading to 10 different cases.The three loci A, B, and C, respectively) for all cases are derived in the Equations (A6)–(A8) in File S1. For example, we obtain for loose linkage . The different systems of equations are available in the Equations (A11)–(A15) in File S1.We investigate two cases of dominance of the epistatic interaction: recessive and codominant epistasis see . AssuminThere are multiple measures for the strength of a genetic barrier between two divergent populations that are connected by gene flow. For example, the gene-flow factor (or the effective migration rate) due to A allele on the island, we have X = A and the strength of the genetic barrier is given by A allele is swamped and the locus is fixed for the continental a allele. Analogously, the strength of a genetic barrier with three polymorphic loci and island alleles A, b, and C (say) is denoted as Specifically, we define the barrier strength e.g., c). While Below, we consider how the strength of an existing genetic barrier changes under further evolution. We then denote the original barrier strength, which serves as the reference point, as The authors state that all data necessary for confirming the conclusions presented in the article are represented fully within the article.i.e., at a locus In the first part of the results section, we study the case where further adaptation happens directly at an already existing barrier locus  and the barrier strength A and C.Assume that, initially, e island : the conWe thus see that further adaptation at a single polymorphic locus will usually strengthen the genetic barrier, rather than weaken it. In particular, this holds for any further adaptation on the island. A three-locus architecture, Note that the genetic barrier formed by a single locus relies exclusively on local adaptation: any isolation observed is due to the impossibility of coexisting in a common environment and not due to a genetic mechanism. This is different for barriers with multiple interacting loci, which is our focus in the remainder of the manuscript.aB, and Ab is the fittest haplotype on the island. A new mutation appears on the island at locus e.g., AC), which we will call the A’ allele in the following. We denote the fitness advantage of the new allele A’ as B allele at the a, A, and A’) that interacts with a loosely linked biallelic locus in the genomic background :A”  will only originate by mutation or rare recombination after one of the alleles a, A, or A’ is lost. This leads again to the three-allele dynamics described by Equation (2). Results for the four-allele dynamics are given in section C2.4 in File S3. Further scenarios with adaptation at the File S2.Assume now that we start with a two-locus polymorphism at two incompatible loci File S2, we show that alleles A and A’ can never coexist at a stable equilibrium . In conFile S3, blue dashed line). However, note that such an optimum at intermediate recombination distances can occur in stochastic models  an optimal architecture involving intermediate recombination, even if the genetic barrier is no longer a monotonic function of recombination and local maxima of the barrier strength as a function of the recombination rate exist in some cases  either in the same environment  or in a different environment (divergent evolution). Since crosses between individuals under different selective pressure tended to generate more unfit individuals, they concluded that genes involved in early divergence also generate genetic incompatibilities [see also Solanum genus.see also . This coi.e., only selection against hybrids) can only be maintained in the face of gene flow when the DMI loci are far away from each other. This is indeed the case for Our model relies on a number of hypotheses, most of them shared with e.g., wind, water current, or flowering time) or if the contribution of both populations to a common migrant pool is strongly biased . If there is weak back migration, the effects described here should still hold, as long as the island adaptations are not advantageous on the continent. For strong bidirectional migration, generalist genotypes can gain an advantage and different results are obtained . We assume that all loci are autosomal. Due to the complexity of the system, we restrict our analytical analysis to the limiting cases of recombination (Finally, we have restricted our detailed analysis in this paper to epistasis schemes with only pairwise interactions. Complex epistasis networks with interactions linking three or more loci offer further routes to strengthen a genetic barrier that will be explored in a forthcoming study.www.genetics.org/lookup/suppl/doi:10.1534/genetics.117.300652/-/DC1.Supplemental material is available online at Click here for additional data file.Click here for additional data file.Click here for additional data file.


Brachypodium distachyon (Poaceae), an annual Mediterranean Aluminum (Al)-sensitive grass, is currently being used as a model species to provide new information on cereals and biofuel crops. The plant has a short life cycle and one of the smallest genomes in the grasses being well suited to experimental manipulation. Its genome has been fully sequenced and several genomic resources are being developed to elucidate key traits and gene functions. A reliable germplasm collection that reflects the natural diversity of this species is therefore needed for all these genomic resources. However, despite being a model plant, we still know very little about its genetic diversity. As a first step to overcome this gap, we used nuclear Simple Sequence Repeats (nSSR) to study the patterns of genetic diversity and population structure of B. distachyon in 14 populations sampled across the Iberian Peninsula (Spain), one of its best known areas.B. distachyon, congruent with a highly selfing system. Our results indicate the existence of at least three genetic clusters providing additional evidence for the existence of a significant genetic structure in the Iberian Peninsula and supporting this geographical area as an important genetic reservoir. Several hotspots of genetic diversity were detected and populations growing on basic soils were significantly more diverse than those growing in acidic soils. A partial Mantel test confirmed a statistically significant Isolation-By-Distance (IBD) among all studied populations, as well as a statistically significant Isolation-By-Environment (IBE) revealing the presence of environmental-driven isolation as one explanation for the genetic patterns found in the Iberian Peninsula.We found very low levels of genetic diversity, allelic number and heterozygosity in B. distachyon populations, potentially causing the existence of more genetically depauperated individuals.The finding of higher genetic diversity in eastern Iberian populations occurring in basic soils suggests that these populations can be better adapted than those occurring in western areas of the Iberian Peninsula where the soils are more acidic and accumulate toxic Al ions. This suggests that the western Iberian acidic soils might prevent the establishment of Al-sensitive The online version of this article (doi:10.1186/s12862-017-0996-x) contains supplementary material, which is available to authorized users. Brachypodium distachyon (L.) P. Beauv. has become one of the most important model systems for functional genomic studies of temperate cereals and forage grasses and for bioenergy crops ).er plant –19, 23 ber plant , as weller plant . Thus, ter plant ).B. distachyon in Spain can be grouped (at least) in three clusters congruent with their geographical origin. NE, NW and S Iberian populations all formed separate and homogeneous groups except in the STRUCTURE analysis that clustered all NE and NW populations in one single group  and GBS techniques might reconstruct similar but slightly different stories [Senecio boissieri DC.: [Gentiana alpina Vill., Kernera saxatilis (L.) Rchb. and Silene rupestris L.: [Ferula loscosii Willk.: [Cheirolophus intybaceus (Lam.) Dostál: [Gentiana lutea L.: [The results of BAPS Fig.  and PCoAoup Fig. . AlthougBS study . This stBS study . But con stories , severaleri DC.: ; Gentiantris L.: ; Ferula  Willk.: ; Cheirol Dostál: ; Gentianutea L.: ).B. stacei both distributed in southern Spain (S and SE Spain groups), from where it colonized the Mediterranean islands of Minorca and Majorca (SE Spanish group) and the oceanic Canary Islands (S Spanish group) apparently through different long distance dispersal (LDD) events [B. distachyon lineages across the Mediterranean area [B. distachyon, the low sharing of alleles between geographical regions, and the highly selfing nature of this species support vicariance rather than long-distance dispersal as our preferred explanation for the patterns found in our study.Using the same set of nSSRs, we found two main genetic groups in the close relative ) events . The potean area , 18. HerB. distachyon in the Iberian Peninsula seems to be also shaped by environmental isolation. Isolation by environment, in which genetic differentiation increases with environmental differences, independent of geographic distance, is one of the most important patterns that contribute to genetic divergence in nature [B. distachyon is significantly positively associated with soil pH and the temperature of the wettest quarter, suggesting that the lower these variables are the less would be the genetic variability of the B. distachyon in the populations studied. By contrast, the significantly negative association found between the genetic diversity parameters and isothermality, as well as with the temperature of the driest quartersuggests contrary results.Besides historical factors , the genetic diversity of n nature . Howevern nature , 66. HerB. distachyon  and in the S Iberian area , suggests that this area is also an important source of genetic diversity in this species, in accordance with several other phylogeographic studies . Pal. PalB. ds  since although genetic diversity is lower in introduced areas, invasiveness might have also triggered the activation of new allelic variants in this species.The finding of a higher level of genetic variability and adaptation of  species . This spg eroded  and are g eroded , 82. Due andAdditional file 1: Table S1.Brachypodium distachyon individuals based on nSSR analysis (TXT 12 kb)Genotypes of the studied Spanish Additional file 2: Table S2.http://www.worldclim.org/bioclim) used in the correlation analysis (XLSX 10 kb)Description of the bioclimatic worldclim layers  Mean log probability of data LnP(D) over 10 runs for each K value as a function of K (error bars represent standard deviation). (B) Evanno’s ad hoc statistic; DK as a function of K (PDF 376 kb)STRUCTURE analysis of Additional file 4: Figure S2.Brachypodium distachyon. A. BIO3. B. BIO8. C. BIO9 (PDF 9255 kb)Bioclimatic variables significantly associated to genetic diversity in


FST, Nei's D) were very low, and AMOVA revealed no evidence for genetic variation structured by transect or by low and high elevation sites within transects. In addition, we found no consistent evidence for strong parallel allele frequency divergence between low and high elevation sites within the three transects. Large elevation-related phenotypic variation may be maintained by strong selection despite gene flow and future work should focus on the mechanisms underlying such variation.Montane habitats are characterized by predictably rapid heterogeneity along elevational gradients and are useful for investigating the consequences of environmental heterogeneity for local adaptation and population genetic structure. Food-caching mountain chickadees inhabit a continuous elevation gradient in the Sierra Nevada, and birds living at harsher, high elevations have better spatial memory ability and exhibit differences in male song structure and female mate preference compared to birds inhabiting milder, low elevations. While high elevation birds breed, on average, two weeks later than low elevation birds, the extent of gene flow between elevations is unknown. Despite phenotypic variation and indirect evidence for local adaptation, population genetic analyses based on 18 073 single nucleotide polymorphisms across three transects of high and low elevation populations provided no evidence for genetic differentiation. Analyses based on individual genotypes revealed no patterns of clustering, pairwise estimates of genetic differentiation ( Geographical isolation can lead to spatial genetic structure by directly limiting gene flow and allowing allele frequencies to differ as a result of genetic drift (isolation by distance ). In addSpatially variable environments play a major role in ecological and evolutionary processes, and offer the opportunity for understanding the factors that shape phenotypic and population genetic variation. Habitats exhibiting environmental variation on relatively small spatial scales could improve our understanding of the relationship among environmental variation, local adaptation and population genetic structure. Montane habitats present a particularly useful setting to investigate these issues, because heterogeneity often occurs rapidly and predictably along elevation gradients ,11. MontPoecile gambeli) are resident, food-caching passerines that inhabit a range of elevations along the Western montane regions of North America [Mountain chickadees ( America . These b America . Birds i America ) and pro America .h2 = 0.32–0.54) [h2 = 0.29–0.95) [Mountain chickadees, like other parids, remain in flocks of unrelated individuals throughout the winter and experience approximately 50% winter mortality . As thei32–0.54) –36 and h29–0.95) ,38 are mIn addition to elevation-related variation in spatial memory and neural morphology, these same populations of high and low elevation birds exhibit differences in social dominance. Low elevation males were dominant to high elevation males in pairwise interactions . DifferePoecile are well known to be highly sedentary with rather short post-natal dispersal distances and limited to no movements once juveniles settle . The. ThePoecle  and low elevation sites 1891–2122 m) in the Sierra Nevada ; Sagehen m in the2.2.DNA was extracted from blood samples using a Qiagen DNeasy Blood and Tissue kit  and quantified using spectrophotometry on a QIAxpert machine . Reduced representation libraries for Illumina sequencing were constructed using a genotyping-by-sequencing (GBS) approach used in multiple previous studies ,55–57. SEscherichia coli; PhiX) and low quality reads. A perl script was then used to identify individual barcodes, correct barcodes with errors, and remove reads containing sequences associated with Illumina adaptors or PCR primers. After this step, fragments were 86–88 bases in length. A random subset of 25 million reads was assembled de novo using the SeqMan ngen software (DNASTAR Inc.), specifying a minimum match percentage of 95 and a gap penalty of 30 (full details of parameter settings are available from the authors by request). Contigs were removed from the reference if they contained fewer than 10 reads, were over-assembled, or were not 84–90 bp in length. This step produced a reference of genomic regions sampled with our GBS approach, providing a template for subsequent reference guided assembly. DNA sequences from each chickadee were subsequently aligned to the reference with bwa v7.5 [aln and samse algorithms and an edit distance of 4. Because all sampled genomic regions begin with the EcoRI cut site and all HiSeq reads contained 100 bases of sequence, these alignments produced consistently rectangular contigs with even positional coverage.The 100 bp reads generated on the HiSeq run were first filtered to remove contaminant DNA  and then converted to a composite genotype likelihood format. Genotype likelihood matrices and assembly related files are available at Dryad and additional information regarding parameter settings is available from the authors upon request.Variant sites (i.e. SNPs) were called and quantified using v.0.1.19 ,60. SNPsTo account for uncertainty associated with variation in coverage depth, a hierarchical Bayesian model  was emplprcomp function in R [adegenet package [tructure [find.clusters function and the appropriate number of retained PCs was estimated using the optim.a.score function. For both PCA and DAPC, permutational multivariate analysis of variance  using the ion in R . As a coion in R  using th package ,63 in R. package . Additiotructure ). The moERMANOVA ) impleme package  of R wasD [FST using Hudson's estimator [ecodist package [D) as the response variable and pairwise elevational and geographical distance as the predictor variables. We estimated geographical distances among sampling sites by calculating Haversine distances from latitude and longitude coordinates using the fossil package in R [poppr package [randtest function in the ade4 R package [We summarized the degree of genetic differentiation among sampling sites by calculating Nei's genetic distance (D ) for eacstimator  as an adstimator ) using t package  in R, wiage in R . Finallyage in R  as imple package ,75. For  package .FST estimates for all loci for the high and low elevation contrasts at each of the three geographical areas. We considered loci residing above the 97th, 98th and 99th quantiles of the FST distribution for each elevational contrast as outliers potentially residing in genomic regions experiencing directional selection. We evaluated potential parallel differentiation using a permutation-based approach to assess significance of the counts of loci that had extreme FST values in more than one elevational contrast. More specifically, the loci in all three elevational comparisons were permuted and then we counted the number of loci that resided in the upper quantile in multiple elevational comparisons; permutations were conducted 10 000 times to create a null distribution.To test for patterns of parallel locus specific allele frequency shifts across low and high elevation groups, we quantified locus specific 3.3.1.De novo assembly of a subset of 25 million reads aligned 21 269 517 reads into 313 976 contigs with a coverage depth greater than 10. The consensus sequences of these contigs were retained as a GBS reference onto which we assembled reads from each individual bird. The final reference based assemblies (executed using bwa) placed 143 928 656 reads across all individuals into alignments. Thus, the final alignments contained 77% of the cleaned reads. After removing loci with minor allele frequency < 0.05, we retained genotype estimates for 37 252 loci in the 167 individual birds. We further filtered for SNPs with a minimum coverage depth of 4× and maximum of 15× per locus and a minimum of coverage depth of 3× and a maximum 10× per individual. After this filtering step we retained a final set of genotypes at 18 073 loci in 151 birds that passed our coverage criteria.One lane of sequencing on the Illumina HiSeq generated 225 302 911 reads. After cleaning contaminants, parsing bar codes, and removing sequences containing pieces of the Illumina adaptors or primers, 185 281 261 reads from 167 individual birds were retained for analyses. R2 = 0.025; F5,145 = 0.742; p = 0.653) . Similarly, pairwise genome-wide FST and Nei's D estimates for samples of chickadees from different sampling sites were consistent with minimal genetic differentiation . Groups of chickadees from high elevation sampling sites did not consistently exhibit significant genetic differentiation from neighbouring low elevation sites, and the genome-wide FST estimates for most contrasts were not significantly different from zero  and elevational distance was unrelated to genetic distance . Hierarchical analysis of genetic variation using AMOVA partitioned nearly all of the variance within samples (99.5%), with minute proportions attributed to variation among transects (0.49%) and across elevations within transects (0.01%). Based on these analyses, there was no evidence for significant genetic structuring among transects  or among different elevations within transects . One PC was retained for the DAPC analysis, which found K = 2 as the most likely number of clusters in explaining the data  . In addirom zero . Althougry small . Consistthe data . Individthe data , consistFST in order to evaluate whether individual loci exhibited elevated differentiation in multiple elevational contrasts, which would be consistent with such loci tagging genomic regions responding to elevation-related selection. There were a small number of loci that were outside the 97th, 98th and 99th quantiles of the genome-wide FST distribution in two elevational contrasts , and few to zero loci that were outside these quantiles in all three elevational contrasts  than expected , we detected little evidence for genetic differentiation between high and low elevation locations or across space for the geographical region we sampled. There was no indication in PCA or DAPC analyses of any population structure figures  and 4, aistances . FinallyParus major [Cyanistes caeruleus [P. major [All chickadee species are known to be resident birds that disperse short distances from their natal site and remain fairly sedentary for the rest of their lives ,45,79. Pus major  and deciaeruleus ). Unlikeaeruleus ,83. WhilP. major )), theseInstances of loci exhibiting exceptional divergence (outlier loci) across replicated environmental gradients could arise when loci are linked to genomic regions involved in local adaptation (e.g. ,10,85,86Second, variation in complex quantitative traits often involves large numbers of small effect loci ,89–95. CPrevious work on this mountain chickadee system has shown that high and low elevation birds exhibit differences in spatial memory ability, hippocampal morphology, social dominance, novel environment exploration, problem solving and proactive aggression ,39,98,99Adaptation in the presence of gene flow is quite common in many species, including birds ,100–104,Overall, our study detected no elevation or geographical related population genetic structure despite numerous phenotypic differences previously documented in mountain chickadees. Future work should now focus on identifying the mechanisms underlying elevation-related phenotypic variation in these birds. In order to understand the role of local adaptation in driving these phenotypic differences, it might be more fruitful to concentrate on measurements of heritability and selection, as opposed to genome-wide association techniques, owing to the well-known difficulties associated with identifying the genetic basis of highly polygenic traits ,95,96.


FST-based and EigenGWAS statistical analysis revealed high genetic variation between the two subpopulations. Analysis of the 10 loci with the strongest selection signals showed that many loci were located in chromosome regions that have previously been identified as quantitative trait loci (QTL) associated with environmental-adaptation-related and yield-related traits. The pattern of diversity among the American and Chinese accessions should help breeders to select appropriate parental accessions to enhance the performance of future soybean cultivars.Soybean is one of the most important economic crops for both China and the United States (US). The exchange of germplasm between these two countries has long been active. In order to investigate genetic relationships between Chinese and US soybean germplasm, 277 Chinese soybean accessions and 300 US soybean accessions from geographically diverse regions were analyzed using 5,361 SNP markers. The genetic diversity and the polymorphism information content (PIC) of the Chinese accessions was higher than that of the US accessions. Population structure analysis, principal component analysis, and cluster analysis all showed that the genetic basis of Chinese soybeans is distinct from that of the USA. The groupings observed in clustering analysis reflected the geographical origins of the accessions; this conclusion was validated with both genetic distance analysis and relative kinship analysis.  Soybean originated in China, and has a history of planting for more than 4,000 years  and 300 from the USA (hereafter termed as US-set). Of the 277 Chinese accessions collected from 11 provinces, 231 accessions were derived from the Northern spring (Nsp) ecotype and 46 were derived from the Huang-huai-hai summer (Hsu) ecotype. Detailed information on the 277 accessions can be found in Supplementary Table Genomic DNA was extracted from soybean seedlings (leaf) following the protocol presented by Kisha et al. . All of r2, calculated by TASSEL 5.0 ; and (nl − 1) is a sampling bias correction. Negative values between two accessions, indicating the existence of a weaker relationship than would be expected between two random individuals, were replaced by zero. Analysis of molecular variance (AMOVA) was performed to estimate the variance between populations and among accessions within populations based on analyses of variance of allele frequencies  ranging from 1 to 10. The number of subpopulations was determined when Δk reached its highest value , and cluster analysis with a neighbor-joining algorithm, were employed to divide the soybean accessions into subgroups. The Bayesian model-based program STRUCTURE 2.3  FST between the CN-set and US-set was calculated for individual SNP using VCFtools (https://vcftools.github.io/index.html)  Finding loci under selection through genome-wide association studies of eigenvectors were implemented by EigenGWAS. There were three steps included. Firstly, genetic relationship matrix was generated for the 577 accessions; secondly, the top 10 eigenvalues and eigenvectors were calculated; and then linear model for selected eigenvectors from the second step was conducted.Three statistical methods were used to detect the loci under selection. (1) Differences in allele frequency between the CN-set and the US-set were tested by Student's A total of 577 soybean accessions were analyzed using the 5,361 SNP markers of the Illumina SoySNP6k iSelect BeadChip. SNP markers with missing data points for more than 20% (166 SNPs) of the accessions were not used for further analysis, so a total of 5,195 SNPs (96.90%) were used in our study. In the CN-set, the average PIC was 0.2643, ranging from 0 to 0.3750, and the average genetic diversity was 0.3307, ranging from 0 to 0.5000. In the US-set, the PIC ranged from 0 to 0.3750, with an average of 0.2408, and genetic diversity ranged from 0 to 0.5000, with an average of 0.2988  analysis was performed based on 5,195 SNPs in both the CN-set and the US-set. The D Figure . In the K = 2 as the highest level of structure  increased continuously, with no obvious inflection point Figure , which ie Figure . There we Figure . Of the In order to validate and gain further insight into the genetic diversity of the soybean germplasm panel, we constructed a neighbor-joining tree based on the frequency of shared alleles among the accessions. The 577 soybean accessions were classified into two major groups Figure . One groOur unbiased population structure, neighbor-joining tree, and PCA analyses all clearly indicated the existence of two distinct subpopulations among the 577 accessions of our study. Clear divergence existed between the Chinese and US soybean accessions. With the exception of a very small number of accessions that were grouped into the subpopulation for the other country (< 7%), most of the accessions from the same origin (country) were clustered into the same genetic group. Therefore, the following studies were based on the two pre-defined genetic pools .FST was 0.1933 (P < 0.001) between the two subpopulations  ranged from 0 to 88.63% between the CN-set and the US-set and was almost symmetrical , and −log(P) of f1−f2 values  are presented in Figure FST, test of f1−f2 values, and results from EigenGWAS were highly consistent over the 20 soybean chromosomes  had pleiotropic effects on all of the following physiological aspects: photoperiod, seed quality, defense, and yield-related traits. Among the top 10 loci ranked based on the strength of their selection signals, ss245627275 on chromosome 5, ss246165779 on chromosome 7, and ss249429323 on chromosome 17 have not been previously reported to be associated with any traits, and one gene near SNP locus ss245627275 was annotated as RAS-related nuclear protein; one gene near SNP locus ss246165779 was annotated as having a function relating to nucleoside diphosphate kinase activity; and one gene near SNP locus ss249429323 was annotated as having a function relating to protein domain specific binding  related to domestication (Ren et al., The identification of loci with selective signals is a centrally important step for understanding how various populations have adapted to particular agronomic practices and/or unique environments. The FST-based statistical analysis and EigenGWAS to identify selection signals at SNP loci. Although, the majority of the loci had low FST-values in pairwise comparisons, our data revealed the presence of genomic regions with high genetic variation between the two subpopulations (Figure Glymo08g09310 controlled seed size was identified (Zhou et al., In the present study, we used an s Figure , and theLQ, DW, ZL, and SW: conceived and designed the experiments; ZL, ZW, and XF: performed the experiments; ZL, HL, YL, RG, and YG: analyzed the data; ZL, HL, and ZW: wrote the paper.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Our proposed approach is in contrast to worldwide current programs, where genetic evaluation is constrained by the range of environments where testing is conducted, which may be insufficient to capture the broad environmental variation necessary to tackle adaptation under changing climate.Traditional gene-resource management programs for forest trees are long-term endeavors requiring sustained organizational commitment covering extensive landscapes. While successful in maintaining adaptation, genetic diversity and capturing traditional growth attributes gains, these programs are dependent on rigid methods requiring elaborate mating schemes, thus making them slow in coping with climate change challenges. Here, we review the significance of Norway spruce in the boreal region and its current management practices. Next, we discuss opportunities offered by novel technologies and, with the use of computer simulations, we propose and evaluate a dynamic landscape gene-resource management in Norway. Our suggested long-term management approach capitalizes on: (1) existing afforestation activities, natural crosses, and DNA-based pedigree assembly to create structured pedigree for evaluation, thus traditional laborious control crosses are avoided and (2) landscape level genetic evaluation, rather than localized traditional progeny trials, allowing for screening of adapted individuals across multiple environmental gradients under changing climate. These advantages lead to greater genetic response to selection in adaptive traits without the traditional breeding and testing scheme, facilitating conservation of genetic resources within the breeding population of the most important forest tree species in Norway. The use of  Picea abies L. Karst) and Scots pine (Pinus sylvestris L.) timber resources across the landscape, utilizing areas traditionally not suitable for effective production of other crops. The landscape delivers important ecosystem services, non-timber values, and is of fundamental importance for the biodiversity. Norway spruce is the most widespread and economically important conifer tree in Europe.Bio-products from forest trees have always been important for societies in boreal regions, and will continue to be important for a successful transfer to a bio-based economy. Production forestry in this region is typically based on Norway spruce  from mature natural grown stands in Norway. Since then, about 3,500 plus-trees have been tested for performance of their progeny (F1) in multiple field trials. Among the F1, a superior population is selected as the basis for long-term breeding, structured in 23 sublines of approximately 50 individuals based on adaptive characteristics (h2) typically range from low (∼0.15) for growth traits, intermediate (∼0.50) for quality traits, to high (>0.60) for adaptive traits such as phenology . Therefore, we propose to work with large populations, rely on natural crosses , and then evaluate very large candidate populations on a landscape basis, rather than dealing with a limited number of progeny test trials. This is also compatible with the genotype × environment interactions commonly observed in forest tree trials and the generally unpredictable effects of long-term selection on genetic covariance among multiple traits  in the plantations. In this step, the breeder simply selects uniform, well-stocked sections of the plantations, and measures contiguous blocks of trees as in a normal progeny trial. Second, a very large population residing in established plantations is screened to identify a small Top Phenotype subpopulation of size = NT. Based on phenotypic assessment, these trees are the top candidates for selection. The idea is to use UAV to screen tens of thousands of trees and make an identification of candidates. Molecular marker analysis and pedigree reconstruction will be done on both NR and NT trees, respectively. Finally, all phenotypic data are utilized to estimate genetic values for all trees (NR + NT), using the reconstructed pedigree data to estimate genetic variance components and calculate Best Linear Unbiased Predictions (BLUPs).For each subline, a breeding arboretum is established as a “breeding population.” Following natural crossing (open-pollination), conventional forest plantations are established from seed collected in the arboretum, and managed as regular commercial plantations. We assume that all the phenotyping, genetic evaluation and selection for the gene conservation and tree breeding program is performed in these plantations. A typical plantation of Norway spruce is assumed to be 3 hectares in size with 6,000 surviving trees when the plantation reaches an appropriate age for evaluation and selection. First, a Random subpopulation of size = Table 1.Computer simulations were done to compare the genetic gain from the proposed approach with a conventional tree improvement program. All simulations were done following the approach presented by P. abies in Norway .A single subline was modeled with constant population size of 50 individuals. The breeding objective was assumed to be height growth at 15 years, and we assumed typical genetic parameters for ’s 50 parents was assumed with the actual variable gametic contributions typical for Norway spruce . The Random subpopulation was held to a constant size NR = 1,800 distributed evenly across all test sites , with the number of test sites ranging from NSITES = 1 to 30. Thus, the total number of trees in the experiment NBwB was NSITES × 6,000. The Top Phenotype subpopulation was also held to a constant size of NT = 600, distributed across test sites, wherever the best phenotypes were found. Subpopulation sizes of NR = 1,800 and NT = 600 resulted in sufficient genetic gains from selection across varying sizes of NBwB, and for different sizes of the selected population, while minimizing costs associated with collecting phenotypic data and lab genotyping.For the proposed BwB strategy, natural open-pollinated mating between the sub-lineFor a comparison with the BwB strategy, we assumed a conventional tree improvement program with full-sib crosses (FS). Under the FS scheme, 25 FS  were generated among the 50 parents with 160 trees per each full-sib family. Progeny trials were established across 4 sites within the breeding zone (40 trees per family per site). The total number of trees in the experiment was 4,000.For both the BwB and the FS strategy, simulated phenotypic and genetic data was generated using the assumed genetic parameters. Genetic evaluation was conducted using a REML & BLUP analysis with ASReml software  with theNe (effective population size) = 20 unrelated parents, and a next-generation breeding arboretum of 80 unrelated selections. With four sublines in a breeding zone, this meant that from a given subline, we made Ne = 5 unrelated selections for the seed orchard, and Ne = 20 unrelated selections for the breeding arboretum. Selection of the best set of offspring for these new populations was done using a linear optimization algorithm which is fully described in Ne, while ensuring that there is no relatedness among the selected trees (to avoid the impact of inbreeding depression on adaptive and other traits).Following the genetic evaluation, selection of the top offspring was done for a seed orchard with Figure 1), we believe the proposed BwB approach is very competitive to the traditional forest tree selective breeding programs. For example, for the Seed Orchard population, gain from the BwB approach surpasses conventional programs with NSITES > 6. The BwB strategy requires a Random subpopulation of 1,800 trees  to be phenotyped and genotyped. An additional 600 Top Phenotype trees are identified by cost-effective UAV, and these trees are also phenotyped and genotyped. This approach will produce genetic gains greater than or equal to conventional breeding approaches, and could be more resource efficient due to reduced cost and time components.Based on simulation results . A t-test was conducted to compare genetic response to selection between the two respective strategies, i.e., BwB and conventional full-sib strategy at each value of NBwB. All differences were statistically significant (P ≤ 0.0001), except for the respective gains for the arboretum population at NBwB = 8. As NBwB) over the full-sib alternative is due to the larger within-family selection intensity (larger size of the candidate population). Results were robust to additional increments in NR, as the current size of 1,800 was sufficient to variance decomposition and prediction of the respective mid-parental additive genetic values, thus further increase in the NR parameter produced non-significant ΔG of the BwB strategy (data not shown). We further investigated the sensitivity of ΔG with respect to the key parameters in the current study. Significant incremental response to selection of the BwB strategy was observed for the studied values of The main features of the proposed gene-resource management could be highlighted as: (1) natural open-pollinated crosses, thus natural selection is targeted at adaptive traits, (2) genetic testing, screening, and evaluation are performed on the landscape level, recognizing the importance of adaptive traits and their interaction with environmental effects and their spatial and temporal change, (3) selection optimally utilizing available information, highlighting the importance of genetic diversity, yet providing sufficient genetic gains in productive traits mainly due to the expansion of the candidate population, thus increasing the within-family selection intensity, and (4) the strategy is logistically simple and reduces complex and expensive operations . After selections, the forest plantations will be managed for production of timber until the economic rotation age is attained, and therefore remain as a possible base for new selections and reservoirs of genetic variability for at least 50 additional years.We believe that once high-density SNP chips for Norway spruce become available, combined with further genotyping cost reductions, it will be possible to replace  the BLUP-based evaluation by the genomic (GBLUP) alternative . This could further enhance the BwB gains due to: (1) accounting for historical relationships, (2) more efficient separation of non-additive genetic variance components, and (3) utilization of the Mendelian sampling term , while sThe proposed approach could be applicable to any species world-wide, where the adaptive response to climate change is becoming a critical component to human population growth (wood demand) and where resources to implement traditional breeding are limited.ML and AS conceived the project and supervised the manuscript. ML, YE-K, TS, GH, JS, and AS drafted the manuscript and contributed to discussions. ML and AS conducted computer simulations.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The application of high-resolution genetic data has revealed that oceanographic connectivity in marine species with planktonic larvae can be surprisingly limited, even in the absence of major barriers to dispersal. Australia’s southern coast represents a particularly interesting system for studying planktonic larval dispersal, as the hydrodynamic regime of the wide continental shelf has potential to facilitate onshore retention of larvae. We used a seascape genetics approach  to assess population genetic structure and self-recruitment in a broadcast-spawning marine gastropod that exists as a single meta-population throughout its temperate Australian range. Levels of self-recruitment were surprisingly high, and oceanographic connectivity simulations indicated that this was a result of low-velocity nearshore currents promoting the retention of planktonic larvae in the vicinity of natal sites. Even though the model applied here is comparatively simple and assumes that the dispersal of planktonic larvae is passive, we find that oceanography alone is sufficient to explain the high levels of genetic structure and self-recruitment. Our study contributes to growing evidence that sophisticated larval behaviour is not a prerequisite for larval retention in the nearshore region in planktonic-developing species. Many coastal marine species have a two-phase life-cycle in which adults are sessile or sedentary, with dispersal instead facilitated by pelagic propagules such as eggs and planktonic larvae146Direct measurements of propagule dispersal are almost invariably difficult to obtain9101113111516Siphonaria diemenensis  is a common rocky shore limpet that occurs throughout southern and eastern temperate Australia23S. diemenensis a particularly suitable model for studying the effect of ocean circulation on the connectivity of rocky shore fauna with theoretically high dispersal potential.In recent years, seascape genetics  has proven to be particularly powerful in helping to identify factors that limit connectivity in the oceans1118Although southern Australia is dominated by three ocean currents , which together can potentially connect the region’s entire fauna over a distance > 5000 km10S. diemenensis were amplified as described inSide01, Side03, Side04, Side05, Side07, Side09, Side12, Side13, Side15, Side17, Side18, Side19 and Side20. Fragments were separated on an ABI 3730 Genetic Analyser (Applied Biosystems) and alleles were scored using GENEMAPPER v. 3.0 (Applied Biosystems). Tests for genotyping errors, null-alleles and large allele drop-out were performed with MICRO-CHECKERTissue samples from the foot of 714 individuals were collected from 16 localities  during 2G″ST in GenAlEx 6.5G′STFSTFST. Significance of both statistics at α = 0.05 was assessed by running 999 permutations in GenAlEx, and the B-Y false discovery rate methodTo measure genetic structure between sites, we calculated pairwise G″ST values and one comprising geographic distances, and to construct a regression plot. Significance for the Mantel test was based on 999 permutations. GenAlEx was also used to perform a multilocus spatial autocorrelation analysis and to calculate within-population relatedness. Both methods are useful to determine whether larval recruitment occurs mostly in close proximity to the parent site, in which case the spatial autocorrelation coefficient rr. Spatial genetic structure is present when the value of r is beyond the confidence interval of the null hypothesis and when the confidence interval of r does not overlap with zero. After exploring a number of distance class sizes, we presented an autocorrelation correlogram with a distance class size of 120 km, as this resulted in particularly good resolution for our sampling design.Geographic distances between sites were measured as minimum coastline distances in ARCMAP 10.1 . GenAlEx was used to perform a paired Mantel test36rrQG to distinguish it from the spatial autocorrelation statistic). Genotypes from all sites were permuted 999 times to calculate 95% confidence intervals for the range of rQG expected under conditions of spatial genetic homogeneity.As an independent and complimentary means of assessing levels of self-recruitment, relatedness or kinship indices have proven to be useful because individuals from the same site are more likely to be closely related to each other than they are to individuals from other sitesS. denticulata, where there is a lag of 2–3 months between hatching and settlement. diemenensisThe effect of ocean circulation on genetic connectivity was assessed using oceanographic connectivity simulations. These were performed on OFES hydrodynamic dataG″ST values and oceanographic connectivity matrices was assessed using Mantel tests, as described earlier, and Multiple Regression on Distance Matrices . As the value of G″ST for each pair of sites is a consequence of both immigration and emigration, a single oceanographic connectivity value was calculated for each pair of sites as the sum of the number of settlers released from a particular site that reached the other site, and the number of settlers received from that site. In addition, as the inferred number of settlers differed by several orders of magnitude, each estimate was corrected for the Mantel test by taking its natural logarithm.Relationships between ices MRDM46. The MAs Mantel tests have been criticised for having an inflated Type I error rateTo determine how well the simulated oceanographic data explained the self-recruitment inferred with the genetic data, Spearman rank correlationsSide18) to 31 (Side20) for the combined data, and for individual sampled sites, it ranged from 2 (Side18 at sites 2 and 13) to 23 (Side09 at site 5). The mean number of alleles across all loci was similar at the different sampled sites and ranged from 11.2 to 12.7 , indicating that genetic structure can be described by a pattern of isolation by geographic distance. This was also evident in the positive correlation identified in the corresponding regression plot , but also at sites 8 and 12.Mean pairwise relatedness within sites  confirmeG″ST revealed a significant negative correlation for both the short spawning model, Model 1  and the long spawning model, Model 2 . For the 5 generation simulations, the relationship was non-significant for Model 1  but remained significant for the more realistic Model 2 . The two matrices have an inverse relationship because the more strongly two sites are connected by migration, the lower G″ST is for that pair of sites . A positive correlation was also found between the number of simulated particles that reached the shelf edge , and there was a negative correlation between those particles that reached the continental shelf and the level of self-recruitment . This indicates that particles that became entrained in the region’s shelf-edge boundary currents were not only less likely to return to the parent habitat, but also had lower recruitment success.Mean relatedness  at each elf edge  and thosEvaluating how planktonic larvae recruit back into their source populations, and how much gene flow takes place between different populations, is not only critical for understanding the population dynamics of coastal species, but also to manage and conserve such populations1per se does not necessarily determine high levels of self-recruitment. Depending on local conditions, larval concentrations may decrease significantly from the coast to the shelf edgeIn the present study, a simple advection connectivity model, which treated larvae as passive particles and assumed that they remained in the surface layer throughout their dispersal phase, was adequate to explain high levels of genetic structure and self-recruitment. The considerable differences in these estimates at specific sites in the sampling region are readily explained by differences in how oceanographic regimes influence larval dispersal. Self-recruitment was particularly high in the west of the sampled range, as indicated by the high proportion of simulated particles that settled at their natal site and the high levels of relatedness among individuals from almost half of the study sites. In contrast, mean relatedness was low at most eastern sites, where the larger number of simulated particles reaching the continental shelf was positively correlated with greater larval loss. This difference between regions is not simply due to the presence of large bays in the west, where larval retention can be expected to be high5356The strong correlation between genetic structure and oceanography identified in this study is likely to apply more widely to most southern Australia’s mollusks that disperse by means of planktotrophic veliger larvae. Unlike many fish and crustaceans, whose late-stage larval often have considerable swimming ability and sensory competency that may reduce the influence of ocean currents on their dispersal60616364S. diemenensis seems to be quite flexible in terms of when it spawns. Recruitment has also been reported between May and July25In addition to assuming passive dispersal, other simplifications may have affected our simulations. First, we simulated larval dispersal from early September to the end of NovemberS. diemenensis. While larval duration can be inferred indirectly on the basis of spawning and recruitment patternsS. diemenensisS. denticulata, whose larvae also hatch well developed and may remain pelagic for up to 10 weeksS. diemenensis. This is also unlikely to significantly affect genetic structure because there is no clear difference in realised dispersal distances of species whose larvae are part of the plankton for a few days compared to those that remain pelagic for weeks72Secondly, our simulations may also have been affected by the fact that there is no detailed information on the planktonic larval duration of Our study contributes to the growing evidence that nearshore hydrodynamic processes can promote the retention of larvae close to parental habitats, even in ‘dispersive’ taxa117475How to cite this article: Teske, P. R. et al. Oceanography promotes self-recruitment in a planktonic larval disperser. Sci. Rep.6, 34205; doi: 10.1038/srep34205 (2016).


Capsicum.Conservation of genetic diversity is an essential prerequisite for developing new cultivars with desirable agronomic traits. Although a large number of germplasm collections have been established worldwide, many of them face major difficulties due to large size and a lack of adequate information about population structure and genetic diversity. Core collection with a minimum number of accessions and maximum genetic diversity of pepper species and its wild relatives will facilitate easy access to genetic material as well as the use of hidden genetic diversity in Capsicum annuum showed the highest genetic diversity , whereas the wild species C. galapagoense showed the lowest genetic diversity . The Capsicum germplasm collection was divided into 10 clusters (cluster 1 to 10) based on population structure analysis, and five groups (group A to E) based on phylogenetic analysis. Capsicum accessions from the five distinct groups in an unrooted phylogenetic tree showed taxonomic distinctness and reflected their geographic origins. Most of the accessions from European countries are distributed in the A and B groups, whereas the accessions from Asian countries are mainly distributed in C and D groups. Five different sampling strategies with diverse genetic clustering methods were used to select the optimal method for constructing the core collection. Using a number of allelic variations based on 48 SNP markers and 32 different phenotypic/morphological traits, a core collection ‘CC240’ with a total of 240 accessions (5.2 %) was selected from within the entire Capsicum germplasm. Compared to the other core collections, CC240 displayed higher genetic diversity (I = 0.95) and genetic evenness (J’ = 0.80), and represented a wider range of phenotypic variation .To explore genetic diversity and population structure, we investigated patterns of molecular diversity using a transcriptome-based 48 single nucleotide polymorphisms (SNPs) in a large germplasm collection comprising 3,821 accessions. Among the 11 species examined, Capsicum accessions based on transcriptome-based 48 SNP markers with genome-wide distribution and 32 traits using a systematic approach. This core collection will be a primary resource for pepper breeders and researchers for further genetic association and functional analyses.A total of 240 accessions were selected from 3,821 The online version of this article (doi:10.1186/s12863-016-0452-8) contains supplementary material, which is available to authorized users. Capsicum spp.) is one of the major vegetable and spice crops grown worldwide, and is rich in bioactive compounds, such as capsaicinoids and carotenoids, which contribute to the improvement of human health [Pepper , the Netherlands [Capscicum spp. for various purposes. Fan et al. [Potato virus Y (PVY), respectively. Hanson et al. [Several research institutions have collected and conserved thousands of herlands  to almosherlands . Researcn et al. , Nicolain et al. , and Zewn et al.  establisn et al. , and Quen et al.  construcn et al.  developen et al. , 12, 14.Capsicum germplasm collection consisting of 3,821 accessions by applying 48 genome-wide SNPs, and selected a core set using the SNP data together with data for 32 morphological traits. This allowed us to 1) examine the level of genetic diversity and the population structure within the worldwide Capsicum germplasm collection; 2) optimize selection methods by comparing different core sets, which were selected using a stepwise selection strategy based on various combinations of data and clustering methods; and 3) ultimately construct a Capsicum core collection that represents the entire germplasm collection without redundancy. Finally, we validated the core collection by evaluating the diversity of a range of traits and genotyping additional molecular markers. This core collection will be a valuable data set for both pepper breeding and genome-wide association studies.In this study, we performed population structure analysis in a large Capsicum accessions used in this study originated from 97 countries and included 11 species: C. annuum, C. baccatum, C. cardenasii, C. chacoense, C. chinense, C. eximium, C. frutescens, C. galapagoense, C. praetermissum, C. pubescens, and C. tovarii. The geographic origin and passport data of the germplasm accessions were obtained from the Rural Development Administration  and Seoul National University . Among the germplasm accessions, 3,599 were obtained from the RDA, and 1,053 were obtained from SNU. Most of the accessions were C. annuum, accounting for 4,163 accessions. Four other domesticated species, C. baccatum, C. chinense, C. frutescens, and C. pubescens accounted for 163, 122, 152, and 11 accessions, respectively. Among the wild Capsicum species, C. cardenasii, C. chacoense, C. eximium, C. galapagoense, C. praetermissum and C. tovarii accounted for 1, 28, 4, 2, 5, and 1 accessions, respectively.A total of 4,652 Two young leaves from each accession were used for DNA extraction. DNA was extracted using the cetyl trimethylammonium bromide (CTAB) method as described previously . The conA set of 48 SNP markers evenly distributed in 12 pepper chromosomes were used in this study  . Principal coordinate analyses were also performed with DARwin 6.0.9 .Capsicum collections. These include levels of observed heterozygosity (HO), expected heterozygosity (HE), polymorphic information content (PIC), genetic differentiation (FST), Shannon’s information index of diversity (I), and genetic evenness (J’). Indices Ho, HE, PIC, and FST were calculated using Power Marker 3.25 [Different indices were used for analysis and comparison of diversity among the ker 3.25 . For anaker 3.25  and Pielker 3.25 , respectker 3.25 .To establish a core collection, five different methods were used. Specifically, core sets were selected based on 1) genotype analysis of the entire collection, 2) genotype analysis of each cluster after grouping based on genotype dissimilarity, 3) phenotype analysis of the entire collection, 4) a combination of genotype and phenotype analysis of entire collection, and 5) a combination of phenotype and genotype analysis of each cluster after grouping based on genotype dissimilarity.Representative accessions were selected based on the advanced M strategy using a modified heuristic algorithm implemented in PowerCore software . CategorTo evaluate each core collection, diverse statistical indicators were calculated for two types of variables, continuous and categorical variables. For continuous variables, the percentage of significant difference between core collections and the entire germplasm collection was calculated based on the mean difference (MD) percentage, the coincidence rate (CR) of range, the variance difference (VD) percentage, and variable rate (VR) of coefficient of variation. Among the candidate core sets selected from each different data set, a core set with MD less than 20 % and CR more than 80 % was considered as a representative collection. In addition, a lower value in VD and higher value in VR was considered to indicate a more effective core collection . For catThree additional markers having multiple alleles, COS643, COS111, and L4RP-3 F, which were selected from the Sol Genomics Network  and YangP < 0.05 was considered to indicate a statistically significant difference. The IBM SPSS Statistics v23 software  was used for analysis.Finally, the core collection (CC240) with the highest genetic diversity and evenness was planted in 2014 in a research farm  to monitor the variation of the diverse traits. Morphological data were obtained for the same accessions that were genotyped. Thirty-two different traits related to plant habit (9), leaf (4), flower (6), fruit (10), and seed (3) were analyzed. Phenotype data were presented as the mean ± SE. The differences between the mean values of individual clusters were assessed using one-way ANOVA and Duncan’s multiple range tests. O values, 673 accessions mostly from C. annuum with Ho value more than 0.3 were considered as F1 hybrids  suggests that there is extensive genetic variation within this species. With the exceptions of C. baccatum and C. pubescens, the other domesticated species showed relatively high HE values, above 0.37. The HO value of C. annuum was 0.12, whereas those of the other species varied from 0.09 to 0.21. Four domesticated species C. annuum, C. baccatum, C. chinense, and C. frutescens and two wild species C. chacoense, and C. eximium had lower values for HO compared to HE,  [Using the SNP genotyping results, the Hsources) , 36. By E) between individuals in each cluster was 0.32. The highest HE value of 0.43 was observed in cluster 5, indicating greater genetic diversity within this cluster, whereas cluster 9 showed the lowest HE value of 0.11. Genetic differentiation (FST) values varied from 0.08 to 0.78 with an average of 0.33. The smallest FST value (0.08) was observed in cluster 5, whereas cluster 9 had the highest FST value (0.78), indicating that accessions in this cluster have several different genotype patterns.The SNP genotyping results were used to perform population structure analysis for the 3,821 accessions under an admixed model using the STRUCTURE program . EstimatC. annuum accessions were found in clusters 1 to 7. C. chinense was mostly distributed in clusters 8 and 9, whereas C. frutescens was mostly found in clusters 4, 9, and 10. By contrast, C. baccatum was distributed in clusters 8, 9, and 10. C. pubescens was placed in cluster 10. Wild species C. chacoense, C. cardenasii, C. eximium, C. praetermissum, and C. tovarii were distributed in cluster 10 along with C. baccatum accessions  in European countries were distributed among upper branches including the clades A and B. C. annuum accessions from Asian countries were distributed among lower branches (clades C and D). The accessions belonging to other species were clustered together in clade E. Within clade E, most of C. chinense accessions were clearly distinguished from those of other species and were placed next to clade D. When the unrooted phylogenetic tree was compared with the clusters obtained from the STRUCTURE analysis, the phylogenic tree matched well with the cluster separation in the STRUCTURE analysis. Accessions in cluster 3 belonged to clade A, accessions in clusters 1 and 2 to clade B, and accessions in clusters 4 to 7 to clade C; some of the accessions in cluster 5 and admixtures belonged to clade D and the accessions in clusters 8 to 10 were in clade E  in which accessions of To determine the best possible method for selection of a core collection, five different sampling strategies were tested Table . The firWhen only genotype data were used, 7 and 53 core entries were selected for Gcc and Ggcc, respectively. Ggcc showed a MD of less than 20 %, which is close to the mean value of the entire collection, whereas Gcc showed a MD of more than 20 %, poorly representing the entire collection. Both of them showed a CR of less than 80 %, demonstrating insufficient coverage of the phenotype variation of the entire collection. However, Ggcc exhibited a smaller percentage of VD and larger percentage of VR, which indicated that selection of a core set after clustering analysis (Ggcc) better represented the entire collection. Furthermore, the comparison of categorical variables including 48 SNPs markers and 15 qualitative traits produced a higher value in I for Ggcc (0.91) than for Gcc (0.88), but a similar value in J’ for Ggcc (0.79) and Gcc (0.77). Therefore, selecting the core entries after clustering analysis is more effective to represent the entire collection in terms of both phenotype and genotype data even using same number of alleles.C. annuum (176), C. baccatum (21), C. chinense (22), C. eximium (2), C. frutescens (18), and C. praetermissum (1) were ultimately selected as a core collection (CC240) , the genotype  and genotype plus phenotype  revealed lower variation in each axis , 9146 (C. annuum), Tabasco (C. frutescens), and 9148 (C. frutescens) were excluded from the phenotype analysis due to poor growth. Thus, phenotype evaluation was performed for 236 accessions for 32 different traits  by hierarchical clustering (UPGMA) based on genetic dissimilarity of 48 SNP markers. Clusters I, II, and III included Capsicum germplasm accessions having been documented worldwide, little is known about their population structure or genetic diversity in contrast to other crops. Previously, Capsicum germplasm collections have been examined for genetic diversity using accessions from Mesoamerica  to survey geographic origin and understand the domestication process [Capsicum germplasm collection with 1,352 accessions, which was grouped into six distinct clusters based on genetic analysis with six SSR markers [Capsicum germplasm collection consisting of 3,821 accessions was divided into ten clusters by STRUCTURE analysis and five distinct groups by phylogenetic analysis . Variance among populations and within a population of five phylogenetic groups were seven and 93 %, respectively and the variance among and within populations of ten STRUCTURE clusters were 31 and 69 %, respectively  as per the population structure analysis, whereas in the unrooted phylogenetic tree accessions corresponding to those two clusters were placed in a same node. Clade E included species other than C. annuum and showed distinct grouping. C. chinense accessions were separated out from other species and closely placed next to C. annuum. In pepper breeding, agriculturally useful traits such as disease resistance, fragrance, yield, and pungency have been introgressed from wild species by interspecific hybridizations. Among the domesticated species, C. chinense has better crossability with C. annuum and is used as a bridge species between C. annuum and other species [C. chinense in this tree, between C. annuum and other species, may explain why C. chinense has played a role as an interspecific cross-bridge. Based the topology of the phylogenic tree, C. annuum accessions in clade D . Therefore, the number of clusters in the collection is not a critical factor to select highly diverse core entries.Use of either genotype or phenotype information only for selection of core collection entries may not be efficient for capturing genetic diversity of the entire germplasm of a species. Therefore, we used both genotype and phenotype information along with clustering to select core collection entries. To determine the optimal core set selection methods, we compared five different methods and found that selection of the core set using genotype and phenotype data after clustering analysis (Gg + Pcc) is the best method Table . MoreoveC. annuum and other two (IV and V) included other species, such as C. baccatum, C. chinense, C. eximium, C. frutescens, and C. praetermissum without clear species distinction. In a previous study, Capsicum germplasm was divided into six clusters [C. annuum accessions. Those three clusters were clearly distinguished mainly by fruit shape, such that cluster 1 was characterized by elongated fruited peppers, thin pericarp and late flowering, whereas cluster 2 exhibited conical fruit and rather thick pericarp, and cluster 3 had large-fruited peppers with thick pericarp and elongated-fruited peppers. Consistent with Nicolai’s work [C. annuum accessions, which are mainly used as fundamental breeding materials, were clustered by breeding features based on food culture. Though, M strategy is the most powerful option for the selection of accessions with rich allelic diversity and for eliminating redundancies from noninformative alleles, it does not consider species composition while selecting core entries, which is a one of the disadvantage of the model based M strategy and therefore, future works should consider other measures of model fit including a rarefaction analysis, which corrects for sample sizes and manual inclusion of some representative wild species depending on the purpose of the core collection.To reveal the phenotype variation in CC240, core accessions were clustered into five distinct subclusters based on genotype relationship  of Capsicum was constructed using 48 SNP markers and 32 different traits. The core collection ‘CC240’ is composed of six Capsicum species from 44 geographic locations and was found to represent the diversity of the entire germplasm collection. This core collection will serve as a primary source for SNP mining and further genetic association and functional analyses for novel genes in Capsicum.Establishing a core collection of