ST value of 0.0024. The sPCA analysis revealed that the major part of Morocco and the Northern part of Algeria were affected by the phenomenon, including most of the breeds considered. Unsupervised cross-breeding with the popular Ouled-Djellal breed was identified as a proximate cause of this homogenization. The issue of transboundary breeds was investigated, and the Hamra breed in particular was examined via ROH fragments analysis. Genetic diversity was considered in the light of historical archives and anthropological works. All of these elements taken together suggest that homogenization as a factor affecting the Maghrebin sheep stock, has been particularly significant over the last few decades, although this process probably started much earlier. In particular, we have identified the policies set by the French administration during the colonial period of the region’s history as a causal factor that probably contributed significantly to this process. The genetic homogenization that we have observed calls into question the integrity of the farm animal genomic resources represented by these local breeds, whose conservation is of critical importance to the future of the livestock sector.Northwest-African sheep represent an ideal case-study for assessing the potential impact of genetic homogenization as a threat to the future of traditional breeds that are adapted to local conditions. We studied ten Algerian and Moroccan breeds of sheep, including three transboundary breeds, distributed over a large part of the Maghreb region, which represents a geographically and historically coherent unit. Our analysis of the dataset that involved carrying out Genome-wide SNP genotyping, revealed a high level of homogenization , in such a way that some breeds from different origins appeared genetically undistinguished: by grouping the eight most admixed populations, we obtained a mean global F Due to these adaptations, local breeds represent valuable genetic resources, especially in the context of climate change2. However, a large proportion of them are at serious risk of extinction3. One of the main threats is uncontrolled cross-breeding  with more productive local or imported breeds being favoured by farmers, who face increasing economic pressure. These practices, assessed by FAO to establish risk status, lead to genetic dilution, i.e. overwhelmingly high numbers of non-local genes lowering the relative frequency of native genes in the population. They favor genetic homogenization via the loss of rare or specific variants, and induce disruption of co-adapted gene complexes, which jeopardizes the integrity and viability of the local populations5.Local breeds are defined as those that have been in a country “for a sufficient time to be genetically adapted to one or more traditional production systems or environments in the country”Ovis aries) provides an ideal case-study for assessing the problem of genetic homogenization among local breeds and its impact on farm animal genetic resources. This stock is 7000 years old6, and contains a remarkable diversity of populations, which have been maintained under traditional farming systems over millennia7. These systems have enabled the emergence of breeds locally adapted. In the Maghreb, the diversity of territories  and climates, in combination with the history of the region’s people, have shaped a rich livestock heritage.The stock of North African domestic sheep , the D’Man breed (mostly encountered in oases), the Tazegzawth breed (from the Kabyle mountains) and the Hamra breed  all appear to be more genetically preserved12.In recent decades it has been observed that in Algeria there is a strong preference among farmers for the Ouled-Djellal breed, due to its larger conformation, and this has led to the intensification of anarchic crossing practices between local breeds. The breeders hope through such practices to increase their productivityth century. For this reason an analysis of the processes shaping these livestock, whose origins date as far back as 7000 years BP, would be better and more coherently approached under a broader geographic scale . The aims of the study were to assess the genetic structuration of the Maghrebin sheep stock. Particular attention was paid to the case of the transboundary  breeds, in order to see how their history and genetic characteristics can provide insights regarding the evolution of the Maghrebin sheep stock.Our study of the genetic diversity of Maghrebin sheep relies on breeds from Morocco and Algeria, two countries that represent a substantial part of the Maghreb area. The stabilization of national borders in the Maghreb region started in the 18A total of 180 sheep (93 from Algeria and 87 from Morocco) from ten breeds , with 129 individuals (i.e. 71.7%) showing negative values. A total of 22 individuals showed a pronounced deficit in heterozygosity with FIS values > 0.1; this deficit was particularly apparent among the Barbarine (BRBA) and Moroccan D’Man (DMNM) breeds, with 40% and 43.3% of individuals displaying such values, respectively.The average FST , with nine of these values not significantly different from zero . (ii) in the second group, which included the five Algerian remaining breeds , the pair-wise values of FST ranged from 0.022  to 0.113 .Calculation of pair-wise FST Table  was usedST value not significantly different from zero), but was clearly differentiated from the Algerian Hamra population preserved in pilot farms (HAMAP) (FST = 0.051); (ii) D’Man populations from Morocco and Algeria (DMNM and DMNA) showed pair-wise FST of 0.026; (iii) Ouled-Djellal populations from Morocco and Algeria (OLDM and OLDA) showed pair-wise FST close to zero (FST = 0.002). The NeighborNet graph based on the FST genetic distances  Beni-Guil  was not differentiated from Hamra (HAMA) sampled in private Algerian farms  error) and showed genetic overlap with Algerian D’Man (DMNA). Increasing K from 3 to 6, assigned clusters to the preserved Hamra population (HAMAP), Tazegzawth (TZGA), Barbarine (BRBA) and Algerian D’Man (DMNA). The second, or “homogeneous”, group was characterized by an important phenomenon of admixture, and included all of the remaining breeds. At K = 7, a few D’Man individuals from Morocco (DMNM) showed genetic distinctness. For K > 7, intra-breed structuration was also apparent for the SDNA, HAMAP, BRBA and DMNA breeds.The ADMIXTURE analysis revealed the same pattern, whereby the sheep were characterized by two groups Fig. : In the IS value > 0.1 were removed from the analysis (see)15, we were led to remove the Barbarine (BRBA) breed, which was then only represented by three individuals. The simulated data network was based on a minimum spanning tree. At fine scale  individual, (ii) and on another side, with the Sidaoun (SDNA) cluster. At k = 75  individual (i.e. a breed reared in Kabyle mountains as TZGA). The tree based on Allele-Sharing Distance (ASD) distance between individuals .NetView was used to represent the genetic structure of the dataset along a gradient of k values from 2 to 100), allowing for the investigation of a range of structures, from fine scale population structures, revealed by a small value of k, to large scale population structures, revealed by a large value of k , most individuals carried few and short ROHs. The 50 K SNP panel did not detect any ROH in Beni-Guil , Hamra sampled in private Algerian farms (HAMA), Algerian Ouled-Djellal population (OLDA) and Rembi (RMBA). Such profiles are characteristic of genetically diverse and most probably admixed populations17. The second group , and also Sardi (SRDM), and the Moroccan D’Man population (DMNM). This second group was characterized by a higher number of ROHs. Tazegzawth (TZGA) showed several long ROH fragments, a pattern characteristic of recent inbreeding, resulting in FROH > 0.1 for four individuals out of the six considered  for two of the five individuals. The Hamra subpopulation, preserved in Algerian pilot farms (HAMAP), received special attention through the analysis of the ROH distribution according to their size   was not to study each breed according to the values obtained, but to assess the extent to which strong patterns, ize Fig.  revealedize Fig.  of eightoup Fig. , includeed Table . Similares Table . We furt1) Table .Figure 3i.e. breeds standardized during the 20th centuries) and South-West Asian breeds (see Supplementary Text 1 and 2) highlighting a clear relationship between the occurrence of admixture and a decrease, in number and length, of IBD segments.IBD segments analysis Table  revealedet al.20 individuals showing high values of FIS (>0.1) were excluded from the analysis. The individuals belonging to the Sidaoun (SDNA) and Algerian D’Man (DMNA) breeds clustered and were separated from the other populations on the basis of shared haplotypes. The Tazegzawth (TZGA) and Algerian preserved Hamra (HAMAP) breeds  with the Hamra preserved in pilot farms (HAMAP) and Tazegzawth (TZGA) as exceptions , Timahdite, Sardi, Hamra/Beni-Guil , D’Man (the population from Morocco but not the population from Algeria), Rembi and Berber. We observed a strong spatial pattern with two major areas identified. The first one was the Northern area, corresponding to the major part of Morocco and the Northern part of Algeria delimited to the South by steppe. This area appeared homogeneous for all the breeds previously mentioned except for two differentiated breeds, 21 gave rise to consistent conclusions. Indeed, most of the breeds mentioned above were characterized by a very limited number of ROHs with very small sizes. In particular, four of them, Rembi, Ouled-Djellal from Algeria and Beni-Guil/Hamra from Morocco and private Algerian farms, were characterized by the quasi absence of ROH, clearly indicating admixture17. Moreover, these breeds were characterized by extremely few or no IBD segments, what was shown to also be a signature of genetic homogenization (see Supplementary Text 1 and 2). Indeed, it seems likely that strong and continuous admixture, in small ruminants (characterized by relatively short generation time) breaks up contiguous genomic blocks resulting in very small haplotypes.Chromopainter analysis showed that breeds in the homogeneous group, clustered together and shared few genetic chunks (haplotypes). The characteristics of ROHs, used as sources of information about the demographic history of the breedsi.e. a few hundred heads) of the present-day Algerian sheep stock. In contrast, the Sidaoun breed, reared by Touareg people, did not appear to be affected by inbreeding. This breed was genetically differentiated from the other studied breeds , but would have experienced a significant level of admixture with breeds/populations not considered in this study. Indeed, the Chromopainter and IBD segments analyses reveal a very particular profile for this breed with quite a large number of intra-breed shared segments, but with such segments characterized as being relatively small in size. Further investigations will be needed, for example focusing on the genetic relationship between this breed and the Malian sheep stock, which is thought to be the cradle of the Sidaoun breed. Another southern breed, the Barbarine, a transboundary breed related to the Tunisian Barbarine (i.e. the main breed in Tunisia), showed clear genetic distinctness. This fat-tail breed originates from West-Asia, which may explain its genetic distance from all other breeds considered in this study. Analyses of ROH (distribution and FROH) raised concerns about possible inbreeding problems, however, only examination of a larger sample could lead to robust conclusions. Such conclusions would not be surprising if we consider the size reductions suffered by the Algerian Barbarine population in recent decades. Indeed, whilst the fat tail of the Algerian Barbarine sheep means that the animal is remarkably well adapted to arid environments, this same trait hinders the animal’s marketability22, its slaughter quality being considered as diminished because of it . The Algerian Barbarine breed has therefore lost the interest of breeders, who have abandoned it in favor of the Ouled-Djellal breed23. The effect of this preference among Algerian breeders is so significant that the analysis of information contained in the DAGRIS database (http://dagris.ilri.cgiar.org/) now indicates that the Barbarine breed is in danger of being “absorbed by Ouled-Djellal” in Algeria.Among the breeds identified as being genetically differentiated from other breeds, our analysis appeared to show that the Tazegzawth breed, reared by Kabyle people in geographically isolated conditions, is threatened by inbreeding . These results were not surprising given that the breed accounts for less than 0.02% . The French agronomist G. Toutain19 (i.e. not a pattern that indicates a strong bottleneck effect), (ii) a low abundance of long ROHs suggesting management practices avoiding inbreeding. Taking all of these elements into account, we can assume that Hamra’s dilution has probably intensified over the past 30 years. Indeed the pilot state farms were established at that time, yet they have visibly allowed for the preservation of at least part of the genetic specificity of the breed, even if the founding effect experienced when the farm was created undoubtedly led to a reduction in genetic diversity. This hypothesis is largely supported by various anthropological studies. These have shown that over the last few decades there has been a significant intensification of crossing practices between Ouled-Djellal and this breed in private farms: Bechchari et al.25 described an “invasion phenomenon” characterized by a growing number of Ouled-Djellal in Beni-Guil herds. According to interviews conducted by Bechchari et al.25, it seems that the expansion of the Ouled-Djellal breed is mainly the result of the sedentarization and intensification of agriculture, i.e., breeders, who are under increasing pressure to improve productivity, favor Ouled-Djellal for its market value. These results were confirmed by Brisebarre26 who, moreover, reported massive inflows of Ouled-Djellal originating from Algeria. A figure of 200,000 to 500,000 heads of Ouled-Djellal (depending on the interviewed source) was estimated have been illegally entering Morocco each year26.In our study another transboundary breed, Hamra/Beni-Guil, was sampled from different sources, with samples taken both in Morocco and in Algeria, and in different types of farms . The study of this breed through these different populations has been particularly instructive. Among the three sample sites, only sheep reared in the Algerian pilot farms appeared not to be genetically diluted . The main state farm in charge of the preservation of the Hamra breed (Algeria), gathered its initial herd at the end of the 1980s. Since then, a few carefully selected progenitors from private farms have occasionally been introduced into the initial core, over the years. Moreover, a strict breeding program was devised to prevent inbreeding. All the genetic results were in accordance with the management practices reported for the pilot farm as they showed (i) a pattern of short to intermediate ROH sizes, which is a pattern specific to that of a population that is isolated but that is nonetheless based on a source population of significant size27. The strong local adaptation of this breed, with respect to climate, vegetation and disease resistance, was emphasised as far back as 1891, in a report commissioned by the General Governor of Algeria28, which concluded that introduction of other breeds to this territory and the crosses induced would jeopardize the survival capacity of the Hamra breed.If we consult the historical archives, we learn that it has long been recognised that the Hamra breed is an ancient one, belonging to a stock that was passed down within a tribe from one generation to the next, and that this practice likely originated with the Zénète people. The breed’s geographic isolation is also related to the specific geological features of the Oran plateau where it lives29, involved more than one million sheep heads per year . Today, the main pilot farm responsible for the breed’s preservation keeps 600 sheep or 1% of the total number of Hamra heads in Algeria. Since 1980 the Moroccan government has implemented countrywide a so-called “sheep strategy”, under which most of the country has been divided into cradles of the main breeds. In these areas, breed phenotypic standardization was highly favored and measures were taken to limit gene flows30. Our study did not identify any preserved areas from a genetic point of view. However, it should be stated that our sampling design in Morocco did not focus on the cradle of breeds, contrary to the Algerian sampling.Moreover, it may be noted that this breed, called “Le petit Oranais” by the settlers, was particularly appreciated in France for its organoleptic qualities and conformation. This breed of sheep was therefore particularly affected by the sheep export activities in Algeria, with the animals transported live by boat. At the end of the 19th century, it is estimated that this trade, from Algeria to Franceth century, studies commissioned by the colonial government concluded that the “white breed” of the steppes was the breed on which all efforts should focus32. The aim of the settlers was to drastically increase the number of sheep by promoting this breed through various means , with guidelines explicitly recommending the elimination of breeds considered useless, of poor quality, or judged “abnormal” with regard to their conformation (the Berber and the Barbarine breeds were the ones that were mainly affected by such guidelines).If we refer to the historical archives, it appears that the colonial period in the region’s history probably contributed significantly to the expansion of the Ouled-Djellal breed. Indeed, at the end of the 19et al.33 in their study looking at the Maghrebin goat in the same area. Indeed, the results of this goat study also showed very weak genetic structuration among Moroccan breeds  and the most widespread of the Algerian breeds (i.e. Arabia). Despite the breeds being from different geographical origins (i.e. Black from Morocco is Berber type and Arabia from Algeria is Sahelian type), they appeared quite indistinct from each other from a genetic perspective (i.e. mean FST was of 0.005 [0.004–0.006]IC95% considering the four breeds together). Two breeds were distinguished from this main homogeneous group: the Algerian ancestral Kabyle and M’Zabite breeds, which have both been reared in isolated conditions, due to social and political factors, since ancient times. In order to assess how the patterns observed in Maghrebin small ruminants were influenced by migration processes, outward from centers of domestication, we examined the results provided by Colli et al.34 for goats. Considering the 12 African countries for which at least two local breeds were genotyped by country , it can be seen that of the 703 pair-wise FST calculated, only 12 showed values lower than 0.01 and none of them had a value lower than 0.006. These results postulate for a clear structuring of African goat breeds, whatever the migration routes considered, with occasional cases of more intense gene flows, generally occurring between two breeds. The data currently available do not allow for a study looking specifically at breeds of sheep in Africa. However, Gaouar et al.12 observed that, out of a total of 4460 pair-wise FST values, and considering Algerian and worldwide breeds  all together, instances of particularly low pair-wise FST values were only found for Algerian breeds and Australian Merino populations (i.e. strains of Merino that are not breeds as such). Thus, to our knowledge, as things currently stand, it seems unlikely that the genetic homogenization identified is an effect that results from ancestral migration processes .The diversity pattern of the Maghrebin sheep appears very similar to that revealed by Ouchene-Khelifi th century, unlike European breeds, and therefore they continue to show intra-breed phenotypic diversity. They could be qualified as landrace breeds, representing an initial stage of breed development, with a genetic specificity mainly resulting from the combination of founder effects, isolation and natural selection. As emphasized by Sponenberg et al.36 isolation, resulting from geographic factors, but also from social practices (e.g. tribe organization) and political contexts, represents a key factor for the development and the preservation of the genetic integrity of landrace breeds. In the Maghreb, such isolation has been disturbed, inducing the genetic homogenization observed in most Maghrebin sheep breeds. In recent decades, motorized transport has certainly played an important role in increasing long-distance gene flow.Maghrebin breeds are representative of traditional breeds more generally, that, worldwide, are a cornerstone of the rural economies on which smallholder farmers and herders rely. These Maghrebin breeds were not standardized during the 202.In conclusion, our study highlights a strong homogenization of the Maghreb sheep stock affecting the major part of Morocco and most of Northern Algeria. We still lack information regarding the scale at which this phenomenon of homogenization might be occurring worldwide. However, the observation of such a phenomenon clearly calls into question the integrity of farm animal genomic resources represented by these local breeds, whose conservation is of critical importance for the development of livestock breeding in the context of global changesSamples were collected by veterinarians during routine blood sampling, for medical care or follow up, such that no ethical authorization was required. All the samples and data processed in our study were obtained with the breeders and breeding organizations’ consent.et al.12. Supplementary Table We studied ten Algerian and Moroccan breeds representing most of the local breeds for this area. Three transboundary breeds were sampled in both countries: Ouled-Djellal, Hamra (Algerian name)/Beni-Guil (Moroccan name) and D’Man. Most of the breeds were “wooled thin-tailed sheep”, but the D’Man breed belongs to the “mixed hair-wool sheep” group, Sidaoun to the “hairy sheep” group and Barbarine was the only “fat-tailed” breed in our study. Algerian breeds were described by Gaouar 37 for Algeria and by Boujenane38 for Morocco, is presented in Fig. th century, responsible for drawing up an inventory of the country’s wealth. In addition, the consultation of several historical archives41 made it possible to record essential information about these landrace breeds, which have been extensively described by zootechnicians and agronomists, including: (i) phenotypic traits specific to the different breeds ; (ii) detailed descriptions of the areas occupied by the breeds , and of the geographical factors implying a more or less extensive isolation of the populations; (iii) history and breeding practices of the tribes that have maintained the traditional breeds; (iv) crossbreeding experiments undertaken by the settlers with imported breeds but also between local breeds. A general observation was the strong consistency of the genetic material within the indigenous breeds as evidenced by the heritability of the characteristic traits.The geographical distribution of the breeds based on their cradle of origin, as described in the 1990s by Chelliget al.12, 87 Moroccan samples provided by the NextGen project (Grant Agreement no. 244356), plus 47 Algerian samples genotyped specially for this study . If we consider the case of the Hamra breed: previously available genotypes12 (n = 6) were only representative of one Algerian pilot farm (Aïn El Hadjar). With the further sampling, 3 samples from two other Algerian state farms and 11 samples from private farms were added to the dataset. The sampling design was developed in such a way that it was possible to study the trajectories of different populations of the same breed. The sheep were sampled, as much as possible, from different flocks in order to limit relatedness among individuals and optimize representativeness of breeds. In cases where several samples were obtained from the same farm, the choice was based on the pedigree documentation (if available) and/or genealogical information provided by the breeder. For a few breeds only few samples were available, but this did not affect the robustness of the results, as almost all analytical methods used were based on the consideration of individuals . Moreover, we used the Weir & Cockerham FST estimator for quantifying differentiation between breeds, since in contrast to the Reynolds’ distance it is not significantly biased by a small sample size provided that, as in our case, the number of markers used is large43.The whole dataset included 46 Algerian samples from Gaouar 44. All Algerian animals were genotyped for 54,241 SNPs, using the Illumina® Ovine SNP50 BeadChip , following standard operating procedures recommended by the manufacturer. Genotyping was performed by the Van Haeringen Laboratorium . Moroccan SNP data were extracted from the WGS variation using the Ovine SNP50 BeadChip coordinates of SNPs on the OAR v3.1 reference genome assembly using Vcftools45. High concordance between 12× re-sequencing data and the Ovine SNP50 BeadChip was demonstrated in sheep by Benjelloun et al.46. Moroccan and Algerian datasets were merged, and SNPs and animals were pruned with PLINK v1.0747 using the following filtering out parameters: (i) SNP call rate ≤ 97%, (ii) SNP minor allele frequency (MAF) ≤ 1%, (iii) animals displaying ≥ 10% of missing genotypes.Blood samples were cryo-preserved until DNA extraction and analysis were carried out. Genomic DNA was purified from whole blood by protease K digestion and a salting-out procedureIS) per individual was assessed in each breed using PLINK. Runs of homozygosity (ROHs) were identified with PLINK using a sliding windows approach. The fraction of 50 SNP-windows that were almost completely homozygous  was calculated following recommendations of Manunza et al.19. Indeed, it has been shown that this approach (i.e. considering windows of SNPs that are not completely homozygous) is more powerful in detecting truly autozygous segments48. To reduce the occurrence of spurious runs of homozygosity (ROHs), the minimum number of SNPs required to constitute a ROH was estimated using the method proposed by Lencz et al.49. The inbreeding coefficient based on ROH (FROH)50 was calculated by dividing total ROH length per individual by total autosomal SNP coverage (2.44 Gb).Inbreeding coefficient  software53.The extent of population subdivision was examined by calculating pair-wise F54 available in R. This visualization tool allows for the analysis of complex genetic structure via the construction of population networks through mutual k-nearest neighbours thresholds applied to genome-wide SNPs. PLINK was used to construct the initial ASD matrix . As recommended by Neuditschko et al.15, the dataset was first subjected to quality control  and significant deviation from Hardy–Weinberg equilibrium (P < 0.001)). We explored the data in a range of k  from 2 to 100 that was determined following recommendation of Neuditschko et al.15 with the “selection plot” function.We used the network-based approach implemented in NetView v.1.1ST genetic distance were constructed using Splitstree55.A tree based on ASD distance between individuals and a NeighborNet graph based on F56 was used to investigate the relationship between sheep breeds. ADMIXTURE was run for K = 2 through K = 14, and ten independent runs were performed for each value of K. The program CLUMPAK57 available at http://clumpak.tau.ac.il, was used to analyze the multiple independent runs at a single K and visualize the results.ADMIXTURE software58 based on haplotypes was also used. The dataset was filtered and phased using SHAPEIT ver. 259. We used CHROMOPAINTER58 to analyze the painted dataset in order to identify homogenous clusters. Visualization of the posterior distribution of clusters was then performed using the tree-building algorithm of fineSTRUCTURE.The approach of fineSTRUCTURE60 package ADEGENET61, using the Delaunay triangulation as connection network62. Monte Carlo tests were used to check the statistical significance of spatial structures  for 10,000 iterations. The sPCA results were visualized by plotting the samples according to their geographic coordinates and coloring them according to their respective scores along the third first sPCA components.Spatial analysis of Principal Components (sPCA) was performed with the RSupplementary Information


Elymus species. Elymus excelsus Turcz. ex Griseb, a perennial forage grass with strong tolerance to environmental stresses, such as drought, cold and soil impoverishment, can be appropriately used for grassland establishment due to its high seed production. To provide basic information for collection, breeding strategies and utilization of E. excelsus germplasm, microsatellite markers (SSR) were employed in the present study to determine the genetic variation and population structure of 25 wild accessions of E. excelsus from Xinjiang (XJC) and the QTP, including Sichuan (SCC) and Gansu (GSC) of western China. Based on the 159 polymorphic bands amplified by 35 primer pairs developed from three related species, the average values of the polymorphic information content (PIC), marker index (MI), resolving power (Rp), Nei’s genetic diversity (H) and Shannon’s diversity index (I) of each pair of primers were 0.289, 1.348, 1.897, 0.301 and 0.459, respectively, validating that these SSR markers can also be used for the evaluation of genetic diversity of E. excelsus germplasms, and demonstrating the superior versatility of EST-SSR vs. G-SSR. We found a relatively moderate differentiation (Fst = 0.151) among the XJC, SCC and GSC geo-groups, and it is worth noting that, the intra-group genetic diversity of the SCC group (He = 0.197) was greater than that of the GSC (He = 0.176) and XJC (He = 0.148) groups. Both the Unweighted Pair Group Method with Arithmetic (UPGMA) clustering and principal coordinates analysis (PCoA) divided the 25 accessions into three groups, whereas the Bayesian STRUCTURE analysis suggested that E. excelsus accessions fell into four main clusters. Besides, this study suggested that geographical distance and environmental variables , especially for QTP accessions, should be combined to explain the population genetic differentiation among the divergent geographical regions. These data provided comprehensive information about these valuable E. excelsus germplasm resources for the protection and collection of germplasms and for breeding strategies in areas of Xinjiang and QTP in western China.Hosting unique and important plant germplasms, the Qinghai-Tibet Plateau (QTP), as the third pole of the world, and Xinjiang, located in the centre of the Eurasian continent, are major distribution areas of perennial Triticeae grasses, especially the widespread  Elymus Linn. is the most diverse and largest genus in Triticeae, with approximately 12 species widely distributed in northern China  Hand.-Mazzand. and E. duhuricus Turcz. ex Griseb. , good generalizability, high mutation rate, co-dominant inheritance and suitability for automated allele sizing  evaluating the transference of EST-SSRs and G-SSRs developed from related species of Triticeae to E. excelsus accessions and the discrimination power of the tested accessions; (ii) determining the genetic diversity and population structure of the wild accessions; and (iii) assessing the effects of climatic and geographical variables on genetic diversity and population structure.The existing data show that wide hybridization, geographical isolation or local adaptations  will promote the genetic diversity of the  species . A combi ignored . Here, 2E. excelsus wild accessions (https://www.qgis.org/en/site/) was used to georeference the E. excelsus accessions on a map . Ten seeds per accession of E. excelsus were placed in the culture dishes to germinate under uniform conditions in an incubator. Total DNA was isolated from the bulked young leaf tissues of each accession using a DNA extraction kit . The concentration of each DNA sample was measured using a NanoDrop1 ND-1000 Spectrophotometer , and then, the extracted DNA was diluted to approximately 20 ng µL−1 for SSR analysis.A wide collection including 25 cessions  was usedcessions . The QGIon a map  and its E. sibiricus  (Pseudoroegneria spicata (with the prefixes ‘Elymus’) (E. sibiricus (with the prefixes ‘ESGS’) (−1) template DNA, 0.8 µL (5 pmol µL−1) forward and reverse primers, 7.5 µL mix , 0.4 µL Taq enzyme (2.5 U µL−1)  and the remaining volume was supplemented with ddH2O. A PCR program was used with the following cycling parameters: first pre-denaturation step at 94 °C for 4 min, followed by 35 cycles of 30 s at 94 °C, annealing for 30 s at 51–66 °C   and PseuElymus’) , and the ‘ESGS’) . Followi51–66 °C , 1 min aq2-p2  is: Ib = 1-(2 × |0.5 − Pi|), where Pi is the amplification bands frequency in the studied accessions  for further study owing to the difficulty of unambiguously ascertaining allele dosage in allohexaploid. Based on the number of polymorphic bands (NPB), which was estimated according to the band frequency of less than 95% and more than 5%, the discriminatory power of each SSR marker was evaluated by computing the percentage of polymorphic bands (PPB), Shannon diversity index (I) , polymorq2-p2 . Nei’s gq2-p2 . The Rp cessions . Finallya), number of effective alleles (Ne), Shannon’s information index (Hj), expected heterozygosity (He), Nei’s genetic distance and pairwise population PhiPT values (Fst) among the geographical groups.Based on DICE coefficient , a binarK = 2 to 10. Four independent runs were assessed for each fixed K and each run consisted of a ”Burnin Period” of 50,000 and “After Burnin” MCMCC (Markov Chain Monte Carlo) replicates of 10,0000. The optimal potential clusters  were determined by the statistic ΔK method on the SRTUCTURE Harvester v.0.6.93 program  were used to evaluate the association with genetic distance  obtained from DIVA-GIS , and both PIC and H were significantly correlated with I . It is noteworthy that the results revealed a higher transferability of the EST-SSR (30.61%) than the G-SSR (17.86%), while all the average diversity parameters  of the G-SSRs were higher than those of the EST-SSRs  than those of the EST-SSR .In total, 35 SSR primer pairs, including thirty (30.61% out of 98) EST-SSRs and five (17.86% out of 28) G-SSRs, allowed the identification of 159 reliable polymorphic bands (NPB). The number of polymorphic bands (NPB) of each primer pairs changed from one  to 14 (Elymus 5264) with an average number of 4.54. The total number of bands (TNB) amplified by each primer pairs ranged from four  to 16 (Elymus 5264) with an average of 7.2 and a total of 252 . The perEST-SSRs ; these rThe genetic similarity among the tested accessions ranged from 0.6667 to 0.9496, which were calculated based on the 252 amplified fragments , SCC  and GSC  based on geographical origin, in which the SCC group displayed the highest level of variability  followed by the XJC and GSC groups . AMOVA was used to test the influence of the geographic origin on the genetic diversity of the E. excelsus accessions. Of the total genetic variance covered by the three geo-groups, 15% (P < 0.01). The average fixation index (Fst) among the three groups demonstrated a moderate genetic differentiation (Fst = 0.151). Otherwise, the pairwise Fst value between the XJC and GSC groups was the highest  at the species level  and the QTP groups . Furthermore, a relatively high but non-significant Mantel relationship value between the genetic distance and all of the studied environmental factors were detected in the GSC accessions, which may be due to the small group size (only three accessions).Since Sichuan and Gansu provinces both belong to the QTP areas, here we amalgamated the SCC and GSC groups into a new group, namely, the QTP group. A Mantel test was carried out to explore the influence of geographical distance on genetic variation, which demonstrated a faint but significantly positive correlation between genetic distance and geographical . The results showed that SSR markers could be used to investigate the genetic relationships among wild E. excelsus accessions, and 61.37% of the polymorphic loci were observed, which was higher than that of E. sibiricus (50.7%) (E. nutans (79.75%)   but lowe(79.75%) .E. sibiricus, E. nutans and Pseudoroegneria spicata) and could also be used to distinguish E. excelsus accessions. If the PIC, MI and Rp can be used as indicators to characterize the effectiveness of accessions, Elymus 3207 , Elymus 5264  and ESGS 266  are recommended as the ideal SSR primers.The PIC refers to the discriminatory power or informativeness of markers and has been extensively applied in animals and plants . The MI E. excelsus accessions than G-SSRs (17.86%), which was in accordance with the study results for E. nutans  in Clade I of the UPGMA clustering had obviously distinct genome fractions in the STRUCTURE analysis. That result may be due to the vulnerability of the STRUCTURE program to influences, including gene drift, gene mutation, gene flow and natural selection, etc.  was greater than that of the GSC (He = 0.176) and XJC groups (H e = 0.148); therefore, it was further inferred that a high variable climate and complex topography might increase the genetic diversity of accessions from the southeast boundary of the QTP (The high intra-group variation components (85%) among the three geo-groups might be attributable to the dominantly self-pollinated breeding system of al scale . Due to ocations , will ma the QTP , which hE. excelsus accessions. We also found a weak geographic signature of isolation-by-distance  among the tested E. excelsus accessions, which is similar to a study of the related E. nutans  were emphasized. All results emphasize the significance of local adaptation in the forming of the models of E. excelsus wild accessions, and these findings are beneficial to protection strategies and utilization of germplasm resources.This study indicated that SSR markers developed from related species are powerful tools to characterize the genetic diversity of wild 10.7717/peerj.8038/supp-1Data S1Click here for additional data file.10.7717/peerj.8038/supp-2Figure S1Click here for additional data file.10.7717/peerj.8038/supp-3Table S1MAT, Mean annual temperature; MAP, Mean annual precipitation; MP (May to August), Mean rainfall from May to August.Click here for additional data file.10.7717/peerj.8038/supp-4Table S2Elymus sibiricus L.). Conservation Genetics Resources, 2014, 6(4): 861-864. [2] Zhou Q, Luo D, Ma LC, Xie WG, Wang Y, Wang YR, Liu ZP. Development and cross-species transferability of EST-SSR markers in Siberian wildrye (Elymus sibiricus L.) using Illumina sequencing. SCI REP. 2016;6:20549. [3] Bushman BS. Development and annotation of perennial Triticeae ESTs and SSR markers. Genome. 2008;51:779-788. [4] Luo D, Zhou Q, Ma LC, Xie WG, Wang YR, Hu XW, Liu ZP. Novel polymorphic expressed-sequence tag–simple-sequence repeat markers in Campeiostachys nutans for genetic diversity analyses. Crop Sci. 2015;55:2712-2718.Ta represented the annealing temperature; references were quoted from: [1] Lei Y, Zhao Y, Yu F, et al. Development and characterization of 53 polymorphic genomic-SSR markers in Siberian wildrye (Click here for additional data file.10.7717/peerj.8038/supp-5Table S3Click here for additional data file.10.7717/peerj.8038/supp-6Table S4Click here for additional data file.10.7717/peerj.8038/supp-7Table S5Click here for additional data file.


Genetic diversity within a species reflects population evolution, ecology, and ability to adapt. Genome-wide population surveys of both natural and introduced populations provide insights into genetic diversity, the evolutionary processes and the genetic basis underlying local adaptation. Grass carp is the most important freshwater foodfish species for food and water weed control. However, there is as yet no overall picture on genetic variations and population structure of this species, which is important for its aquaculture.We used 43,310 SNPs to infer the population structure, evidence of local adaptation and sources of introduction. The overall genetic differentiation of this species was low. The native populations were differentiated into three genetic clusters, corresponding to the Yangtze, Pearl and Heilongjiang River Systems, respectively. The populations in Malaysia, India and Nepal were introduced from both the Yangtze and Pearl River Systems. Loci and genes involved in putative local selection for native locations were identified. Evidence of both positive and balancing selection was found in the introduced locations. Genes associated with loci under putative selection were involved in many biological functions. Outlier loci were grouped into clusters as genomic islands within some specific genomic regions, which likely agrees with the divergence hitchhiking scenario of divergence-with-gene-flow.This study, for the first time, sheds novel insights on the population differentiation of the grass carp, genetics of its strong ability in adaption to diverse environments and sources of some introduced grass carp populations. Our data also suggests that the natural populations of the grass carp have been affected by the aquaculture besides neutral and adaptive forces.The online version of this article (10.1186/s12864-019-5872-1) contains supplementary material, which is available to authorized users. Genetic diversity is a central concept of evolutionary biology and molecular ecology, and plays an important role in the complexity of organisms, ecosystem recovery and the ability of a species to adapt diverse environmental conditions , 2. InvePopulations with recent introduction history typically have experienced founder effects due to a small number of introduced individuals. With small effective sizes, these populations are more vulnerable to random genetic drift during the early stages of the introduction . These eCtenopharyngodon idella) is the most important freshwater foodfish species as well as a species for aquatic vegetation control ; t1, t2 and db = [1–150]; t3 = . All the other parameters were using the default settings. A total of 106 simulated datasets were generated for the three competing scenarios to produce reference tables. The scenarios and priors were pre-evaluated and at least one prior–scenario combination was designed to produce simulated datasets that are close to the observed datasets . The comVCFtools  and SNPsR2 = 0.992, P < 0.001). Only the loci consistently identified to be significantly associated with both temperature and latitude were retained to study latitudinal gradients.Evidence of local adaptation was detected for individual loci, using a Bayesian generalized linear mixed model involving covariance of allele frequencies and environmental variables and taking demography into account, with the program Bayenv . A BayesFST-based outlier tests were also performed to identify signatures of local selection. Firstly, a Bayesian simulation-based test implemented in BayeScan 2.1 [FST-based outlier tests do not take demographic history into consideration. Because the null distribution of these outlier tests still does not account for the bottlenecks that have likely occurred in the introduced populations, FST-based outlier tests were separately carried out for native locations and for all locations.Scan 2.1  was usedScan 2.1  for idenScan 2.1  with theLoci under putative selection were mapped against the raw annotation of the grass carp genome. SNPs within both exons and introns were considered to have a gene feature. Coding sequences of the genes were extracted and then functionally annotated by Blast2Go  against The patterns of differentiation across the genome were examined to verify if there were genomic regions where genomic loci under putative selection formed into clusters and showed elevated divergence in comparison to the background. Estimates of genetic differentiation between the locations distributed in the Yangtze and Pearl River Systems were calculated. Fifty, 100, 150 and 200 Kb window-sized FST values were separately calculated based on the 24 longest scaffolds, ranging from 9.6 to 19.1 Mb, of grass carp reference genome using the program populations in stacks. Corresponding window-sized SNP densities were also calculated to assess if the genome-wide patterns of differentiation were independent of the patterns of SNP density.Genotyping by sequencing (GBS) produced an average of 10.17 million raw reads per individual. After quality control, 9.21 million reads per individual were obtained for sequence mapping and SNP discovery. A total of 458,544 RADtags were detected and 280,544 SNPs were discovered across all nine locations, with an average of 0.61 SNPs per RADtag. After removing the loci that failed to meet the filtering criteria, 43,310 SNPs were genotyped across all locations, among which 28,412 (65.6%) showed minor allele frequency (MAF) of > 0.05 across the entire data set.HO, HE and Π, estimated based on all variant SNPs, are shown in Additional file P < 0.01).The measures of genetic diversity, including FST analysis revealed significant genetic differentiation between the native and the introduced populations with FST ranging from 0.1126 between Zhaoqing and Indian populations to 0.2399 between Vietnam and Malaysia  estimated using FST ≈ 1/(4 Nm + 1) ranged from 4.604 to 12.505 among the three river systems. For native locations, genetic divergence at each individual locus was estimated between the most divergent locations (Hanjiang vs Vietnam) and also between the most distant locations (Nenjiang vs Vietnam). We observed that no loci showed FST > 0.5 and only < 15% of loci had FST > 0.1 between these sampling sites.Pairwise R2 = 0.187, P = 0.16; Additional file R2 = 0.876, P < 0.001; Additional file Principal component analysis revealed that the native locations were strikingly differentiated from the introduced locations Fig. a. For thR2 = 0.795, P < 0.001; Additional file In Admixture analysis, the lowest cross-validation error was identified at K = 6 CV, 0.359) followed by at K = 4   were detected in the coding sequences, also showing no deviation from the null hypothesis that 5.6% of all examined loci could hit the coding sequences of a gene in the genome. Fifteen (42.9%) were revealed to cause amino-acid substitutions in known proteins, within which 13 and 2 were associated with putative positive and balancing selection, respectively  were associated with 210 unique genes, showing no significant deviation from the null hypothesis that 39.8% of all examined loci could hit the genomic region of a gene in the genome. A total of 154, 60 and 27 genes were revealed to be related to putative positive selection, balancing selection and latitudinal gradients, respectively  in Australia, which separated from the population in Southeast Asia 1.5 million years ago, lost approximately 60% of genetic diversity, but successfully adapted to and colonized the new continent [In grass carp, we observed that genetic diversity in the introduced locations was significantly reduced in comparison to that in the native locations. This result is consistent with a previous study on assessment of genetic diversity in introduced locations in the USA, Europe and Japan and native locations in the Yangtze River System using mitochondrial DNA . As reveontinent . Here, wontinent . All theTypically, freshwater fishes are more isolated by various geographical factors than marine fishes, and thus have a lower level of gene flow . In thisAccording to the historical records , grass cIt also should be noted that the genetic status of Nenjiang in the Heilongjiang River System much likely has not been significantly influenced by the recent introduction of grass carp from the other river systems. Although ~ 50% of the overall genetic composition of Nenjiang was observed from the Yangtze and Pearl River Systems, some individuals still showed completely native genetic composition of the Heilongjiang River System while most of the others showed a large proportion of genetic composition from the other two river systems. Such results likely suggest that the introduced grass carps have not randomly mated with the natives in Nenjiang. Interestingly, a significant pattern of isolation-by-distance across native locations can be observed when excluding the grass carps in Nenjiang showing significant genetic composition from the other river systems. This is also the differentiation pattern among the locations within the Yangtze and Pearl River Systems. Altogether, our results suggest that the native gene pool of the Heilongjiang River System was still relatively independent and has not been significantly influenced by the immigrants from the other river systems. It may also suggest that the reproduction and hybridization capacity for the immigrants were limited in the extreme environment and due to genomic divergence although this should be experimentally examined in future studies. Above all, the current population structure should be taken into consideration when establishing conservation strategies and utilizing the natural resources for aquaculture, particularly the grass carps of Heilongjiang River System.As discussed above, we observed significant genetic heterogeneity both in terms of genetic diversity and differentiation between the native and the introduced locations, as well as historical population decline in demographic models, suggesting significant founder effects in the introduced locations. It is likely that the introduction of grass carps was not initiated under planned programs or that not all the introduced fish could adapt to the new habitats. As expected, we identified significant population structure and also a clear geographical pattern of differentiation among the three river systems in a background of high gene flow, which allows tracing back the origins of the introduced locations . InteresTaken together, our data suggest that the genetic differentiation among native populations is high enough to allow tracing the origins of the recently introduced grass carp , was under putative balancing selection within the introduced locations of grass carp. This gene plays an important role in disease and stress responses [In comparison to directionally positive selection, many more loci  were consistently identified to be under putative balancing selection by Arlequin (206) and BayeScan (77). In particular, BayeScan showed a more conservative result than Arlequin did, which is likely because the scenarios underlying BayeScan are less sensitive to the changes of effective population size and immigration rates among studied locations . In popuesponses . MaintaiGasterosteus aculeatus [Gadus morhua [FST is not likely to be artifacts resulting from the relatively small number and the uneven distribution of markers. Consistent with conclusions in cichlid, stickleback and some other fish species, the ‘genomic islands’ model might be a common pattern of divergence with frequent gene flow [The ‘divergence hitchhiking’ scenario of divergence-with-gene-flow, in which specific genomic regions show elevated differentiation than average, has been testified in some fish species , the Yangtze River (Y) and the Pearl River (P), and three introduced locations from Malaysia (I), India (I) and Nepal (I). The native and introduced locations are denoted as black and red solid circles, respectively. Detailed sampling information is listed in Table https://www.google.com/permissions/geoguidelines/). (JPG 1359 kb)Additional file 2:Figure S2. The overall pattern of isolation-by-distance for (a) all six native locations, (b) five native locations excluding Nenjiang and (c) all six native locations excluding individuals from Nenjiang (10) showing significant genetic composition from both the Yangtze and Pearl River Systems, examined using Mantel tests based on all genotyped SNPs. Genetic distance was estimated as FST/(1-FST), while geographical distance was the linear distance between sampling localities. (JPG 566 kb)Additional file 3:Figure S3. A phylogenetic tree showing relationships among all nine locations of grass carp, which was constructed using the Neighbour-Joining approach with bootstrap values over loci for 1000 times. (JPG 409 kb)Additional file 4:Figure S4. Plot for cross-validation errors at each K value for nine grass carp locations. (JPG 357 kb)Additional file 5:Figure S5. Population structure across nine locations of grass carp as inferred using Admixture at K = 5. (JPG 80 kb)Additional file 6:Figure S6. Gene ontology annotations of the candidate genes under putative selection identified by outlier tests and Bayenv association tests in grass carp. Three categories: Cellular Component, Molecular Function and Biological Process, were used to visualize the potential functions of enriched genes. (JPG 157 kb)Additional file 7:Figure S7. Genome-wide pattern of differentiation (a) and SNP density (b) calculated using 150 Kb sliding window size. Distribution of SNPs under putative positive selection in four scaffolds of the reference genome with the greatest number of outlier loci are shown in (c), where clusters of SNPs under putative positive selection are in shadow. (JPG 1626 kb)Additional file 8:Table S1. Sampling information of six native and three introduced locations of grass carp including river systems of origin, numbers of samples, sampling localities and dates, and the annual average temperature of each sampling locality. Measures of genetic diversity including observed heterozygosity (HO), expected heterozygosity (HE) and nucleotide diversity (Π) are also indicated. (DOCX 14 kb)Additional file 9:Table S2. Pairwise FST values among each pair of locations of grass carp. Genetic differentiation that was non-significant after Bonferroni corrections (P < 0.001) is denoted in bold. (DOCX 14 kb)Additional file 10:Table S3. Parameters used for Diyabc modelling and its distribution for historical demographic parameters of the best competing scenario for two datasets. (DOCX 16 kb)Additional file 11:Table S4. List of annotated genes under putative selection identified by outlier tests and Bayenv association tests. (XLSX 20 kb)Additional file 12:Table S5. Summary of the SNPs associated with local selection that are identified to be located in coding sequences of genes and showing non-synonymous mutations. Codon variant positions with the two alternative nucleotides and the corresponding amino acid change are shown in the “Codon” and “Amino acid” columns, respectively. (DOCX 17 kb)Additional file 13:Table S6. Enriched KEGG pathways and the gene counts under putative selection identified by outlier tests and Bayenv association tests. (XLSX 10 kb)


Platichthys flesus in the northeast Atlantic (from the Norwegian to the Portuguese coast) and Baltic Sea. Specifically, we used a Bayesian infinite mixture model to infer the most likely number of natal sources of individuals based on otolith near core chemical composition. Simultaneously, we characterised genetic connectivity via microsatellite DNA markers, and evaluated how the combined use of natural tags informed individual movement and long-term population exchange rates. Individual markers provided different insights on movement, with otolith chemistry delineating Norwegian and Baltic Sea sources, whilst genetic markers showed a latitudinal pattern which distinguished southern peripheral populations along the Iberian coast. Overall, the integrated use of natural tags resulted in outcomes that were not readily anticipated by individual movement or gene flow markers alone. Our ecological and evolutionary approach provided a synergistic view on connectivity, which will be paramount to align biological and management units and safeguard species’ biocomplexity.Processes regulating population connectivity are complex, ranging from extrinsic environmental factors to intrinsic individual based features, and are a major force shaping the persistence of fish species and population responses to harvesting and environmental change. Here we developed an integrated assessment of demographic and genetic connectivity of European flounder  The processes driving population homogenization or differentiation are complex and range from extrinsic oceanographic and environmental factors, to intrinsic individual-based or species’ specific features 3. In tandem, selective pressures and adaptation to gradients of environmental conditions influence population dynamics and connectivity over ecological and evolutionary time scales5.Connectivity plays a key role in shaping fish species persistence and responses to harvesting and environmental change8. Yet, disparities between actual biologically discrete populations and management units are still common11, and this mismatch severely impacts resource sustainability, and hinders socio-economic and biodiversity goals13.Understanding population structure is paramount to conservation and fisheries decision making, particularly for species with large transboundary distribution ranges, or whose life histories include spatially segregated life stages or migratory behaviour. Marine fish populations range from broad and homogeneous to complex and interconnected units14. In particular, otolith chemistry is an exceptional marker of fish movement and key to delineating fish populations and management boundaries16, as otoliths are metabolically inert, grow continuously and their chemical composition is in part influenced by physical and chemical properties of the surrounding environment17. Overall, these biogenic carbonate structures are chronological recorders of the environment fish experience throughout their lifetime, but also reflect genetic and physiological influences on element incorporation20, which partly explains their success in discriminating populations within the more homogeneous marine environment21.A variety of techniques can be used to infer connectivity and population structure, including indirect estimates via abundance-distribution data or life history parameters, or direct quantifications via artificial or natural tags a priori statistical classification approaches 22. However, the inherent assumption that all potential source sites are characterized is hardly met, and likely unattainable due to sampling limitations or knowledge gaps in species ecology and distribution. This leads to biased assignments as these methods do not allow for unsampled sources beyond the characterized baseline. Therefore, there is growing recognition that frameworks that incorporate uncertainty and resolve the challenge of unknown sources in mixed populations are key to connectivity assessments25. In particular, Neubauer et al.26 developed and validated an unconditional Bayesian mixture model, incorporating the main requirements and assumptions employed in otolith chemistry studies, that allows for unknown (infinite) groups and estimates the most likely number of sources represented in a mixed sample.The application of otolith chemistry as natural tags for connectivity studies generally relies on first establishing baseline references from source groups . Then individuals of unknown origin collected from mixed populations are retrospectively assigned to characterized source groups via 28. Overall, dispersal and reproductive movements play a key role in shaping genetic diversity, with many marine fish stocks being amalgamations of multiple geographic components29. Genetic heterogeneity implies limits in connectivity and reproductive movements, with spawning site fidelity over individual life time and over many generations increasing the potential for genetic variability among populations30. Overall, if we lack a clear understanding on genetic structure, life history diversity and the boundaries of biological populations, fisheries management initiatives will fail to protect discrete units or species’ biocomplexity, namely spawning habitats and potential local adaptations29.Concurrently, the use of genetic markers, particularly allele frequency of microsatellite DNA and single-nucleotide polymorphism assays, have long been based on Bayesian methods and are pivotal in assessments of population structure, connectivity and dispersal in marine fish32. The rationale is that natural markers are generated by different biological processes and operate over different spatial and temporal resolutions. In particular, otolith chemistry provides demographic information over an individual’s life time whilst genetic markers resolve connectivity and population structure over generational or evolutionary time scales, depending on the rate of variation at given loci35. These complementary approaches encompass distinct ecological and evolutionary time scales and allow us to unravel individual or stochastic movements as well as to estimate long term population exchange rate and adaptation. In addition, combining them may allow us to overcome the potential limitations of single marker approaches. For example, the lack of spatial variation in otolith chemical composition may not necessarily imply that fish have a common source and may just reflect similar extrinsic and intrinsic factors. In these circumstances, genetic markers may potentially reveal population structuring. Nevertheless, the use of a single framework to truly integrate continuous (i.e. otolith chemistry) and categorical (i.e. genetic markers) data types is still rare36, and we should continue to strive to harness the full power of complementary and synergistic interdisciplinary approaches38.Recently, there has been an increase in the application of multi-marker approaches to address population structure and connectivity in fish39. Yet, for most marine species we lack baseline information on connectivity, relative importance of spawning grounds, or population structure and genetic differentiation on which to base management initiatives12. Here we use a Bayesian infinite mixture model to characterize population structure of a flatfish species, European flounder Platichthys flesus (L.), throughout the northeast Atlantic  assessed the spatial separation of early life history stages, estimating the most likely number of source groups  to the mixed populations based on otolith chemical composition and using a Bayesian infinite mixture model26; ii) examined the genetic differentiation of these populations using microsatellite DNA markers; and iii) evaluated the combined use of these two marker types with distinct ecological and evolutionary resolutions to support biologically defined management initiatives.Many commercially important fish species have large transboundary distributions throughout the northeast Atlantic, where waning recruitment and decreased spawning stock biomass have led to precautionary or emergency management measuresoto = 102) and genetic samples (Ngen = 318) as well as the number of individuals used in the combined analysis (Ncombi = 99) per collection location are shown in Table The numbers of otolith Noto = 02 and geSpatial patterns were evident in the MDS based on otolith near core chemical composition, namely individuals collected in the Baltic Sea (Swedish and Polish coast) and the Norwegian coast were clearly segregated from each other and from the remaining samples. Otolith composition in fish from the remaining collection locations covering the North Sea, Bay of Biscay and the Atlantic Iberian Peninsula overlapped in multidimensional space, though with some evidence that individuals from the Bay of Biscay separated more from the remaining locations Fig. .Figure 2Using the unconditional Bayesian mixture model approach on otolith near core chemical composition, two main clusters were identified Fig. . The firP. flesus including the mean number of alleles per locus, expected heterozygosity (He) and mean allelic richness (AR) are summarized in Table He < 0.61; 4.78 < AR < 5.59). Pairwise comparisons of genetic differentiation between populations  were significant with the exception of the Polish coast samples versus North Sea samples and the two most southern locations , with the highest differentiation attained between the Swedish and the Portuguese coasts. . Deviations from HWE were only randomly significant in limited cases Table . Thus, as. Table .Table 2MP. flesus in the northeast Atlantic , and results of PCoA reflected a North-South cline along the first axis, while the second axis clearly separated the two locations within the Baltic Sea (Swedish and Polish coasts). Considering both axes of the PCoA, samples from the Norwegian coast were clustered near the Polish coast and the North Sea  and the more central locations , yet they also highlighted increased differentiation of individuals from Norway and the Swedish Coast.We found a significant isolation by distance (IBD) pattern for Sea Fig. . FurtherSea Fig. . SpecifiThe combination of results from individual otolith and genetic markers increased the spatial differentiation among locations. The three-dimensional graphical approach highlighted how genetic data contributed to the separation of the central and southern populations of the northeast Atlantic whilst otolith chemical data were more important for disentangling the genetically homogeneous northern locations Fig. . The cleP. flesus collected throughout the northeast Atlantic. Furthermore, we added information from microsatellite DNA markers to otolith chemical data, thus combining two different natural markers with distinct ecological and evolutionary perspectives to increase the likelihood of accurately depicting this species’ population connectivity40.The application of different statistical approaches that tackle uncertainty and incomplete baseline data is required to resolve connectivity patterns of commercially exploited marine fish species with large contiguous geographical distributions. Here we applied an infinite Bayesian mixture model to otolith chemical data of 41. Our results represent a first attempt toward this end but in future studies the number of samples to best evaluate population structure of this species using otolith chemistry should be increased. In particular, as in infinite mixture models the number of individual samples needs to increase exponentially with an increasing number of anticipated group signatures26.The infinite mixture model successfully differentiated source groups within our otolith near core chemical data, and in general there was an alignment between these results and the MDS used to visualize data. One of the advantages of the infinite mixture model is that the most probable number of sources is inferred by the algorithm whereas in MDS the identification or separation of groups can be subjective. A clear separation of early life history stages among populations from the Norwegian coast and the Baltic Sea was evident. Individuals from the remaining locations had otolith near core chemical signatures that were not sufficiently differentiated to separate source groups. Identifying the number of sources contributing to a mixed population is a difficult task at best, and in reality likely dependent on the dimensionality of the data and how source groups are segregated in multivariate spaceP. flesus, no latitudinal gradient was detected from the North Sea to the Iberian Peninsula. Otolith near core chemical composition of individuals collected along the Norwegian coast and the Baltic Sea were clearly differentiated. Although otolith chemistry has been shown to be influenced by physiology and genetics21, environmental factors are still considered the main drivers of variation for certain elements 43. In addition to water chemical composition, temperature and salinity may play an important role in the differentiation of the chemical composition of the individuals collected in the two locations within the Baltic Sea and in the northernmost collection site (Norwegian coast). The Baltic Sea is a large brackish water body characterized by strong vertical and horizontal salinity gradients. These gradients are clearly reflected in dissolved inorganic carbon (δ13CDIC) and water δ18O, with more depleted values in low salinity areas44 and thus in otoliths45. Furthermore, otolith chemical studies in the Baltic have shown increased Mn:Ca values due to hypoxia47. These physico-chemical properties partly contributed to the differentiation of source areas of individuals collected in the Baltic Sea. Similarly, the distinctive otolith chemical composition of individuals from the Norwegian coast is potentially related to lower temperature of this area compared with the remaining collection locations, and varying salinity due to estuarine-like conditions that result from the intricate system of fjords48.Regarding otolith chemistry of P. flesus occurs offshore, and this likely contributed to our inability to differentiate otolith near core compositions of individuals collected from the North Sea to the Iberian Peninsula as the physico-chemical properties of the marine environments in these regions are homogeneous in comparison to near- and inshore environments (e.g. estuaries)50. Most of the studies attempting to resolve population structure in marine fish species have overlooked the effects of physiology and genetics which can contribute to variations in otolith chemistry and to the success in identifying collection locations in marine environments21. One key aspect to continue to pursue is identifying other elemental and isotopic markers that may enable the differentiation of locations in marine environments using otolith chemistry. Nevertheless, the possibility of a single source seeding the area from the North Sea to Portuguese waters is highly unlikely and the results of microsatellite DNA markers indicated population structuring in this area. Pelagic larval duration of P. flesus (25–35 days51) allows potential large scale dispersal of early life stages and mixing of contributing sources, yet oceanographic and topographic features may conduce to higher retention rates of early life stages . Overall, connectivity in marine fish cannot be readily predicted from early life history traits and dispersal potential as multiple biophysical processes are involved in shaping connectivity patterns52.Spawning of et al.53 already showed genetic differentiation between P. flesus populations from the Mondego estuary  and estuaries along the English Channel. In fact, the Portuguese coast is acknowledged as the southern limit of distribution of P. flesus54, and there is likely a combination of adaptive processes to increased temperature and reduced gene flow taking place here due to its peripheral geographic location55. Regarding the Norwegian coast, though otolith chemistry suggests this was a well separated source group, additional genetic markers and samples are necessary to substantiate potential genetic divergence of this population. A further spatially saturated sampling design will aid in disentangling the location of the genetic split towards the Iberian populations, and could simultaneously contribute to better understanding the genetic structure of P. flesus populations at its northern distribution limit. On the other hand, it is well documented that P. flesus has distinct pelagic and demersal spawning behaviours in the Baltic Sea, and this likely explains the genetic differentiation observed between the two geographically close populations57.Complementary to the otolith chemical markers, microsatellite DNA markers highlighted a genetic split between the Bay of Biscay and the Iberian Peninsula, and in general genetic differentiation followed a latitudinal gradient. Calvès, 58. An integrated approach should provide more robust conclusions regarding population identity, and this was manifest in our results, as markers provided complementary perspectives on P. flesus population structure. Both markers showed differentiation among northern and Baltic populations. However, otolith chemistry was more successful in identifying different source groups in these northern areas, whilst only microsatellite DNA markers detected population structuring towards the southern limit of P. flesus distribution. Considering the different nature of demographic and genetic markers, reconciling contrasting connectivity patterns can be highly informative59, whilst disregarding information from one of them can lead to misconceptions in fish population connectivity. Therefore, synthesizing data from a suite of markers and compiling congruent information at different spatio-temporal scales is the best practice to provide robust information to define and support actions and decisions in fisheries and conservation13.The combined analysis of otolith chemical and genetic markers on the same individuals increases the likelihood of correctly characterizing population connectivity, as they reflect ecological and evolutionary processes37, we have yet to establish a common statistical approach that can adequately resolve the limitations associated with the different nature of the data obtained from distinct markers. Specifically, one of the main hurdles to overcome is combining the categorical data from genetic markers with the continuous data from most other markers 13. On the other hand, Bayesian approaches dealing with probabilities are able to define realistic models that incorporate uncertainty at various levels60, and handle scenarios with unknown sources to a mixed population26, as seen here with the infinite mixture model. To date, studies have combined the information resulting from independent analysis of different datasets31, synthesized multiple marker information in quantitative or qualitative indices32, and developed approaches that allow integration in specific contexts or add information in a priori classification-based methods11. To our knowledge, only one study has taken full advantage of the available information by effectively integrating otolith chemistry and genetic markers to determine the proportion of groups of origin in a mixed population of Atlantic cod36. Adapting Bayesian infinite mixture models to adequately accommodate different data types will contribute towards integration of multiple marker data, and represent a breakthrough in ascertaining the biological boundaries among populations or the sources contributing to mixed populations.Despite the increased recognition of the importance of conducting multidisciplinary population assessments based on the consensus and complementarity of multiple markersCharacterizing the biocomplexity of fish species that span large spatial ranges is critical to avoid mismatches between management and biological units. Here, we showed how the complementary use of two markers with distinct ecological and evolutionary resolutions can enhance our perspective of population connectivity. Results highlighted how single marker approaches may lead to the misinterpretation of a species’ population structure. Overall, disregarding demographic and genetic connectivity in management decisions will have significant ecological and economic consequences, particularly in the current context of general over exploitation of fisheries resources.Platichthys flesus (L.) has a complex life cycle with adults spawning at sea whilst juveniles use nearshore to inshore environments with large-scale movements between the different habitats15. A total of 318 individuals of P. flesus [mean (and standard deviation) of fish total length: 292 (56) mm] were obtained between 2012 and 2014 directly from commercial fishers and research surveys at locations throughout the northeast Atlantic Ocean, including the Baltic Sea  were removed and stored in 100% ethanol for genetic analysis  were selected  otoliths were ground to the midplane using lapping paper (30 µm and 3 µm) until the core was exposed. In a class-100 clean room, otoliths were immersed in ultrapure water, sonicated for 3 min, triple rinsed with ultrapure water, and then mounted on new glass slides in random sets of 30 using double sided tape. All glass and plasticware used for otolith processing were previously decontaminated in a 10% nitric acid (HNO3) wash for 24 h, rinsed with ultrapure water and dried in a laminar flow positive pressure fume hood.Otolith preparation and chemical analysis followed Tanner, 25Mg, 48Ca, 55Mn, 88Sr, and 138Ba on a Thermo Finnigan Element2 single collector inductively coupled plasma mass spectrometer (ICP-MS) coupled to a New Wave 193 nm excimer laser ablation system. 450 µm long rasters with a 30 µm beam diameter were ablated in the near core region, corresponding to the marine larval life history stage. Ablated material was transported via a He gas stream to the dual-inlet quartz spray chamber and mixed with a 2% HNO3 aerosol from a self-aspirating PFA 20 μl min−1 nebulizer. Resulting analyte was transported to the ICP-MS via an Ar carrier gas. Instrumental blanks (2% HNO3) were run at the beginning and end of each set of 10 otoliths. Blank correction of all measured raw values was done by calculating a blank value for each sample by linear interpolation of the measured blanks. A dissolved otolith certified reference material (CRM)63 at Ca concentration of 40 μg g−1 was used to correct for instrument mass bias. Instrument precision was assessed by running another CRM64, dissolved and diluted to a Ca concentration of 40 μg g−1. External precision (relative standard deviation) for this CRM (n = 46) was as follows: Mg:Ca = 0.59%, Mn:Ca = 14.30%, Sr:Ca = 0.51%, and Ba:Ca = 1.29%. All otolith elemental concentration data were converted to element:Ca ratios.Otolith elemental ratios were quantified by measuring 13C and δ18O, blind side (left) otoliths were ground to the midplane using lapping paper (30 µm and 3 µm) until the core region began to be exposed. A computer-controlled micromill (ESI New Wave Research Micromill) was used to extract otolith material from the near core area using the same sized rasters as for elemental analysis (drilling depth 100 µm) until c. 50 µg of otolith material was obtained taking into account the circular structure of otoliths. Thus, otolith material for isotopic analysis represents the same time period as that targeted in the right otolith for elemental analysis. Samples were analyzed on a Thermo Scientific MAT 253 isotope ratio mass spectrometer (IRMS) with dual inlet coupled to a Kiel IV device for automated sample preparation following standard methods65. Isotopic values were reported relative to Vienna Pee Dee belemnite (VPDB) and expressed in standard δ notation. Long term analytical uncertainties for δ13C and δ18O based on repeat measurements of NBS-19 certified reference material are 0.04‰ and 0.06‰, respectively.For stable isotope analysis of δP. flesus throughout the northeast Atlantic, only unconstrained statistical methods were used when assessing near core chemical composition. When sources in mixed samples are uncertain, identifying the origin of individuals becomes a difficult statistical problem as any classification will be biased a priori by the exclusion of potential sources26. We used multidimensional scaling (MDS) analysis to visualize data in reduced space and examine the occurrence of potential groupings, using Vegan package in R. Second, we adapted the unconditional Bayesian mixture model approach  from Neubauer, et al.26. This approach implements a Dirichlet process mixture that allows unknown (infinite) sources and estimates the most likely number of sources represented in a mixed sample. However, the core functions of PopR are implemented in Julia, which brings gains in computation time but opens up a number of issues. In particular, Julia is a recent programming language and is still being actively developed rendering its functionality and synchronization with R unstable. Thus, to address these issues, the unmixR package was developed translating and updating all of PopR’s Julia components to C++, making extensive use of the Rcpp and RcppArmadillo packages. This removed the need for an external interpreter and the creation of intermediate files . No changes were made to the original PopR algorithms.As there is no prior information regarding potential spawning grounds and larval distributions for 66. Simulation parameters for the unmixR analysis were 2000 iterations and thinning was set to 1.Preliminary analysis of the otolith chemical data indicated that best results were obtained by using both elemental and isotopic variables therefore the full set of variables was used for subsequent analyses. All data treatment and statistical analyses were made in R67. Twelve microsatellites  were grouped into 3 multiplex PCR and genotyping groups68. PCR reactions were run with the Type-it Microsatellite PCR Kit (Qiagen TM) containing 5 µl of Master Mix, 1 µl Q-solution, 2 µl RNase-free water, 1 µl of primer mix and 1 µl genomic DNA (c. 50 ng). Amplification conditions were: 95 °C for 10 min followed by 40 cycles of 95 °C for 30 s, 55 °C for 30 s and 72 °C for 1 min and an extension step of 10 min at 72 °C68.Total genomic DNA was extracted from fin clips using a standard phenol-chloroform extraction protocolSamples were genotyped in an ABI PRISM 310 Genetic Analyzer and fragments were sized with GeneScan-500 LIZ Size Standard using GeneMapper 3.7 (Applied Biosystems), and blindly scored by at least two readers to minimize genotyping errors.69. Genetic diversity was measured as the mean allelic richness (AR) calculated and corrected for sample size by rarefaction using HP-Rare70; observed heterozygosity (Ho), unbiased expected heterozygosity (He)71 both per loci and multilocus, mean number of alleles across loci (NA) using ARLEQUIN 3.1172; and fixation index within samples (FIS), inferred using GENETIX 4.05.273. Deviations from HWE and linkage disequilibrium were tested using ARLEQUIN per locus and Genepop 3.174 for all loci, using 100 000 Markov chain and 1000 dememorization steps. ARLEQUIN software was also used to calculate population differentiation through pairwise comparisons of FST (fixation index between samples and 100 000 steps for Exact tests) and p-value significance was assumed using the Bonferroni correction for multiple comparison tests.Microsatellite loci were first tested for the presence of null alleles and genotyping errors like null alleles, stuttering and large allele dropout using MICRO-CHECKER 2.2.3FST values against the shortest waterway distance between collection locations of P. flesus using a Mantel-test implemented in the Vegan package in R. Statistical significance was assessed with 1000 permutations of the data.Isolation by distance (IBD) was tested by comparing pairwise 75.Patterns of differentiation were visualized by principal coordinates analysis (PCoA), a multivariate technique that allows major patterns within a multivariate dataset of multiple loci and samples to be discerned. This analysis was computed using GenAlEx 6.576 was used to infer possible genetic clusters, with parameters of 1000000 Monte Carlo Markov Chain interactions following a burn-in period of 1000000 for K = 1–7, as number of tested clusters, and replicated 20 times. Evanno’s index AK77 was evaluated using STRUCTURE HARVESTER 0.6.9378.A Bayesian clustering method (STRUCTURE 2.3.4)36. The potential benefits of integrating the two markers were evaluated graphically with a three-dimensional scatterplot combining the probability of each individual belonging to one of the identified genetic clusters and the MDS matrix scores from individual otolith data. Thus, the data used for each individual originated from the results obtained using the full data sets for each marker. One advantage of this integrated graphical approach relies on the combination of results from continuous and categorical data without requiring re-analysis of the data.Due to the differing nature of otolith chemistry data and genetic data, a straightforward integration of the datasets is challengingThe datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.


FST) and the overlap of ecological niches  among genetic groups resulting from spatial Bayesian genetic clustering in the wolf (Canis lupus) in the Italian peninsula. Within the Italian wolf population, four genetic clusters were detected, and these clusters showed different ecological niches. Moreover, different wolf clusters were significantly related to differences in land cover and human disturbance features. Such differences in the ecological niches of genetic clusters should be interpreted in light of neutral processes that hinder movement, dispersal, and gene flow among the genetic clusters, in order to not prematurely assume any selective or adaptive processes. In the present study, we found that both the plasticity of wolves—a habitat generalist—to cope with different environmental conditions and the occurrence of barriers that limit gene flow lead to the formation of genetic intraspecific genetic clusters and their distinct ecological niches.The distribution of intraspecific genetic variation and how it relates to environmental factors is of increasing interest to researchers in macroecology and biogeography. Recent studies investigated the relationships between the environment and patterns of intraspecific genetic variation across species ranges but only few rigorously tested the relation between genetic groups and their ecological niches. We quantified the relationship of genetic differentiation ( Its distribution across species ranges can reveal intraspecific information that is not necessarily represented in simple species occurrence patterns  and ecological niche overlap between identified genetic clusters, and (c) identified the ecological factors related to the spatial location of genetic clusters. For this purpose, we used a genetic data set on wolves (Canis lupus), which were molecularly identified during multiyear large‐scale monitoring projects based on carcasses, live‐trapped individuals, and non‐invasively collected samples from the central Apennines to the western Alps in Italy  estimated the pairwise ecological niche overlap between genetic groups resulting from spatial Bayesian clustering, (b) determined the relationship between genetic distances  were collected by more than 400 trained operators . Training was done by wolf experts, and operators were instructed to collect samples as fresh‐looking as possible, excluding degraded ones. Samples were collected along randomly chosen trails and country roads across all habitat types, at least once per season. Sampling was part of a long‐term monitoring program on wolves in Italy  were determined using 12 unlinked neutral microsatellites, a sex‐specific restriction site and taxon‐specific markers following the methods described in Caniglia et al.,  using three different values of the spatial interaction parameter h .To identify genetic clusters of Italian wolves, we considered the 923 wolf individuals and used the Bayesian genetic clustering method implemented in TESS 2.3  plot , we obtained the topographic variables altitude, slope, and landscape roughness (Table https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/population-distribution-demography) as well as the presence and distance from human settlements .We collected a total of 13 predictor variables encompassing environmental, topographic, and anthropogenic factors. Specifically, we estimated habitat diversity and the percentages of six land cover types derived from CORINE Land Cover 2006 IV Level, with a minimum mapping unit/width of 25 ha/100 m and a thematic accuracy ≥85%  for all the variables and removed predictor variables with values higher than 3  and the corresponding ecological niche overlap between two clusters (fixed effect) in a linear mixed‐effects model  values correspond to a large presumed effect of the predictor on the response variable. We expected an inverse relationship between Fst‐values and ecological niche overlap , as genetic differentiation should decrease with increasing niche overlap. We used 10,000 permutations to assess significance of linear mixed‐effects models using the PGIRMESS package  as well as significance and importance  of all the predictor variables entered in the best model(s) per genetic cluster through model averaging. We carried out these analyses using the R package MUMIN  developed using the SPDEP package in R  tested. We thus based further analyses on the default spatial interaction factor of h = 0.6 and the resulting four genetic clusters. The highest number of wolf individuals was recorded in cluster 1 (N = 329), followed by cluster 3 (N = 239), cluster 4 (N = 217), and cluster 2 (N = 138). Cluster 1 was located on the eastern side of the central Apennines (EC hereafter), cluster 2 occurred in the western Alps (WA), cluster 3 on western side of the central Apennines (WC), and cluster 4 in the northern Apennines  genetic differentiation among clusters. Ecological niche overlap between clusters was moderate, with values ranging between 0.361 and 0.451  with ecological niche overlap between clusters with a standardized β‐coefficient of 0.557.Linear mixed‐effects models with pairwise genetic differentiation N = 11; AIC ranging between 995.43 and 997.42, better than a random model at AIC = 1,029.28) included altitude, meadows, and habitat diversity, which were all positively related to cluster membership and had the highest W and p < 0.001  included altitude, shrublands, coniferous, and deciduous forests  with the highest W and p < 0.001  included deciduous forests and mixed woods, both positively related to cluster membership, with the highest W and p < 0.001  altitude, deciduous forests, and meadows  showed the highest W and p < 0.001 , we identified four genetic clusters of wolves in Italy and developed cluster‐specific ENMs to estimate niche overlap between them. We found moderate ecological niche overlap between the resulting clusters, which was nevertheless significantly related to the genetic distances between them, suggesting that genetically different clusters live in different environmental conditions. Accordingly, we identified cluster‐specific combinations of environmental factors influencing wolf occurrence in Italy.4.1Why should genetic clusters reflect differences in ecological niches among clusters? There are two main possible explanations: (a) genetic clusters might reflect differential adaptation to different environments (as detected by ENMs) caused by selection; (b) genetic clusters occupy different ecological niches, but the genetic differences among them are caused by restricted gene flow. Though the two processes are different in nature, they are not mutually exclusive  markers or a larger number of SNPs. While the majority of SSRs is neutral by definition and is usually not linked to adaptive regions within the genome of a study species, a smaller or larger proportion of SNPs  in Italy . In addition, the wolf is a territorial carnivore living in hierarchical packs and the occurrence of established packs can thus result in moving animals not settling down, because of aggressive behavior and competition of resident individuals, resulting in limited gene flow, finally contributing to genetic clustering in Italian wolves.In this study, we found that different genetic clusters of wolves in Italy are differently related to land cover types, probably caused by two main effects. First, the plasticity of the wolf—a distinct habitat generalist , the data of the present study cannot be made openly accessible. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Prunus mongolica (Rosaceae), a relict flora endemic in arid areas of the Asian interior, to investigate the genetic diversity, phylogeography, population structure, and history of the species.Microsatellite primers were developed in P. triloba, P. davidiana, P. persica, P. cerasifera, and P. serrulata).Fifty‐one microsatellite loci, including di‐, tri‐, and tetranucelotide repeats, were identified using transcriptome sequencing and bioinformatic screening. The number of alleles ranged from seven to 11 and the levels of observed and expected heterozygosity ranged from 0.545 to 1.000 and 0.600 to 0.989, respectively. Most of the primers also amplified in a group of congeneric species (P. mongolica. In addition, they can also be used to investigate the population structure, phylogeography, and landscape genetic patterns of congeneric species.This set of microsatellite loci is useful for studying the genetic diversity of  Prunus mongolica Maxim. (Rosaceae) is a relict flora that is endemic to arid areas of the Asian interior. Prunus mongolica was listed in the China Plant Red Data Book as endangered; this has resulted from mining, firewood collection, industrialization, and urbanization, which have led to habitat loss and fragmentation , have been widely used as DNA markers in studies of population genetics, phylogeography, landscape genetics, and parentage analysis due to their high level of polymorphism, putative neutrality, codominant inheritance, and ease of scoring  following the manufacturer's protocol. A cDNA library was prepared using the TruSeq Stranded Total RNA Sample Prep Kit  and sequenced on a HiSeq 3000 sequencing platform (Illumina).Total RNA was extracted from fresh leaves of one individual of Extra (adapters and other Illumina‐specific sequences) and low‐quality sequences were removed from the raw data using the software Trim Galore version 0.4.4  Franch., P. persica (L.) Batsch, P. cerasifera Ehrh., and P. serrulata Lindl.; most primers amplified in these five species except P. triloba .In our study, a total of 19,498 SSRs were identified from 15,837 contigs, and 6069 microsatellite primers were designed. Among the identified SSR loci, 51 microsatellite primer pairs were polymorphic. The 51 novel microsatellite markers can be used to study the genetic diversity, landscape genetics, and phylogeography of the species. The microsatellite primers can also be used to genotype congeneric species (