This study was conducted to study the genetic and population structure of local (Awassi) and exotic  sheep breeds in Jordan using eight microsatellite markers.A total of 125 sheep were used (25 from each breed) in the study. The number of alleles (A), the mean values of observed (Ho) and expected (He) heterozygosity, polymorphism information content (PIC), fixation index as a measure of heterozygote deficiency or excess, and Hardy–Weinberg equilibrium (HWE) were analyzed using PopGen and CERVUS softwares. Nei’s standard genetic distances among breeds and dendrogram of unweighted pair group method with arithmetic mean (UPGMA) were calculated and constructed using PopGen software.A total of 40 alleles were detected with an average number of alleles of 5. The mean Ho value was higher than the mean He value for all breeds. Awassi breed showed the highest average PIC value while Romanov had the lowest. There was a significant (p<0.05) deviation from HWE at each locus within and between breeds. Deviations from HWE were found to be highly significant for all markers except OARFCP304 locus. The genetic distance estimates revealed a close relationship between Romanov and Charollais and between Awassi and Charollais. In the UPGMA dendrogram, Charollais, Romanov, and Awassi breeds were placed together in one main cluster while Assaf was in a different subcluster. Awassi was placed alone in a second main cluster.Results of this study offer insight toward the genetic conservation of the studied breeds and a base on which breeding plans can be made. Sheep raising is considered an important source of livelihood in Jordan . The locMicrosatellites are commonly used to estimate the genetic diversity among different farm animal species ,4. SeverIn Jordan, genetic similarity and distance have been reported within and between Awassi sheep lines  using random amplified polymorphic DNA markers .The genetic structure, genetic diversity, and relatedness between Awassi sheep and four of the most common imported sheep breeds Assaf, Suffolk, Romanov, and Charollais were never been studied before. Therefore, the aim of this study was to investigate the genetic structure and variability of Romanov, Charollais, Assaf, Awassi, and Suffolk sheep breeds in Jordan.All experimental procedures in this study were approved  by the Jordan University of Science and Technology Animal Use and Care Committee.A total of 125 blood samples were collected from five different sheep breeds (25 samples each) including Awassi, Romanov, Charollais, Assaf, and Suffolk. Whole blood was collected by jugular vein puncture using Vacutainer blood collection tubes containing EDTA and stored at 4°C until analysis.DNA was extracted from whole blood samples using the E. Z. N. blood DNA extraction kit according to manufacturer’s recommendations . The extracted DNA was stored at −20°C until further analysis.DNA samples were amplified with eight microsatellite markers . The PCRThe Ho and He, fixation index (Fis) as a measure of heterozygote deficiency or excess, PIC, and Hardy–Weinberg equilibrium (HWE) were analyzed using PopGen  and CERVThe total numbers of alleles (A), Ho, He, PIC, and HWE for each of the eight microsatellites used in this study are presented in The number of alleles (A), Ho, He, Fis, and PIC for eight microsatellites in Romanov, Charollais, and Suffolk, Awassi, and Assaf sheep breeds are presented in The matrix of Nei’s standard genetic distances among the studied sheep breeds is presented in In the UPGMA dendrogram , two maist time using microsatellite markers. The level of variation depicted by the number of alleles at each locus serves as a measure of the impact of each of the studied breeds on differentiation within livestock populations in Jordan. In this study, the number of alleles per locus was ranged from 3 to 9. Similar results were reported in Muzzafarnagri sheep [In this study, the genetic contribution of four exotic sheep breeds  and their relationship with the local Awassi breed was studied for the 1ri sheep  and Bangri sheep , while ari sheep . Variatiri sheep .The mean value of the Ho for all loci in this study was higher than the overall mean value of He. Furthermore, the overall mean value of Ho was higher than the He value for the five studied breeds. These results indicate high genetic variability among studied breeds. Slightly lower Ho values were reported previously in Awassi sheep . It was In this study, Awassi breed showed the highest average PIC value (0.59) while Romanov had the lowest PIC value (0.48). These results are lower than that reported in Kilakarsal sheep breed , but simAll markers in this study revealed a significant departure from HWE except for OARFCP304 marker. This may indicate migration or high mutation rate in microsatellites. It also could be due to artificial selection in the studied breeds . Other fLow value of the genetic distance between breeds indicates close relationship or common origin . In thisst time in this study using microsatellite markers. The results indicate high genetic diversity within the studied breeds. At the same time, a close relationship was found between Awassi and Romanov and Awassi and Charollais breeds, whereas distant relationships existed between Awassi and Assaf and between Assaf and Suffolk breeds. This information offers insight toward the genetic conservation of the studied breeds and a base on which breeding plans can be made.The genetic diversity and relatedness of local Awassi sheep to four exotic breeds in Jordan were investigated for the 1KJ: Designed the experiment and performed the laboratory work, the genetic and statistical analysis and manuscript writing. MA: Supervised the molecular experimental work, ZBI: Performed data interpretation and manuscript writing and revision, AMY: Collected samples and performed laboratory work, IAS: Help in the laboratory work. All authors read and approved the final manuscript.


The objective of a conservation program is to maintain maximum genetic diversity and preserve the viability of a breed. However, the efficiency of a program is influenced by the ability to accurately measure and predict genetic diversity.To examine this question, we conducted a simulation in which common measures (i.e. heterozygosity) and novel measures  were used to estimate genetic diversity within a conserved population using double-labeled single nucleotide polymorphism markers.The results showed that the accuracy and sensitivity of identity-by-state probabilities and heterozygosity were close to identity by descent (IBD) probabilities, which reflect the true genetic diversity. Expected heterozygosity most closely aligned with IBD. All common measures suggested that practices used in the current Chinese pig conservation program result in a ~5% loss in genetic diversity every 10 generations. Parental genomic components were also analyzed to monitor real-time changes in genomic components for each male and female ancestor. The analysis showed that ~7.5% of male families and ~30% of female families were lost every 5 generations. After 50 generations of simulated conservation, 4 male families lost ~50% of their initial genomic components, and the genomic components for 24.8% of the female families were lost entirely.In summary, compared with the true genetic diversity value obtained using double-labeled markers, expected heterozygosity appears to be the optimal indicator. Parental genomic components analysis provides a more detailed picture of genetic diversity and can be used to guide conservation management practices. The primary goal of a conservation program is to maintain the maximum genetic diversity of a conserved stock , effectiSeveral approaches, including pedigree-based and molecular marker-based methods, have been applied to estimate genetic diversity ,5. HowevHo and He), observed and effective number of alleles (Ao and Ae), and polymorphism information content (PIC), have been used for this purpose [Due to the fact that SNPs are bi-allelic, optimal measures with sufficient sensitivity and accuracy are essential for monitoring the genetic diversity in conserved stocks. Measures based on gene frequency, such as the observed and expected heterozygosity , were measured to assess the current practices used in the Chinese pig conservation program (see methods as below). All simulation data was generated using Fortran 90 codes, with averaging over 100 replicates.A base population at mutation-drift equilibrium was prepared using t = 0). The population comprised 120 individuals, consisting of 20 males and 100 females. Population size was kept constant across generations. We simulated 1,200 SNPs per chromosome and these SNPs were evenly distributed across each of the 18 chromosome in the pig genome. All marker loci were initially fixed at the “1” allele  and selected loci were permitted to mutate to allele “2”. The mutation rate per locus in each generation was μ = 2.5×10−4. The number of new mutations in each generation was drawn from a Poisson distribution with mean 2Nencμnl, where Ne was the effective population size, nc was the total number of chromosomes, and nl was the marker number per chromosome [t = 0 [The base population was generated by simulation through 5,000 generations of random mating until reaching mutation-drift equilibrium. This population is defined as generation zero . The population size is kept constant during the conservation period. He, Ho, Ao, Ae, PIC, and number of rare alleles (RA) were used to measure the diversity over the whole genome and for each chromosome, which were calculated as previous study [The initial population was managed for 50 generations. The mutation rate during the conservation period was set to 2.5× 10t = 0. To observe the dynamic changes of SNP distribution, SNP frequencies were sorted into 10 bins. Genealogical inbreeding coefficients were calculated using the formula as follows with the assumption that the individuals were unrelated at t = 0.In addition, the rate of decline for each diversity parameter was calculated between adjacent generations. To express the rate of decline, we determined the number of generations required to reduce a given parameter by 5% from its initial value at t, generation; Nm, male number of conservation population; Nf, female number of conservation population.Of which, t=0)/(G-hom + G-her), where G-IBS was the number of IBS loci in the whole genome, and (G-IBS)t=0 was the number of IBS loci in the whole genome at t = 0. Similarly, IBD and IBS probabilities were calculated for each chromosome.The IBD probabilities were first introduced as estimates of genetic diversity based on neutral SNP markers . GeneticNe) for each 5 generations were estimated according to the random mating model of linkage disequilibrium using NEESTIMATOR v2.01 [Ne = N/(1+F), N = 4NmNf/(Nm+Nf). F has been estimated in the above; Nm, male number of conservation population; Nf, female number of conservation population.Simulated SNP information of conserved stock were exported after managing for 50 generations. The kinship between individuals were calculated with GCTA v1.92.1beta6  and reshOR v2.01 . While t(t)/(Nm×nl×nc), where M1 was the number of males with ID = 1 at generation t, Nm was the population size of males, nc was the total number of chromosomes, and nl was the number of markers per chromosome. Meanwhile, all male and female family was calculated as the formula described. The relative genomic components (RGC) for each generation relative to the initial generation was calculated as (PGC(t=n) – PGC(t=0))/PGC(t=0).Using double-labeled markers, we could trace marker origin in all descendants. The proportion of parental genome in the genome of a descendant genome was defined as the parental genomic component (PGC). Using the first male family as an example, PGC was calculated as M1He and Ho decreased in the conserved population throughout the 50-generation simulation . The Ao decreased from 2 to 1.76 during the simulation, while Ae decreased from 1.55 to 1.43, representing reductions of 12.2% and 7.63% from the values at t = 0, respectively  at t = 0. The number of RA increased sharply to 660.52 at t = 9, and then fluctuated over a small range (data not shown). Additional genetic diversity parameters for each chromosome were calculated  into 10 bins every five generations, with two additional bins to account for “lost” and “fixed” alleles . The disNe was 47.1 at t = 1 and the highest was 70.1 at t = 5. While the theoretical effective population size was calculated as the formula described in the material section, which showed a continuous and slow decline across the conserved generations.Actual effective population size was estimated based on the SNPs of conserved stock for every five generations , which pt = 3, an increase of ~10% relative to t = 0. However, the genomic components derived from M1 at t = 5 were 4.15%, a 17% reduction compared to the initial generation  under conservation are shown in The conserved stock had 100 female families in total, and the genomic components contributed by each female family was therefore 1% at t t = 50 . The ratis stage . To furtHe, Ho, Ae, Ao, and Pp) and coalescent genetic diversity  across 50 generations during a simulated conservation program.Simulations have been used to predict the efficiency of strategies that rely on SNP markers to maintain genetic diversity in conserved stock ,19. The Ao, Ae, and GD_IBD was strong , which is considered to reflect true genetic diversity . Althougs strong , the obvs strong . Since SIn our study, genetic diversity was estimated using marker loci. Genetic diversity has previously been studied in a simulated neutral genome without selection, or with selectively neutral markers ,12,20–22t = 5 (t = 5 (data not shown). Moreover, loss of genetic diversity could be observed in other genetic diversity parameters , coalescent genetic diversity , and allele frequency.In our simulation, parental genomic components could be tracked using the parental labels accompanying each SNP. In practice, such labels are unavailable. Nevertheless, the parental genomic components estimated in this study are still useful for predicting the loss of genetic diversity in the current Chinese conservation program. Because the population size and management strategy in the simulation are identical to those used in the program, the PGC dynamics we observed potentially reflect actual trends in conserved swine stock. Of most concern is the loss in genetic diversity. For example, the genomic components of three male families  were reduced by more than 50% at t = 5 . The gent = 5 , and 35 t = 5 due to genetic drift. The most direct strategy to rescue the loss of genetic diversity is to enlarge the population size with additional genetic resources from same breed. Accordingly, we could infer that it is appropriate to introduce ~10% additional genetic resources into a managed stock every 5 generations (about 12.5 years in Chinese pig conservation program). This practice would ensure that the conserved stock maintains a high level of genetic diversity in the long term. Additional genetic resources could be obtained from live pigs from another conserved stock of the same breed, or even frozen semen and embryos, thus combining in vivo and in vitro conservation and reducing overall conservation costs.The simulated conserved stock with its small effective population size had already lost ~10% of genetic diversity at in vivo and in vitro conservation to maintain of genetic diversity.We are the first to exploit true IBD probabilities using double-labeled SNP markers in a simulated conservation program. After comparing the performance of GD_IBD and other predictors, we recommend GD_IBS and heterozygosity rather than allele number to monitor dynamic changes in genetic diversity when the marker density is on the order of 1,000 SNPs per chromosome. Additionally, we have incorporated parental genomic components into our assessment of a conservation program, making it possible to develop improved strategies for maintaining conserved stock at a high level of genetic diversity over the long term. Our results also provide a theoretical foundation for combining


Historical factors, demography, reproduction and dispersal are crucial in determining the genetic structure of seabirds. In the Antarctic marine environment, penguins are a major component of the avian biomass, dominant predators and important bioindicators of ecological change. Populations of chinstrap penguins have decreased in nearly all their breeding sites, and their range is expanding throughout the Antarctic Peninsula. Population genetic structure of this species has been studied in some colonies, but not between breeding colonies in the Antarctic Peninsula or at the species’ easternmost breeding colony (Bouvetøya).loci and a mitochondrial DNA region (HVRI) in 12 breeding colonies in the South Shetland Islands (SSI) and the Western Antarctic Peninsula (WAP), and one previously unstudied sub-Antarctic island, 3600 km away from the WAP (Bouvetøya). High genetic diversity, evidence of female bias-dispersal and a sign of population expansion after the last glacial maximum around 10,000 mya were detected. Limited population genetic structure and lack of isolation by distance throughout the region were found, along with no differentiation between the WAP and Bouvetøya , indicating long distance dispersal. Therefore, genetic assignment tests could not assign individuals to their population(s) of origin. The most differentiated location was Georges Point, one of the southernmost breeding colonies of this species in the WAP.Connectivity, sex-biased dispersal, diversity, genetic structure and demographic history were studied using 12 microsatellite Pygoscelis species.The subtle differentiation found may be explained by some combination of low natal philopatric behavior, high rates of dispersal and/or generally high mobility among colonies of chinstrap penguins compared to other The online version of this article (10.1186/s12862-018-1207-0) contains supplementary material, which is available to authorized users. Investigating population genetic structure is important for understanding evolutionary processes , and estPopulation genetic structure is typically investigated using indirect methods, such as inferring gene flow levels among colonies . As levePygoscelis penguins breed sympatrically: Adélie (Pygoscelis adelie), gentoo (P. papua) and chinstrap (P. antarcticus) [In the Southern Ocean, penguins are a major component of the avian biomass , dominanrcticus) . Populatrcticus) –23. In crcticus) , 24. Thi. To exemplify, Trivelpiece et al. [Pygoscelis species show some degree of natal philopatry, chinstrap penguins are the least philopatric of the genus [loci [Unlike Adélies and gentoos, almost the entire breeding distribution of chinstrap penguins is restricted to the Antarctic Peninsula (up to approximately 64° S) and the South Shetland, South Orkney, and South Sandwich Islands in the Scotia Sea region , 25–29. e et al.  demonstre et al.  describehe genus , 33. At he genus , 34, 35.us [loci . Howeverus [loci –46.Pygoscelis penguins, and the connectivity between breeding colonies in the WAP, or between the WAP and the easternmost breeding colony in the species’ distribution (Bouvetøya), is still unknown. Population declines reported in numerous colonies highlight the importance of investigating the connectivity of breeding colonies in terms of source and sink population dynamics, and other genetic effects these reductions might have. Indeed, in the context of conservation biology, the proper identification of population genetic structure is crucial [P. antarcticus in a few locations and in another species of the genera with similar ecological features (P. adeliae), and (b) strong genetic structure between Antarctica and Bouvetøya, due to isolation explained by large geographical distances.Population genetic structure of chinstrap penguins has been studied in some parts of their range , 34, 35. crucial . Thus, tn = 183), two in the Antarctic Peninsula (n = 45) and Bouvetøya (n = 23) , a total of 251 blood samples from chinstrap penguins were collected at 13 sites, including 10 locations in the South Shetland Islands , and reverse primer AH530 (5′- CTGATTTCACGTGAGGAGACCG-3′) [A 305 base pair (bp) of the mitochondrial DNA (mtDNA) HVRI was amplified using forward primer Forw2 (5’-ACAGTACGAGATAAGTCATGGTTCC-3′) or L-tRNAACCG-3′) . The PCRACCG-3′) . The mtDloci  isolated from the genome of three species of Pygoscelis penguin sequenced by NGS (Next Generation Sequencing), as part of another study [loci with overlapping size ranges. Protocols applied for primer’s sequences, PCR conditions, and amplification cycles for microsatellite loci were those described in Vianna et al. [Genetic diversity and population differentiation were examined at 12 tetranucleotide microsatellite er study . Forwarda et al. . DNA samThe mtDNA sequences (HVRI) were aligned and edited according to the chromatogram utilizing Sequencher v.5.1 . Polymorphic sites (S), number of haplotypes (h), haplotype diversity (Hd), average number of differences between pairs of sequences (∏), and nucleotide diversity (π) were estimated with Arlequin v.3.5.1.2 , applyinlocus, and evaluate the observed (Ho) and expected heterozygosities (He). Expectations for Hardy-Weinberg equilibrium (HWE) were estimated as deviation of the Wright’s FIS index and these were tested for each locus, for all loci, and for each population utilizing randomization procedures using 10,000 permutations with GENETIX v.4.05.2 [For all microsatellite data, PGDSpider v.2.1.0.1 software was used as an automated data conversion tool . For thev.4.05.2 . To testv.4.05.2 , 58.FST and ФST between pairwise populations on microsatellite and mtDNA sequence data using 10,000 permutations. P values were corrected with the FDR method for multiple tests [loci, isolation by distance was evaluated by means of the Adegenet package in R [Arlequin v.3.5.1.2  was utille tests , 58. Empage in R . For thiage in R . Google K), multilocus genotypes were analyzed through Bayesian clustering methods implemented in STRUCTURE v2.3.4 [K) set from 1 to 13. For each K, the model was run 10 times with a burn-in length of 100,000 iterations followed by 1,000,000 Markov Chain Monte Carlo (MCMC) subsequent iterations. The optimum number of clusters was inferred by deriving the posterior probability of K (LnP(D)) from each independent run. As the ΔK method of Evanno’s does not allow K = 1 to be tested [K was higher than one for log-likelihood using STRUCTURE HARVESTER [To determine the most likely number of clusters  was performed using a combination of analytical and stochastic methods, based on molecular markers and geographical sampling . CalculaK value was determined based on the highest averaged maximum likelihood score of the models.An analysis of spatial structure using the R package GENELAND v3.1 was carried out to determine the most likely number of populations and to assign individuals to population clusters. This program is based on an algorithm which includes not only genotypes, but also the geographical location of all individuals to estimate the number of groups and delineate their spatial boundaries . AnalyseK-means and model selection to identify genetic clusters [Additionally, a Discriminant Analysis of Principal Components (DAPC) was carried out to determine the number of clusters of genetically related individuals, using a non-Bayesian approach. DAPC uses sequential clusters . The Adeclusters  was usedloci was done using GENECLASS2 v.2.0.h [To assign or exclude individual colonies as being the origins of individuals based on genotype data, assignment testing of microsatellite  v.2.0.h . Two sep v.2.0.h , and the v.2.0.h . The pro v.2.0.h . The samTo evaluate deviations from Wright-Fisher equilibrium, two neutrality tests were applied: Tajima  and Fu . Both te2, 100 μM dNTPs and 0.7 U Taq DNA polymerase (Invitrogen Life Technologies). The reactions’ conditions were as follows: an initial denaturing step at 94 °C for 5 min; followed by 45 cycles at 94 °C for 30 s, 46 °C for 45 s, and 72 °C for 25 s; and a final extension step at 72 °C for 5 min. All reactions were conducted on an Applied Biosystem machine. The amplification products were separated on 2% agarose gel for approximately 1 h at 150 V and visualized with GelRed® under UV light.For molecular sex identification, a region of the Chromosome-helicase-DNA binding protein (CHD1) gene was amplified, with primer pair 2550F/2718R . PCRs weIS), fixation index (FST), relatedness between individuals (r), mean Assignment Index (mAIc) and variance of Assignment Indices (vAIc) between sexes were calculated. FST, r and mAIc were expected to be lower in the sex that disperses most, whereas FIS and vAIc were expected to be higher [p values of each test were estimated using 10,000 randomizations.Using microsatellite data of the sex-identified individuals by molecular techniques, sex-biased dispersal was evaluated with FSTAT v.2.9.3.2 . Two hype higher . The p vn = 207, S = 55) were found, along with high haplotype diversity  ranging from 0.95 (HP) to 1 , and low nucleotide diversity , so it was not used in further analyses. The remaining 11 microsatellite loci were polymorphic for all populations, except for AP-3 at Baily Head (BH)  was low in all populations, and no significant p-values were found, indicating no significant heterozygote excess or deficiencies. Therefore, deviations from HWE were not detected in populations at the 11 loci  and 12 (locus GP-15), with an average of 5.69 alleles for all sample sites. The expected heterozygosity ranged from 0.62  to 0.71 (BH), with an average of 0.65. The observed heterozygosity exhibits a similar level of variation with an average of 0.63 over all locations. Values of allelic richness ranged between 5.00 to 6.46 per sample site. Locus-by-locus allelic richness and diversity measures for each sample location are shown in the Additional file For microsatellite data, only one ST = − 0.004, p = 0.766) . However, statistically significant FST values varied from 0.031 to 0.054, indicating a weak differentiation between GP and other colonies . Although studies have questioned the performance of the Mantel test [For microsatellite ant Fig. . Seven otel test , 84, it tel test .loci, yielding different optimal numbers of clusters. Using the mean log-likelihood in STRUCTURE, the analysis inferred that the number of populations (K) was one, for seven of the eight tested different model assumptions. Only one model selected K = 3, therefore it was also evaluated by Evanno’s method, which suggested K = 2. Nonetheless, when analyzing individual assignment plots, no group could be identified  Kopaitic Island, (2) Georges Point, and (3) northern WAP locations and Bouvetøya . Models and estimated number of populations (K) for all Bayesian programs used are summarized in Table K = 6, however, they were geographically meaningless and overlapped extensively  , combineST > 0.05 [loci showed absence or reduced population structure among chinstrap penguins breeding in the WAP. Interestingly, absence of structure between the WAP and Bouvetøya was observed  in peripheral populations [Using mtDNA (HVRI) from four breeding sites, Clucas et al.  found weT > 0.05 . Microsaulations . Anotherulations . Numerouulations –95. Duriulations .IS, negative mAIc and higher vAIC) pointing towards a female-biased dispersal, though none of the indexes were significant [Dispersal has significant effects on population size (growth or reduction), species’ persistence and genetics . In mostnificant . In the nificant , 33. Natnificant . Howevernificant , and a snificant . Indeed,nificant . Evolutinificant . Philopanificant . Howevernificant .Pygoscelis penguins in the WAP [Finally, chinstrap penguins also show a signature of population expansion after the Last Glacial Maximum, similar to that detected for other  the WAP , 22, 24.Most of the data analyses suggests a single large population of chinstrap penguins throughout the Southern Ocean, with minimal population structure in the WAP region, and absence of genetic differentiations between the WAP and a sub-Antarctic island located 3600 km away. Georges Point, one of the southernmost breeding colonies of chinstrap penguin in the Antarctic Peninsula, was the most differentiated of all. The lack of genetic structure among distant reproductive colonies of chinstrap penguins in the Southern Ocean may be due to different factors, such as a historical large population size making it unyielding to drift, long-range gene flow between breeding colonies, stressful environmental conditions forcing penguins to increase dispersion rates and/or post-LGM recolonization between WAP and Bouvetøya.Additional file 1:Figure S1. Plot of assignment probabilities from STRUCTURE. A vertical bar represents an individual, and colors represent the different clusters found. All plots were generated via running 10 replicates. Figures show the optimal number of clusters for no admixture model, with independent allele frequency and no location information supplied using A) Posterior probability of K (LnP(D)) and B) Evanno’s method. (DOCX 20 kb)Additional file 2:Figure S2. Plot of assignment probabilities from BAPS. Vertical lines represent each individual and the color refers to clusters found through this analysis. A) spatial with both, mixture and admixture models (K = 1) and B) non-spatial admixture model (K = 7). (DOCX 16 kb)Additional file 3:Figure S3. Posterior probabilities of population membership from the spatial model with correlated allele frequencies’ model. Lighter colors indicate higher probabilities of population membership. Three genetic clusters were identified using GENELAND. Left: Kopaitic Island, middle: Georges Point, and right: northern WAP locations and Bouvetøya. (DOCX 16 kb)Additional file 4:Figure S4. Discriminant Analysis of Principal Components (DAPC). The six genetic clusters identified by Adegenet are shown in different colors. All six groups overlapped extensively, and none of them represent a specific colony. (DOCX 16 kb)Additional file 5:Figure S5. Skyline plot mtDNA (HVRI) for chinstrap penguins. (JPG 144 kb)Additional file 6:Figure S6. Chinstrap penguins south of their traditional breeding range. A) Breeding pairs at Waterboat Point , B) a single individual surrounded by gentoo colonies on Doumer Island  and c) another single bird on Avian Island  in the midst of Adélie penguins. (JPG 43 kb)Additional file 7:Table S1. Allelic richness (A), expected (HE) and observed heterozigosity (HO) values for 11 microsatellite loci, for all examined populations. Study site (collection location) abbreviations correspond to EI: Elephant Island, PI: Penguin Island, BP: Barton Peninsula, AI: Ardley Island, GI: Greenwich Island, MB: Miers Bluff, HP: Hannah Point, CS: Cape Shirreff, BH: Baily Head, VC: Vapour Col, KI: Kopaitic Island, GP: Georges Point, and BI: Bouvetøya. (JPG 424 kb)Additional file 8:Table S2. Summary of pairwise genetic differences (ΦST) between chinstrap penguin colonies for mtDNA marker (HVRI). Bellow the diagonal are ΦST values, and their corresponding p-values above the diagonal. (TIFF 2112 kb)Additional file 9:Table S3. Summary of pairwise genetic differences (FST) between chinstrap penguin colonies for mtDNA marker (HVRI). Below the diagonal are FST values, and their corresponding p-values above the diagonal. (DOCX 18 kb)Additional file 10:Table S4. Summary of pairwise genetic differences between chinstrap penguin colonies (FST) calculated from the 11 microsatellite loci. Below the diagonal are FST values, and corresponding p-values above the diagonal. (JPG 76 kb)Additional file 11:Table S5. GeneClass2 percentage test results using microsatellite data for chinstrap penguins from 13 colonies for (a) genetic assignment using Paetkau et al. (1995) criterion and (b) first-generation migrant. Lines indicate the samples’ site collection and columns indicate the colonies to which the individuals were assigned. Colony self-assignments are in bold. (JPG 5437 kb)


Pinus taeda L.) by determining how ectomycorrhizal (EM) colonization relates to pathogen susceptibility. We detected interactions between susceptibility to pathogenic fungi and soil inoculation source on loblolly pine compatibility with the EM fungi Thelephora, and on relative growth rate of loblolly pine. Additionally, we detected spatial variation in the loblolly pine–EM fungi interaction, and found that variation in colonization rates by some members of the EM community is not dictated by genetic variation in the host plant but rather soil inoculation source alone. The work presented here illustrates the potential for indirect selection on compatibility with symbiotic EM fungi as a result of selection for resistance to fungal pathogens. Additionally, we present evidence that the host plant does not have a single “mycorrhizal trait” governing interactions with all EM fungi, but rather that it can interact with different fungal taxa independently. Synthesis. An understanding of the genetic architecture of essential traits in focal species is crucial if we are to anticipate and manage the results of natural and artificial selection. As demonstrated here, an essential but often overlooked symbiosis  may be indirectly influenced by directed selection on the host plant.Selection on genetically correlated traits within species can create indirect effects on one trait by selection on another. The consequences of these trait correlations are of interest because they may influence how suites of traits within species evolve under differing selection pressures, both natural and artificial. By utilizing genetic families of loblolly pine either tolerant (t) or susceptible (s) to two different suites of pathogenic fungi responsible for causing either pine decline or fusiform rust disease, we investigated trait variation and trait correlations within loblolly pine ( Such genetic correlations between traits can determine how populations evolve under multiple, conflicting selection pressures , Kranabetter  Miyabe ex Shirai f. sp. fusiforme) and those associated with the pine decline complex. The pine decline complex is associated with several abiotic and biotic factors, including Leptographium and Grosmannia pathogenic fungal species, with symptoms including short, chlorotic needles, and thinned crowns . These fungal pathogens have had negative economic and environmental impacts on both natural and agriculturally managed loblolly pine stands, causing substantial damage yearly. Several studies have shown variation among loblolly pine genetic families in their susceptibility to both fusiform rust disease  to show greater vegetative and reproductive responsiveness to mycorrhizal colonization than wild strains. These studies suggest that although artificial selection in these plants was for other, agriculturally relevant traits, the association with mycorrhizal fungi was indirectly affected. Studies of pinyon pine (Pinus edulis) have shown that trees differing genetically in tolerance to insect pests also host different EM fungal communities, whether or not herbivory has occurred  has revealed a quantitative trait locus associated with compatibility with a particular EM fungal species that maps near a linkage group determined to be involved in tolerance to rust fungi , we may be able to understand how indirect selection may be driving evolution in other traits, such as compatibility with particular mycorrhizal fungi. Examination of mycorrhizal traits in loblolly pine families that vary in susceptibility to fungal pathogens, but have not been exposed to the pathogens, will also help disentangle patterns seen in field, where it is difficult to establish the mechanism behind observed correlations between traits. For example, correlations between mycorrhizal traits and pathogen tolerance could be a product of genes influencing both traits directly, or it could be that fungal pathogens induce a response in the host plant that affects its association with mycorrhizal fungi. Additionally, by utilizing soil from multiple locations within the range of loblolly pine, this field soil inoculation experiment allows exploration of variation in EM fungal community composition and tests the potential for host genotypes to be expressed differently in different biotic environments.Do different soil inoculation sources within the natural range of loblolly pine yield different mycorrhizal fungal communities?Do individual EM fungi respond differently to host genetic variation in pathogen tolerance and does this response depend on origin of the fungal community?How do other traits  respond to host genetic variation in pathogen tolerance and does this depend on origin of the fungal community?Specifically, we aimed to explore genetic variation, trait correlations, and geographic variation in the loblolly pine–mycorrhizal fungi interaction by answering these questions:22.1−2 s−1), receiving weekly deionized water sufficient to completely soak the soil. Six weeks after planting, seedlings from each family were transplanted to bleach‐sterilized Ray Leach cone‐tainers .Loblolly pine seeds were obtained from open‐pollinated families that fell into one of four genetic categories: pine decline tolerant (PDt: 4 families), pine decline susceptible , fusiform rust tolerant , and fusiform rust susceptible . These categories were determined in previous pathogen inoculation trials, and tolerant and susceptible families were chosen from the upper and lower ends of the genetic distribution of tolerance to each pathogen  . Transplant mortality was assessed 55 days after initial planting: 586 of the 2,563 seedlings (22.9%) had died, with no significant difference among soil types or plant family categories. High seedling mortality was attributed to transplanting and transportation. Additional mortality occurred throughout the 22‐week growth period, with final replicate numbers in each genetic category as follows: PDt: MS n = 50, AL n = 48, GA n = 23; PDs: MS n = 43, AL n = 50, GA n = 41; FRt: MS n = 42, AL n = 52, GA n = 37; FRs: MS n = 54, AL n = 63, GA n = 55 (Table −2 s−1), receiving a weekly watering sufficient to completely soak the soil. Seedlings in cone‐tainers were randomized in trays within each soil type (to avoid contamination between soils) at the beginning of the experiment and then re‐randomized after 10 weeks of growth. Each tray contained seedlings of the same inoculation treatment to avoid splash contamination during watering. The locations of the trays in the growth chamber were also randomized. Seedling height was measured at planting (Ht1) and upon harvest (Ht2), which took place 22 weeks after planting in treatment soil; this allowed for calculation of RGR of height (RGR = (ln(Ht2) − ln(Ht1))/(no. days of growth)). All plants were assayed for mycorrhizal fungal colonization characteristics including colonization intensity (number of root tips colonized per cm root) and abundance of different EM morphotypes  were transplanted into pots containing the three different field soil  treatments  and was found to vary among soil types ; however, given the large sample sizes and visual confirmation of community differences, we treated this as an acceptable violation. The fungal morphotypes included in this and all subsequent analyses were only those four morphotypes that comprised >5% of the total number of root tips colonized in each soil type. In removing the rarer species, we follow recommendations by some statisticians , root:shoot ratio, and percentage of total colonized root tips that were colonized by each of the four dominant fungal morphotypes. The fixed factors in each model were Soil , Genetic Category , and Soil × Genetic Category interaction, with loblolly seed family included as a random effect within Genetic Category. When the Soil × Genetic Category interaction was found to be significant, we performed pre‐determined contrasts  for each response variable to determine differences between plants tolerant and susceptible to each of the fungal pathogens within and between soils, using the glht function from the multcomp package  to separate means. Comparisons were not made across pathogen categories  because the seed families obtained were only categorized as either resistant or susceptible to one or the other fungal pathogen. Additionally, we analyzed the relationship between RGR and the five EM colonization measurements: percentage of total colonized root tips that were colonized by each of the four dominant fungal morphotypes, and number of colonized root tips per centimeter root . This was achieved by analyzing separate univariate Type III ANOVA models using the lmer function from the lme4 package   , but the two categories did not differ in either Mississippi or Alabama soils; fusiform rust‐tolerant and susceptible plants did not differ in Thelephora colonization , which was greater than that in either Alabama , or Mississippi   , which was higher than that of plants in Alabama soil , and marginally greater than plants in Georgia soil   Figure , Table 3Rhizopogon colonization was highest in Georgia soil , Cenococcum colonization was highest in Alabama soil , and Wilcoxina colonization was highest in Mississippi soil  . Similarly, we found no significant variation for root–shoot ratio of the seedlings, the average of which was 0.9536 (±0.002 SE). On average, loblolly seedlings had 1.02 (±0.02425 SE) root tips colonized by EM fungi per centimeter of root length, and did not differ among treatments. Additionally, RGR did not relate to any of the fungal colonization metrics.The average dry biomass of the seedlings was 2.84 g, and showed very little variation , four morphotypes of fungi were identified as the most dominant root tip colonizers, e Figure . This ree Figure . Althouge Figure , the impe Figure . As suchPinus radiata D. Don) populations showed genetic variation in patterns of compatibility with different members of the dominant EM fungal community. Specifically, it was found that patterns of genetic variation in compatibility among Monterey pine populations differed among three species of EM fungi: Rhizopogon roseolus, Wilcoxina1, and Pyronemataceae1  the EM fungal community as a whole. Consequently, such diverse mutualisms are not necessarily governed by “diffuse coevolution” in which coevolution is weakened by conflicting selection on the same trait , there could be conflicts arising from selection pressure for fungal pathogen tolerance and the need for beneficial root symbionts. A similar scenario would be predicted with RGR, an important adaptive trait of the host plant that was also found to vary according to an interaction between plant genetic category and biotic environment. A more thorough exploration of site variation would be needed in order to strengthen the value of these predictions; however, the results shown here do provide an alluring avenue for future studies in the system. It is worth noting that pine decline‐tolerant plants had greater colonization by Thelephora than susceptible plants in Georgia inoculated soils  and fungi  had components of fitness that depended on the interaction between the genotype of the associated partner and the abiotic environment  and red crossbills (Loxia curvirostra complex) differed depending on the presence or absence of a third species and superior seed competitor, the red squirrel (Tamiasciurus hudsonicus) , and its resident ant community ranged from beneficial to costly for the host plant, depending on the presence of herbivores. While the work described here focuses on the interaction of pines and mycorrhizal fungi, the findings add to a broader body of work aimed at understanding the diversity and geographic structure of potentially coevolving species interactions.Selection mosaics have been explored in previous work in several systems, including plants and mycorrhizal fungi. Piculell, Hoeksema, and Thompson  demonstrnd Smith  found thBenkman, . SimilarBenkman,  found th4.3Rhizopogon, Cenococcum, and Wilcoxina was influenced solely by soil inoculation source. Loblolly pine occurs in a large, continuous population across its range with little genetic structure due to high gene flow via widespread windborne pollen  seedlings grown from open‐pollinated seed trees known to be either resistant or susceptible to WPBR showed differences in stress tolerance traits, whether or not they had been exposed to the fungal pathogen , a disease caused by the pathogenic fungus When selective breeding focuses on one trait, such as pathogen tolerance and growth, there may also be indirect selection on genetically correlated traits. It is important, therefore, to understand the genetic architecture of essential traits in these focal species to better anticipate and manage the results of breeding practices. This study joins others in lending insight into the underlying genetic structure of traits governing species interactions, and how that genetic structure may affect the evolution and adaptation of species. In addition, understanding the genomic architecture of how important adaptive traits are related to one another is an essential component of successful breeding and management strategies.None declared.B.J. Piculell, L.G. Eckhardt, and J.D. Hoeksema conceived and designed the experiments. B.J. Piculell implemented the experiments, collected, and analyzed data with assistance from J.D. Hoeksema. B.J. Piculell authored the manuscript, with editorial comments from L.G. Eckhardt and J.D. Hoeksema.https://doi.org/10.5061/dryad.n13h839).Mycorrhizal colonization data have been archived as supporting information on Dryad ( Click here for additional data file.


Argania spinosa L. was estimated using inter-simple sequence repeats (ISSRs). Mutation screening and genotyping by high-resolution melting (HRM) was performed to detect delta-6-desaturase (D6D) variants in the tested individuals, and finally fatty acid analysis of argan leaves with gas chromatography (GC) was performed. The plant materials used in this study originated from four different sites in Tunisia. Analysis of morphological characteristics showed large variability both within and between the studied collections. The analysis of ISSR polymorphisms gave information about the diversity within and between populations. HRM analysis showed that all 60 argan individuals were grouped into 10 different categories. The results of the gas chromatography analysis showed that the presence of omega-3 fatty acids EPA and DHA was noticeable in some argan leaves.Argan trees are normally endemic to Morocco and Algeria, but hundreds of argan trees exist in Tunisia, some introduced from Morocco and some from unknown origins. The aim of the present study was to evaluate the genetic, morphological, and biochemical diversity of the argan trees in Tunisia. In this study, we used morphometric data collected from vegetative tissue, as well as pomological characteristics related to fruits, stones, and kernels. Genetic variation in 60 trees of Tunisian  The argan tree is a thorny evergreen tree and one of the most remarkable species of North Africa, both in part to its botanical and bio-ecological interest as well as its social and medical value. These properties have led, in 1925, to the enactment of a special legislation for this species, which in 1998 was declared a Biosphere Reserve Species by the UNESCO and the Biosphere Program. Argan trees are a multi-purpose tree , and oil extracted from argan seeds is rich in unsaturated fatty acids [Argania spinosa is a diploid species (2n = 24) [ species n = 24 [22n = 24) . Argan t2n = 24) . The gen2n = 24) ,7. This 2n = 24) . In cont2n = 24) . This stA. spinosa contains oleic acid  and alpha-linolenic acid  which have high nutritional and industrial value due to their considerable presence in many essential fatty acids. Delta-6 desaturase is an enzyme that respectively converts linoleic acid  and alpha-linolenic acid  to gamma-linolenic acid  and stearidonic acid  [C18:4ω3) . In geneC18:4ω3) . The AmeC18:4ω3) . PolyunsC18:4ω3) .A. spinosa leaf samples were collected from 60 trees and analyzed by GC MS. These results were discussed comparatively with those obtained from ISSR and HRM to assess the industrial usage and taxonomic classification of this species.To quantify fatty acid contents within A. spinosa in general. The earliest research into the genetics of this species looked at isozyme markers isolated from chloroplast DNA using Polymerase Chain Reaction- Restriction Fragment Length Polymorphism (PCR-RFLP) [Argania spinosa [Opuntia ficus indica) [Quercus infectoria [Cassia fistula L [Nothapodytes nimmoniana [Myrciaria tenella [The genetic diversity of argan trees in Tunisia has not been investigated so far. In fact, not many studies have looked at the genetic variability of CR-RFLP) ,7. LaterCR-RFLP) ,15, and CR-RFLP) ,17,18 anCR-RFLP) , and morCR-RFLP) . On the CR-RFLP) . ISSR ma spinosa ,17,18, b indica) , Quercusfectoria , Cassia istula L , Nothapommoniana , and Myr tenella .A. spinosa within Tunisia, in order to develop a preservation management program for this species.The purpose of the present study was to evaluate the genetic, morphological, and biochemical variability of natural populations of The principal aim of the morphological analysis was to determine the morphological variability of argan between and within the four sites. In this study, a morphological analysis of both vegetative and reproductive tissues collected from 60 individuals across the different sites was conducted . ObservaLeaves were observed to be three shades of green, as well as fruits, which came in three colors  in the same period. In addition, five forms of fruit shapes including oval, apiculate, fusiform, round, and an intermediate form were observed .P < 0.001) indicating high morphological variability between individuals involved in this analysis. The morphological parameters displaying the most variability are primarily those related to fruits and seeds. Some parameters related to kernels and leaves are characterized by a value of F higher than variables that have marked values of p > 0.05 and therefore have non-significant differences such as   of quantitative measurements collected from fruits and leaves allows for the determination of which variables best discriminate the groups studied. The ANOVA performed on the 22 morphological parameters made it possible to determine the variables that discriminates best individuals from the four sites studied. Many characters present significant differences between the different sites,  .Principal Component Analysis (PCA) was used to demonstrate the variability in a large number of samples with multiple variables . In ordeConcerning axis 2, there is an overlap between individuals from all sites . IndividThe Ward dendrogram was established on the basis of Gower distances. This analysis was based mainly on the quantitative characters since the qualitative characters did not contribute affectively in the PCA. The dendrogram  shows thIn order to optimize the PCR reaction, differentin each primer we played on two variables which are the concentration of magnesium concentrations and the annealing temperatures were tried for each primers. Ten primer pairs were used to produce clear and reproducible fragments. The concentration of magnesium and the annealing temperatures used in this study varied depending on which primers were used . Each prAmong the four argan provenances, the genetic diversity showed that the number of polymorphic loci extend from 90 to 94, and the percentages of polymorphism loci (PPL) range from 77.59% to 81.03%. The observed numbers of alleles (na) were between 1.81 ± 0.39 to 1.77 ± 0.41. The effective numbers of alleles (ne) ranged from 1.41 ± 0.35 to 1.46 ± 0.35 .The range of Nei’s genetic diversity (h) was between 0.25 ± 0.18 and 0.27 ± 0.18. Shannon’s information indexes (I) ranged between 0.38 ± 0.25 and 0.41 ± 0.25. Among the four populations studied, “A” showed the highest genetic variability , while “H” showed the lowest variability  .P < 0.001) genetic differentiation among populations were discovered. Results in Using Analysis of Molecular Variance (AMOVA), highly significant (The results obtained from Nei’s genetic distances  showed tKorbous (A) and Souss (H) were the closest populations and formed a single clustered group, while the majority of individuals from Sfax (S), the rest of individuals from Sousse (H) and the botanical garden (JB) formed single groups except for a few exceptions . The relationship among the 60 individuals studied was also demonstrated using Factorial analysis .The results obtained from these analyses show that individuals were clearly divided into three distinct groups. Using a UPGMA tree , the firThe program STRUCTURE was used to investigate population structure. With the aim of assigning individuals to populations, STRUCTURE was able to confirm the results obtained by using the UPGMA tree and factorial analysis. This program uses a Bayesian algorithm to assess similarities among individuals studied within a known number of genetic groups . Analyzing the results obtained by STRUCTURE using the program Structure Harvester is a fast way to assess and visualize genetic similarities across multiple populations, allowing for the visualization of the number of genetic groups best fitted to the resulting data set. When K = 3, an analysis of assignment probabilities for the 60 individuals studied reveals an increase in cluster resolution. The genotypes were thus grouped into three distinct clusters, the first group (red) contains trees from A and H, the second group (green) contains trees from JB, and the third group (blue) contains individuals from S . S and tThe assignment of individuals to genetic clusters at K = 3, with the y-axis representing genetic distance and the x-axis the accession and subgroup indicated by color, and with each color representing a single gene pool.D6D) gene in 60 argan samples. The samples from 60 argan individuals were collected from the four sites studied; Korbus (A), Sousse (H), Sfax (S), and Botanical Garden (JB). HRM classified 60 argan individuals into 10 different groups carrying sequence variances for the D6D gene  is a fast, simple, sensitive, and cheap method for genotyping and mutation screening in variants. The normalized and temperature-shifted difference plots were obtained by HRM assays using the primer pair for the 130-bp region of the Delta-6 desaturase  and docosahexaenoic acid (DHA). These are omega-3 fatty acids, which are normally found in cold water fish. EPA and DHA are highly unsaturated fats because they contain six and five double bonds on their long structural chains. The quantities of EPA and DHA vary from one tree to another, some trees are characterized by having high quantities of EPA and DHA while others contain only trace amounts. Individuals containing the highest quantity of EPA and DHA are those inhabiting Korbous; A2 (2.903 g/100 g), A3 (1.631 g/100 g), A8 (1.002), A10 (0.823), while individuals with the second highest quantity of EPA and DHA are found in Sfax S3 (0.788 g/100 g), S4 (0.563 g/100 g), S8 (0.632), S12 (0.866 g/100 g), S14 (0.585 g/100 g) . Figure Argan trees show high levels of morphological diversity both within and between different regions within Tunisia. The variability among trees, such as the morphology of both vegetative and reproductive tissue, is visible to the naked eye. This variability was further highlighted using statistical analysis.The results obtained from ANOVA show discriminating parameters allowing for the subdivision of argan trees into three groups. Principal component analysis (PCA) results show existence of morphological heterogeneity at both intra- and inter-site levels, taking into account quantitative and qualitative variables. These analyses prove that argan trees display considerable morphological variability. Finally, dendrogram classifying argan trees from the four populations studied indicates the presence of substantial morphological variability at the intra- and inter-site level. This variability may be a result of their geographical origin, as well as the detachment of some of these individuals from their original site. We can conclude that argan trees originating from the four sites studied demonstrate a considerable level of morphological variability at the intra- and inter-site level, which could be a result of the geographical origin of the trees themselves. These observations prove contrary to those obtained by Zahidi , whose rThe degree of genetic diversity between and within populations is the result of a combination of factors such as gene flow, the selection effect, inbreeding, genetic drift, and mutation . When itArgania spinosa [In order to adapt to and survive environmental changes, the genetic diversity of a species is vital; the higher the genetic diversity of a species, the higher its chance for survival in a changing climate. ISSRs are efficacious markers used to evaluate polymorphisms as well as the variability of many forest plant species including  spinosa ,37,38,39This is the first report of genetic diversity of argan trees in Tunisia. According to the results obtained from this study, argan trees from the provenance Korbus (A), located in the North not far away from the sea, showed the highest genetic variability , while the provenance Sousse (H) showed the lowest variability  . HoweverA. spinosa and showed a lower Nei’s genetic diversity in all the populations studied compared to the result found in the present study. A third study [In the present study, the 10 ISSR primers showed high levels of polymorphisms when compared to those revealed using different molecular markers on the same species, such as RAPD and allozymes analysis ,14. Whenrd study  on ArganA. spinosa in Tunisia, an unsurprising result for an endangered species characterized by small populations [Genetic variability parameters showed high levels of genetic variability among the four provenances of ulations ,41.A. spinosa in Morocco which indicated high genetic differentiation (Gst = 0.40), (Gst = 0.20), and (Gst = 0.54) [Pinus sylvestris with Gst = 0.3965 [Cupressus gigantea (Gst = 0.36) [Machilus thunbergii (Gst = 0.4118) [Rheum palmatum and Rheum tanguticum  [Multi locus analysis estimated from 116 loci showed diversity within the four provenances studied; , which is considered lower than the average genetic differentiation coefficient (Gst) among populations of  Gst = 0., and  ; Machilu= 0.497) . This maAn ANOVA analysis has confirmed a high genetic variability within provenances, showing that 18% of the total variance was between populations while 82% was within populations. The genetic variability of argan could be explained by various factors, including their geographical localization, soil composition, and climatic changes between sites, and their breeding system .We observed a genetic link between “H” Souss and Korbus “A” (Souss plain), while the two other sites were detached forming separate groups which means that gene flow between those two populations is limited. This genetic diversity can be explained by some factors as geographical barriers, wind direction, and also mode of reproduction as reported by El Mousadik and Petit that they have used allozyme markers (Gst = 0.25) PCR-RFLP and the DNAcp markers Gst = 0.60) in their study 0 in thei,48.In this study, the gene flow in and among sites was high as 2.42 compared to ((Nm = 0.4), (Nm = 1.78) and (Nm = 0.73)) argan populations in Morocco ,17,18,20Argania spinosa is important for possible ex situ conservation. The results that we obtained in this study improve our understanding on population structure of the argan tree and give valuable information about management implications for this species, as this diversity among populations should be maintained for later conservation of this species [Spatial genetic structure analysis results showed that all populations were assigned to three groups, as indicated by the phylogenetic tree UPGMA , factori species .A. spinosa individuals which have not reported before. The HRM analysis of gDNA PCR amplicons is a fast, simple, and cost-effective method for identification of variations of argan individuals. A 130-bp region of the D6D gene was scanned by HRM analysis to identify polymorphic variants in 60 argan individuals collected from four different regions of Tunisia. Sixty individuals were grouped into 10 different categories after HRM analysis. The HRM analysis demonstrated that there are large variations among the argan individuals for the D6D gene, even if they grow in the same location under similar conditions. Analysis of fatty acid content, especially LA and ALA, supports the HRM results. LA and ALA contents show high variability in argan individuals. The 10 different “groups” must be verified with sequencing of the PCR products to reveal the polymorphisms in an upcoming study, to reveal the polymorphisms.In this work, we reported the use of HRM analysis in plants for scanning and genotyping of Omega-3 Fatty acids (omega-3 FAs) are found normally in seafood, some plants and livestock in limited concentrations. Normally fish oils are the only concentrated source of eicosapentaenoic acid  and docosahexaenoic acid  . ClinicaEPA and DHA actually represent a subject of much interest, because of their important roles in human health and nutrition, such as neonatal retinal and brain development, as well as cardiovascular health and disease prevention . EPA is Beta maritima L., Cakile maritima Scopoli, Portulaca oleracea L., Amaranthus viridis L., Chenopodium album L., Sonchus oleraceus L. and Stellaria media Villars, Crithmum maritimum L., Malva sylvestris L., Parietaria diffusa Mert., Pichris echioides L., Rumex crispus L., Salicornia europaea L., Sisymbrium irio L., Sonchus oleraceus L., Sonchus tenerrimus L., Stellaria media Villars, and Verbena officinalis L. In this study, some of these mentioned plants scored the presence of EPA and DHA. The content in EPA in these different plants went from 0 to 0.9 g/100 g, the content of DHA went from 0 to 2.3 g/100 g [According to the results in this study, the quantity of EPA and DHA is found high compared to some plants from other studies, as proved in many articles EPA and DHA are present in higher quantities than marine organisms such as fish, fish oil, shrimp. However, the quantity present in plants is normally too low, and the content in EPA and DHA has not exceeded the 0.01 g/100 g in the majority of plants which are considered as traces. Another study on Purslane leaves showed that it contains a quantity of EPA and DHA of about 0.36 g/100 g in leaves  which pr g/100 g , howeverThe main known sources of EPA and DHA are different kinds of seafood  . Additiop = 0.481, alpha = 0.05). This indicates that the great majority of the genetic variation resided within populations.The Mantel test was used to calculate the correlation between the genetic and geographical distances. A correlation test was conducted using Rstudio software  to deterThe samples of leaves and fruits used in this study were collected from 60 trees located in four different sites in Tunisia , Table 7Fifteen trees from each site were chosen to carry out this study. The assessment of morphological variability was based on the observation and measurement of morphological characters related to leaves, fruits, seeds, kernels, flowers, and the general appearance of selected trees.Several characters were chosen according to the Avocado descriptor  since there is not yet a descriptor dedicated to argan trees. Morphometric analysis is essential to make a first “inventory” to identify the most discriminating and informative characters in the description of phenotypic variability and its organization. Sixty argan trees were analyzed based on quantitative and qualitative morphological characters .Ten fruits from each of the 60 trees were collected from different levels and positions of the tree to measure the pomological characters. The sample collection was made between June and August since argan fruits do not reach maturity at the same time. The fruits were pitted to separate the pericarp from the seeds. A morphological study was carried out on the seeds. Subsequently, the kernels of each seed were removed, and a measure of the selected kernel-related traits was established .The genomic DNA was extracted from 60 trees, using a modified and optimized CTAB protocol . DifficuCassia fistula L. [Murraya koenigii [Apterosperma oblata [2 concentration and annealing temperature varied according to the primers. The PCR reactions were performed in 25 μL reaction mixture containing 50 ng of genomic DNA, 1× PCR buffer Taq polymerase buffer, Taq polymerase (1 U), 0.2 mM of dNTP, and 0.2 mM of primers. The concentration of MgCl2 depends on the nature of the primers. The program performed starts with an initial denaturation cycle at 94 °C for 5 min; followed by 30 cycles of 30 s denaturation at 94 °C, annealing at 47–60 °C for 30 s (depending on the type of primers) and 45 s extension at 72 °C; and ending with final extension cycle at 72 °C, for 10 min. The PCR products were visualized by 2% agarose gel.In this study, 10 ISSR primers that produced high polymorphism were used to estimate the genetic variability of 60 samples. These primers present di- and tri-repeat motifs , previoustula L. , Murrayakoenigii , Apterosa oblata . The ampD6D) variants in 60 A. spinosa individuals. In the first round, D6D (Accession number: AY131238.1) was amplified using 20 ng genomic DNA with primers D6D-F1:5′-CGATTCTTGGGAAAGCCTATGA-3′ and D6D-R1:5′-CATGTAGAGGCAGGATGGAATG-3′. PCR reactions included 0.4 µM forward and reverse primers ). PCR cycling conditions were programmed as initial denaturation at 95 °C for 10 min, followed by 35 amplification cycles of denaturation at 95 °C for 15 s, annealing at 60 °C for 15 s, and elongation at 72 °C for 15 s with a final extension at 72 °C for 5 min in a thermal cycler . Resulting PCR products were analyzed in 1.2% agarose gels. In the second round, HRM-PCR reactions were performed in a Light Cycler 480 II  and first-round PCR products, diluted 50 fold, were used as DNA template in HRM-PCR reactions. HRM-PCR reactions were performed in a final volume of 10 µL containing 1× the Luminaris Color HRM qPCR Master Mix , 0.25 µM of each internal primer, and diluted first round PCR products. Amplification and melting conditions were as follows: 95 °C for 10 min, 35 cycles at 95 °C for 15 s, 60 °C for 15 s, 72 °C for 15 s, and melting at a gradient from 60 to 95 °C at 0.02 °C per second, 25 fluorescence acquisitions per 1 °C [HRM reactions were carried out in two rounds to detect delta-6-desaturase  temperature 240 °C and column temperature, 120 °C for 5 min, programmed at 5 °C increments per min up to 240 °C for 15 min . Fatty acid methyl esters were identified by comparison of their retention times with a known reference material (Certificated Argan oil) and authentic standards . Two replicate GC analyses were performed, and the results of fatty acid methyl esters were calculated as %. Fatty acid as mg/100 g dried leaves were calculated according to Equation (1):Total lipids were extracted from lyophilized leaves. After the 24 h rotary extraction with petrolium ether, methyl esters were analyzed . The anaThe morphological data was analyzed by different statistical procedures of Rstudio software  and XLSTFurthermore, a joint analysis of all the parameters was applied to study the morphological variability and its structuring by performing a principal component analysis (PCA) for the quantitative variables. A classification dendrogram was constructed according to Ward’s method based on gower distance all with the software Rstudio. Genetic determinism of ISSR primers is dominant. The amplified fragments on the agarose gel, presented bands in different level according to their molecular weight (bp) the bands were scored using a binary code (present (1) or absent (0)). The exact location of bands and the exact molecular weight were determined with gelpro32 . Each of these fragments represents a locus. Data was compiled in a 0/1 binary data matrix of 60 individuals using MS Excel. The parameters related to the genetic variability in and between sites , Nei’s gene diversity (h), Nei’s genetic differentiation index among populations (Gst), allelic richness (A), and gene flow (Nm)) were estimated using POPgen 1.32 . An estiA Mantel test was performed to determine the correlation between the genetic distance  and the A correlation test was conducted using Rstudio software  to deterIn order to study the morphological, molecular, and biochemical diversity of argan trees in Tunisia, 60 trees were selected for this study, and leaves and fruits from each tree were collected. With regard to edaphic conditions, the argan tree has no requirement for soil type, based on the results obtained.In the first part of this study it appears that the argan tree normally develops in different types of soils (sandy and loamy), tolerates a wide pH range from 4 to 8.3, and a high concentration of limestone. The argan tree is a thermophilic and xerophilic tree, whose range overlaps with both humid and arid bioclimates. The introduction of this species into reforestation programs offers a sustainable reforestation solution in the arid and semi-arid zones and also in those affected by salinity and would therefore allow diversified exploitations. For the morphological characterization part, results have shown the existence of different forms, colors, and sizes of fruit and leaves. The results obtained from ANOVA showed that some are more discriminating than others which allowed to subdivide the 60 argan trees in three groups. Principal component analysis (PCA) showed that individuals belonging to the four sites exhibit considerable morphological variability, taking into account quantitative and qualitative variables. The results obtained from Ward’s dendrogram showed a classification of argan trees studied. These results indicated the presence of substantial morphological variability at the intra- and inter-site level. These groups could be dependent on the geographical origin, and we noticed also a formation of three groups; trees from Sfax and Sousse overlapped in same group while trees from Botanical garden and Korbous formed individual groups, although some detachment of some individuals from their original sites was observed.In the second part of this study, a great level of genetic variation among ISSR markers was shown, as a result of this study, proving that ISSR was an efficient method to assess the genetic differentiation in argan trees. A high amount of genetic diversity within argan populations was shown by AMOVA analysis, which indicates that every site is very important for the argan tree gene pool. The results with UPGMA, factorial analysis, and STRUCTURE analyses divided all the individuals into three groups; one group contains individuals from both sites Korbous and Sousse the two other sites formed individual groups. This result did not correlate with the morphological characterization results, which leads to the conclusion that the morphological diversity determined by ISSR is not related to the morphological variability determined with the chosen variables.D6D gene polymorphisms, which divided the 60 individuals to 10 groups. We could not correlate this result to the results of molecular diversity with ISSR or with the other parts of this study. In fact, each study divided the samples into different groups. Conservation strategies should be planned to preserve this high genetic diversity within populations and to prevent potential extinction of this important species in Tunisia.In the third part of this study, HRM was performed to detect Based on our results we suggest possible strategies for conservation of argan trees. In the last part, fatty acid analysis by gas chromatography showed that argan tree leaves are rich in omega 3 and 6, and more importantly in eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) which is interesting since normally plants do not produce EPA or DHA. Both EPA and DHA quantities produced in some trees are even more than that produced in salmon fish or comparable to herring fish which are known for its high concentration of EPA and DHA, so this result can be revolutionary. In view of these results, it would be interesting to grow these particular trees that scored the highest quantity of EPA and DHA, in order to develop a genetic selection that would increase the ratio of EPA/DHA.-The first most important management practice is to increase awareness about the importance of this species and start exploiting these trees with moderation especially trees with high genetic diversity. At the same time, we should allow populations to increase in size through natural regeneration and also try to grow argan trees in different locations in Tunisia, especially locations under the threat of desertification since it tolerates high temperatures and salinity and fights desertification.-The second possible strategy is to focus on the establishment of an ex situ conservation program by seed storage from all the four sites to preserve most of the genetic variation.-The third possible strategy consists of building a gene bank with a large number of plantations in a natural reserve, taking into consideration all the populations and genetic groups so that we can increase the possibility of gene exchange and ensure long-term conservation of this genetic variation. Knowing the economical, ecological, nutritional, and pharmaceutical value of this tree, it is important to increase awareness in Tunisia about the importance of this tree in order to preserve it. In addition, it would be interesting to combine the studies on the distribution of genetic diversity with other information such as oil composition and morphological characters per population.Argan trees in Tunisia are not valued or exploited in any field, and they are totally neglected, contrary to argan in Morocco which are overexploited.


Lagostomus maximus inhabits primarily the Pampean and adjoining Espinal, Monte and Chaquenean regions of Argentina. In order to study the population genetic structure of L. maximus, a fragment of 560 bp of the mitochondrial DNA hypervariable region 1from 90 individuals collected from the 3 subspecies and 8 groups along Argentina was amplified and analyzed. We found 9 haplotypes. The haplotype network did not show an apparent phylogeographical signal. Although low levels of genetic variation were found in all the subspecies and groups analyzed, a radiation of L. maximus would have occurred from the North and Center of the Pampean region toward the rest of its geographic range in Argentina. Low levels of genetic diversity, the existence of a single genetically distinct population in Argentina and changes of its effective size indicate that metapopulation processes and changes in human population dynamics during the late-Holocene were important factors shaping the population genetic structure of L. maximus in Argentina.The South American plains vizcacha,  Studying the amount and the pattern of genetic diversity found within and between populations is one of the central aims of population genetics. Furthermore, elucidating the population genetic structure of a species provides ecological and evolutionary information that allows the identification of conservation units. Nevertheless, the population genetic structure of many species is currently poorly understood.Lagostomus maximus, is a hystricognath rodent belonging to the family Chinchillidae. The family also comprises the chinchillas (Chinchilla spp.) and mountain vizcachas (Lagidium spp.). L. maximus inhabits primarily the Pampean and adjoining Espinal, Monte and Chaquenean regions of Argentina, though it is also found in southeastern Bolivia and western Paraguay ) pattern ,5, the m pattern ,19 and aCLUSTALX 2.0.11  was usedTo study patterns of geographical distribution and haplotype relationships we performed a Median-Joining network , as implIn order to further evaluate our data set, we grouped the samples by subspecies and geographical proximity . Subspecem). We used the finite sites (HKY) model. Ten independent runs of 2 x 106 iterations each and a burn-in of 5 x 105 iterations were performed. Likelihood values with the highest posterior probability were accepted as the best estimates.For each subspecies and defined group, haplotype (h) and nucleotide diversity (π) of the data set were assessed using Arlequin v3.5 . AdditioST and ΦST statistics were computed in Arlequin v3.5 ).L. maximus  along its range, it would be expected to have the potential to adapt to climate changes. However, this apparent population stability showed in the BSP should be regarded with caution as it could be an artifice of the analysis. Population local contractions and posterior increases can promote the disappearance of some haplotypes lineages, with the subsequent loss of genetic information. In turn, the BSP could display a flat portion preceding a population expansion even though changes in the population effective size of L. maximus occurred )lopatric ,5. ThereL. maximus is classified as "Least Concern" by the International Union for Conservation of Nature [L. maximus in Argentina reported in this study and the anthropogenic pressure to which the species is subjected to in some areas of Argentina should be a matter of concern.Usually, genetic variation is considered to be critical for the long-term survival of a species. Low levels of genetic variation can affect the ability of a species to respond to stochastic factors , as well as to deterministic factors  ,66. Addif Nature  and the f Nature , its pref Nature –72). TheL. maximus as different management units. Furthermore, since the optimal definition of management units and the development of effective management and conservation plans need to be made upon reliable demographic data, demographic studies are crucial in order to ensure the long-term survival of L. maximus.Also, since we found a single genetically distinct population in this study, Argentina can be considered as a single management unit . This prL. maximus along most of its geographic range employing mtDNA. Despite the existence of three morphological distinct subspecies and the wide geographic extension analyzed in this study, our results suggest low levels of genetic diversity in all the analyzed subspecies and groups and a single genetically distinct population along Argentina that displayed changes of its effective size during the last 1,500 YBP. Surprisingly, these results were found by analyzing a genetic marker that is inherited by the philopatric sex. This indicates that different factors, such as metapopulation processes and changes in human population dynamics during the late-Holocene, led to the lack of population genetic structure of L. maximus in Argentina. Furthermore, since L. maximus can be considered an ecosystem engineer, our results provide valuable information for future reintroduction plans, especially in degraded areas. Further analyses including highly polymorphic, biparentally inherited microsatellites, as well as ancient DNA from archaeological and paleontological samples, could help to enlarge the results showed herein and improve our knowledge of the population genetic structure at a fine scale and the recent population history of L. maximus.In this study we analyzed for the first time the population genetic structure of


Panthera onca) populations will ensure natural gene flow and the long-term survival of the species throughout its range. Jaguar conservation efforts have focused primarily on connecting suitable habitat in a broad-scale. Accelerated habitat reduction, human-wildlife conflict, limited funding, and the complexity of jaguar behaviour have proven challenging to maintain connectivity between populations effectively. Here, we used non-invasive genetic sampling and individual-based conservation genetic analyses to assess genetic diversity and levels of genetic connectivity between individuals in the Cockscomb Basin Wildlife Sanctuary and the Maya Forest Corridor. We used expert knowledge and scientific literature to develop models of landscape permeability based on circuit theory with fine-scale landscape features as ecosystem types, distance to human settlements and roads to predict the most probable jaguar movement across central Belize.Connectivity among jaguar . Using Bayesian clustering and multivariate models to assess gene flow and genetic structure, we identified one single group of jaguars (K = 1). We identified critical areas for jaguar movement that fall outside the boundaries of current protected areas in central Belize. We detected two main areas of high landscape permeability in a stretch of approximately 18 km between Sittee River Forest Reserve and Manatee Forest Reserve that may increase functional connectivity and facilitate jaguar dispersal from and to Cockscomb Basin Wildlife Sanctuary. Our analysis provides important insights on fine-scale genetic and landscape connectivity of jaguars in central Belize, an area of conservation concern.We used 12 highly polymorphic microsatellite loci to identify 50 individual jaguars. We detected high levels of genetic diversity across loci (HThe results of our study demonstrate high levels of relatively recent gene flow for jaguars between two study sites in central Belize. Our landscape analysis detected corridors of expected jaguar movement between the Cockscomb Basin Wildlife Sanctuary and the Maya Forest Corridor. We highlight the importance of maintaining already established corridors and consolidating new areas that further promote jaguar movement across suitable habitat beyond the boundaries of currently protected areas. Continued conservation efforts within identified corridors will further maintain and increase genetic connectivity in central Belize. Panthera onca), listed as near threatened by the IUCN Red List of Threatened, species b (H151496S cp-R) . SpeciesP. onca were genotyped for twelve polymorphic microsatellite loci  [Samples identified as and 225) . Each saPCR reaction conditions were optimised in the annealing temperature for each set of primers as follows: 95 °C for 15 min, followed by 13 cycles of 94 °C for 30s, 57.4–62.4 °C for 90s, 72 °C for 60s, followed by 32 cycles of 94 °C for 30s, 55–60 °C for 90 s, 72 °C for 60s and 60 °C for 30 min  based on zinc finger alleles (ZFX/ZFY). The two forward primers (ZF-1F and ZFY-2F) were fluorescently labelled with FAM and MAX, respectively. Amplification was performed using G&E Ready-to-go PCR Beads  in a final volume of 25ul containing 2 mM of each primer and 5ul of DNA. The PCR conditions were as follows: 95 °C for 5 min, followed by 13 cycles of 95 °C for 45 s, 63 °C for 60s, 72 °C for 60s, followed by 35 cycles of 95 °C for 45 s, 53 °C for 45 s, 72 °C for 60s and 72 °C for 10 min  and to compare allelic richness (AR). Genetic differentiation among sampled individuals was summarised in a Discriminant analysis of principal components (DAPC) based on the allele frequencies. Linkage disequilibrium (LD) between pairs of loci was performed in GENEPOP v. 4.2 [PT matrix and geographic distance so-called Isolation by Distance (IBD) via Mantel tests of matrix correspondence; we assessed the partitioning of genetic variation between sampling localities with Analysis of Molecular Variance (AMOVA).MICROCHECKER v 2.2.3  was usedP v. 4.2  with defP v. 4.2  to test max. We also used a spatial clustering approach in GENELAND v4.0.6 [We estimated population genetic structure using Bayesian assignment methods with STRUCTURE v2.3.4 , which aD v4.0.6  as an adTo further explore genetic diversity and structure among individuals, we reduced the dimensions via a Discriminant Principal Component Analysis (DAPC) without a priori group assignment using the ADEGENET package in R v2.0.1 , 66. TheLevels of genetic relatedness were calculated using seven estimators as implemented in the RELATED package in R v1.0 . PairwisWe used ArcGIS Desktop v10 to model resistance surfaces of the landscape to jaguar movement and CIRCUITSCAPE v3.5  to identThe probability of jaguar occurrence has been positively associated with forest cover and negatively associated with human activities . We assuEach pixel was assigned a cost value in a scale of 1–9, which represents the relative effort required to move from one point to another  Table . The aveAdditional file 1: Figure S1. Probability graphs of K as calculated by Evanno et al. (2005). Figure S2. Box plots, and density plot of relatedness values for simulated pairs of individuals of known relatedness. Figure S3. Map of ecosystem types in Belize (2017). Table S2. Results from MICROCHECKER for 12 microsatellite loci.Additional file 2: Table S1. Excel template with PCR profiles for species identification, microsatellite multiplexes and sex identification.Additional file 3: Table S3. List of genotypes for 50 individual jaguars identified in the Cockscomb Basin Wildlife Sanctuary and the Maya Forest Corridor.Additional file 4. TIF file to be open in GIS with cumulative current map projections of potential movement of P. onca between the Cockscomb Basin Wildlife Sanctuary and the Maya Forest Corridor, Belize.