Sebastes diaconus), a recently described species found in the northeast Pacific Ocean. We investigated population structure among fish sampled from two nearshore reefs  and one offshore site  within a <50‐km2 area off the Oregon coast. Fish from the three sample sites exhibited small but statistically significant differences based on genetic variation at >15,000 neutral loci, whether analyzed independently or classified into nearshore and offshore groups. Male and females were readily distinguished using genetic data and 92 outlier loci were associated with sex, potentially indicating differential selection between males and females. Morphometric results indicated that there was significant secondary sexual dimorphism in otolith shape, but further sampling is required to disentangle potential confounding influence of age. This study is the first step toward understanding intraspecific variation within the deacon rockfish and the potential management implications. Since differentiation among the three sample sites was small, we consider the results to be suggestive of a single stock. However, future studies should evaluate how the stock is affected by differences in sex, age, and gene flow between the nearshore and offshore environments.Little is known about intraspecific variation within the deacon rockfish ( Otolith shape and genetic variation within the recently described Deacon Rockfish was investigated for the first time. Small but significant differences were detected among fish sampled from two nearshore reefs and one offshore site within a small geographic radius. Males and females differed in otolith shape and genetically which may indicate secondary sexual dimorphism and differential selection between the sexes, respectively. Although superficially similar, these species represent distinct genetic lineages and exhibit key phenotypic differences such as body coloration and cranial morphology , the Yelloweye Rockfish Conservation Area was established at Stonewall Bank in 2005, a previously heavily fished 10‐km‐long rocky outcrop that rises 20 m above the surrounding seafloor  from Puget Sound and the outer coastal area were concluded to represent a single genetic population  to identify fish stocks has been used since Lea's  seminal Reinhardtius hippoglossoides   . The three sample sites were analyzed independently, and differences between nearshore and offshore samples were tested as well. In order to assess the influence of sex in our analyses, we tested for otolith shape and genetic differences between males and females. To disentangle any potential interaction between sample location and sex, we also analyzed genetic variation among the three sample sites using males and females separately.The aim of this study was to use an interdisciplinary approach , Seal Rock , and Stonewall Bank  . Prior efforts to collect deacon rockfish off Oregon have shown that Sabiki rigs are capable of capturing a wide size range of individuals (~8–40 cm in this study), which helped offset gear‐related bias in size selectivity of typical hook and line fishing gear Ralston, . At sea,n = 9 per area). Sampling efforts each month were mostly constrained to a 24‐hr period, although low catch rates at Seal Rock meant that fish collected on August 8th and 16th 2017 were combined to achieve adequate sampling. Age was determined for all otolith samples using the break and burn method  was indeterminable; therefore, a slightly smaller dataset was used to estimate otolith shape differences between males and females  where the left otolith was damaged or unavailable and the right otolith was used instead. Right side otolith images were horizontally transformed using adobe photoshop CS6 to correct the orientation in the digitization process. The nonparametric PERMANOVA test implemented in the shaper 0.1‐5 R package . Each dataset was divided evenly between sampling from the nearshore and offshore areas, and we tested the discrimination of those groups.2.3A piece of caudal fin was taken from each fish and stored in a 5‐ml vial filled with 95% ethanol. Whole genomic DNA was extracted following the protocol and buffer solutions described by Ivanova, Dewaard, and Hebert . DNA wasN = 96; Table n = 94; Table n = 73; Table n = 20; Table n = 50; Table All available DNA samples were used to analyze variation between the potential nearshore and offshore populations . DNA extractions for 96 deacon rockfish individuals, as well as six quality control repeats of specimens , were organized randomly across three 96‐well sequencing plates (Table 100 ng DNA was prepared for restriction site‐associated DNA sequencing (RADseq). Genomic DNA was digested with es Table . Each pl2.4stacks 1.47  used in our final analyses were judged to provide a high number of stacks, consistent with other parametrizations , the maximum distance (in nucleotides) allowed between stacks was two (‐M 2), and the maximum distance allowed to align secondary reads to primary stacks was four (‐N 4). A bounded SNP model was applied with the error rate not being allowed to exceed 5% (‐‐bound_high 0.05). In cstacks, the number of mismatches allowed between sample loci when building a catalog was one (‐n 1). Locus coverage depth per individual was similar across the tested datasets, although some individuals yielded more loci with adequate coverage than others. As an example, Figure N = 96), where the overall mean coverage depth was 18.7 reads .Reads were assembled into stacks of similar DNA sequences and then into catalogs of reads for each investigated dataset using s et al. , many ofly Table . In ustans Table , with a populations component of the stacks pipeline. In populations, the minimum stack depth required for individuals at a locus was set at five (‐m 5). Samples were organized into multiple, independent datasets, which differed in the number of individuals and designated populations used to construct a loci catalog  so that these sites were removed from subsequent analyses  were identified using the python and R scripts for  Mortiz, , which ies Table . We test2.5HO) and expected heterozygosity (HE) for each group tested in the separate loci datasets, using the adegenet 2.1.1 R package  for each tested group. The whoa 0.01 R package was used to investigate genotype frequencies, as well as the relationship between read depth per locus and heterozygote miscall rates , again implemented in Jombart, . For PCA2.6fsthet 1.01  to estimate outliers, alongside otherwise default settings, and the number of PCs used for each analysis (K) was determined using the aforementioned broken‐stick test on PC Eigen values. The qvalue 2.12.0 R package was used to estimate FDR for pcadapt , implemented in the stampp 1.5.1 R package using 5,000 bootstraps . For each value of K, five replications of the admixture model with independent allele frequencies were applied, with an MCMC length of 50,000 generations and a 10% burn‐in. The optimal number of clusters was determined by examining estimates of mean K probability for a given value of K  and gopher rockfish Sebastes carnatus  by Fowler and Buonaccorsi  (RefSeq: GCA_000475215.1). This species was used as it the closest available relative to deacon rockfish , of the flag rockfish against annotated genomes of several other fish species: three‐spined stickleback Gasterosteus aculeatus  (RefSeq: GCA_000180675.1), fugu Takifugu rubripes  (RefSeq: GCF_000180615.1), and Japanese medaka Oryzias latipes  (RefSeq: GCF_002234675.1).Using a modified approach of Fowler and Buonaccorsi , we inveat Kent,  was used33.1F‐values for these comparisons were similar  and among sites  when comparing the three sample sites or the nearshore and offshore groups (Table FST and observed heterozygosity (HO) for each locus in the sex comparison dataset, as used by fsthet, are shown in Figure No outlier loci were identified by all four genome scan programs , whereas the FST value estimated for outlier loci was much higher at 0.45 , we found statistically significant differences for two out of three pairwise comparisons based on variation at the neutral loci  and Stonewall Bank , but Seal Rock was not significantly different from Stonewall Bank , one out of the three pairwise comparisons was significant. Again, Siletz Reef was significantly different from Stonewall Bank , but Siletz Reef and Seal Rock  and Seal Rock and Stonewall Bank were not significantly different  and Larimichthys crocea . A subset of outliers aligned with highest confidence to unannotated nuclear DNA sequences of Sparus aurata , which were previously estimated as quantitative trait loci associated with sex determination and body growth . Using BLASTN, most outlier sequences aligned with highest confidence to nuclear DNA sequences of 44.1F‐values for the PERMANOVA test  could be distinguished using both otolith shape and genetic data when analyzed independently or organized into the nearshore and offshore groups. Although there was substantial overlap, statistically significant differences in otolith shape were found among the sample sites and the tested groups Table . Estimatst Table  were simFST and found that the three sample sites and the nearshore and offshore groups were significantly different based on neutral loci  reported by other RAD sequencing studies of marine fishes  have been reported by population genetic studies of other rockfish species sampled over larger geographic scales , and which used the same SbfI restriction enzyme  was low and comparable to results for the sample sites, whereas the FST estimate based on the outlier loci was much higher . In concordance, genotypic cluster analysis in structure also did not identify any population structure among the three sample sites using either sex‐specific dataset. Altogether, these results suggest that genetic differences between male and females are unlikely to have influenced comparisons of the three sample sites.Pairwise es Table . The FSet Table D,E. HoweSebastes rastrelliger , sex was not recorded and PCA plots were not presented , and no obvious geographic structure was observed for either dataset using PCA  found that males typically possessed the minor allele, whereas females had the major  between males and females for autosomal or pseudoautosomal regions (segments of sex chromosomes that recombine) in the deacon rockfish genome. Adaptive genetic differences can occur between the sexes if males and females are placed under different selection pressures for traits such as reproduction and behavior . Since males and females of this species demonstrated similar hearing ability, it was hypothesized that differences in habitat preference  were responsible for the observed dimorphism  seems remarkable. This statistical significance may reflect the large amount of information  provided by the RAD sequencing method. Further genetic sampling across the range of deacon rockfish may help to interpret the scale and significance of variation observed in this study, and improve the distinction of potential stocks. Future genetic studies of rockfish should record the sex of samples and take into account the sex‐ratio and age distribution of sample groups. Inattention to such factors may have already biased the results of previous genetic studies on rockfish (Waples et al., Although differentiation was low, the fact that we detected statistically significant otolith shape and genotypic differences over such a small geographic scale (<50 kmNonetheless, given that we ultimately regard a “fish stock” as a practical management decision (Carvalho & Hauser, 4.6We found small but statistically significant otolith shape and genetic differences among deacon rockfish sampled off the Oregon coast, regardless of whether the three sample sites were analyzed independently or organized into nearshore and offshore groups. We suggest that deacon rockfish from the nearshore and offshore are managed separately until further genetic, environmental, and demographic data are available, requiring no practical change from current management practices.Sebastes species.Sex mattered in our otolith shape and genetic analyses. We found evidence for secondary sexual dimorphism in otolith shape, which may reflect differences in the growth, age, and lifespan of males and females. Males and females were readily distinguished in our genetic data, although this is unlikely to have affected comparisons of the sample sites. Our results concur with previous studies that sex should be considered in population genetic research (Benestan et al., We identified 92 outlier loci that are associated with sex in deacon rockfish. These sites likely reflect differential selection between males and females, which should be investigated in other rockfish species with potentially different sex determination systems. A possible biological cause for this selective difference is uncertain in deacon rockfish, due to the recent discovery of the species. This subject warrants further investigation, as the genetic variation may reflect differences between males and females for habitat usage, which in turn could result in different management requirements.Sebastes rockfish diversity. The sequence data are compatible with reads from previous RADseq studies of other rockfish species that also used the SbfI restriction enzyme (Andrews et al., shaper otolith digitization method (Libungan & Pálsson, The data generated in this study can contribute to future, more extensive studies of This study provides a first step toward the investigation of intraspecific variation in a recently described species, and the results emphasize the potential of RAD sequencing to provide substantial population and sexual genetic information for species that have not been previously studied.None declared.Felix Vaux conducted the genetic analysis and wrote much of the manuscript. Leif K. Rasmuson designed the study, collected samples, conducted the shape analysis, and wrote much of the manuscript. Lisa A. Kautzi processed and photographed the otoliths and contributed to writing in the manuscript. Polly S. Rankin designed the study, collected samples, and contributed to writing in the manuscript. Matthew T.O. Blume designed the study, collected samples, and contributed to writing in the manuscript. Kelly A. Lawrence collected samples, produced Figure  Click here for additional data file.


Thirty-three autosomal short tandem repeat (STR) markers were used to evaluate genetic heterogeneity and diversity in 525 golden retrievers (GRs). This breed was selected because of its popularity and artificial selection for conformation vs. performance phenotypes. Seven additional STRs were used to evaluate the highly polymorphic dog leukocyte antigen (DLA) class I and class II regions. From 3 to 13 alleles were found at each of the 33 loci (mean 7) and the average effective alleles (Ne) was 3.34. The observed heterozygosity was 0.65 and the expected heterozygosity was 0.68. The resulting fixation index was 0.035 indicating that the population was randomly breeding. We found that modern GRs retain 46% of genomic diversity present in all canids and 21/175 (12%) and 20/90 (22%) of the known DLA class I and class II haplotypes, respectively. Selection for performance or conformation led to a narrowing of genomic and DLA diversity with conformation having a greater effect than performance. A comparison was made between coefficient of inbreeding (COI) determined from 10 or 12 generation pedigrees and DNA based internal relatedness values. A weak but significant correlation was observed between IR score and 10 or 12 generation COI . IR values were higher in conformation than performance lines but only significant at p = 0.17. This was supported by 10 and 12 generation COI values that were significantly (p<0.0001) higher in conformation than performance lines. We demonstrate herein that a low density of STR markers can be utilized to study the genetic makeup of GRs. Purebred dogs originate from a highly select group of founders that are pre-defined by a homogenous standard. Once a breed is registered, further gene flow is prohibited thus ensuring the purity and maintenance of the breed. Therefore, to continue to achieve a desired phenotype, many purebred dog breeders utilize line-breeding and may overuse popular sires . AlthougTo help maintain the genetic diversity of purebred dogs, breeders have relied on pedigree-derived coefficient of inbreeding (COI) percentages calculated over a few recent generations. One general purpose of utilizing a COI percentage is to produce healthier offspring with limited incidence of inherited diseases . HoweverA resolution to the issue of COI percentages that lack depth is utilization of modern genetic techniques involving DNA analysis. To date, the two methods available to evaluate the genetic contribution of the founding population to a dog is via short-tandem repeat (STR) markers and single nucleotide polymorphisms (SNPs) –14. It wWith regard to STR markers, they are contiguous repeated segments of DNA consisting of two or more nucleotides. In humans, forensic DNA analysis utilizes 10–15 STRs to identify individuals, illustrating their utility for genetic profiling . More reIn this manuscript, we chose to utilize two different methods to evaluate GR genetic diversity: an STR marker panel derived internal relatedness (IR) score and pedigree derived 10-generation (10-G) and 12-generation (12-G) COI percentages. The GR breed is an ideal breed for this type of analysis due to the large breed population size, which is assumed to be genetically diverse. Additionally, the GR breed has been subjected to two major artificial selection pressures resulting in two distinct lineage groups: conformation and performance. We used an STR marker panel instead of a SNP panel to assess the genetic heterogeneity and diversity of golden retrievers (GRs) due to logistics, price, and practicality. Furthermore, public pedigree database records are also available to GR breeders that include 10-G and 12-G COI percentages. We hypothesize that breeding practices that result in distinct GR lineage groups impact the overall genetic diversity for this breed.Pedigreed North American golden retrievers (GRs) were recruited to participate in this study via open enrollment at multiple breed events and website advertisement. Cytology brushes and pedigree information for all participating GRs were collected. A comprehensive questionnaire that included owner-reported GR lineage and demographic data was required for study enrollment. Lineage of the GR was recorded as conformation or performance.Approval from the UC Davis Institutional Animal Care and Use Committee was obtained (Protocol Number: 20047) prior to collection of samples. DNA was extracted from cytology brushes as previously described . BrieflyAMELX and AMELY) genes for sex determination [To determine the genetic diversity of GRs we utilized 33 STR markers that were previously validated –11. The mination –22. The IR = (2H-Σfi)/(2N-Σfi) where H corresponds to the number of loci that are homozygous for an individual, N is the total number of loci genotyped, and fi is the frequency of the i-th allele [STR results were used to calculate an IR score for each dog using the following formula: h allele . After, h allele . This anh allele . AdditioTo determine DLA class I and II haplotypes, we utilized the STRs flanking these regions as previously described and validated –11. LinkFinally, to analyze the number of distinct STR and DLA markers for the GR breed, we obtained the number of overall alleles reported for 17 breeds tested to date by the UCD VGL.f = (1/2)i-1x (1+FCA) [i is the number of individuals in the path and FCA is the inbreeding coefficient of the shared ancestor [Both a GR 10-G and 12-G COI percentage were obtained from the GR public pedigree database website k9data.com. This percentage is calculated from pedigree analysis and is a measure of shared ancestors. Briefly, COI percentages are calculated based on the sum of the paths in a pedigree with the following formula:  (1+FCA) . A path  (1+FCA) . In the ancestor . We seleancestor , 28. AddDescriptive statistics were conducted for COI percentages and IR scores to evaluate diversity of the overall population and each lineage group. D’Agostino-Pearson normality test was utilized to determine if COI percentages and IR scores were normally distributed. If the data is normally distributed, we reported the mean +/- SD and for nonparametric data median and interquartile range (IQR) was reported. A Mann-Whitney test was conducted to evaluate if IR scores and COI percentages differed significantly by lineage. Spearman nonparametric correlation analysis was used to evaluate correlations between IR score and 10-G or 12-G COI percentage. GraphPad Prism v7  was used for all statistical analysis performed in this study.We further evaluated the 33 STR markers allele frequencies and seven DLA class I and II STR loci associated allele frequencies using GenAlEx 6.5 . This alA total of 529 GRs from across North America were recruited to participate in this study. We had equal representation of GRs from all geographical regions of the United States . AdditioWe determined the allele frequencies for 133 STR markers in the 525 GR samples enrolled in this study . OverallTo determine the genetic relationship between conformation and performance lines, we performed PCoA using the 33 STR markers. Overall, the GR population formed one cluster with considerable diversity as indicated by genetic outliers . There wAllele frequencies for 33 STR markers were used to calculate IR scores for individual dogs in the entire population. The median IR scores ranged -0.266 (most heterogenous) to 0.501 (least heterogenous). The median for the population was 0.049 (IQR: -0.040 to 0.143), for conformation lines the median was 0.063 (IQR: -0.027 to 0.15), and for performance lines the median was 0.032 (IQR: -0.039 to 0.12). We did not identify any statistically significant differences for IR between the conformation and performance lines (p = 0.165) .We next evaluated DLA class I and II STR haplotypes for the 522 of the 525 GRs and identified a total of 21 class I and 20 class II haplotypes in the GR breed. The GR possessed only 12% and 22% of the known canid class I and class II DLA haplotypes, respectively. The majority of class I haplotypes identified in the GR were shared among all dog breeds tested, except for DLA class I 1069 which was unique to the GR and found in 3% individuals . DLA claWe identified 14 DLA class I and 16 DLA class II haplotypes for GRs grouped by lineage . The conThree hundred ninety-eight of the 525 GRs in our sample population had COIs calculated from 10-G and 12-G pedigrees. The median 10-G COI for 398 GR samples in this study was 5.82% (IQR: 2.79 to 9.14) and the median 12-G COI was 7.47% (IQR: 3.75 to 11.70) . Only 32Spearman nonparametric correlation analysis was used to compare IR scores with 10-G or 12-G COIs from the same 398 dogs. The r-values for 10-G and 12-G comparisons were 0.38 and 0.40, respectively . Nine peThe aim of this manuscript was to illustrate how DNA and 33 autosomal STR loci can be utilized to evaluate the genetic diversity of pure breeds of dogs. We have chosen to model the GR breed because of its popularity and reported heterogeneity. A low density STR marker panel was chosen over a high-density SNP array due to logistics, price, and practicality. Results of this study indicated that the GR breed has retained 46% of the known genetic diversity in all dogs. The average number of alleles per loci and the proportion of alleles contributing to heterozygosity, observed and expected heterozygosity and resulting fixation index, indicated that the breed was in good genetic health. However, IR scores calculated from the same allele frequencies indicated that a proportion of individuals was more inbred and outbred than the average for the population. Such information is important for breeders to better manage genetic diversity in individuals as well as the overall population. A reduction in heterozygosity across the breed or within a subpopulation can decrease health, litter size, and lifespan .DNA testing of this type can also be of value at looking at subpopulations within the breed that have been created by artificial selection. These subpopulations can occur from the use of sire that gain notoriety in areas such as conformation or performance. The evolution of such populations may grow to an extent as to effect genetic diversity within the subpopulation (line) or the entire population. In order to study the effects of such selection we chose to look at GRs bred for conformation or performance. Observed and expected heterozygosity for both groups was similar, illustrating that each group was genetically heterogenous. Fixation index evaluation illustrated that the performance lines were more heterogenous compared to conformation lines. IR scores also tended to be lower in the performance lines. However, the level of significance was only p = 0.17. This result is similar to previous findings that illustrated that conformation lines have reduced heterogeneity and genetic diversity compared to performance lines although both groups have decreased genetic heterogeneity and diversity when compared to village dogs .Evaluation of the DLA haplotypes indicate that GRs had retained 12% and 22% of the respective DLA class I and class II haplotypes known to exist in modern canids. This suggest that the breed has evolved from no more than 16 founder lines, each possessing the existing haplotypes. The confirmation GRs contained less haplotypes compared to performance lines. Furthermore, the two most common haplotypes identified for DLA class I and class II were different between each lineage group. The presence of these haplotypes relates to the artificial selection that were used to select for conformation or performance. This also supports the findings from the 33 autosomal STRs. It is important for breeders to always consider the genetic diversity of the DLA in breed management because polymorphisms within genes of the DLA play a crucial role in autoimmunity , 30, 31.COIs calculated from relatively shallow pedigrees have been historically used by breeders to monitor heterozygosity and genetic diversity within their dog breeds, 32. It This study demonstrated how DNA analysis based on 33 autosomal STR loci markers can evaluate genetic heterogeneity and diversity in a pure breed such as the GR. We found the 525 GRs tested to be heterogenous by standard genetic assessment and to segregate as a single population by PCoA. Overall, the breed has retained about half of known alleles present in all canids. Although the population appeared heterogenous, IR values indicated that portions of the population were either more inbred or outbred than the average GR. Artificial selection for conformation or performance led to sub-structuring in the population with conformation having a greater effect than performance. Seven additional STR markers within the DLA identified 21 class I and 20 class II haplotypes. This was a small proportion of all known haplotypes and indicative of a low number of breed founders. Additional narrowing of DLA haplotypes was observed in both conformation and performance subpopulations. A weak but significant relationship was found between IR calculated from DNA and from COIs calculated from 10 or 12 generation pedigrees. DNA testing using a low density STR marker panel provided an accurate assessment of genetic heterogeneity, population substructure, and diversity in a model breed such as the GR.S1 TableThe number of known alleles for all dogs tested by the UC Davis Veterinary Genetics Laboratory are listed under the locus name in parenthesis. The percent of known alleles detected in the Golden Retriever breed are listed in bold at the end of the allele list.(DOCX)Click here for additional data file.S2 TableLoci with increased heterozygosity are in listed in bold letters.(DOCX)Click here for additional data file.S3 TableTotal number of DLA class I and class II haplotypes identified in all dogs are listed in parenthesis.(DOCX)Click here for additional data file.


Panulirus homarus) is a popular seafood commodity worldwide and an important export item from Oman. Annual catches in commercial fisheries are in serious decline, which has resulted in calls for the development of an integrated stock management approach. In Oman, the scalloped spiny lobster is currently treated as a single management unit (MU) or stock and there is an absence of information on the genetic population structure of the species that can inform management decisions, particularly at a fine-scale level. This work is the first to identify genome-wide single nucleotide polymorphisms (SNPs) for P. homarus using Diversity Arrays Technology sequencing (DArT-seq) and to elucidate any stock structure in the species.The scalloped spiny lobster . Analysis of genetic variation using putatively directional FST outliers (504 SNPs) revealed higher and significant pairwise differentiation (p < 0.01) for all locations, with Ashuwaymiyah being the most diverged population (Ashuwaymiyah pairwise FST range = 0.0288–0.0736). Analysis of population structure using Discriminant Analysis of Principal Components (DAPC) revealed a broad admixture among P. homarus, however, Ashuwaymiyah stock appeared to be potentially under local adaptive pressures. Fine scale analysis using Netview R provided further support for the general admixture of P. homarus.After stringent filtering, 7988 high utility SNPs were discovered and used to assess the genetic diversity, connectivity and structure of P. homarus along the Omani coastline are admixed. Yet, fishery managers need to treat the lobster stock from Ashuwaymiyah with caution as it might be subject to local adaptive pressures. We emphasize further study with larger number of samples to confirm the genetic status of the Ashuwaymiyah stock. The approach utilised in this study has high transferability in conservation and management of other marine stocks with similar biological and ecological attributes.Findings here suggested that stocks of The online version of this article (10.1186/s12864-018-5044-8) contains supplementary material, which is available to authorized users. Severe decline of many commercial fish stocks highlights the emerging need for sustainable management plans for regulation and conservation of marine biodiversity. Managing marine stocks sustainably is a dynamic process and requires an in-depth understanding of the stock and its spatial boundaries, along with biological, ecological, evolutionary, economic, social or even political factors that influence the fishery , 2. WhilP. homarus (Linnaeus 1758) is characterised by a relatively long pelagic larval duration (PLD) of about 4.5–6.5 months [P. homarus have studied sub-species resolution, phylogeography throughout its wide range [The scalloped spiny lobster 5 months , during 5 months , 29. The5 months  and in t5 months , 32, emp5 months , 33. Mande range –37 or dide range . Howeverde range .P. homarus in Oman, its levels of genetic fitness and relatedness in this region. Hence, the lobster stock along the Omani coastline is currently treated as one single management unit. This study is the first to assess the genetic structure of P. homarus in Oman using high resolution genome-wide SNPs genotyping. The findings provide valuable insight into the connectivity of the Omani P. homarus population and will aid in the identification of management units for the fishery of this commercially important crustacean.Commercial spiny lobster fisheries have a long tradition in Oman, with the country currently being one of the major suppliers to the global market . Of concp < 0.000004 after Bonferroni correction). After the removal of these SNP loci deviating from HWE, significant skew in estimation of genetic diversity indices (Fis and Ho) was observed. Thus, indicating the status of those SNPs as putative null alleles or genotypic artifacts. Additionally, nine individuals were excluded from the dataset due to poor genotyping coverage < 80%.A primary dataset of 48,140 SNPs was filtered to retain 7988 SNPs  across populations ranged from 0.1660 to 0.1840 and were generally lower than the expected heterozygosity values (Hn.b)  (Table Fis) was significantly high across all populations,  slightly differentiated from zero (0.0003–0.0038). However, this differentiation was statistically significant (i.e. 95% C. I. does not overlap zero) in two locations  varied from 5507.5 for Haitam and 10,305.8 for Al Ashkharah to an infinite value for other populations.Observed heterozygosity (HFST) using 7988 SNPs indicated very low levels of genetic differentiation, with average FST = 0.0004 (±SD = 0.1843), with only seven out of 28 pairwise comparisons being statistically significant , with highest differentiation between Ashuwaymiyah and Ras Madrakah (0.0736) and the lowest between Masirah and Dhalkut (0.0288)  potentially under divergent selection, 168 SNPs (2.10%) under balancing selection and 7316 SNPs (91.59%) likely to be neutral  were observed across all populations, apparently caused by a heterozygote deficit as represented by significantly high Fis values . Despite repetitive culling of SNPs deviating from HWE, notably lower, but still significantly high Fis values (0.2094–0.2861) were observed. Heterozygote deficits have been observed in many marine invertebrates [2) slightly differentiated from zero  [Pinctada margaritifera (0.0520–0.1030) [P. maxima (0.3030–0.3110) [Fis values are unlikely due to reduction in genomic heterozygosity. Multi locus heterozygosity may reflect genome wide heterozygosity especially when thousands of genomic markers are used [Additionally, average multi locus heterozygosity estimates (Av.MLH) showed a moderate to high level of genomic heterozygosity (0.1683–0.1858) compared to some marine invertebrates e.g. –0.1720) , Pinctad–0.1030)  and P. m–0.3110) . This maifera 0.00–0.1030 are used .Fis values are caused by the Wahlund effect, as Netview analyses indicated high levels of admixture  and contribute to the organism’s fitness and adaptation . It has FST) analysis using 7988 SNP loci, showed low but statistically significant (p < 0.05) differentiation between Ashuwaymiyah and other locations, except for Al Ashkharah and Mirbat   and preserved immediately in 95% ethanol until DNA extraction. All samples were purchased from local fishermen in March 2015 during the legal fishing season. Genomic DNA was extracted from tissue samples using a modified cetyl trimethyl ammonium bromide (CTAB)/ Chloroform-Isoamyl method [The commercial km) Fig. . Sampleskm) Fig. . All saml method . DNA extP. homarus DNA samples being digested using a combination of PtsI and HpaII restriction enzymes. Multiplexed reduced representation libraries were then sequenced on the Illumina HiSeq2500 platform for 77 cycles.Genomic DNA extracts were standardised to 50 ng/μl, and sent for sequencing and genotyping using DArTseq™ technology, with Diversity Arrays Technology, Canberra, Australia , 89. LibDArTsoft14 algorithm within the KDCompute framework developed by Diversity Arrays Technology (http://www.kddart.org/kdcompute.html), with initial calling parameters and filtering methods as described in Morse et al. [To call SNPs and genotype each individual, raw Illumina HiSeq2500 data was first de-multiplexed into individual samples, based on sample-specific barcode sequences. De-multiplexed samples were then assessed for overall sequence quality, with any fragments with an average Q-score of < 25 being removed from the dataset. Sequences were also compared to public databases for identification of contaminant sequences, and any non-target sequences  were removed. SNP calling was conducted using the e et al. .https://github.com/esteinig/dartQC) [To eliminate potentially aberrant SNPs, stringent quality controls were applied using custom python scripts within the DArTQC pipeline (/dartQC) . Initial/dartQC) . Further/dartQC) .Fis and Ho were calculated for each population at different reads depth (Average SNP Counts) thresholds  to discover the degree of potential bias caused by lower call depths. Accordingly, four subsets of SNPs were generated at these sequencing depths. To detect potential genotyping artifacts, SNPs were tested for significant deviation from Hardy-Weinberg equilibrium (HWE) using Arlequin v.3.5.2.2 [p < 0.000004). To assess the impact of deviation from HWE, Fis and Ho were calculated before and after removal of significantly deviated SNPs.To investigate the effect of sequencing depth, .3.5.2.2 . Any SNPHo), average expected heterozygosity corrected for population sample size (Hn.b.) and inbreeding coefficient (Fis) were calculated using Genetix v.4.05.2 [NeLD) was computed with NeEstimator [2) were calculated for all individuals using the R package inbreedR [To estimate the genetic diversity within populations, standard allelic diversity indices including average observed heterozygosity Genotypic data of Additional file 2:P. homarus in Oman. The plot was generated using the most informative 13 PCs identified from all 7988 SNPs dataset across 164 P. homarus individuals in the R package adegenet. (TXT 9105 kb)A plot of the Discriminant Analysis of Principal Components (DAPC) against the discriminant function retained. The plot is indicating presence of two genetic clusters of Additional file 3:P. homarus for 164 individuals from Oman using 504 putatively directional SNPs. Genotypes are in genetix format. (PDF 389 kb)Genotypic data of


Camellia sinensis (C. sinensis) population and aims to (i) identify single nucleotide polymorphisms (SNPs) on the genome level, (ii) investigate the genetic diversity and population structure, and (iii) characterize the linkage disequilibrium (LD) pattern to facilitate next genome-wide association mapping and marker-assisted selection.To efficiently protect and exploit germplasm resources for marker development and breeding purposes, we must accurately depict the features of the tea populations. This study focuses on the We collected 415 tea accessions from the Origin Center and analyzed the genetic diversity, population structure and LD pattern using the genotyping-by-sequencing (GBS) approach. A total of 79,016 high-quality SNPs were identified; the polymorphism information content (PIC) and genetic diversity (GD) based on these SNPs showed a higher level of genetic diversity in cultivated type than in wild type. The 415 accessions were clustered into three groups by STRUCTURE software and confirmed using principal component analyses (PCA)—wild type, cultivated type, and admixed wild type. However, unweighted pair group method with arithmetic mean (UPGMA) trees indicated the accessions should be grouped into more clusters. Further analyses identified four groups, the Pure Wild Type, Admixed Wild Type, ancient landraces and modern landraces using STRUCTURE, and the results were confirmed by PCA and UPGMA tree method. A higher level of genetic diversity was detected in ancient landraces and Admixed Wild Type than that in the Pure Wild Type and modern landraces. The highest differentiation was between the Pure Wild Type and modern landraces. A relatively fast LD decay with a short range (kb) was observed, and the LD decays of four inferred populations were different.This study is, to our knowledge, the first population genetic analysis of tea germplasm from the Origin Center, Guizhou Plateau, using GBS. The LD pattern, population structure and genetic differentiation of the tea population revealed by our study will benefit further genetic studies, germplasm protection, and breeding.The online version of this article (10.1186/s12870-019-1917-5) contains supplementary material, which is available to authorized users. Camellia sinensis (L.) O. Kuntze, Theaceae (C. sinensis), has been grown in the Yunnan-Guizhou Plateau in southwest China for approximately 5,000 years and is now widely cultivated all over the world [Tea is one of the most popular beverages worldwide , 2 with he world . The Guihe world , 5, wherhe world . Large sSect. Thea (L.) Dyer, and are defined as varieties grown for more than 100 years. Wild teas, including wild type and self-wild type, are valuable for scientific research and application as they have mainly undergone natural selection and were only minimally affected by artificial selection. Analyzing genetic diversity and population genetic structure is significant to depicte the domestication event and genetic relationships of tea plants. It is also helpful for expediting the development on breeding strategies [Ancient tea plants belong to rategies . Moleculrategies , nSSR [1rategies , gSSRs [rategies , SSR [10rategies , SNP [12rategies , ISSR [1rategies , EST-SSRrategies , 16, etcrategies , Guangxirategies  or acrosLD is defined as the association of allelesat different loci within a given population. Understanding the LD pattern is crucial for tea breeding –21. GBS GBS was performed on 415 tea accessions using Illumina HiSeq X ten. After the primary quality filtering step, 390.3 Gb clean data were obtained with an average of 0.94 Gb clean data per accession , observed heterozygosity (Ho) and polymorphism information content (PIC) of 415 tea accessions were 0.257, 0.247 and 0.214, respectively Table . Among tWe used STRUCTURE and PCA to analyze the genetic structure of the tea accessions. Both analyses were performed using 1,135 LD-pruned SNPs. Based on the genetic distance matrix of the 415 tea accessions, we used TASSEL v.5.2.37 to build an UPGMA tree.Camellia tachangensis F.C.Zhang, of which most were from the zones IV, III and II .The number of clusters was estimated based on the ΔK method , 36 and C.tachangensis, C.sinensis and C. remotiserrata)  Fig. d of tea The plateau criterion was also used to estimate the number of clusters –40. As s2 values were below 0.16  at approximately 35 kb. Conversely, LD declined the most rapidly in GP02—r2 = 0.08 corresponded to a physical distance of approximately 1 kb—followed by subgroup GP03–1, in which r2 = 0.08 corresponded to approximately 2 kb. The LD of subgroup GP03–2 declined below r2 = 0.08 at approximately 25 kb.LD decay in the four inferred groups was estimated  to analyze the population structure of the 415 tea varieties, and the results we obtained complemented the previous studies. STRUCTURE could effectively identify global clusters, which were subsequently validated by PCA. However, the two parameters we used to determine the number of clusters in STRUCTURE yielded different K values—the Evanno’s ΔK method identified K = 2 when analyzing the entire germplasm collection and the cryptic structure. Evanno’s method focuses exclusively on the change in slope, therefore, it estimates the uppermost level structure of the data which may cause ΔK to be artificially maximal at K = 2 in some cases, as reported previously by Campoy JA et al. . We usedWe detected the lowest genetic differentiation and genetic distance between the modern and ancient landraces. The Pure Wild Type and modern landraces exhibited the largest genetic differentiation and genetic distance, followed by that between the Pure Wild Type and ancient landraces, and that between the Admixed Wild Type and ancient landraces. These results support the notion that the evolution of tea plants was related to the historical tea cultivation in the Guizhou Plateau. The Pure Wild Type is the most primitive resource that originated in the region, and the retained species purity was owing to the isolated ecological environment. The ancient landraces and the Admixed Wild Type likely emerged in the Ming Dynasty, when local landraces, introduced landraces, and wild species were co-cultivated. The co-cultivation facilitated cross-pollination among different germplasms, which reduced the genetic distance and differentiation between the ancient landraces and the Admixed Wild Type and significantly increased the diversity of the ancient landraces and the Admixed Wild Type among all inferred groups. Most modern landraces and breeding varieties were assigned to GP03–2, reflecting a narrowed genetic basis of the modern landraces due to breeding practice.We observed the lowest genetic differentiation between GP03–1 and GP03–2, suggesting that human activities may have caused frequent gene exchange between these two subgroups. GP01 and GP03–2 showed the highest level of genetic differentiation and distance, implying that geographic isolation has restricted the gene flow among populations. This observation could also be a result of the reproductive isolation between species. According to our results, GP03–1 and GP02 exhibited a higher genetic diversity compared with GP01 and GP03–2, therefore, varieties in GP03–1 and GP02 can be used for tea improvement. As revealed by our data, the differences between species did not affect clustering, which reflected the complexity and uncertainty of the tea classification systems. Thus, it is necessary to establish a more scientific classification system. In addition, natural hybridization between tea species may be another explanation of the results mentioned above  and two subpopulations (GP03–1 and GP03–2). The ancient landrace group was found to have a more complex genetic structure than the wild and modern landraces. These data will inform the collection, conservation, and application of the tea varieties in the Guizhou Plateau.Camellia sinensis (L.) O. Ktze, 100 Camellia tachangensis (F.C.Zhang), 59 Camellia remotiserrata (Zhang) and five near Camellia taliensis (W.W.Smith) were identified  were included in this study  to isolate genomic DNA from the samples. DNA integrity was tested on 1% agarose gel, and DNA purity was tested and quantified using Qubit Fluorometer (Invitrogen).We used 5 U of SacI and MseI (NEB) and 1 × restriction buffer in a 25 μl reaction to digest 100 ng genomic DNA. After digestion, SacAD and MseAD adaptors were ligated to the digested DNA fragments; 12 samples were pooled in equal volumes and purified using the QIAquick PCR Purification Kit (Qiagen) . We thenhttp://www.plantkingdomgdb.com/tea_tree/) [-T 20 -k 30’ [The barcodes were used to de-multiplex the raw DNA reads, and a custom perl script was used to trim the adaptors. Only the reads with quality values > 5 were retained as the clean data, and then aligned to the reference genome (a_tree/)  using BW0 -k 30’ . GATK (Vr2 threshold of 0.2 by Plink (v1.9). After the filtering, 415 accessions and 79,016 SNPs were retained and used for further analysis.The SNPs were filtered according to the methods used by Hussain et al. , Chen etThe polymorphism information content (PIC) values for the SNP data were calculated using the following equation .\documenThe mean number of observed alleles per locus and the observed heterozygosity (Ho) were calculated for each group using TASSEL v.5.2.37 . Geneticr2 threshold was 0.4. PLINK was used to measure pairwise LD between multi-SNPs [r2) using PopLDdecay program1. To summarize the relationship between LD decay, we fitted a locally-weighted linear regression (loess) model to the r2 data [http://www.R-project.org/) [r2 summarizing both the recombinational and mutational history [Prior to the PCA and STRUCTURE analyses, we LD-pruned the SNPs again using Plink (v1.9)  with a wlti-SNPs , 54. The r2 data , 57 usinct.org/)  with r2  history . The LD Population structure was analyzed using the model-based Bayesian analysis implemented in STRUCTURE . The numAdditional file 1:Table S1. The quality control (QC) data of each sample. Table S2. Statistics of individual heterozygosity of 287,408 SNPs based on GBS. Table S3.. Statistics of individual heterozygosity of 79,016 SNPs based on GBS. Table S4. SNP density of scaffolds based on GBS. Table S5. The p-value of genetic diversity parameters in Table Table S6. Genetic diversity parameters of three species of tea plants in Guizhou Plateau. Table S7. Fst and pairwise genetic distance among three Species of tea plant in Guizhou Plateau (XLSX 117 kb)Additional file 2:Information of 415 tea accessions used in this study, including the accession/clone/collection, the accession name, the zone, the cultivation status, growth habits, the species, the STRUCTURE URE-based grouping (Qi ≥0.8) at K = 2, the notes, the source, and the inferred populations (XLSX 45 kb)Additional file 3:Figure S1. Graphical method allowing the detection of the number of groups using ∆K inferred population structure of the 263 Pure Cultivation Type. Figure S2. Inferred population structure of the 263Pure Cultivation Type using STRUCTURE software. Bar plot of individual ancestry proportions for the genetic clusters inferred using STRUCTURE (K = 2) and the reduced dataset. Individual ancestry proportions  are sorted within each cluster. Admixture model, independent frequencies, 30,000 burn-in iterations, 100,000 Markov Chain Monte Carlo iterations were used for this analysis. Ancient landraces (GP03–1) and modern landraces (GP03–2) are shown in yellow and green, respectively. Figure S3.. Four inferred populations of the 415tea accessions using STRUCTURE (K = 3). GP01 are shown in red, GP02 are shown in red and blue, GP03–1 are shown in blue, and GP03–2 are shown in green. (PDF 207 kb)Additional file 4:2) estimated against the genetic distance for pairs of linked SNP across all scaffolds longer than 500 kb in the 415 accessions (ALL) and four inferred groups . (PDF 220 kb)Average LD decay  The geographical position of Guizhou province in China. (B) Agriculture climate regionalization map for tea plant growth in Guizhou Plateau . Ia: Are


Triticum turgidum L. subsp. durum) landraces, 13 landraces from the broader Mediterranean basin and 22 modern varieties. Cluster analysis depicted a clear separation among modern varieties and landraces, regardless of their origin. Landraces presented the highest genetic variation (average discriminating power of 0.89) and a high number of private alleles (131) was detected; underlying the unique genetic mark-up of this genepool. AMOVA revealed that the highest variability was detected within the landraces originating from Cyprus and landraces from the broader Mediterranean basin. The Cypriot landrace ‘Kyperounda’ was selected for further evaluation of its’ intra-genetic variation and it was determined that genetic diversity was higher in accessions conserved as sublines (He 0.643–0.731) than bulks (He 0.384–0.469). Bayesian analysis revealed substantial admixture within ‘Kyperounda’ accessions, depicted also by Principal Coordinate Analysis. The findings of the current manuscript emphasize that high intra-genetic diversity is retained when landraces are conserved as sublines in ex situ collections, while landraces that are conserved as bulks have a higher risk of bottleneck. Hence, a more exhausting diversity evaluation is needed in order to fully utilize landraces in breeding schemes and to prevent the loss of genetic variation.Genetic characterization enhances the development of rational conservation strategies and the utilization of germplasm to plant breeding programs. In the present study, 19 microsatellite markers were employed to evaluate the genetic diversity and the genetic affiliations across 20 Cypriot durum wheat ( Triticum turgidum L. subsp. durum) developed a definite agricultural significance and gradually predominated its ancestors [The center of wheat domestication is located at the Fertile Crescent, where its cultivation is evident since 12.000 Before Present [ncestors –6. In thncestors ,8.ex situ conserved in national, regional, and international genebanks. Historically, two main strategies have been followed for the collection of wheat landraces: either creating bulks from random sampling of individual spikes or collecting individual spikes based on phenotypic differences (ear lines or sublines) [Despite their good adaptation to local conditions, resulting from their long presence in the area , landracublines) .Nowadays, there is an increasing trend of reviving the forgotten landraces via their utilization in plant breeding programs. Given their millennia evolutionary history and adaptation to low-input agricultural systems, landraces can indeed contribute to breed novel varieties with improved productivity, adaptation, resilience to climate change, nutritional value ,13 and qex situ conservation and enhances the germplasm use in breeding programs [in situ and ex situ genetic variation in landraces collections, remains undiscovered [Genetic characterization with robust molecular markers, such as microsatellites –29, alloprograms ,30–32. Dprograms ,33 and tprograms ,31. Despprograms , 6,19, ascovered ,30,32. Iscovered , 31. As ex situ conservation method [ex situ conservation and superficial estimations could undermine the full exploitation of landraces in plant breeding schemes.Although by definition landraces are genetically diverse dynamic populations , the knon method . Still, Triticum turgidum L. subsp. durum), landraces from the broader Mediterranean basin and modern varieties. Furthermore, we aim to in-depth describe their genetic affiliations, as a first step to enhance their use to plant breeding programs in the Mediterranean Basin. The second part focuses on the Cypriot landrace ‘Kyperounda’, which was the predominant landrace in Cyprus, known for its high phenotypic diversity and adaption to hot and dry winds and a very wide variety of soil types [ex situ conservation and the within genetic diversity of durum wheat landrace populations are discussed.The current study is divided in two sections. The first part aims to evaluate the genetic diversity across Cypriot durum wheat landraces  accessions were selected for the present study, the majority being landraces originated from Cyprus and other Mediterranean countries. These accessions were assigned into four groups, according to distinct origin, category and conservation method (Triticum aestivum L.), provided by the Leibniz-Institut für Pflanzengenetik und Kulturpflanzenforschung (IPK) genebank was also included as an outgroup.Fifty-five durum wheat (n method . Group ILandrace accessions in genebanks can be conserved as bulk (seeds from different ears are mixed together) or sublines (each ear is conserved separately). The landraces at the ARI genebank were collected during 1978 and are conserved as sublines i.e. 50–150 distinct ear lines, for each accession, depending on the phenotypic variability observed at the collecting site . The lanFive ‘Kyperounda’ accessions were selected to further investigate the intra-genetic diversity and their genetic affinity. Three accessions were selected from the germplasm conserved at the ARI genebank  and two accessions from entries conserved and kindly provided by USDA-ARS  . The latThe accessions in ARI genebank are conserved as sublines. Forty seeds were sown from each subline to one-meter-long rows at the Athalassa experimental station. In total, 160 sublines from the three ARI accessions were examined . Off-typ® Spin Plant Mini Kit , following the manufacturer’s instructions. DNA concentration and quality was determined by Nanodrop 1000  and verified in agarose electrophoresis. Nineteen microsatellite markers (SSRs) were selected based on their polymorphism and chromosomal location. These markers have been previously described [Genomic DNA was extracted using the Invisorbescribed –27  and 0.2 μM of each primer (the forward primers were 5´-end labeled with FAM—5-carboxy-fluorescent). PCR amplification was performed in a PTC-200 thermocycler  under the following temperature profile: 5 min at 95°C, followed by 30 cycles (40 cycles were used for primer WMC161), each one including 30 sec at 95°C, 90 sec at annealing temperature depending of the primer pair  was added to each sample to delineate allele sizes. Data were analyzed using GeneMapper Software version 4.1 .Amplified PCR products were run on an ABI3130 genetic analyzer . Size standard GeneScanThe DNA extraction procedure was as described above. Five primer pairs  were selected from the complete set of the 19 microsatellite markers, based on the number of alleles detected in the ‘Kyperounda’ and ‘Maurotheri’ accessions . AmplifiMulti-alleles were detected on the landraces bulked accessions composed of several plants. Hence, for the analysis of bulked accessions we applied a model  that does not require evolutionary assumptions in order to calculate genetic affiliations among accessions. On the other hand, analysis in Kyperounda populations was conducted on allele fragments .As DNA extraction was performed on a bulk sample containing tissue from around 50 seedlings per accession, the average number of alleles per primer and accession was calculated as an indicator of the heterogeneity within accessions. The discriminating capacity and the level of polymorphism /informativeness described by the indexes of number of alleles, number of rare alleles, allele range, number of private alleles, Discriminating Power (Dj), and Resolving Power (RP) were calculated as previously reported –44. The The five ‘Kyperounda’ accessions were treated as populations. An analysis of variance (ANOVA) for heading date was carried between the three ARI populations conserved as sublines. Mean comparison was done with the Tuckey-b test. Box plots were constructed to depict the variation for heading data and plant height. Box plots and ANOVA were performed with SPSS V.22 (IBM).https://taylor0.biology.ucla.edu/structureHarvester). A subline / plant was considered to belong to a cluster if its membership coefficient was ≥ 0.8 [Principal Coordinate Analysis (PCoA) was conducted to determine the associations between sublines / plants of the five populations. AMOVA was carried out to assess the within and between variance across populations. AMOVA, PCoA and the calculation of genetic variation indices  were performed using GenAlEx 6.4 . The Botas ≥ 0.8 .Modern varieties were found genetically homogeneous in comparison to landraces. The average number of alleles per primer and accession was 1.99 and 1.25 for landraces and modern varieties, respectively. The landraces conserved as sublines exhibited higher heterogeneity compared to the landraces conserved as bulks ; their ahttps://wheat.pw.usda.gov/GG3/) (All microsatellite primers used in the present study were found to be polymorphic . The aveov/GG3/) .In total, 224 alleles were detected with an average of 11.79 alleles per locus. Forty-nine alleles were classified as rare, since they appeared with a frequency lower than 0.05. Landraces had a higher number of private alleles compared to modern varieties . Thirty-PhiPT = 0.230, PhiRT = 0.165, PhiPR = 0.078 respectively; p = 0.001). The highest variability was recorded for landraces originating from Cyprus , followed by landraces from other Mediterranean countries , modern varieties from other breeding programs  and modern varieties from Cyprus . The most diverged groups were varieties originating from foreign breeding programs, and Cypriot landraces , while genetic affinity was observed between the two landrace groups .Analysis of molecular variance (AMOVA) revealed that 77% of the total variation was attributed to the genetic variation among accessions within groups, 16% to the genetic variation among landraces and modern varieties, while the remaining 7% to the genetic variation among groups within landraces and modern varieties . The thrTriticum aestivum accession, was out-grouped from the core of durum wheat entries. With the exception of ‘Simeto’, all other modern varieties were clustered together. Landraces did not follow a specific geographical pattern. However, ‘Kyperounda’ and ‘Maurotheri’ accessions were clustered together alongside to ‘Cyprus-IG96271’, ‘LIVIA-IG98726’ and ‘MAROCCO-IG96437’.p<0,0001) in heading date; population 1 differed significantly from the other two populations. As expected, early heading sublines reached maturity earlier than late heading sublines , followed by Populations 1 and 4 , while the greatest genetic proximity was observed between Populations 2 and 3  and Populations 3 and 4  (The least affinity among populations was found among Populations 4 and 5 (= 0.001) .ad hoc quantity, based on the second order rate of change of the likelihood function with respect to ΔK, was observed for K = 4  in landraces when compared to modern varieties. This is the outcome of the larger heterogeneity in the lineage of landraces since they are mixtures of discrete genotypes. In particular, the mean Dj value was 0.89 for landraces and 0.55 for modern varieties  species, like durum wheat, fixation indices are expected to have upper limit values (+1). Lower ranges can result from the polyploidy of durum wheat, or higher than expected rates of outcrossing. Soriano et al.  reportedex situ, high number of seeds are required during regeneration in order to avoid bottleneck effect and the risk to lose rare alleles [Two Phase Model (TPM) and the Step-wise Mutation Model (SMM) revealed heterozygosity excess to Population 5, indicating that this population experiences a reduction of its effective size . In this alleles . The lat alleles .The highest genetic diversity among the ‘Kyperounda’ entries was observed within population 2 which was collected from a remote area located to the western part of the island, where traditional farming systems and landraces are still used. Mountainous and remote areas are considered to be the hot spots of diversity ,33. AdveThe genetic differentiation between accessions of individual landraces observed in the present study, which results from their dynamic nature ,35, reinex situ conservation and characterization of the within individual local landraces genetic variability.The results of the present study underline the ‘hidden’ genetic diversity within individual landraces conserved as sublines. Further to the molecular tools, indigenous knowledge should be employed for optimizing sampling of sublines of each particular landrace . As a fuS1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S1 Fig(TIF)Click here for additional data file.S2 Figshort beak and slithly colored ear (A), short beak and intermediate colored ear (B) short beak and intesively colored ear (C), presence of hairiness on the clums and slithly colored ear (D), long beak and slithly coloured ear (E), and long beak and intermdediate colored ear (F).(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.


Many of the results are in line with expectations from urban theory and with the behaviour of analogous quantities in other urban systems in both high and middle-income nations. India is a continental scale, fast developing urban system, and consequently there are also a number of interesting exceptions and surprises related to both particular quantities and strong regional patterns of variation. Specifically, these relate to the potential salience of gender and caste in driving sub-linear scaling of crime and to the geography of technological innovation. We characterize these patterns in detail for crime and invention, and connect them to the existing literature on their determinants in a specifically Indian context. The paucity of data at the urban level and the absence of official definitions for functional cities in India create a number of limitations and caveats to any present analysis. We discuss these shortcomings and spell out the challenge for a systematic statistical data collection relevant to cities and urban development in India.This paper attempts to create a first comprehensive analysis of the integrated characteristics of contemporary Indian cities, using scaling and geographical analysis over a set of diverse indicators. We use data of  This phenomenon is only expected to intensify, with the UN projecting that 66% of the global population will live in cities by 2050, and that over 90% of this increase is expected to be focused in Asia and Africa . Given tAgainst this general backdrop of worldwide urbanization, one particular country—India—accounts for the most momentous transformation of all. India is on track to becoming the largest nation in the world by population, expected to surpass China in the next few years with a population around 1.4 billion by 2024 [The prospect of guiding this massive urbanization successfully in the next few decades demands a much deeper assessment and understanding of Indian cities and their trajectories. Although there is a rich history of case studies, demographic analyses –5 and soTo do this, we use the framework of urban scaling, together with ideas from urban geography –14. UrbaiY for city i, with population size iN(t), at time t can be described as:Y0(t) is independent of size and carries the physical dimensions of the relevant quantity (e.g. money per year for gross domestic product (GDP)). Its change in time signifies systemic change for all cities, such as national level economic growth. The scaling exponent β is the elasticity of the quantity iY relative to population at fixed t,The starting point of the analysis is very simple: any extensive urban indicator iξ(t) are city specific, scale independent deviations from the scaling law, also known as scale-adjusted metropolitan indicators (SAMIs),The quantities β is observed empirically to take similar values for broad classes of urban indicators [β ≃ 7/6 > 1, for socioeconomic rates, β ≃ 5/6 < 1, for spatial density and several kinds of infrastructure, and β ≃ 1, for household quantities expressing typical individual needs . Second, these specific exponent values can be computed from urban scaling theory [The point about scaling analysis is twofold. First, the exponent dicators ,14, withg theory –21.urban agglomerations, which are only an approximation to these concepts. Urban agglomerations are defined by spatial contiguity, under several additional conditions . This is an important caveat because urban units of analysis that are ‘not functional’, and are instead defined through metrics such as density, or by political boundaries, sometimes are found not to display urban agglomeration properties [In this way urban scaling theory—like most earlier mathematical models of urban economics and regional science—defines cities through a budget constraint balancing urban incomes and costs of real estate and transportation ,19. Thisoperties ,25.Despite these caveats, which are at present hard to resolve, we believe that the scaling analysis of urban agglomerations is a fundamental first step for a systemic understanding of Indian cities. Below, we explore the scaling relationships of various spatial, socio economic and infrastructural characteristics of Indian cities with their populations. We analyse the geography of exceptions to average scaling and attempt to make sense of these deviations by invoking a deeper understanding of the geography and history of India. Finally, we discuss how data for Indian cities must improve in the near future and point out priorities in light of our results and other general considerations from the emerging field of urban sciences.2.We start by exploring the nature of scaling relationships for three sets of urban characteristics: public infrastructure, social interactions and individual household needs. We present the detailed discussion on urban units of analysis, data and statistical methods used in electronic supplementary material, appendix A. We structure our results in two parts—the first deals with the analysis of scaling (agglomeration) effects in Indian cities, while the second pays closer attention to two quantities of great interest, namely crime and technological innovation measured via patent counts. Both these quantities show strong regional patterns, which we analyse systematically.β ≃ 5/6), with an observed sublinear exponent β ≃ 0.88 (b) [β ≃ 0.96 (a). It is typical of fast developing nations that formal infrastructure and services are first developed in larger cities and then gradually become more common in smaller places throughout the urban system [First, we find that land area shows a behaviour analogous to other urban systems, historically and throughout the world, and with urban scaling theory , where the scaling exponent is found to be significantly above 1, though below 1.5 [We turn to assessing technological innovation rates in Indian cities. The classic quantitative measure for technological innovation is patent applications. However, the annual number of patents is zero for some cities in certain years. To overcome this issue, we use logarithmic binning of cities and data  . Figure Figure 2elow 1.5 ,12. Theoelow 1.5 ,29. For b shows the scaling of total crimes  with city population. We find a clear sublinear scaling relationship with population, with an exponent β ≃ 0.87. This result stands in stark contrast with the results for crime scaling in other contexts such as the United States [Finally, we analyse crime rates in Indian cities. Figure 2d States  and seved States , where tc plots the total numbers of murders and culpable homicides against population. This analysis confirms the sublinear scaling relationship for crime, showing an estimated exponent that is in fact smaller than that for total crime (β ≃ 0.78). An observed sublinear scaling versus an expected superlinear trend  means, of course, that in large Indian cities socialization is substantially more peaceful than in smaller ones , which characterizes city i at time t:Deviations from the average population size dependence  estimated in the previous section, give us a principled way to assess and characterize these local and regional effects ,31,32. TThis is a dimensionless metric that makes direct comparison between the performance of different cities possible because population size agglomeration effects have been factored out .We now calculate and analyse the SAMIs for patents and crime (only murder and culpable homicide) in India. This will allow us to better assess the nature of local dynamics and also, in the case of crime, potentially explain the counterintuitive observed sublinear scaling effect.a plots the SAMIs for crime in Indian cities in rank order from more crime than expected to less. Aligarh (Uttar Pradesh) is ranked 1 (worst), while Malappuram  is the best performing (safest) city in India. To illustrate the difference between the standard per capita measure of crime (incorporating only murder and culpable homicide) and crime SAMIs (which removes population size bias) we show the expectations from these two approaches for the 10 largest Indian cities. Each one of these cities performs worse when ranked by SAMIs than when measured by a standard crime per capita measure (b). For instance, the per capita crime measure shows us that only one of these 10 cities is in the top half of cities ranked in descending order of crime, while the corresponding SAMI analysis shows that five out of these 10 cities are in the top half. This is because, crime rates being sublinear in population, the SAMIs contain the expectation that larger cities should have less crime per capita and measures only the deviation from such expectation.Figure 3 measure b. For inCrime in India is arguably an under-researched subject. Studies that explore the theme as a sociological phenomenon do so through the lens of gender ,33 and cGender is the other sociological lens applied to understand crime in India. Dreze & Khera  find a ra maps the spread of crime SAMIs in India and we can clearly see the distinct geographical tilt in crime behaviour in India. Even a cursory visual inspection makes it clear that a plurality of cities in north and central India, large and small, have high crime SAMIs, while those in western and southern India have much lower crime SAMIs. The higher crime SAMIs in the smaller cities of north-central India, especially given the salience of caste in generating crime, is possibly correlated to the fact that smaller settlements have a higher proportion of Dalit populations. We find evidence for higher Dalit populations in smaller cities when we examine the scaling of Dalit population with city size and discover slightly sublinear scaling with an exponent of 0.96.We now assess the geographical spread of crime SAMIs to investigate if there is support for these contentions. Figure 4b shows that the set of northern-central Indian cities has a significantly lower β than the southern-western cities group (0.63 compared to 0.92) because small and medium cities in the northern-central group have higher crime SAMIs than comparable cities in the southern-western set. It is also important to note that even though there is a significant difference in β between the two groups, both are still sublinear. This is potentially a reflection of the fact that despite significant differences in crime behaviour across geographies in India, there is indeed an underlying sociology of culture-specific crime (honour and caste-based crime) that is at work across the nation.We also split the scaling plot for crime into two groups of cities—one group for cities in north and central India and another group for southern and western cities. Figure 4c, which plots the temporal evolution for crime SAMIs for cities in five Indian states between 1991 and 2011. The temporal evolution of crime SAMIs in Uttar Pradesh, Madhya Pradesh and Bihar show that deviations from scaling have remained high throughout this period, while the temporal pattern of crime SAMIs for cities in the southern states of Kerala and Tamil Nadu have remained low. This is an indication that even as cities gain population over decades, relative local characteristics can persist over long time periods [These geographical differences also appear to be contingent on history, as evinced in figure 4 periods .a and suggests that regional variations in caste and gender dynamics could be critical to the nature of homicides and murders observed in India.We now seek to validate these patterns more formally by attempting to cluster cities based on the distance between their SAMIs. figure 4i and j, ijc, is computed as the equal-time cross-correlation of their SAMI time series [We also assess how spatial distance affects crime SAMI behaviour. Spatial similarity between cities e series :4.2cij=1This measure ensures that cities with similar SAMIs and time series have high correlation. To conclude our analysis of crime in Indian cities, we provide some international comparisons to put the Indian evidence into a broader context. UNODC data reveal that, at present, while the global intentional homicide rate is 6.2 (per 100 000 inhabitants), there are significant regional variations, with the Americas and Africa exhibiting high rates of 16.3 and 12.5, and Europe, Oceania, and Asia showing much lower rates of around 3 . Within 5.a. Again, we find significant discrepancies between the rank ordering of cities based on innovation SAMIs and technological innovation measured as patents per capita (figure 7b). When ordered by patents per capita, five of the 10 largest Indian cities are ranked among the top 10 most inventive cities, but when ranked by innovation SAMI, only one of them, Bangalore, appears in the top 10. This finding points to many medium-sized cities that are quite inventive for their population size and that should be the focus of some additional attention in terms of both scholarship and, where applicable, policy.We now turn to an analysis of the deviations in innovation SAMIs, figure 7We also map the innovation SAMIs across India in As before, we tried to confirm this impression by clustering cities based on the Euclidean distance between their innovation SAMI. 6.We have analysed emerging data for Indian cities in light of urban theory and known statistical patterns for other urban systems, with the aim of characterizing the tell-tale signals of urbanization in terms of scaling and agglomeration economies, and geographical patterns of development and variation.Although empirical information at the level of cities is becoming more available in India from official sources, such as the Census (2011), the paucity of data for functional urban definitions relative to most other high- and middle-income countries makes our analysis necessarily limited and tentative. Within these limitations, we find patterns of urban density scaling with population size roughly in line with other urban systems and historical cases. Regarding infrastructure delivery, large Indian cities seem to have an advantage relative to smaller towns, which is a pattern typical of other urban systems where basic infrastructure such as roads, sanitation and electricity access are not yet universal and spread from larger urban areas to other parts of the country . One of Arguably, the biggest surprise about Indian cities is the sublinear pattern of crime, including murders and homicides, which—unlike in most high-income nations—translates to higher rates of violence per capita in smaller towns, relative to the nation's largest cities. Though many questions about the data remain, we were able to derive the geography of crime across India and relate it to more specific studies that identify most sources of violence in the country associated with issues of gender and caste. These show a strong regional signature and have been discussed by sociologists and anthropologists as a predominantly rural or small city phenomenon.Indian urbanization, currently estimated at 33%, is expected to rise to 53% by 2050 . Success


Cataglyphis is considered to be a model genus in regard to such diversity. The present study sought to determine the social and population structure of the recently described C. israelensis species in Israel. For this purpose we employed a multidisciplinary approach, rather than the commonly used single approach that is mostly based on genetics. Our study encompassed behavior (nest insularity/openness), chemistry , and genetics (microsatellite polymorphism). Each approach has been shown to possess both advantages and disadvantages, depending on the studied species. Our findings reveal that C. israelensis colonies are headed by a single, multiply inseminated queen and that the population structure is polydomous, with each colony comprising one main nest and several additional satellite nests. Moreover, our findings demonstrate that none of the above‐noted approaches, when employed individually, is suitable or sufficient in itself for delineating population structure, thus emphasizing the importance of using multiple approaches when assessing such complex systems.The remarkable diversity of ant social organization is reflected in both their life history and population kin structure. Different species demonstrate a high variation with respect to both social structure and mating strategies: from the ancestral colony type that is composed of a single queen (monogyny), singly inseminated (monoandry), to the more derived states of colonies headed by a multiply inseminated queen (polyandry), to colonies composed of multiple queens (polygyny) that are either singly or multiply inseminated. Moreover, the population structure of an ant species can range from multicoloniality to polydomy to supercoloniality, and  DNA was extracted with 5% CHELEX (BIO‐RAD) and amplified with eight microsatellite markers that had been designed previously for er Earl, . PCA of 2.3stats plotting was done by autoplot in ggplot2); however, this unsupervised analysis does not provide much information when the differences between samples are small. We then used k‐means clustering in order to understand the structuring of the population, and assessed the most probable K using k‐means function in stats, as well as mclust in mclust and nbclust in NbClust libraries in R. Plot was done by clusplot function in cluster library. Hydrocarbon profile specificity was also determined using multivariate statistics, that is, discriminant analysis using the stepwise forward mode, using IBM SPSS statistics 21. This module of the program works initially as a principle component analysis for reducing the number of variables to fit the number of cases and then performs the discriminant function. We used the colonies suggested by the STRUCTURE program for discrimination.For cuticular hydrocarbon analysis, we immersed individual heads in hexane (5–10 ants from each of 36 nests), containing 100 ng/µl of C20 as internal standard. Initial analysis was conducted by gas chromatography/mass spectrometry (GC/MS), using a VF‐5 ms capillary column, temperature‐programed from 60 to 300°C  at a rate of 10°C per min, with a final hold of 15 min. Compound identification was achieved according to their fragmentation pattern, and the respective retention indices were compared to authentic compounds. Quantitative analyses were performed by flame ionization gas chromatography (GC/FID), using the above running conditions. Peak integration was performed using the program Galaxie Varian 1.9. Only hydrocarbons , which presumably play a major role in nestmate recognition  colonies comprising approximately 200 workers that were maintained in nesting boxes prior to the experiment. Using various combinations, we conducted merging assays between two colonies, which were chosen either according to the dissimilarity of CHC profiles or to nest metric distance Table . We also2.5SYNCSA, library matrix for data preparation, library ggplot2 and ggpmisc for visualization in R version 3.3.3). Mantel test was calculated by using the mantel.rtest function (library ada in R version 3.3.3) between all types of distances.The pattern of nest distribution was assessed using the mean distance between nearest neighbors equation as described in Clark and Evans . Pearsondendextend, cluster analyses using each of the following distances: geographical , genetical (Fst's) and chemical . We then checked for association between the methods, in order to understand how they help to elucidate the population structure, by using cluster analysis (method: average), using the hclust algorithm in R version 3.3.3. Since each method resulted in a specific and slightly different dendogram, we assessed their validity by calculating the entanglement level between pairs of dendograms. Entanglement, a function used in the library dendextend, is measured by assigning values of consecutive numbers to the left dendogram's labels and then matching these numbers to those assigned to the right dendogram. Entanglement is the L‐norm distance between these two vectors. In other words, we take the sum of the absolute difference L) and divide it by the “worst case” entanglement level . L determines the penalty level . L > 1 means that we give a big penalty for sharp angles; while L‐> 0 means that whenever something is not a straight horizontal line, it receives a large penalty If L = 0.1, it means that we give higher preference to horizontal lines over nonhorizontal lines. We used L = 1.5 to allow for some flexibility; using lower L values results in higher entanglement value. A lower entanglement coefficient corresponds to a better alignment.To test the hypothesis that the population is polydomous, we performed, using library 33.1Ho = 0.65 (range 0.44–0.69) He = 0.107 (range 0.065–0.128). The fixation index Fit was slightly higher than zero (0.001 ± 0.03 SE jackknife) indicating random mating in the tested population. Fst was very low at 0.15, while the within‐nest relatedness was 0.3 (±0.019 SE jackknife), which is considerably lower than assumed for a monogynous, monandrous nest (0.75). Nest excavations, in completion, revealed a single queen in nine nests. In 11 nests, COLONY genetic data analyses showed that all of the workers genotypes matched to a single queen, confirming unequivocally monogyny. In the other nests, some of the workers’ genotypes  did not perfectly match the assigned queens’ genotype. These workers were observed both in QL (queenless) and in QR (queenright) nests; noteworthy, these QR nests had a single queen. Similar reconstructed matrilines are assumed to belong to the same colony (Supporting Information Table SE = 0.28).None of the eight microsatellite markers used in this study showed indications of null alleles or linkage disequilibrium. The number of alleles ranged from 4 to 29. Mean heterozygosity was Population analysis using the program STRUCTURE indicated that the population consisted in six or 22 colonies out of the 35 nests analyzed Figure a–c. In H3.2k‐means approach to determine the level of structuring in the samples. The most probable number of K's was two according to all three analysis methods used, with the two components explaining only 51% of the variability . There was, however, also a clear distinction between sites. Colonies five and six, which were very distinct from all other colonies, originated from the Habonim population; while those from the Atlit population were clumped together, but still distinct from each other.Analyses of the hexane head extracts revealed a complex profile comprising 24 identified hydrocarbons Figure . We then4Nest merging was carried out between QR nests of differing proximities , and positive correlation between genetic with geographic distances , as well as between chemical and genetic distances .Figure R value of 0.419 (R = mean distance of nearest neighbor observed/expected), indicating that the nests are aggregated more than expected under random distribution. We therefore checked for nest clusters using cluster analysis based on average geographic distances between nests (GPS locations). In Atlit there were eight main clusters of nests that were less than 20 meters apart from each other. Using the same nests, we performed a second cluster analysis based on average genetic distances (Fst's). In this clustering, we obtained a slightly larger number of clusters , with Fst < 0.05. Aligning the two dendograms into a tanglegram resulted in a high level of entanglement (E = 0.63 in Atlit only), indicating a poor match between the dendograms. A third cluster analysis with the same nest population was performed using the average chemical Mahalanobis distances identified nine clusters (x < 15), but nest composition of each of these clusters differed from that based on geographic distances, as revealed by the high‐entanglement value (0.75). Finally, comparison of the dendograms based on genetic and chemical distances similarly resulted in a mismatch with an entanglement value of 0.64.Calculation of the nearest‐neighbor extrapolated‐nest‐density revealed an 5Cataglyphis israelensis is distributed along the central mountain ridge in Israel, but has also a small extension along the coast, at the foot of Mount Carmel, where it is sympatric with C. savignyi. In this study, we determined the social and population structure of C. israelensis in its coastal distribution. The results of nest excavations indicate that this species is monogynous. This was also supported by the genetic analysis of several nests where all workers’ genotypes matched that of their mother queen. However, in some of the nests, workers’ genotypes did not correspond with the assumed queen's genotypes. Two possible explanations can account for this genotype discrepancy. Some of the nest may have had multiple queens, which we may have missed during excavation. Alternatively, these workers may represent drifting workers that entered and were accepted in the wrong nest. The phenomenon of drifting workers is also known to occur in other Cataglyphis species , as well as other social insects  indicated a highly polydomous population, and possibly a supercolony. However, although the mean Fst was rather low (0.15), it was not as low as that of a previously described supercolony of C. niger  due to the high number of matings per queen, but the Fst values still differed from zero, indicating a genetic separation between nests. Thus, colonies can be closely related but nonetheless do not form a unicolonial structure. This might also be due to a reasonably high level of drifting workers. Comparing nests with similar matrilines (using COLONY) seems to give a more accurate and biologically meaningful result, however, because the method of STRUCTURE is commonly used, the comparison is noteworthy.The newly‐described C. niger . CHC profiles are influenced both genetically and environmentally. Thus, in a small area like the one examined here, the possibility of isolation by distance (as suggested from the genetic data) superimposed by similarity in prey composition, may make differences between colonies hard to detect. Another variable to consider is that in our analysis we examined only the hydrocarbon constituents . The basis for such population structure could be due to difficulty of dispersal, properties of the substrate, and/or richness of the habitat. When isolation by distance occurs, new gynes hardly disperse and daughter colonies are located in high proximity to mother colonies. In soft substrate like sand, it is easier to dig new nests and, therefore, nest turnover may be large. Finally, a high availability of food increases nest mass and is perhaps conducive to colony spread.In conclusion, the most probable population structure of Our study also emphasizes the importance of using a multi‐method approach in population structure studies. A multi‐method approach is often used for delimiting species’ boundaries and we suggest its use also in delimiting colony boundaries. Most of the methods frequently used to determine a specific colony signature are affected by factors such as isolation by distance, food abundance and distribution, social structure, and mating systems. Thus, using a single method for assessment is insufficient and the importance of behavioral assays cannot be overemphasized.TRB and AH conceived the ideas and designed the methodology; TRB collected the data; TRB analyzed the genetic and behavioral data; TRB and AH analyzed the chemical data; TRB and AH led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication.https://doi.org/10.5061/dryad.6gb3977.Sampling locations, CHC data and microsatellite genotypes are available at: Dryad  Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.