Canis lupus) are affected by hybridization, particularly in southern Europe, where ongoing recolonization of historic ranges is augmenting gene flow among divergent populations. Our aim was to provide diagnostic canid markers focused on the long-divergent Iberian, Italian and Dinaric wolf populations, based on existing genomic resources. We used 158 canid samples to select a panel of highly informative single nucleotide polymorphisms (SNPs) to (i) distinguish wolves in the three regions from domestic dogs (C. l. familiaris) and golden jackals (C. aureus), and (ii) identify their first two hybrid generations. The resulting 192 SNPs correctly identified the five canid groups, all simulated first-generation (F1) hybrids  and all first backcross (BC1) individuals . An assay design and test with invasive and non-invasive canid samples performed successfully for 178 SNPs. By separating natural population admixture from inter-specific hybridization, our reduced panel can help advance evolutionary research, monitoring, and timely conservation management.Intra- and inter-specific gene flow are natural evolutionary processes. However, human-induced hybridization is a global conservation concern across taxa, and the development of discriminant genetic markers to differentiate among gene flow processes is essential. Wolves ( Canis lupus) caused considerable genetic drift that is now apparent in their genome-wide profiles5. However, the ongoing spatial and demographic recovery of most European wolf populations, mainly due to legal protection and increased prey and habitat availability7, has prompted a gradual process of natural contact with dispersal and gene flow among discrete populations10. This tendency is represented, for instance, by the wolf pack in Lessinia Regional Park in northern Italy, which was established in 2012 by a female from Italy and an immigrant male from the Dinaric Mountains in southwestern Slovenia near the border with Croatia12, followed by back-migration of their descendants to Slovenia detected during the national genetic wolf monitoring program13. Further examples are provided by the detection of Italian or Dinaric individuals re-sampled in the Iberian Peninsula14 and in southern Germany15.During the past centuries, long-term isolation and demographic declines in southern European peninsular populations of wolves (C. l. familiaris)17 and possibly with golden jackals 19. Wolf and jackal ranges are showing an increasing degree of overlap in several countries22, and hybridization between jackals and dogs has been confirmed23. The ability to rapidly and accurately discriminate natural population admixture from inter-specific hybridization is therefore especially important in areas where active interventions may be needed to mitigate anthropogenic hybridization, while allowing or facilitating inter-population gene flow26.However, despite growing numbers, several European wolf populations are now threatened by anthropogenic hybridization with free ranging domestic dogs . However, although multi-allelic microsatellites have a higher per-locus information content than bi-allelic single nucleotide polymorphism (SNP) markers and may have benefits for applications involving short spatiotemporal scales29, their use requires the calibration of results among laboratories when different datasets need to be merged. As an alternative, small panels of SNP markers, suitable also for non-invasive samples, have been developed for various species to address evolutionary and conservation questions . Although a larger number of SNPs are needed for tasks such as individual identification, SNPs permit high-throughput analyses where results are directly comparable among laboratories without the need for calibration—a major advantage when timely results are needed for cross-boundary conservation management decisions33.Earlier studies, prior to the development of genomic tools, provided key insights toward understanding population processes and wolf-dog hybridization based on traditional microsatellite loci  where they noted that overall amplification rates were higher for SNPs than for microsatellites. However, new high-throughput sequencing approaches are providing important improvements also for microsatellite markers, as demonstrated for brown bears (Ursus arctos)35. These recent developments can permit the integration of different marker types and maximize the information obtained from non-invasive monitoring efforts. Because SNPs also permit insights into selective processes37, such markers can offer additional information for evolutionary research and may serve as indicators of responses to environmental change38.von Thaden et al39). SNP genotyping has also offered major advances for non-model species in fields such as biogeography40 and conservation genomics41. Modern genomic platforms provide extensive and cost-effective screening of thousands of SNPs, from which small panels of highly ancestry-informative markers (AIMs) can be selected depending on the specific research or monitoring questions at hand. These panels can be applied in extensive monitoring of endangered taxa or priority management units through microfluidic or quantitative PCR techniques42, which allow cost-effective genotyping of dozens of samples and markers at a time, including low quality DNA from non-invasively collected materials43, where multiple replicates can be performed when needed (see details in Ref.33).Genomic methods are thus essential tools in wildlife conservation and management, and provide opportunities to track allele frequency changes in adaptive and deleterious loci for improved population monitoring . Strong wolf population substructure and the increasing gene flow among wolf populations could therefore make individual assignment more difficult44. The 93-SNP panel proposed by Harmoinen et al.15 performs well for the distinct populations of Italy, Iberia, and the Dinaric region in detecting wolf x dog hybrids. The identification of additional highly informative SNPs can nevertheless help to distinguish natural admixture between wolves from these genetically divergent populations  from anthropogenic hybridization with dogs, a serious and increasing concern in all three regions46, and possible inter-specific hybridization with jackals . Building on the work of Harmoinen et al.15, we therefore selected an additional panel of highly informative SNPs. Our objectives were to: (i) identify dogs, jackals, and wolves from the Italian, Dinaric and Iberian populations in southern Europe and (ii) distinguish inter- and intra-specific first-generation hybrids (F1) and the first generation of backcrosses (BC1) with parental groups, to help prioritize conservation actions, especially in areas with high levels of anthropogenic hybridization48. Two additional objectives were to (iii) assess the SNP panel for the presence of loci under possible selection, and (iv) evaluate whether the SNP panel exhibits any clear bias in reconstructing wolf population divergence patterns, compared to existing results based on genome-wide profiles. These additional two assessments were performed to obtain a better understanding of the identified SNPs and their representation of the investigated wolf populations.A first set of ancestry-informative markers (AIMs) to accurately detect wolf 49 with unpublished data from Europe. European wolves included individuals from population ranges broadly defined in previous SNP analyses3 as Dinaric-Balkan (n = 112), Iberia (n = 25), and Italy (n = 77). Dog profiles included village dogs from Italy (n = 30), Croatia (n = 11) and Bulgaria (n = 2), considered more likely to hybridize with wolves49 or jackals23, and nine common dog breeds whose size is compatible with potential crossing with wolves , genotyped by the LUPA project51. Jackal samples originated primarily from Bulgaria (n = 18), where possible hybridization between wolves and jackals have been reported19 and included samples from Croatia (n = 3) where hybrids with dogs have been documented23.We combined existing SNP genotypes from wolves, dogs, and jackals typed on the Illumina CanineHD BeadChip including over 170,000 SNPshttp://genome.ucsc.edu/cgi-bin/hgLiftOver) and imported into the SNP&Variant Suite 8.0.1 . Nine of the SNPs included had previously been associated with three phenotypic traits  to determine overall population structure. To exclude possible hybrids, we ran pairwise comparisons, each time with K = 2, for each of the three wolf populations and dogs, then wolves and jackals, and finally dogs and jackals. We retained only wolves and jackals assigned with qi ≥ 0.95 to their respective population clusters, and dogs assigned with qi ≥ 0.90, considering their higher within-group variability.We performed an initial quality control of the dataset, by first applying a filter based on individual genotyping success and retaining profiles with ≥ 90% success rate. Next, we screened the data with a per-SNP genotyping threshold of > 90%. We then pruned the dataset for loci in strong linkage disequilibrium (LD) in SVS using the default settings: a LD-threshold of 0.5 and sliding windows of 50 SNPs. None of the nine phenotypic loci passed the initial filtering process, owing to missing values in the SNP data set. However, given their potential information value, we nevertheless selected three SNPs, one for each trait, for inclusion in the candidate panel  wolves vs. jackals, (b) FST values across pairwise comparisons for the three regional wolf populations, (c) Iberian vs. Italian wolves, (d) Italian vs. Dinaric wolves, (e) dogs vs. Italian wolves, (f) dogs vs. Iberian wolves, (g) dogs vs. Dinaric wolves, and (h) wolves vs. dogs across all three regions.AIMs to identify admixture between dogs, jackals and the three regionally distinct wolf populations were selected by performing pairwise 56 with the Adegenet package 2.1.357 to detect patterns of genetic differentiation among groups and individuals58. The “find.clusters” function was initially used to determine the best-supported number of genetic clusters using the Bayesian Information Criterion (BIC) running successive numbers (1–10) of K-means clusters of the individuals and the “table.value” function was used to graphically visualize the corresponding best clustering of the individuals. The optimal number of principal components (PCs) was identified using the “optim.a.score” function and the Discriminant Analysis (DA) was then run on the retained principal components using the “dapc” function. Finally, after selecting the optimal number of eigenvalues for the DA analysis, the DAPC results were graphically visualized with the “scatter.dapc” function and the assignment probability of individuals to each cluster were calculated and plotted using the “assignplot” function59.We used the resulting SNPs in explorative multivariate analyses by performing a Discriminant Analysis of Principal Components (DAPC) in R 3.6.6Structure 2.3.460. Five independent runs were performed for increasing values of K (the number of genetic clusters) from 1 to 10 using 500,000 Markov chain Monte Carlo (MCMC) iterations, after a burnin of 50,000 iterations, assuming no prior information (option Usepopinfo not activated), and choosing the “Admixture” and “Independent Allele Frequency” models59. We used the highest rate of increase in the posterior probability LnP(K) between consecutive values of K to estimate the most likely K-value, and then assessed the average (Qi) and individual (qi) proportions of membership in each cluster61. The software Clumpp 1.1.162 was used to concatenate the data from the five independent runs for each K value, and Distruct 1.163 to graphically display the results.We used the same dataset to perform Bayesian clustering analyses in HybridLab 1.064 to simulate jackal x wolf, wolf x dog, and wolf x wolf hybrids. The five canid groups were used as reference populations, and we simulated pairwise crosses to generate 10 F1-hybrids and 10 BC1-individuals with the wild parental populations for each comparison  and Structure as described above with the 192 SNPs and a larger dataset of 191 individuals. This dataset included the five reference groups plus additional canids categorized as admixed or not admixed in earlier studies65 to test the discriminating power of the selected loci when applied to empirical data . To check the consistency of the inferred genetic structure, all clustering analyses were run again in Admixture to reassign each sample to its group of origin, assuming K-values from 1 to 10. The most likely number of clusters was identified based on the lowest cross-validation error55.Subsequently, we used on Table . The resO and AE), observed and unbiased expected heterozygosity (HO and uHE), numbers of private alleles (NP), Probabilities of Identity among unrelated (PID) and among full sib (PIDsibs) individuals66, FST values, number of migrants (NM), and analysis of molecular variance (AMOVA) were computed using GenAlex 6.50267. The polymorphic information content (PIC) and the mean proportion of successfully genotyped loci (GL) were computed using Cervus 3.0.368. Allelic richness (AR), which corrects the observed number of alleles for differences in sample sizes, was computed with Fstat 2.9.3.269, and values of the inbreeding coefficient Wright’s FIS were calculated using Genetix 4.0370.The proportions of polymorphic (PL) and monomorphic (ML) loci observed and effective allele numbers  84 SNPtype™ genotyping assays previously developed by Harmoinen et al.15 (Panel 1 in Table Vulpes vulpes) to check for other possible cross-species amplifications. Additionally, we included B) 60 non-invasive DNA samples obtained from faecal swabs71 including 11 Italian wolves, 4 dogs, and 45 hybrids previously identified using traditional microsatellites26. The samples in B) were collected during ongoing non-invasive genetic monitoring of the Italian wolf population. Finally, three blank controls were included. Invasive and non-invasive samples were genotyped 2–3 times and replicates were used to reconstruct consensus genotypes and calculate amplification success (henceforth AS) and error  rates in Gimlet 1.3.372. Reliable Fluidigm consensus genotypes were then used to perform an additional Principal Coordinates Analysis (PCoA) using GenAlex 6.50267.To verify the applicability of the resulting SNPs see “” in futuFST tests for regional wolf populations, (c) 23 SNPs fixed between Italian and Iberian wolves, (d) 2 SNPs fixed between Italian and Dinaric wolves, (e) 13 SNPs fixed between dogs and Italian wolves, (f) 2 SNPs fixed between dogs and Iberian wolves, (g) 3 highly discriminant SNPs between dogs and Dinaric wolves and (h) 10 SNPs highly discriminant for wolf-dog tests in all the three geographic regions. These 105 SNPs were added to the three phenotypic loci  available for a dataset of 158 canids, namely 52 dogs, 21 jackals (18 from Bulgaria and 3 from Croatia), and 30 Italian, 30 Dinaric, and 25 Iberian wolves. Following the selection process for highly discriminant loci, we retained an initial panel of 198 candidate SNPs Table . These ips Table .K-means clustering analysis identified the best-supported number of genetic clusters at K = 5, which showed the lowest BIC value  and distinguished dogs from all of the wild canids, which were visibly separated along the second axis, describing c. 19% of the genetic diversity  of the clusters until K = 5  in individual coefficient values (qi) among runs , dogs (mean estimated membership of population to the assigned cluster Q1 = 0.999) were clearly separated from the other four taxa (mean Q2 = 0.999). At K = 3, dogs (Q1 = 0.998) clustered separately from Italian wolves (Q2 = 1.000) and from the other wild canids (Q3 = 0.990). At K = 4, dogs were included in cluster 1 (Q1 = 0.997), Italian wolves in cluster 2 (Q2 = 0.999), jackals in cluster 3 (Q3 = 0.999), and Dinaric wolves together with Iberian wolves in cluster 4 (Q4 = 0.994). At K = 5 , Italian wolves to cluster 2 (Q2 = 0.999), jackals to cluster 3 (Q3 = 0.999), Dinaric wolves to cluster 4 (Q4 = 0.995) and Iberian wolves to cluster 5 (Q5 = 0.998). For K > 5, the LnP(K) reached a plateau and no further interpretable substructures were observed in the data  with the 192 SNPs was highly coherent with results obtained through an additional Structure analysis performed with the entire panel of 98,004 unlinked SNPs  between their respective parental groups , all known wolf x dog (0.441 ≤ qi ≤ 0.847) and jackal x dog (0.486 ≤ qi ≤ 0.744) hybrid individuals  .Bayesian clustering procedures run with the five canid groups plus their simulated first two generations of hybrids clearly confirmed that 998 Fig. . As expels  Fig. . Bayesiapes Fig. , S8. PreO, AE, AR, PIC, HO and uHE were observed in dogs and the lowest values were seen in jackals (Table P < 0.001) partitioned among the five groups. All pairwise FST values (Table P < 0.0001) and aligned with the very low corresponding effective FST-based migration rates  in the three wolf populations  and not significantly different  from the results obtained with 192 SNPs.We successfully designed primer pairs and assays for all the new 108 SNPs that satisfied the recommended parameters (minimum separation distance of 100 base pairs), avoiding regions with large repeats or otherwise difficult for the interpretation of results. After amplifying the 93 DNA samples 2–3 times at the 192 SNPs, we discarded 14 loci among the newly-designed SNPs  that failed amplifications in all reactions Tables , and S5,ypes Fig  and S12,ypes Fig  and S13 ypes Fig , were al15 and in this study can therefore provide informative results for monitoring, research, and conservation management.We employed a reduced panel of 192 SNPs to reliably discriminate between anthropogenic hybridization and natural gene flow among populations in five canid groups, centered on wolves in southern Europe. Of these SNPs, 178 were successfully amplified on the Fluidigm platform, and provided the same results as the 192 SNPs for analysis of population structure. Results from empirical data and simulated profiles suggest that the selected marker set is well-suited for this purpose, and our analyses unambiguously identified all F1-hybrids and all BC1-individuals. Our results have shown that the panel performs well, also for non-invasively collected samples, and the reduced set of SNPs presented by Harmoinen et al.26. The identified loci incorporate the panel of 93 SNPs developed for microfluidic genotyping by Harmoinen et al.15, who found almost identical genotypes in a test of 30 invasive and non-invasive samples from the same wolves. As a complement to the European wolf-dog hybrid panel presented by Harmoinen et al.15, we therefore propose the selected SNPs for a more detailed evaluation of population admixture versus hybridization in southern European populations, including possible events of jackal-hybridization23.Our results based on empirical data and simulated profiles indicated that the identified SNPs clearly differentiate F1-hybrids and accurately detect BC1-individuals within and across canid species, showing no type I  nor type II  errors74, which is highly relevant for tracking the expanding distribution of jackals. Their rapid range expansion across Europe22 and within our study area, recently illustrated by the confirmation of jackal presence south of the Po River in northern Italy , highlights the urgent need to account for this species in future canid monitoring and management plans. Our results indicated that the selected SNPs perform well for identification of F1-hybrids and BC1-individuals for all canid group combinations. Detecting the presence and distribution of these early-generation hybrids is typically a higher priority than that of individuals with lower proportions of dog ancestry, to prioritize scarce conservation resources26. The findings from our empirical and simulated data therefore indicate that our panel can reliably identify the animals that are most essential for monitoring and conservation management .The presence of fixed alleles in jackals, dogs, and between wolf populations contributed to the discriminant power of certain loci, which can be particularly valuable as diagnostic markers for monitoring purposes. Where incomplete profiles are obtained from non-invasive samples, the results from a few such markers can be enough to allow species identification.15 was developed and successfully tested with non-invasive samples, and the additional loci presented in this study also performed well with non-invasive sources of DNA, we underline that the proposed panel may need future modifications given the highly dynamic situation for canids in our study area. In view of the recent gene flow between Italian Alpine and Dinaric wolves12, the detection of dog-jackal hybridization23 and the increasing overlap seen in wolf and jackal distributions throughout Europe21, it will also be necessary to monitor for possible changes in the spatial and temporal distribution of allele frequencies in the selected SNPs26. However, ongoing international collaborations in population monitoring, as illustrated by the LIFE WolfAlps EU project , will provide such information for wolves and other wide-ranging carnivore species. Accordingly, the opportunities for data sharing offered by new genomic approaches will contribute toward keeping these tools as useful and updated as possible.Although the original panel of 93 SNPs from Harmoinen et ala priori sampling groups emerged as well-separated genetic clusters based on multivariate and Bayesian clustering analyses of the identified SNPs. As expected, dogs represented the most divergent group, and Italian wolves were also clearly differentiated from the other wolves, as observed in earlier analyses of SNP profiles3 and supported by earlier findings from analyses of mitochondrial DNA and microsatellite markers76. Moreover, pairwise values of genetic distances between populations align with previous findings3 for southern European wolf populations. Notably, jackals emerged as a distinct group only at K = 4 population clusters, which is likely a reflection of the ascertainment bias in the SNP panel originally developed for dogs1.All five 3. It is nonetheless important to note that the reported levels of diversity differ among analyses, and that the a priori selection of loci for the purposes of our study likely influenced the results. The SNP array that formed the basis of our study is centered on genetic variation in dogs77, magnifying their genetic diversity while underestimating that of wolves and the more distantly related jackals1. Furthermore, our selection of markers focused on SNPs with high FST-values. For these reasons, direct comparisons of genetic variability among taxa should be treated with caution. Future developments based on whole-genome data may provide additional new markers less affected by ascertainment bias. However, the selection of loci from the existing—and commonly used—SNP panel facilitates the use of already-published canid profiles from earlier projects as reference populations, which could increase the utility of the panel.The lower diversity values for Italian wolves, followed by Iberian wolves and Dinaric wolves, accord with earlier SNP analyses of European wolf populationssibs, where values calculated across 21 jackals  were substantially higher than those calculated for the 30 Italian wolves  that represented the 2nd-highest values. Nonetheless, our selected SNPs provide essential information, given that the initial 93-SNP panel by Harmoinen et al.15 did not distinguish among wild canids . Although a higher number of SNPs are required to obtain PID values similar to standard microsatellite panels43, careful testing have shown that such panels can still work well across broad geographic regions and divergent populations15.Notwithstanding, we report various diversity parameters for comparative purposes, which might also inform future efforts for different canid groups or other taxa. Relevant examples are the values for PID and PID78, future research with more samples and genomic data are needed to evaluate these findings, including the potential presence of false positives among our results. Our population divergence modeling  indicated sequential population splits and bottlenecks broadly consistent with whole-genome results5, although our results showed very wide confidence intervals and could be partly affected by ascertainment bias, mutation rates, marker numbers, and secondary gene flow79.We detected a limited number of loci under possible selection , may also offer opportunities to combine hypervariable microsatellites with SNP loci possibly associated with adaptive genetic variation. The selected SNPs can provide timely identification of dispersers and ensure that such important individuals are not erroneously classified as hybrids, with the subsequent risk of management decisions that could be harmful for conservation. The proposed panel will also facilitate the monitoring of jackals, which are rapidly expanding their ranges across Europe, and contribute to research, monitoring, and conservation management of wild European canids.We found that the identified SNPs can reliably distinguish natural population admixture from inter-specific hybridization in five canid groups comprising dogs, jackals, and the long-divergent Iberian, Italian and Dinaric wolf populations, up to the first generation of backcrosses, using various sources of DNA, including non-invasively collected materials. New genomic tools provide opportunities to integrate data from different laboratories without the need for calibration, which is essential for wide-ranging species such as wolves. The sequencing of microsatellite markersSupplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.Supplementary Information 6.Supplementary Information 7.


Sakesphorus luctuosus  to understand the processes shaping the evolutionary history of Amazonian floodplains and address unresolved taxonomic controversies surrounding its species limits. By targeting ultraconserved elements (UCEs) from 32 specimens of S. luctuosus, we identified independent lineages and estimated their differentiation, divergence times, and migration rates. We also estimated current and past demographic histories for each recovered lineage. We found evidence confirming that S. luctuosus consists of a single species, comprising at least four populations, with some highly admixed individuals and overall similar levels of migration between populations. We confirmed the differentiation of the Araguaia River basin population (S. l. araguayae) and gathered circumstantial evidence indicating that the taxon S. hagmanni may represent a highly introgressed population between three distinct phylogroups of S. luctuosus. Divergences between populations occurred during the last 1.2 mya. Signs of population expansions were detected for populations attributed to subspecies S. l. luctuosus, but not for the S. l. araguayae population. Our results support that S. luctuosus has had a complex population history, resulting from a high dependence on southeastern “clear water” seasonally flooded habitats and their availability through time. Spatial and demographic expansions toward the western “white water” flooded forests might be related to recent changes in connectivity and availability of these habitats. Our study reinforces the view that isolation due to absence of suitable habitat has been an important driver of population differentiation within Amazonian flooded forests, but also that differences between várzeas  and igapós  should be further explored as drivers of micro‐evolution for terrestrial species.We assessed population structure and the spatio‐temporal pattern of diversification in the Glossy Antshrike  Sakesphorus luctuosus  to understand the processes shaping the evolutionary history of Amazonian floodplains and address unresolved taxonomic controversies surrounding its species limits. We found evidence confirming that S. luctuosus consists of a single species, comprising at least four populations, with some highly admixed individuals and similar levels of migration between populations. Our study reinforces the view that isolation due to absence of suitable habitat has been an important driver of population differentiation within Amazonian flooded forests, but also that differences between várzeas  and igapós  should be further explored as powerful drivers of micro‐evolution.We assessed population structure and the spatio‐temporal pattern of diversification in the Glossy Antshrike  Eastern rivers, such as the Tapajós and Xingu, are “clear water” rivers, with intermediate fertility. Conversely, the Negro River sub‐Basin and a few other tributaries are constituted by low‐fertility “black waters”  and their resultant western and eastern distribution strongholds, respectively  for DNA extraction, and Qubit® 2.0 Fluorometer (Life Technologies) to assess quantity and quality of the extracted DNA. Sequence capture and sequencing of Ultra Conserved Elements (hereafter UCEs) were performed according to Faircloth et al. .We followed the PHYLUCE pipeline , to annotate loci linked to the Z chromosome. These loci were removed from further analyses , we first performed a principal component analysis in the SNPs dataset to set a preliminary population structure using PAST3  were included and attributed to a population of origin considering a geographic criterion, except for specimen T22906 (putative hagmanni), which could not be attributed to either a geographic or a genetic population . The second set comprised all individuals assigned as: (a) W+ highly introgressed T22906; (b) SE+N (including specimens from the Paru River margins); and (c) CE populations. Overall, our range expansion scenarios estimated with rangeExpansion need to be interpreted with caution, since the movement of glossy antshrikes is essentially restricted to river channels . However, the demographic models implemented in rangeExpansion consider uniform probabilities of dispersal throughout the species range , setting priors for τ gamma distribution as α = 2 and β = 2,000. Nonetheless, a model considering old divergences was also included (α = 1 and β = 10). Different prior sets to model effective population sizes were set either considering small or large sizes (effective population size estimated as θ = 4Neμ), α = 2 and β = 2,000 and α = 1 and β = 10, respectively. Migration (M = m/μ) was set to α = 0.002 and β = 0.00001. Each analysis was run during 106 generations after a burn in of 104 generations, with finetune parameters. TRACER was used to summarize statistics for each run and also to check for convergence  of 6.75 × 10−10 mutations/site/year estimated for UCEs based on comparisons across the Passeriformes phylogeny and its sister group (Psittaciformes) using complete genomes and fossil‐dated divergence ages  of 2.81 year estimated for the Glossy Antshrike was considered . FST values among populations were all statistically significant (p < .05) and superior to 0.12 . Of these, 61 loci were likely linked to the Z chromosome Table  and one s Figure . Specimes Figure . This adaraguayae (as this seems a more differentiated population), a likely significant deviation from isolation by distance equilibrium was recovered across the range of S. luctuosus, with small strength of the founder effect, and large founder distances , with the most recent events at 125 kya  and 83 kya , followed by southeastern and western populations and smaller for the northern population . Overall, mean levels of migration were similar between pairs of populations for any of the models considered, but confidence intervals did vary between estimates, with multiple statistical outliers . The Glossy Antshrike has experienced very recent (Holocene) population expansion within western , concluded that hagmanni was distinct enough to warrant formal taxonomic recognition, although the lack of additional specimens prevented any conclusion concerning its alternative ranking as species or subspecies , the status of S. hagmanni still needs further evaluation  are located on the contact zone among the N, SE, and W phylogroups of S. luctuosus .Populations attributed to the 4.2FST = 0.12 and western vs. araguayae FST = 0.27). Yet, a well‐known distribution gap within the middle and upper Xingu River , suggesting that isolation might have contributed to population differentiation. In this region, the flooding period is limited to less than half a year and elevation increases to more than 300 m  at the mouth of these rivers , and all populations sampled can be associated with clear or white water types  and western (white water) populations is the smallest observed among all populations of S. luctuosus  west of Purus River and so likely imposing ecologic exclusion, would also explain the western limit of distribution for the Glossy Antshrike.Nonetheless, connection of eastern e Figure , while re Figure . Connecte Figure  and, duee Figure . Moreove4.3Sakesphorus luctuosus is not a complex of species and suggest it is more closely associated with clear water habitats than previously thought ; Data curation (lead); Formal analysis (lead); Investigation ; Methodology ; Project administration (lead); Software (lead); Validation ; Visualization (lead); Writing‐original draft (lead); Writing‐review & editing . Camila C. Ribas: Conceptualization ; Data curation (supporting); Formal analysis (supporting); Funding acquisition (lead); Investigation ; Methodology ; Project administration (supporting); Resources ; Supervision (supporting); Validation ; Writing‐review & editing (supporting). Alexandre Aleixo: Conceptualization ; Formal analysis (supporting); Funding acquisition (supporting); Investigation ; Methodology ; Project administration (supporting); Resources ; Supervision (lead); Validation ; Writing‐review & editing .Supplementary MaterialClick here for additional data file.


South Africa and Australia shares multiple important sheep breeds. For some of these breeds, genomic breeding values are provided to breeders in Australia, but not yet in South Africa. Combining genomic resources could facilitate development for across country selection, but the influence of population structures could be important to the compatability of genomic data from varying origins. The genetic structure within and across breeds, countries and strains was evaluated in this study by population genomic parameters derived from SNP-marker data. Populations were first analysed by breed and country of origin and then by subpopulations of South African and Australian Merinos.ST: 0.04–0.12) were sometimes more distant than across some breeds (FST: 0.05–0.24), but the divergence of common breeds from their across-country equivalents was weak (FST: 0.01–0.04). According to mean relatedness, FST, PCA and Admixture, the Australian Ultrafine line was better connected to the SA Cradock Fine Wool flock than with other AUS bloodlines. Levels of linkage disequilibrium (LD) between adjacent markers was generally low, but also varied across breeds (r2: 0.14–0.22) as well as bloodlines (r2: 0.15–0.19). Patterns of LD decay was also unique to breeds, but bloodlines differed only at the absolute level. Estimates of effective population size (Ne) showed genetic diversity to be high for the majority of breeds (Ne: 128–418) but also for bloodlines (Ne: 137–369).Mean estimated relatedness according to the genomic relationship matrix varied by breed (-0.11 to 0.16) and bloodline (-0.08 to 0.06) groups and depended on co-ancestry as well as recent genetic links. Measures of divergence across bloodlines (FThis study reinforced the genetic complexity and diversity of important sheep breeds, especially the Merino breed. The results also showed that implications of isolation can be highly variable and extended beyond breed structures. However, knowledge of useful links across these population substructures allows for a fine-tuned approach in the combination of genomic resources. Isolation across country rarely proved restricting compared to other structures considered. Consequently, research into the accuracy of across-country genomic prediction is recommended.The online version contains supplementary material available at 10.1186/s12864-021-08020-3. In South Africa (SA) and Australia (AUS), sheep production is dominated by a few major breeds. In SA, wool- and dual-purpose breeds such as the Merino, Dohne Merino and the SA Mutton Merino (SAMM) contributed nearly 70 % of all weaning weight records submitted to the National Small Stock Improvement Scheme between 2010 and 2011  and the The use of genomic information to increase the accuracy of Australian Sheep Breeding Values has been well established , but is e) describe the diversity of populations and are important factors in the accuracy of genomic selection [Population genetic parameters such as linkage disequilibrium (LD) and effective population size  allow for informative estimates of genetic variation and relatedness across and within population groups, including examples related outside of pedigrees.The objective of this study was to quantify a range of population genomic parameters from a medium density (50 K) SNP data panels to characterize genetic variation between and within subpopulations defined by different hierarchical structures such as breed, country, bloodline or flock of origin. The results should contribute to the knowledge of the genetic make-up underlying these sheep populations and could also facilitate the combining of genomic resources between AUS and SA, with a particular interest in building toward across country genomic prediction of Merinos.The average expected . In turn, the SA Dorper and SA Dormer displayed the highest FVR (0.21), while FVR-values of the AUS Border Leicester (0.20) and Poll Dorset (0.19) were also high. A narrower range (0.02 to 0.08) of generally low FVR  and highly inbred is common in most populations.The mean level of inbreeding . With consistently high FST estimates, the Border Leicester and Poll Dorset breeds diverged highly from all SA populations reported here, but a similar pattern was observed in relation to AUS breed groups  and the smallest between the Industry and Grootfontein lines (0.04). All the comparisons within SA-only bloodlines were below 0.08. Of note was the low across-country estimate observed between the Cradock and Ultrafine lines (0.05). Also, most of the within-country AUS relationships between bloodlines were between 0.08 and 0.11, a magnitude similar to some FST values observed across breeds  genetic relationship of animals to their contemporaries in the same breed or bloodline group Table . The AUSThe range of internal relatedness of Merino bloodline groups was smaller than for breed groups, but there were notable differences amongst them Table . Accordins Table . The Finentclass1pt{minimaA decomposition of the genomic relationship matrix (GRM) into its initial principal components provided a clear distinction between animals by breed structure Fig. . PlottinPCA of the Merino bloodlines Fig.  showed lAll animals with positive values of PC1 were from AUS, whereas those with negative values of PC1 were all linked to SA. In PC1, the Elsenburg and Strong lines were separated the most. The Cradock line clustered near the Ultrafine and Fine-Medium-2 lines. PC2 separated the Elsenburg line and Strong lines from the Fine-Medium-1 line and an overlap between the Cradock and Fine-Medium-2 and the Ultrafine lines was observed. In general, the lines of AUS origin had more distinct clusters compared to the SA lines which varied more within groups on PC1 and PC2. The POVs explained by PC1 and PC2  is only reported for groups consisting of samples sizes larger than 64, but an exception was made for the SA ‘Industry’ group (N = 41), and results for this group should be treated with precaution.From the non-syntenic LD of randomly sampled groups, a strong relationship with 1/N was observed, indicating that measures of LD is rapidly inflated as the sample size decreased below N = 64. This trend can be seen in Fig. The estimates of average LD between adjacent markers (separated by ~ 55 KB) showed substantial variation in the breed and bloodline groups. The lowest LD values were estimated for the AUS Merino (0.138) and second-lowest for the SA Merino 0.156). The highest LD values were estimated for the Poll Dorset (0.224) and Border Leicester (0.226) breeds. The persistence of LD over physical distance was different for each breed group  the correlation between randomly sampled alleles within subpopulations relative to the total population or (2) the proportion of genetic variance that can be attributed to variance in allele frequencies between subpopulations [ST values were higher than previous estimates of 0.062 and 0.053 between the Merino and, respectively, the Poll Dorset and Border Leicester groups [ST values between bloodlines were comparable or larger than certain across breed estimates . The lower level of relatedness between the AUS and SA Dohne Merino populations could be due to a more diverse SA population, as the SA Dohne Merino maintained a lower FVR and internal relatedness compared to the SA SAMM and SA Dorper groups.Estimates of relatedness according to ST results reported here.For the Merino breed, the across-country relationship is known to be connected by both deep ancestral and relatively recent relationships, but the positive relationship was expected to be low for multiple reasons. Both groups were internally diverse, and it can be reasoned that a population cannot be more related, on average, to another population than its internal level of relatedness (than it is to itself). Also, the ancestral relationship between SA and AUS Merinos is defined by a distant linkage in the original development of the AUS Merino . Lastly,www.coopworth.org.au).This study also reported across breed relationships that were comparatively high in magnitude. These cannot be attributed to recent links and must thus be prompted only by a deeper co-ancestry. For example, the SAMM and Dohne Merino share origins to the German Mutton Merino , and theFrom Results from PC- and ADMIXTURE analysis generally indicated very similar clusters of breed and bloodline populations. The discrete breed structures of PC1 and PC2 appeared to capture deep ancestral relationships and very little within-group variation. From ADMIXTURE analysis, the distinct genetic structures of Merino, Border Leicester and Poll Dorset agreed with those three breeds occupying the most distant branches of PC1 and PC2.The ADMIXTURE and PCA results also agreed well with previous parameters that indicated only small effects of across-country separation. However, a study that imputed SA breeds from AUS reference panels showed a markedly higher accuracy of imputation for Dorpers compared to Dohne Merinos . AlthougGenerally, breed structures did not dissipate in succeeding principal components as groups remained clustered beyond PC1 and PC2 Fig. , and theThe ADMIXTURE analysis of bloodlines revealed further complexities of these population structures Fig. b. The ide and relatively low internal relatedness. However, this is possibly due to the orthogonal nature of principal components, which separated the ancestral relationships for which the Ultrafine line appears uniform, from within-line variation between sampled individuals. This could also partly explain the homogenous composition observed in ADMIXTURE analysis. If the Ultrafine line did consist of many small substructures as previously speculated in this paper, the level of K was likely too low to capture such structures for a population with a strongly defined ancestry.The highly defined population structure of the Ultrafine line is notable considering the high estimates of diversity according to NDespite the good agreement and accuracy of PCA and ADMIXTURE analysis in identifying the known groups of origin, the low POV explained by the initial principal components in Fig. e, and thus a decrease in LD. This trade-off could be an important determinant in breeding program design, and the more diverse and diverged the populations, the more challenging this trade-off is likely to be. Thus, an alternative consideration is also valid that a population of high diversity could benefit from being subset into smaller groups of better-connected animals. Regarding genomic selection, Van der Werf et al. [ST indicated that these two groups are likely to be the best starting point for an across-country platform. However, these ‘bloodlines’ were the only examples of some, albeit low, linkage by pedigree. A minimum level of genetic exchange is thus likely to remain an important factor unless more distant population structures are better accounted for in future evaluations.The combining of populations into the same pool - such as the current scenario of merging bloodlines into respective SA and AUS Merino groups - hopes to benefit prediction by increasing sample size. In the presence of heterogeneity this will be accompanied by an associated increase in Nf et al.  showed tf et al. . FurtherAccounting for population structures derived from the extended pedigree have delivered increased accuracies for predicted genomic breeding values in the AUS Merino . IncludiThese results provide valuable information on the population structures of important sheep breeds in South Africa and Australia. According to multiple parameters, the isolation across some bloodlines was as influential as that observed across breeds and showed that important division into subpopulations can be extended beyond breed structures. The connectedness of SA resource flocks suggested they could be valuable for contributing to a pooled SA reference population in combination with industry animals. In the prospect of across-country amalgamation, isolation by country rarely proved restricting. While the need for genetic links remains important, the generated knowledge delivers potential to maximize the benefit of such relationships by a narrow spectrum approach to combining populations. However, the wide range of characteristics observed across and within breeds, bloodlines and flocks suggests that the optimal approach would be unique to any given set of populations and breeding objectives. Investigating across-country genomic prediction and imputation of these populations is recommended.This study examined 3509 genotypes, which are summarised by breed and country in Table The AUS Merinos are historically grouped into the bloodline of origin, partly by performance in fibre diameter, but also by region, with the ‘fine’-type strain popular in high rainfall zones, the ‘medium’ in cropping zones and the ‘strong’ more common in drier pastoral areas . The ElsAnalyses were firstly conducted for (n = 13) subpopulations defined by breed and country of origin (for common breeds), with combinations examined. Second, analyses were repeated only for Merinos with populations defined by the (n = 9) bloodlines as described above. However, the ‘Langgewens’ group was often excluded from analyses due to the small number of genotypes, and the fact that this small subgroup was not pertinent to the outcome of the study as representing SA Merinos.Genotyping was performed using the OvineSNP50 Chip . Quality control measures were applied to SNPs  and samples . Randomly missing SNPs were imputed with ‘FIMPUTE’ (Version 2.2)  and ‘BeaST statistics as measures of divergence. Relationships based on the genomic relationship matrix (GRM) [e). Relationships between more closely linked markers tend to reflect deeper histories since it would require an increasing number of generations for recombination to segregate markers more closely linked [e at a given number of generations ago, which can be used to deduce the population history by trends in the effective population size. Unless stated otherwise, plots were prepared in the ‘ggplot2’ ‘R’ package [Mean allele frequencies within populations were used to derive expected and observed levels of heterozygosity as summary statistics of overall gene diversity. Proportional differences in frequencies between subpopulations were represented by Fix (GRM)  and decoix (GRM)  was usedy linked . Furthery linked  showed t package .pi is the frequency of the ith allele in a defined population. Genetic distances between populations were determined by pairwise FST statistics which was calculated for each marker, and averaged over all markers by deriving The expected  .Genetic relationships were examined by calculating the genomic relationship matrix (GRM) in R according to VanRaden :\documenm x n  and m x n) where the jth of m replicates of the allele frequency VR) as where  package  with the package . The firGenetic group proportions and ancestry for each of the samples were estimated by unsupervised runs of ADMIXTURE with a varying number of groups, K. ADMIXTURE produces a cross-validation statistic, ‘CV error’, as an indication of accuracy relative to the choice of the number of groups, K. The lowest CV error is considered the ‘best’ choice of K for determining proportions of shared chromosome segments from co-ancestry. ADMIXTURE analysis was repeated for K = 3 to K = 20 and results of K = 3, 5, 7 and 10 were chosen for visualization by stacked bar plots. This entailed plotting the proportions of each genetic group (Q) within an individual animal by a stacked bar graph. The CV error statistic for K between 3 and 20 was also plotted.Linkage disequilibrium (LD) between any two markers was calculated by:r2 estimate . A physical distance was assumed for LD decay by multiplying the depth of pairwise SNPs with the mean distance between adjacent SNPs (~ 55 KB). No attempt was made to estimate LD at intervals shorter than ~ 55 KB spacing, as this would rely on much fewer SNPs that are only sporadically associated by shorter distances. The LD procedure was repeated with a more stringent filter on MAF (> 0.1), but effects were negligible (results not shown). The pattern of LD decay over distance was plotted for breed and bloodline groups. For non-syntenic LD, results were subset to include only pairwise comparisons from different chromosomes and a single mean was calculated as there was no need to observe non-syntenic decay over distance.n where n = 1 to 11.75) where a given population at given sample size N is sampled from the full genotype matrix at random.To investigate the effect of sample size on LD estimates, the procedure for non-syntenic LD was repeated for a range of sample sizes (N = 2t) as:LD estimates were used to calculate historical effective population size  with the reciprocal of sample size (N) and the correction factor suggested by Hill and Robertson, (1968).Additional file 2: Fig. S2. The third (PC3) to fourteenth (PC14) principal components of genotypes identified by breed group.Additional file 3: Fig. S3. The third (PC3) to fourteenth (PC14) principal components of genotypes identified by bloodline.Additional file 4: Fig. S4. Historical effective population sizes (Ne) for Merino bloodline groups from 2 to 400 generations ago. The vertical-line represents the timepoint used for ‘current’ NeAdditional file 5: Fig. S5. (A) Cumulative proportions of variance (POV) explained by the first 40 principal components of 'by breed' and 'by Merino bloodline' analysis. (B) The "CV-error" statistic resulting from sequential ADMIXTURE runs or K = 3 to K = 20 of 'by breed' and 'by bloodline' analysis.Additional file 6: Table S1 Mean relatedness across breed groups according to the genomic relationship matrix (G). Estimates in bold are the internal relatedness for a breed group calculated without the diagonal of (G).Additional file 7: Table S2 Mean relatedness across Merino bloodline groups according to the subset genomic relationship matrix (GM). Estimates in bold are the internal relatedness for a bloodline group calculated without the diagonal of (GM).


FIS) values indicated the existence of population substructure with evidence of inbreeding. In commercial layers, a high expected heterozygosity He = 0.640 ± 0.042) and a negative FIS were observed. The positive FIS and high He estimates observed in village populations were due to the heterogeneity of samples, owing to free mating facilitated by communal feeding patterns. Highly admixed nature of phenotypes was explained as a result of rearing many phenotypes by households (58%) and interactions of chickens among neighboring households (53%). A weak substructure was evident due to the mating system, which disregarded the phenotypes. Based on genetic distances, crown chickens had the highest distance to other phenotypes, while the highest similarity was observed between non-descript village chickens and naked neck birds. The finding confirms the genetic wealth conserved within the populations as a result of the breeding system commonly practiced by chicken owners. Thus, the existing local chicken populations should be considered as a harbor of gene pool, which can be readily utilized in developing locally adapted and improved chicken breeds in the future.Local chicken populations belonging to five villages in two geographically separated provinces of Sri Lanka were analyzed using 20 microsatellite markers to determine the genetic diversity of local chickens. Population genetic parameters were estimated separately for five populations based on geographic locations and for eight populations based on phenotypes, such as naked neck, long legged, crested or crown, frizzle feathered, Giriraj, commercial layer, crossbreds, and non-descript chicken. The analysis revealed that there was a high genetic diversity among local chickens with high number of unique alleles, mean number of alleles per locus (MNA), and total number of alleles per locus per population. A total of 185 microsatellite alleles were detected in 192 samples, indicating a high allelic diversity. The MNA ranged from 8.10 (non-descript village chicken) to 3.50 (Giriraj) among phenotypes and from 7.30 (Tabbowa) to 6.50 (Labunoruwa) among village populations. In phenotypic groups, positive inbreeding coefficient ( The choice of chickens by rural small-scale poultry farmers in Sri Lanka is often indigenous chickens for both egg and meat production, owing to numerous advantages they bring, such as disease resistance, high fertility, good maternal quality, longevity, ability to utilize poor-quality feeds, and most, importantly, the ability to manage them under a harsh environment condition with low level of management inputs despite their poor productivity compared with commercial layers and broilers . MoreoveHowever, such valuable indigenous genetic pool with undiscovered genetic potential has been eroding due to various reasons, especially in developing counties. Furthermore, some of the poultry breeds in Sri Lanka have already been lost or are at the risk of extinction . ConservSo far, a large number of studies have been conducted to characterize the chicken populations in Asia and Africa using microsatellite markers due to their highly polymorphic nature and abundance throughout the entire genome . With reIn this context, this study was conducted to determine the genetic diversity of local chicken populations in two selected areas and among identified phenotypes in Sri Lanka using microsatellite markers.Two veterinary divisions, Thirappane  in the North Central Province and Karuwalagaswewa  in the North Western Province were selected for sample collection based on the highest density of local chickens in Sri Lanka . Accordi® filter paper  and stored at room temperature until it was used for the analysis . Among 818 samples, 192 samples were purposively selected for genotyping based on locations, households, and phenotypes –FAO Advisory Group on Animal Genetic Diversity . The PCRThe total number of samples used in the analysis was divided into five populations according to the geographical locations (villages) of sampling as DM, LA, OT, TB and TH , and to p-value of the test  for all locus-population combinations and linkage disequilibria (LD) between all pairs of loci were determined using the program Genepop version 4.1.3 . HWE wasthe test  for eachAllelic frequency, mean number of alleles per locus (MNA), observed heterozygosity (Ho), and expected heterozygosity (He) were calculated to quantify the genetic variation within populations using Microsatellite Toolkit version 3.1  and FSTAK) values at 1–6 with 30 simulations for each K, assuming an admixture model with correlated allele frequencies using a burn-in of 100,000 followed by 100,000 Markov Chain Monte Carlo iterations to check the level of admixture and substructuring of the populations. The most probable number of the clusters (K) was selected based on the highest averaged log probability of data [Ln Pr(X/K)] with the lowest variation {Var[Ln Pr(X/K)]} among the 30 runs. Ad hoc quantity (ΔK) based on the rate of change in the log probability of data between successive K values was calculated to verify the best K using the web-based STRUCTURE HARVESTER  was performed using GENETIX 4.05 , and correlation between distance matrices  were computed using Arlequin version 3.5.2.2  dendrogram between populations was constructed from Nei’s standard genetic distances DS;  using DI 3.5.2.2 . The SplT values .The present study assessed the genetic diversity and genetic structure within Sri Lankan local chicken populations, which have been defined using two different boundaries of geographical boundaries (five villages) and phenotype-based boundaries (eight phenotypes). The results are also presented accordingly.p < 0.05) while no pair of loci rejected LD (p > 0.05).It was observed that among 20 loci across five geographic populations used in the analysis, several loci in individual populations  were deviated from HWE  to 16 (LEI0234 in DM). Moreover, 27 private alleles, which were unique to one population, were found among five populations, e.g., 6 in DM, 4 in LA, 6 in OT, 9 in TB, and 2 in TH. Furthermore, both the mean frequency of individual private alleles across the five populations (1.97%) and the population-specific frequency of the private alleles (1.3%) were very low, including a high number of migrants per population (8.33), similar to what was reported by FIS indicates important properties of the mating system within populations , NN (13), LL (3), and CL (1). The MNA per locus per phenotype ranged from 8.1 in VC to 3.5 in GR .The genetic relationships between populations were determined using Reynolds’ genetic distances assuming that the genetic differentiation occurs solely due to genetic drift .The two villages of TB and TH at Karuwalagaswewa clustered into one group separating other three villages  at Thirappane. The largest (0.0240) and the smallest (0.0090) pairwise genetic distances were observed between TH and OT and between TH and TB, respectively . FurtherFST estimates. Two main clusters were identified, where VC, NN, LL, and FF, were clustering together, while CL, GR, and CR formed a separate cluster, leaving CC as a unique phenotype. Similarly, the highest genetic distance in CC to other phenotypes was found in NeighborNet tree  against each K indicated that a higher ΔK value at K = 2. Therefore, K = 2 was selected as the most probable number of the clusters to reveal the population structure in both situations. Structure analysis indicated the absence of population genetic structure among local chicken populations in Sri Lanka.Graphic displays of the estimated membership coefficients of each individual to each population based on the phenotypes and based on villages at 2 ≤  K value . AccordiCompared with the phylogenetic reconstruction methods, PCA provided a better understanding of the genetic relationship among the local chicken populations, precisely the level of admixture . SimilarThe present study investigated the genetic diversity and population genetic structure of two categories of indigenous chicken populations defined by geographical boundaries and phenotypic boundaries in two geographically distant regions of Sri Lanka.The result showed that some loci were deviated from HWE, probably due to the deficiencies of heterozygotes. Several factors, like the within-population fragmentation created by clusters of households, which were involved in our stratified sampling , could bThe total number of alleles per locus per population, the number of private alleles, and MNA per locus across the populations all indicated a high level of genetic diversity within the indigenous chicken populations of Sri Lanka. Furthermore, the highest MNA was exhibited by VC, NN, and LL, followed by CL, CR, FF, CC, and GR. Comparatively, low MNAs were recorded from other local chicken populations in the world, for example, Vietnamese local chickens (5.1) , HungariFIS value (−0.016) were observed in CL  and non-random mating  in the five populations or in the eight phenotypes, suggesting a non-selective mating regime practiced among the Sri Lankan local chickens.However, the expected and observed heterozygosity values were not significantly different  compared with village populations of TH and OT, which were located around 120 km far apart and separated by the highest genetic distance . HoweverK = 3 and 4, as represented by yellow color, where most of them had an exotic genetic background. Interestingly, several birds in naked neck phenotype showed the same clustering pattern along different K values, showing some substructure as illustrated in The graphical illustration based on structure analysis of chicken phenotypes did not show a distinguished population genetic structure, and the birds shared a highly admixed genetic background . This waK (K = 2), a similar population structure was observed in the two villages of LA and OT but leaving DM separated.Similarly, a high level of admixture was observed among the five geographical populations too , though Similar results were also found in AMOVA where most variability was found at intra-individual level (89%) both within the populations defined by geographical boundaries and phenotypic boundaries. In addition, the observed genetic distances between the populations also confirmed the argument.Local chickens of Sri Lanka do not have a distinguished population genetic structure, for example, a specific breed. However, a weak clustering was evident with the households due to the mating system practiced. One of the earlier studies by Gallus gallus) subspecies and other three wild Jungle Fowl species, it is evident that the backyard chicken populations of Sri Lanka rooted back to the Red Jungle Fowl that is not inhabitant in Sri Lanka, but not to the endemic species of Ceylon Jungle Fowl  of the country  and Institutional Animal Care and Use Committees (IACUC) of the International Livestock Research Institute (ILRI), Nairobi, Kenya.MI, JH, and PS conceptualized and designed the study. RL acquired the data. AS and JH analyzed the data. AS, JH, and PS interpreted the results. AS and PS drafted the article. AO, JH, and PS critically revised the article. MI, AO, JH, and PS gave the final approval of the version to be published. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Euphydryas aurinia) is a critically endangered butterfly species in Denmark known to be particularly vulnerable to habitat fragmentation due to its poor dispersal capacity. We identified and genotyped 318 novel SNP loci across 273 individuals obtained from 10 small and fragmented populations in Denmark using a genotyping‐by‐sequencing (GBS) approach to investigate its population genetic structure. Our results showed clear genetic substructuring and highly significant population differentiation based on genetic divergence (FST) among the 10 populations. The populations clustered in three overall clusters, and due to further substructuring among these, it was possible to clearly distinguish six clusters in total. We found highly significant deviations from Hardy–Weinberg equilibrium due to heterozygote deficiency within every population investigated, which indicates substructuring and/or inbreeding . The stringent filtering procedure that we have applied to our genotype quality could have overestimated the heterozygote deficiency and the degree of substructuring of our clusters but is allowing relative comparisons of the genetic parameters among clusters. Genetic divergence increased significantly with geographic distance, suggesting limited gene flow at spatial scales comparable to the dispersal distance of individual butterflies and strong isolation by distance. Altogether, our results clearly indicate that the marsh fritillary populations are genetically isolated. Further, our results highlight that the relevant spatial scale for conservation of rare, low mobile species may be smaller than previously anticipated.The marsh fritillary ( The marsh fritillary (Euphydryas aurinia) is a critically endangered butterfly species in Denmark known to be particularly vulnerable to habitat fragmentation due to its poor dispersal capacity. We identified and genotyped 318 novel SNP loci across 273 individuals obtained from 10 populations in Denmark using a genotyping‐by‐sequencing (GBS) approach to investigate its population genetic structure. Altogether, our results clearly indicate genetic substructuring and reduced gene‐flow among populations of the marsh fritillary, which are seriously affecting the genetic variability and level of inbreeding present. IncreasConserving genetic diversity is a key priority for biodiversity management to ensure adaptive potential, yet population fluctuations lead to low effective genetic population size  butterfly species is likely to be a case in point. This species has a short flight season in May–June and females emerge with several hundred fully developed eggs, mate shortly after emergence, and typically lay these eggs within meters of their pupation site. Under good conditions, females can produce additional egg batches that may be laid further from the emergence site, but dispersal in the adult stage is mostly rather limited. Similarly, the larvae, which overwinter in family clusters, only move short distances and mostly in the final instar in spring prior to pupation  , which is a reproducible, highly multiplexed next‐generation sequencing approach that uses restriction enzymes to reduce genome complexity allowing for simultaneous single nucleotide polymorphism (SNP) discovery and genotyping  Is there evidence of isolation by distance in Danish populations of the marsh fritillary and at what spatial scales can it be detected? (2) Do populations with lower levels of inbreeding also show reduced genetic diversity? and (3) Are the levels of genetic variability and inbreeding related to the genetic isolation among populations?22.1A total of 300 3rd or 4th instar larvae of the marsh fritillary were collected from 10 sites in Denmark .All the genomic analysis and the bioinformatic filtering of the data have been outsourced to the Cornell University Genomic Diversity Facility (US)  and by running 100 ng of each DNA sample on a 1% agarose gel, respectively.The total genomic DNA was extracted from larvae using the DNeasy® Blood & Tissue Kit  according to the manufacturer's protocol for purification of total DNA from insects. DNA quantity and quality was verified using a fluorometer  was digested for 2 hr with the restriction enzymes ApeKI, EcoT22I, and PstI, in separate essays, using a tenfold excess of enzyme and reaction conditions as specified by the endonuclease manufacturer (New England Biolabs). After ligation of appropriate adapters  control, were prepared according to Elshire et al. (n = 15) were discarded and not submitted for sequencing. Briefly, individual DNA samples were digested with the restriction enzyme and adapters were ligated as described previously. The adapters comprised a set of 96 different barcodes containing adapters and a “common” adapter. Individual ligations were pooled, and purified using QIAquick PCR purification kit (Qiagen). Genomic fragments were then amplified in a 50‐μl volume containing 2‐μl pooled DNA fragments, 1× Taq Master Mix (New England Biolabs), and 25 pmol, each, of the following primers: (a) 5′‐AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT‐3′ and (b) 5′‐CAAGCAGAAGACGGCATACGAGATCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT‐3′. PCR cycling consisted of 72°C for 5 min, 98°C for 30 s, followed by 18 cycles of 98°C for 30 s, 65°C for 30 s, and 72°C for 30 s, with a final extension step at 72°C for 5 min. The EcoT22I GBS library was purified again, as above, and an aliquot was run on an Agilent BioAnalyzer 2100 for evaluation of fragment sizes and the presence of adapter dimers. After quantification on a Nanodrop 2000 (Thermo Scientific), the three 96‐plex libraries were diluted and sequenced  using three lanes on the Illumina HiSeq 2000. GBS library preparation .Illumina raw reads were processed using the default parameter of the Universal Network‐Enabled Analysis Kit (UNEAK)  in a window of 50 SNPs (sliding window overlap 10 SNPs at a time). This filtering process is described in Figure S2.The resulting raw SNP dataset from the UNEAK pipeline was further filtered using Golden Helix SNP and Variation Suite  and PLINK v1.07 , expected heterozygosity (HE), unbiased heterozygosity (uHE) and inbreeding coefficient (FIS), mean percentage polymorphic loci (%P), and mean effective alleles (Ne) were likewise calculated using GenAlEx 6.501 , based on the genetic distances using GenAlEx v6.501 to examine how populations would cluster along principal component axes 1 and 2. Finally, Mantel's tests were performed on all the populations investigated in order to determine whether there was an overall correlation between geographic distance and genetic divergence . However, most of these SNPs had low coverage or were only present in a small number of individuals. After the complete filtering procedure, 318 SNPs were maintained in our matrix for 273 individuals with an overall call rate of 93.57% . Over all of these loci, the mean coverage per locus per individual was 87.88 .The UNEAK pipeline recovered 30,137 bi‐allelic SNP loci , both when pooled altogether and when considered singularly. All the deviations were due to heterozygote deficiency as can be seen by the positive FIS values ranging from 0.1 to 0.228 (Table O), unbiased heterozygosity (uHE) and inbreeding coefficient (FIS), mean percentage polymorphic loci (%P), and mean effective alleles (Ne), are listed in Table FST values were highly significant (p < .001).Deviations from HWE were found to be highly significant for all the 10 populations investigated  and each of the sampled populations: Region A (population 1) is characterized by a single genetic cluster; Region B is subdivided into two different clusters, one including populations 2 and 3 (dark blue) and an additional cluster exclusively including population 4 (light blue); and Region C genetic subdivision is more complex and characterized by 3 private clusters . However, while population 10 is mainly characterized by a single genetic component (orange cluster), populations 5 to 9 showed varying levels of genetic admixture of red and green clusters, with populations 5, 7, and 8 showing a predominance of red cluster and 6 and 9 populations a predominance of the green genetic component. Overall, ADMIXTURE results for K ≥ 6 provided similar outputs, but progressively increased the level of genetic resolution within the E. aurinia populations, with K = 10 providing a genetic clustering result mainly differentiating each of the sampled marsh fritillary populations with varying levels of genetic admixture  . The graphical visualization of the ADMIXTURE results for the 273 individuals and K ranging from two to 12 clusters is shown in Figure 5 . The regression of genetic divergence (FST) against log10 of geographic distance in km was highly significant for all pairwise population comparisons  approach to increase the knowledge on the population genetic structure of the critically endangered marsh fritillary butterfly in Denmark. This study documents the identification of an informative SNP loci panel and demonstrates that the GBS approach represents a powerful tool to define genetic relationships at the intraspecific level. Moreover, this SNP panel provides an important genetic resource for further genetic studies of the marsh fritillary and is a cost‐effective and rapid method that can well describe the genetic variability of other nonmodel species with limited genetic resources.The stringent filtering procedure that we have applied (GQ = 98)  could have overestimated the heterozygote deficiency and the degree of substructuring of our clusters  clearly indicating a high level of inbreeding within populations which indicates substructuring and/or inbreeding due to demographic stochasticity . The demographic stochasticity is increasing the rate at which inbreeding increased. The consequences of the increased inbreeding are reinforced by the limited dispersal of the butterflies, which is limiting the gene flow among populations. The limited gene flow is also confirmed by the fact that strong evidence for isolation by distance was found, even when accounting for the spatial structure in our data by omitting comparisons among the three regions in which the marsh fritillary is found ; Data curation ; Formal analysis ; Funding acquisition ; Investigation ; Methodology (lead); Project administration ; Resources ; Software ; Supervision ; Validation (lead); Visualization ; Writing‐original draft (lead); Writing‐review & editing . Aritz Ruiz‐Gonzalez: Conceptualization ; Data curation ; Formal analysis ; Funding acquisition ; Investigation ; Methodology ; Project administration ; Resources ; Software ; Supervision ; Validation ; Visualization ; Writing‐original draft ; Writing‐review & editing . Simon Bahrndorff: Conceptualization ; Data curation ; Formal analysis ; Funding acquisition ; Investigation ; Methodology ; Project administration ; Resources ; Software ; Supervision ; Validation ; Visualization ; Writing‐original draft ; Writing‐review & editing . Nanna Renee Lauridsen: Conceptualization (supporting); Data curation (supporting); Formal analysis (supporting). Thøger Nisbeth Henriksen: Conceptualization (supporting); Data curation (supporting). Anne Eskildsen: Conceptualization ; Data curation ; Formal analysis ; Funding acquisition ; Investigation ; Methodology ; Project administration ; Resources ; Software ; Supervision ; Validation ; Visualization ; Writing‐original draft ; Writing‐review & editing . Toke Thomas Høye: Conceptualization ; Data curation ; Formal analysis ; Funding acquisition ; Investigation ; Methodology ; Project administration ; Resources ; Software ; Supervision ; Validation ; Visualization ; Writing‐original draft ; Writing‐review & editing .Appendix S1Click here for additional data file.


Hermetia illucens) is the most promising insect candidate for nutrient-recycling through bioconversion of organic waste into biomass, thereby improving sustainability of protein supplies for animal feed and facilitating transition to a circular economy. Contrary to conventional livestock, genetic resources of farmed insects remain poorly characterised. We present the first comprehensive population genetic characterisation of H. illucens. Based on 15 novel microsatellite markers, we genotyped and analysed 2862 individuals from 150 wild and captive populations originating from 57 countries on seven subcontinents.The black soldier fly (H. illucens, and ongoing introgression between domesticated strains globally traded for large-scale farming and wild populations in some regions.We identified 16 well-distinguished genetic clusters indicating substantial global population structure. The data revealed genetic hotspots in central South America and successive northwards range expansions within the indigenous ranges of the Americas. Colonisations and naturalisations of largely unique genetic profiles occurred on all non-native continents, either preceded by demographically independent founder events from various single sources or involving admixture scenarios. A decisive primarily admixed Polynesian bridgehead population serially colonised the entire Australasian region and its secondarily admixed descendants successively mediated invasions into Africa and Europe. Conversely, captive populations from several continents traced back to a single North American origin and exhibit considerably reduced genetic diversity, although some farmed strains carry distinct genetic signatures. We highlight genetic footprints characteristic of progressing domestication due to increasing socio-economic importance of H. illucens. Upon the recent advent of its agronomic commercialisation, however, current human-mediated translocations of the black soldier fly largely involve genetically highly uniform domesticated strains, which meanwhile threaten the genetic integrity of differentiated unique local resources through introgression. Our in-depth reconstruction of the contemporary and historical demographic trajectories of H. illucens emphasises benchmarking potential for applied future research on this emerging model of the prospering insect-livestock sector.We document the dynamic population genetic history of a cosmopolitan dipteran of South American origin shaped by striking geographic patterns. These reflect both ancient dispersal routes, and stochastic and heterogeneous anthropogenic introductions during the last century leading to pronounced diversification of worldwide structure of The online version contains supplementary material available at 10.1186/s12915-021-01029-w. Insects are considered one of the most promising agricultural resources to address the socio-economic challenges of a continuously growing human population due to their dual sustainability advantage –3. On thHermetia illucens , is a particularly promising candidate and considered the ‘crown jewel’ of the fast-growing insect-farming industry [The black soldier fly (BSF), industry . BSF larindustry –18. Remaindustry –21. Giveindustry , 23, BSFindustry , 25, swiindustry , 27 and industry , 29. Morindustry , 31. AccIn contrast to conventional livestock, genetic resources of farmed insects remain poorly characterised . PhenotyIn this context, historic documentation of organism distributions represents a crucial basis. The BSF is considered cosmopolitan across tropical, subtropical and temperate regions –39, and Contrary to ancient natural dispersal and presumably highly stochastic, unintended anthropogenic introductions of BSF into non-native areas that occurred in recent history, the presently increased global farming activity is likely to result in more extensive translocations of genetically uniform captive populations, possibly reinforcing one-directional admixture worldwide. A recent investigation of the global phylogeography of BSF based on mitochondrial COI marker sequences  detectedH. illucens. A comprehensive sample of 2862 individuals from 150 wild and captive populations collected in 57 countries on seven subcontinents were genotyped based on 15 newly developed polymorphic microsatellites. We used this large-scale dataset for population genetics analyses to characterise genetic diversity and to identify distinct genetic clusters of BSF worldwide. We further applied coalescence-based simulations for demographic inference with Approximate Bayesian Computation (ABC). These complementary analyses allowed the following questions to be addressed: (1) Can genetic hotspots reveal the geographic origin of the species? (2) Do population genetic patterns allow the reconstruction of native range expansions and the demographic trajectories of historic dispersal routes into non-native areas? and (3) To what extent do population genetic characteristics of indigenous and naturalised wild populations differ from captive populations in modern mass production facilities in various regions worldwide? We moreover hypothesise that more than two decades of captive breeding and common global trade of a well-described North American BSF laboratory population has left a detectable genetic footprint accompanying domestication. Based on that conjecture, we further aimed to investigate possible human-mediated impacts of domesticated BSF strains increasingly used for farming on present biogeographic population genetic patterns of this cosmopolitan insect, with special emphasis on the direction and the extent of local genetic introgression.This study aimed to ascertain the global population genetic structure and elucidate the contemporary demographic history of H. illucens chromosomal assembly GCA_905115235.1 [All 2862 individuals represented unique multilocus genotypes , is shown in Fig. FST = 0 to 0.626  to document substantial global genetic structure with an overall FST = 0.239  is shown in Fig. We characterised general patterns of population differentiation and applied complementary approaches to identify key factors shaping BSF global population genetic structure. Maximum-likelihood (ML)-based cluster analyses and model selection using KIC goodness-of-fit statistics supported AR), analysis of molecular variance  clearly indicates genetic mismatches of wild and captive populations in some regions, as also reflected by the FCA , the difference was much stronger across Australasia (r = 0.236 vs. r = 0.413), and substantial in Europe (r = 0.019 vs. r = 0.389).On a global scale, AR  than South American populations , but not different from North American wild populations. Only wild populations from central-east Africa and two wild populations from west Africa grouped close to captive African populations and North American wild populations  and within (DIS) populations [DST/DIS ratios than both wild and captive populations in other clusters, but no significant differences were detected between the two latter groups. By contrast, no significant differences among the three groups were found across the remaining 98 pairs of loci.The majority of captive populations from North America, Europe, Africa and Asia grouped in clusters 1–4, which exhibited shallow but distinct structure and similar levels of ts Table . Populatulations  across aP = 0.47; Figure S6-B, Tables S11-13, Additional file Captive populations predominating across North America, Europe, Africa and (to a lesser extent) Asia were closely related but markedly distinct from the geographically closest wild populations Fig. . The cloA better understanding of the frequency, extent and directionality of introgression between globally structured wild populations and genetically highly uniform and distinct captive strains is key to evaluate the impact of increasing global trade and large-scale BSF farming on indigenous and naturalised populations. Further, the characterisation of genetic origins of cross-bred captive strains could support future breeding efforts and traceability.1 hybrids and backcrosses with parental groups represented by west African wild populations and European captive populations (cluster 2) reportedly translocated to a nearby BSF facility 2 years prior  and regionally abundant captive populations (clusters 1–3)  and captive populations of North American origin (clusters 1–3) were identified in Asian clusters 4 and 5 Table . While cSeveral wild populations sampled across considerable distances in Africa demonstrate introgression from modern BSF farms and research facilities Figs. c and 3, Interestingly, recent human-mediated admixture between distantly related clusters contributed to increased genetic variance between individuals within populations, as exemplified for Asian populations Table b, ratherOur comprehensive population genetic study of wild and captive populations of BSF on a global scale using highly discriminating microsatellites permitted a fundamental genetic characterisation of this commercially important insect. Our data provided novel insights into (1) the geographic distribution of genetic variation and population structure of wild and captive populations; (2) the origin of specific colonisations and general patterns of range expansions across the world, including basic delimitations of historic vs. contemporary events; and (3) the genetic relationships between wild and captive populations, which exhibit genetic footprints of domestication, and local gene flow between the two.Hermetia [Worldwide patterns of nuclear genetic diversity are consistent with the previously presumed origin of BSF in the Americas , 45. In Hermetia , South AHermetia . Both coReduced genetic diversity in Asia and Australia indicates BSF is not native to these regions. Characteristic genetic signatures and IBD patterns of naturalised BSF populations across the entire Australasian region suggest a single successful colonisation event followed by successive range expansion, resulting in a unique population structure Fig. , Table 2We infer that Africa was colonised via three independent demographic events in different parts of the continent Fig. . ConsistEuropean wild populations are all highly distinct from European captive populations and appear to stem from at least three independent introductions from the Americas plus a fourth lineage that was the result of a remarkable admixture event. Clusters 15 and 14 from Central and South America, which dominated southern and south-eastern European populations, respectively, were otherwise not detected outside their indigenous ranges and are thus both considered unique introductions  would be plausible for all non-native regions. Not only in the Mediterranean, where our finding of a Central American origin supports previous speculations on local BSF occurrence by the 16th century , but alsripennis , 54–57. ripennis , 58. Admripennis , 59, 60.ripennis . Yet, onripennis .After initial establishment (with or without admixture), human activity could have mediated numerous unintended secondary translocations within non-native continents that may have accelerated range expansions even prior to the recent advent of BSF farming. However, long-distance dispersal of initial colonisers did not disturb pronounced IBD patterns of naturalised populations at large and medium geographic scales  originating from a single source in south-eastern North America that were spread successively across other continents, notably Europe, Africa and Asia. This conclusion is supported by a recent phylogeographic study based on mitochondrial COI markers . This foNorth American captive populations of cluster 1 Fig.  harbour D-statistics suggested that wild-derived captive populations assigned to other clusters may have been similarly affected by genome-wide demographic effects. Thus, the maintenance (or restoration) of genetic signatures characteristic of the original North American strain potentially reflects common selective pressures in modern BSF farming that counter the decay of inter-chromosomal long-distance LD [Strikingly elevated levels of LD of seven specific locus pairs that are characteristic of the closely related clusters 1–4 further support their shared demographic origin . Such getance LD . We therMoreover, despite substantially decreased genetic diversity compared to native wild populations, severe inbreeding of the domesticated populations was not indicated. The maintenance of moderate genetic diversity in domesticated strains may indicate past , 71 and/Ne, which varied across populations but were well comparable between provenances in all regions and even tended to be lower for wild populations outside South and Central America  and based on a single female from the breeding population at the Research Institute of Organic Agriculture (FiBL), Switzerland, which was reared isolated in captivity for about one decade. The library was analysed on an Illumina MiSeq platform using the Nano 2 × 250 v2 format. Microsatellite inserts with a tetra- or a trinucleotide motif of at least 6 repeat units or a dinucleotide motif of at least 10 repeat units were available in 2856 assembled contigs or singlets. Suitable primer design was possible for 2228 microsatellite candidates. Based on an initial screening of specimens deriving from few selected populations supposedly reflecting the species’ global distribution range, fifteen di- and trinucleotide microsatellite candidates, which were polymorphic and amplified without null alleles, were selected for optimising our multiplex PCR protocols , 1 μL  genomic DNA and 0.2–0.9 μM of both forward and reverse primers for each marker. Total primer concentrations as well as specific ratios of labelled and unlabelled primers  were adjusted according to relative marker-specific amplification effectiveness within optimised multiplex reactions as specified in Table S2, Additional file Genomic DNA was extracted from adult thorax muscle or from larval heads including one or two (depending on size) larval segments. Tissue was ground in 250 μL of 5% Chelex solution , incubated at 65 °C for 15 min, boiled at 98 °C for 10 min and then centrifuged at 13,000 rpm for 3 min before transferring the supernatant. Three multiplexed PCRs with five markers each were developed, considering dye combinations based on fragment size ranges , North America, Africa, Asia (including Sunda Islands), Australia  and Europe , rarefied to the lowest population sample size , were evaluated using the package PopGenReport v. 3.0.4 [AR was further analysed with linear mixed models [lme4 v. 1.1-21 [multcomp v. 1.4-10 [F-statistics (FST and FIS), observed and expected heterozygosity, as well as tests for deviations from Hardy-Weinberg-Equilibrium (HWE) within populations were computed and visualised using adegent, hierfstat, pegas v. 0.12 [strataG v. 2.0.2 [ggplot v. 3.2.1 [FIS were subjected to weighted linear regression for specific population groups of interest, with weights being set as the inverse of the residual variances of the response to account for lower variances of global wild populations, complemented by significance testing using Tukey contrasts. The software Arlequin v. 3.5.2.2 [D-statistics according to Ohta [GENETIX v. 4.05.2 [DST/DIS were log-transformed for variance stabilisation and subjected to linear mixed models, including locus pair as random effect and population groups of interest  as fixed effect, and respective post hoc Tukey contrasts between fixed effects.Data were analysed with R v. 3.6.1 , unless v. 2.8.3 , 102. Lov. 2.1.1 , 104 and 0.04-22 . Indicatv. 3.0.4 , 107. ARd models  using th. 1.1-21 . Locus w. 1.4-10 . Overall v. 0.12 , strataGv. 2.0.2  and ggplv. 3.2.1 . Cross-l 3.5.2.2  was used to Ohta  were com. 4.05.2 . Ratios FIS were adjusted based on the total number of populations, not accounting for tests across loci within populations.Alpha levels or confidence limits for assessing locus-specific (or pairwise) deviations from HWE and LD, as well as significant estimates of null allele frequencies, were adjusted based on the number of tests within single populations . Likewise, confidence limits for tests of population-specific poppr and subjected to significance testing as implemented in the package ade4 v. 1.7-13 [Analysis of molecular variance (AMOVA) was performed using . 1.7-13 –119 baseFST and corresponding significance testing , as provided by strataG. For pairwise FST tests, α was arbitrarily set to 0.0001 as the maximum conservative adjustment for that number of permutations. The resulting genetic distance matrix was visualised as an unrooted neighbour-joining tree using the package Ape [DCH, [poppr.Population structure was also explored through pairwise kage Ape . An addipe [DCH, ) betweenvegan v. 2.5-6 [FST / (1 − FST), and log-transformed geographic distances (in kilometres) between populations [sp v. 1.3-1 [SciViews v. 13.1 [Isolation by distance (IBD) patterns was evaluated using Mantel tests  based onv. 2.5-6 . Specifiulations . Based ov. 1.3-1 , 126 and v. 13.1 . In addisnapclust function of adegenet, which provides a rapidly converging maximum-likelihood solution by combining a geometric approach and the Expectation-Maximisation algorithm, to infer the optimal number of genetic clusters (K) in the data by applying KIC goodness-of-fit statistics for model selection [ggplot. Discriminant analysis of principal components was applied to depict genetic structure across individual MLGs based on cluster assignment (or sampling populations) as implemented in adegenet. This multivariate method focuses on variances between groups while minimising within group variation, and it characterises population subdivision with similar accuracy but faster than common Bayesian clustering algorithms [GENETIX v. 4.05.2 was conducted.We used the election , 129, segorithms . Retaine1 hybrid (0.5:0.5) and first generation backcross (0.25:0.75 or 0.75:0.25) detection, using the snapclust function as implemented in adegenet [To infer regional hybridisation and introgression based on estimates of ancestry coefficients, specifically suspected populations (or entire clusters) of interest and respectively suggested parental groups were subjected to dedicated analyses of Fadegenet .Ne) were calculated using the LD test, considering a mating system equivalent to lifetime monogamy and conservatively excluding all singleton alleles within populations, in NeEstimator v.2.1 [Effective population sizes  based on coalescent simulations as implemented in DIYABC v.2.1; ) to comp.1;  to cμ) drawn from a uniform prior distribution ranging from 10−5 to 10−3. For the coefficient P (the parameter of the geometric distribution describing the length variation of microsatellite loci) and mean single-nucleotide indel (SNI) mutation rate, we used uniform prior distributions ranging from 0.1 to 0.99 and from 10−8 to 10−5, respectively. We further chose a combination of three one-sample summary statistics that included mean number of alleles, mean genetic diversity [FST; [2; [To compare observed and simulated data, we used the allelic information of all 15 microsatellite loci  and genFST; [2; ). For eaAdditional file 1: Table S1. Allelic states at 15 novel microsatellite markers for 2862 unique multilocus genotypes of the black soldier fly, Hermetia illucens, from 150 wild and captive populations sampled from 57 countries on seven subcontinents.Additional file 2: Table S2. Microsatellite marker characteristics of the novel Hermetia illucens population genetics tool kit. Table S3. Population genetic characteristics of 150 wild and captive Hermetia illucens populations. Table S4. Countries of origin of investigated Hermetia illucens populations. Table S5. Population genetic characteristics according to subcontinental origin and provenance status (wild vs. captive). Table S6. Diversity and pairwise differentiation of globally inferred genetic clusters of Hermetia illucens. Table S7. AMOVA nesting provenance (wild vs. captive) within subcontinents for worldwide Hermetia illucens populations. Table S8. Model-based estimates of contrasts and significance levels for population-specific allelic richness. Table S9. Genetic isolation by distance for selected hierarchical groupings. Table S10. Specifications on the global distribution of Hermetia illucens genetic clusters presented in Fig. Table S11. Details on ABC analyses. Table S12. Posterior probabilities of demographic models inferred from ABC analyses. Table S13. Estimates of posterior distributions of population genetic parameters inferred from ABC analyses. Table S14. Selected group comparisons of inbreeding coefficients FIS. Table S15. Group-specific comparisons of variance component ratios for linkage disequilibrium between (DST) and within (DIS) populations. Table S16. Microsatellite properties relevant for ABC analyses. Figure S1. Discriminatory power of the novel microsatellite marker set for Hermetia illucens genotyping. Figure S2. Significant deviations from Hardy-Weinberg equilibrium for individual microsatellite loci tested within populations. Figure S3. Computational details on genetic cluster analyses and retaining discriminatory functions for visualisation. Figure S4. Global population genetic patterns of Hermetia illucens according to provenance (wild vs. captive) nested within subcontinent of origin. Figure S5. Neighbour-joining tree based on population pairwise FST across 150 Hermetia illucens populations. Figure S6. Demographic inference with ABC. Figure S7. Detection of hybrids and backcrosses: a west African case of introgression. Figure S8. Detection of hybrids and backcrosses: case-specific analyses of central-east African populations and predominantly farmed populations from Asia.