Boloria eunomia in Belgium surveyed over three generations using synchronized demographic and genetic datasets with the aim to characterize its genetic structure, its dispersal dynamics, and its demographic stability. By comparing the census and effective population sizes, and the estimates of dispersal rates, we found evidence of stability at several levels: constant inter-generational ranking of population sizes without drastic historical changes, stable genetic structure and geographically-influenced dispersal movements. Interestingly, contemporary dispersal estimates matched between direct field and indirect genetic assessments. We discuss the eco-evolutionary mechanisms that could explain the described stability of the metapopulation, and suggest that destabilizing agents like inter-generational fluctuations in population sizes could be controlled by a long adaptive history of the species to its dynamic local environment. We finally propose methodological avenues to further improve the match between demographic and genetic estimates of dispersal.Understanding the functioning of natural metapopulations at relevant spatial and temporal scales is necessary to accurately feed both theoretical eco-evolutionary models and conservation plans. One key metric to describe the dynamics of metapopulations is dispersal rate. It can be estimated with either direct field estimates of individual movements or with indirect molecular methods, but the two approaches do not necessarily match. We present a field study in a large natural metapopulation of the butterfly  The metapopulation concept provides an operational framework for both (evolutionary) ecologists and conservation managers . ClassicDispersal, individual movements potentially leading to gene flow ,10, is tDispersal rate is the proportion of individuals that move from a population to another. It can be estimated on the field through two approaches relying on distinct theoretical frameworks: demography and genetics. Demographic estimates of dispersal rates are obtained either through the direct monitoring of individual movements using visual observation or telemetric systems depending on landscapes and taxa ,21,22, oSTF is used as a proxy for historical effective number of dispersers  between pairs of populations [The genetic approach indirectly estimates effective dispersal rates through its impact on the genetic structure at the metapopulation level, determined from allelic composition and frequencies in the local populations . Most often, the genetic differentiation index ulations . A serieulations . Refinemulations ), and coulations ,32,33). ulations , methodsulations ,36) or pulations  have beeulations ,32,33,38ulations ,32, see ulations ,39 for e2) over three generations (three years). The bog fritillary Boloria eunomia has long been used as a model species in the metapopulation literature because of its patchy distribution and sensitivity to habitat fragmentation. Over the last three decades, important knowledge has accumulated regarding B. eunomia habitat use [(i)B. eunomia metapopulation based on genetic material collected on more than 1000 individuals over three generations across nine local populations;determine the genetic structure within a (ii)describe the dynamics of the whole-metapopulation through a thorough comparison between genetic and demographic estimates of dispersal rates;(iii)evaluate the long-term demographic stability of the metapopulation.Here, we present a field study in which we analyzed the population structure, the dynamics, and the stability of a natural butterfly metapopulation in the Belgian Ardenne combining demographic and genetic datasets obtained at a large spatial scale  long-distance dispersal movements, particularly difficult to record in the field, are not too frequent; (iii) a relatively high proportion of the dispersal movements are effective in terms of gene flow among populations.Despite important inter-annual fluctuations in census population sizes, local population extinctions were very rarely observed over two decades of field sampling in this metapopulation . We thusB. eunomia (formerly known as Proclossiana eunomia) is a Holarctic butterfly species occurring in wet meadows and some peat bogs in middle Europe. In this area, the species is a specialist, strictly associated with the bistort Polygonum bistorta  into improved pastures or spruce Picea abies plantations . We col Ardenne  in 2009,N) [For the three successive years of sampling, each site was visited daily during the flight period (excluding rainy days), and Capture-Mark-Recapture (CMR) sessions performed with a sampling effort normalized according to habitat patch area. We captured butterflies with a net, sexed and marked each of them with a unique identifier using a thin pen on the left hindwing at the first capture . We markN) . For detN) . To testAmong the 5481 individuals marked during CMR, one leg was preserved from 1217 of them in absolute ethanol and total genomic DNA later extracted using the column version of the DNeasy Blood and Tissue kit . From these 1217 DNA extractions, we multiplexed and amplified by PCR 12 microsatellite loci as described before . Tests fSTF [STF values was determined based on 10,000 random permutations, and p-values were adjusted with the Benjamini-Yekutieli correction [F model), which assumes that allele frequencies in different populations are likely to be similar. Five independent runs for each value of K (the number of clusters) ranging from one to nine were performed, using 500,000 iterations and a burn-in period of 50,000 steps. To detect the number of clusters that best fit the data, we estimated the rate of change in the log probability of the data between successive K values and the corresponding variance of log probabilities [p-values were calculated using 10,000 permutations. Despite the existence of two significant major genetic clusters, a large part of the genetic variance remained unexplained (see Results). We therefore performed supplementary clustering runs within each first-order cluster to search for significant sub-structuring, and continued the process across hierarchical levels until no more structure was detected [STF methods revealed that genetic structuring was significant at the level of our nine predefined populations (see Results). We thus continued with the genetic analyses for the nine populations separately, thus matching the partition used in the demographic estimates described above.Genetic differentiation between all pairs of populations was estimated with Wright’s pairwise fixation index FST  using ArFST  for eachrrection . Geneticrrection  includinbilities . We compdetected . SignifiSTF/(1–STF) against the natural logarithm of geographic distance between all pairs of samples is generally performed [STF stem from the balance between gene flow on the one hand and genetic drift on the other hand, STF estimates cannot be considered a proper proxy for gene flow, especially when population sizes are unequal [STF values using the method developed in [di metric of Spatial Heterogeneity in Effective population sizes (SHNe) as the sum of the inverse of population census sizes N inferred from demographic data. For each generation separately, we then computed the residuals of the linear regression between STF/(1–STF) and di across all pairs of populations, and used a simple Mantel test with 10,000 permutations to assess significance of the relationship between these residuals and the natural logarithm of geographic distances.To test for Isolation By Distance (IBD), that is the effect of Euclidian distance between populations on gene flow, a Mantel test regressing erformed . However unequal . We thusloped in . For eacNa), the expected (He) and observed (Ho) heterozygosities, and the ISF were calculated using Arlequin and averaged over all loci. The number of private alleles (Np) was estimated using Convert v.1.31 [A) and Private Allelic Richness (Ap) based on the minimum sample size were estimated by the rarefaction method implemented in HP-Rare v.1.1. We estimated contemporary effective population sizes (Ne) for each generation using the bias-corrected version of the linkage disequilibrium method described in [Ne (effective population size) and N (census population size) estimates for the nine local populations was assessed using a Spearman correlation test for each year separately.For the three generations and each of the nine populations, the number of alleles , and the two-phased Mutation model (TPM), with more than one-repeat mutations occurring at frequencies of either 10 and 20%. Wilcoxon sign-rank tests were used to determine significant departures from null distributions, with 10,000 iterations, and they were adjusted with the Benjamini-Yekutieli correction [We finally searched for the existence of past demographic events, i.e., bottlenecks and exponential changes in population sizes. We used Bottleneck v.1.2.02  to calcurrection . As a qurrection .N) of each of the nine populations separately . Regarding individual dispersal movements, we recorded a total of 49 inter-population movements over the three years, of which only one was considered as a long-distance movement, i.e., more than 5 km , 0.536 (p < 0.0001) and 0.467 (p = 0.049) for 2009, 2010 and 2011 respectively after SHNe correction . Over all years and populations, the Ne/N ratio was 0.38. At this contemporary timescale, we also detected significant correlation between the number of effective dispersal movements at the preceding generation (we detected 46 movements with GenClass2) and the number of movements observed by CMR, when pooling the three years . We also emphasize crucial methodological limits to our study (third section), which could affect the congruence between demographic and genetic parameters, notably due to the lack of integration of the effect of landscape structure on dispersal.Understanding the dynamics of natural metapopulations at relevant spatial and temporal scales is of prime importance to accurately feed both theoretical eco-evolutionary models and conservation plans . Adding iNpop/jNpop ratio. Comparing census and effective populations sizes, we found the mean Ne/N ratio to be 0.38 over years and populations. This is higher than the value usually observed in many systems , increasing their chance to reproduce at least once. Besides, the large availability of the host plant, combined with continuous egg laying in small batches over the whole female lifetime, increases reproductive success by creating a situation where mortality risks for eggs and larvae are spread over space and time. Although relatively high given the census population sizes, Ne were systematically far below the threshold of 1000 reproductive individuals ensuring high evolutionary potential to local populations [We estimated several important metrics summarizing the structure and dynamics of a natural butterfly metapopulation aiming at evaluating its stability. First, the ranking of local population sizes as estimated from intensive CMR campaigns  was consistent across generations. This means that, despite large observed fluctuations in population size between years in many of the local populations ,48, they0.1–0.2, ). We sugulations . AccordiSTF values than Pisserotte. While these two populations probably exchange a lot with the other populations of the network, Grande Fange could be more isolated than Pisserotte, and could have particular local dynamics allowing private genetic diversity to be maintained. Grande Fange could have experienced a recent founding effect followed by an expansion (as suggested by its deficit in heterozygous loci), but could also have acted as a past local refugia [STF values. The IBD analysis confirmed the suspected impact of geographic distance on genetic differentiation: about 25% of the genetic variance between populations could be attributed to Euclidian distances. It is noteworthy that both genetic structure and IBD were highly consistent over the three generations, meaning that the above-described temporal stability in the ranking of population sizes goes along with equilibrium in genetic structure. Nevertheless, the time lag between demographic processes and the genetic response could have hindered our ability to detect recent changes in population structure, even with fast evolving markers such as microsatellites [Second, we observed a stable genetic structure of the metapopulation over the three years of sampling. Bayesian clustering  revealed a first level of genetic partition separating the southern populations of Bérismenil, Mormont and Chapons from the six northern other populations. The Pisserotte and Grande Fange populations had the highest mixed ancestry among all populations, which agrees with their central positions in the network. However, Grande Fange had higher level of private allelic richness and  refugia  or funct refugia , the twotellites . Future B. eunomia dispersal movements overestimated the real number of effective short-distance movements  estimates of dispersal rates. There was a significant correlation between the demographic and genetic approaches, showing that dispersal movements were more frequent between close populations. This pattern agrees with the significant IBD pattern. As expected, CMR campaigns detected less long-distance dispersal movements (more than 5 km) than the genetic approach ,73. We yovements , as expeB. eunomia metapopulation, although we cannot exclude a role of weak source/sink patterns, like the putative case of Grande Fange . We hypothesize that the observed stability probably results from other eco-evolutionary processes and discuss this possibility hereafter. Metapopulation functioning might rely on source/sink dynamics, the permanent dispersal of individuals from a source population of good quality to a receiving population with demographic deficit living in habitats of poorer quality . On the B. eunomia is no exception. For instance, its larvae are attacked by a tiny hymenopteran parasitoid, which abundance regulates the butterfly local population size [B. eunomia emigrate seeking for patches with low male density, while males emigrate seeking for patches with high female density [B. eunomia to its local environment, including its predictable oscillations. Habitat quality and butterfly density within local populations are fine-grained at the scale of a few hundred meters [Castor spp.) in Belgium. The construction of beaver dams along the Lienne river created frequent and long-lasting floods during the winter in the wet meadows inhabited by Boloria eunomia. Although this is still an unproven but likely hypothesis, these floods should lead to very high mortality rates of the diapausing caterpillars, susceptible to cause this large local population to vanish over a couple of years: from over 1000 individuals in 2010 and 2011, only 6 males were captured in 2016, and none in the next years. We hope to be able to estimate the impact of the Prés de la Lienne extinction on the stability of the whole metapopulation in the forthcoming years.The focal metapopulation has been surveyed for decades, and we accumulated knowledge about various eco-evolutionary processes that might further explain its stability. Population parameters are often subject to oscillations as a result of the confrontation of intrinsic (phenotypes) to extrinsic (environments) factors (see review in ), and B.ion size ,76,77. Bion size . Togetheion size ,78 for dion size . The habion size . Adult m density . Besides density . Such sed meters , which sd meters . Howeverr = 0.5). We highlight below a few critical points that could improve the match between demographic and genetic approaches in B. eunomia (and beyond) in future works.The ability of demographic approaches such as CMR to estimate effective dispersal is still a matter of debate. It supposedly suffers from several biases: difficulties in the acquisition of field data, adequation of demographic models to natural situations, definition of dispersal movements, scale effects, etc. ,32,33,81B. eunomia, unsampled distant populations nonetheless connected by rare dispersal movements to our metapopulation could serve as reservoirs feeding the general stability of the whole-system, i.e., undetected source/sink dynamics. The eco-evolutionary mechanisms we proposed above to explain equilibrium  could in this case be less predominant. Underestimation of long-distance dispersal and overestimation of short-distance movements with the demographic approach raise one important question about the origin of the uncommon variance between the two approaches (~75% in our case). Can we attribute most of this unexplained variance to this CMR campaign effect, or should we look for other explanations? A number of other methodological biases can be mentioned. They include for instance the ability of genetic methods to effectively detect first-generation dispersers, incomplete genetic sampling, or asymmetric reproductive success. It will be difficult to tackle these issues in our current dataset. An easy improvement would nonetheless be the use of more numerous molecular markers, such as SNPs, to get more accurate genetic estimates.Despite intensive field work covering the whole flight period in a well-studied butterfly system, we were unable to capture the vast majority of long-distance dispersal movements using CMR data. This recurrent difficulty in the monitoring of individual movements has strong consequences on the ability to construct appropriate dispersal kernels, where long-distance movements play a key role, but also to define the exact contours of natural metapopulations. In Boloria eunomia presents distinct behaviors when encountering different matrix types [Finally, we observed a strong IBD pattern, which makes sense given that long-distance dispersal movements are less numerous than short-distance movements in the studied metapopulation. Nonetheless, ~75% of the genetic variance remains unexplained by the Euclidian distance between populations. ix types . AlthougB. eunomia provides an example of a metapopulation where destabilizing agents like inter-generational fluctuations in population sizes seem to be controlled by a long adaptive history of the species to its dynamic local environment, including the evolution of appropriate rates of dispersal. In such a case, population extinction should be the result of rare catastrophic events, whose consequences on short-term dynamics and long-term stability are of prime interest to study in this butterfly of conservation concern. In our study system, genetic and demographic approaches provided congruent estimates of dispersal rates. This comfortable situation may not hide the necessity to integrate, e.g., functional connectivity to fully capture the functioning of our metapopulation. Indeed, a non-negligible part of the variance in genetic differentiation remained unexplained. We hope that other case studies of such stable ‘unclassical natural metapopulations’ will be available to test for the generality of the mechanisms we have proposed to explain metapopulation equilibrium in B. eunomia.There is no doubt that classical metapopulations do exist, but they might not be as widespread as generally supposed, because extinction/recolonization cycles are not necessary characteristics of metapopulation functioning ,7. This


G−1) and pedigree  and weighted ssGBLUP (WssGBLUP) methods to predict Genomic Estimated Breeding Values (GEBVs) of crossbred animals. Different training population scenarios were evaluated: (SC1) ssGBLUP based on a single-trait model considering purebred and crossbred animals in a joint training population; (SC2) ssGBLUP based on a multiple-trait model to enable considering phenotypes recorded in purebred and crossbred training animals as different traits; (SC3) WssGBLUP based on a single-trait model considering purebred and crossbred animals jointly in the training population (both populations were used for SNP weights' estimation); (SC4) WssGBLUP based on a single-trait model considering only purebred animals in the training population (crossbred population only used for SNP weights' estimation); (SC5) WssGBLUP based on a single-trait model and the training population characterized by purebred animals (purebred population used for SNP weights' estimation). A complex trait was simulated assuming alternative genetic architectures. Different scaling factors to blend the inverse of the genomic ( Bos taurus indicus; Zebu breeds) and productive performance  traits  with the genomic relationship matrix (G) into a hybrid matrix (H). This increases the accuracy and reduces the prediction bias of GEBVs when compared to those yielded from multi-step genomic predictions  explained by them  simulated datasets were used in this study. Therefore, the approval of an Institutional Animal Care and Use Committee was not required.Bos taurus indicus  or Bos taurus taurus  animals. Crossbred animals (F1) were originated from the crossing between females from Line1 and males from Line2. Phenotypes and TBVs were simulated for a trait with a h2 equal to 0.33 and phenotypic variance equal to 0.13. This was done to mimic the trait residual feed intake , which is a very important trait in beef cattle breeding programs  was created by reducing the total number of individuals from 2,000 to 1,500 , which were also randomly mated. Thereafter, a second “bottleneck” was created by randomly sampling 100 males and 100 females from generation zero  of the historical population. These 200 individuals were used to create the expansion population (POP) containing 64,000 individuals. The population reductions (“bottlenecks”) were simulated to create an initial level of linkage disequilibrium (LD), which will be further explained.The historical population consisted of 1,020 generations . During Bos taurus indicus), and 30 in Line2 (Bos taurus taurus), the average LD values (between adjacent SNPs) were similar to those reported for Bos taurus indicus (r2 = 0.20) and Bos taurus taurus (r2 = 0.33) , and the replacement ratio for sires and dams was 0.60 and 0.20, respectively. Selection and culling in both Line1 and Line2 were performed based on the lowest and highest Estimated Breeding Values (EBVs), respectively. EBVs were estimated based on the Best Linear Unbiased Prediction method  F1-1: generations seven and 27; (ii) F1-2: eight and 28; (iii) F1-3: nine, and 29; (iv) F1-4: ten and 30; in Line1 and Line2, respectively. The differences in the generation of Line1 and Line2  are due to the simulation scheme designed to mimic the current pattern of LD and genetic distance between Nellore and Angus, represented by Line1 and Line2, respectively.https://support.illumina.com/downloads/bovinesnp50v2.html), as suggested by Matukumalli et al. , generations 26 to 28 (for Line2), and all F1 individuals. Animals from the last two generations of the purebred lines  were not included in the analyses in order to maintain a genetic distance between training and validation populations (described below). The simulated genotypes consisted of 52,886 bi-allelic SNPs distributed across 29 chromosomes (autosomes), mimicking the bovine genome. The size of the whole genome was ~2,696.54 cM. The number of SNPs and the size of each chromosome was defined based on information retrieved from the Illumina Bovine 50 K Beadchip ; (iv) h2 , and 198 QTLs (SIM4); (v) h2 and 4,500 QTLs (SIM5). The heritability only due to the QTL effects, Different −5 per generation and locus. The QTL effect captured by the SNP marker can potentially change across populations and generations due to the population-specific allele frequency and LD levels between SNP markers and QTLs. In order to minimize the effects of the simulation  in the results, ten independent replicates were carried out for each scenario. Simulations were performed using the QMSim software  and replicated. The genotype quality control kept SNPs with minor allele frequency (MAF) higher 0.05, and departure from the Hardy–Weinberg Equilibrium (estimated as the difference between expected and observed frequency of heterozygous) lower than 0.15. Only common SNPs across populations were kept for further analyses. A summary of the descriptive statistics for Line1, Line2, and F1 in each scenario is shown in pca” of PLINK 2.0 . Principal components were assessed using the flag “–2, in which r2 = D = f(AB)−f(A)f(B), and f(AB), f(A), f(a), f(B), and f(b) are observed frequencies of haplotype AB and alleles A, a, B, and b, respectively  values between two populations . The LD level between two SNP markers was measured by rfreq” from PLINK 2.0 . A total of five alternative scenarios (SC) were investigated: (SC1) ssGBLUP based on a single-trait model considering both purebred and crossbred animals in the training population; (SC2) ssGBLUP based on a multiple-trait model considering phenotypes recorded on purebred and crossbred training animals as different traits; (SC3) WssGBLUP based on a single-trait model including both purebred and crossbred animal datasets in the training population (and information from the three populations to estimate the SNP weights—further described); (SC4) WssGBLUP based on a single-trait model considering only purebred animals in the training population ; (SC5) WssGBLUP based on a single-trait model considering only purebred animals in the training population (and their information to estimate the SNP weights). The main goal of SC4 was to account for the crossbred allele frequencies during the The animals included in the training populations were purebred animals from generations six, seven, and eight (Line1), and generations 26, 27, and 28 (Line2). When crossbred animals were included in the training population, animals from F1-1 and F1-2 populations were used. The scenarios used to create the different training populations are summarized in H matrix . In the ssGBLUP analyses, the D matrix was assumed as an identity matrix. In the WssGBLUP analyses, D was a diagonal matrix with values given by weights derived from the SNP solutions, as described by Wang et al. (D matrix as an identity matrix (I). Then, the SNP weights were derived based on Strandén and Garrick  were derived from the SNP effects as SNP variances:dj is the j SNP weight ; û is a vector of estimated j SNP effect; and p is the allele frequency of j SNP.Where D matrix derived from SNP solutions) were used in the WssGBLUP because the second iteration provided higher GEBV accuracies in the preliminary analysis , τ and ω parameters were only tested using SC1. Thereafter, the tuning parameters that increased the accuracy and reduced the prediction bias of GEBVs were used in all analyses. Details about the methods used to calculate the accuracy and bias (based on regression coefficient) of GEBVs are described in section accuracy and regression coefficient. The inbreeding coefficient was estimated using the BLUPF90 family software ; additive genetic random effects, u ~ N, additive genetic random effects, u3~ N, and, random residuals, e3~ N, respectively. X3 and Z3 are the incidence matrices for the fixed and additive genetic effects, respectively. G0 and R are the additive genetic and residual variance-covariance matrices, respectively, described as:Where u is the additive genetic (co)variance between pairs of populations; Where t test  between accuracies and the regression coefficient from different scheme pairs by using the t-test function available in the R software  of F1 populations. The main goal of the current study was to evaluate the predictive performance of genomic models when purebred parents are selected to produce crossbred progeny with higher genetic breeding value and improved performance, both indicated by higher GEBVs. Therefore, accuracies of genomic predictions were estimated as the Pearson correlation coefficients calculated between GEBVs and TBVs, for the validation populations (F1-3 and F1-4). In addition, the regression coefficient (an indicator of inflation or deflation of the TBVs on GEBVs) was assessed using a linear regression model of TBVs on GEBVs, for the validation animals. Paired Student's  Rosner,  was applA matrix are shown in h2 equal to 0.33). For the Line2 and F1 populations in the SIM4, additive genetic variance and h2 were underestimated  in comparison to the other scenarios. Genetic correlations across populations in the different scenarios ranged from moderate to high (from 0.71 to 0.99).Genetic parameters and (co)variance components estimated in the different simulated scenarios using the Both purebred and F1 populations clustered separately, and the F1 animals clustered between both purebreds (as expected). This is shown by the first and second principal components (PC) of the genomic relationship matrix, in which the first principal component explained from 79 to 82% of the total variation . There wAs presented in G−1 and A22−1 matrices. Changes in accuracies and regression coefficients when using these different values are shown in Different values for τ (from 0.9 to 2.5) and ω (from 0.5 to 1.2) parameters were tested in SC1 when combining the h2 was attributed to the QTL effects h2 explained by them was equal to 0.33 , greater validation accuracies were observed  of the trait observed  and the observed .Due to a large number of scenarios investigated, the Results section will be split according to the validation population (F1-3 or F1-4).SIM1 is the simulation scenario that yielded the lowest GEBV accuracy and the highest bias estimates . The average GEBV accuracies in SIM1 ranged from 0.14 (SC3 and SC4) to 0.15  from the SIM1 dataset, the GEBV validation accuracy reduced by 13.98% when compared to the F1-3 validation set. Thus, the GEBV accuracies ranged from 0.12  to 0.15  are presented in Genetic correlations for the simulated trait across populations in the different scenarios ranged from moderate-to-high, which indicates that Line1, Line2, and F1 are moderate-to-high genetically correlated. Núñez-Dominguez et al.  reportedPrincipal Components Analysis absorbs the information of allele frequencies into a reduced number of independent variables, facilitating the interpretation of potential population structure. The first two PCs showed a clear separation between populations Line1 and Line2, and the F1 animals clustered between both purebred lines . AdditioThe first principal component (PC1) was strongly correlated with Line1 in all simulation scenarios, except for SIM2 . This faThe improvement of the predictive ability of two distinct training and validation populations  depends on the similarity or consistency of gametic phase between the SNPs and QTLs across populations. By increasing the relationship distance between individuals, the genomic distance in which the linkage phase will be consistent across populations decreases. As presented in Both results, PCA and consistency of gametic phase, suggest that better accuracies of genomic predictions could be attained when using a single-training population as the SNP effects seem to be population-specific. In other words, the lower predictive ability could be expected when SNP effects estimated based on Line1 is applied to Line2, or across any combination presented. However, those assumptions are contrasted by the genetic correlation between Line1 and Line2 .Even though a moderate-to-high genetic correlation was observed between Line1 and Line2, there was still population stratification. The contrasting results from both analyses  might be explained by: (i) the similar selection direction for all populations , which could result in a high genetic correlation across these populations for the trait under selection; (ii) single-trait selection, in which only the alleles associated with the trait (or in high LD) would contribute to higher genetic correlation between the populations, but not all the markers spread across the genome; and, (iii) specific population parameters . In other words, when simulating a genomic dataset, one needs to specify: (1) the number of QTLs affecting the trait , and (2) the number of markers in the dataset, in which some will be in LD with the QTLs simulated, while the others might be non-related to the trait and spread out across the whole genome. Thus, it is not surprising that the QTL effects  and their allele frequencies across populations (Line1 and Line2) for the trait under study were similar, which is realistic.G−1 and A22−1 matrices are usually not on the same scale  yielded the highest GEBV accuracies. This suggests that the ssGBLUP method, using either a single- or multiple-trait model, performs better than WssGBLUP for polygenic traits in crossbred animals. We expected that WssGBLUP would perform better for the scenarios SIM2 through SIM5, and especially for SIM4 and SIM5. Lourenco et al.  reportedG matrix in the genomic evaluation of crossbred animals. Furthermore, Ibánêz-Escriche et al.  have a greater bias due to the fact that the relationship matrix does not account for an infinite number of loci . Simulated scenarios with a small or null number of QTLs  might lead to higher GEBV accuracy when using Bayesian variable selection models  animals in the training population (SC1), and ssGBLUP based on a multiple-trait model considering phenotypes recorded on purebred and crossbred training animals as different traits (SC2), yielded the highest accuracies and lowest biases of GEBVs. Considering the current stratification of the genotyped population  for the simulated trait across populations, the ssGBLUP using a single-trait model and a purebred and crossbred (F1) training population's scenario (SC1) is recommended. The SC1 resulted in a similar performance of genomic evaluations in crossbred animals and it is reasonably easy to be implemented in practical situations. Further studies using real datasets should be performed to validate these findings.alvarena@purdue.edu) or Luiz F. Brito (britol@purdue.edu).The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher. Requests to access the datasets should be directed to Amanda B. Alvarenga (AA carried out all the analyses, and wrote the original draft. AA, RV, HO, FS, and LB conceived and designed this study. AA, HO, FS, RV, and LB interpreted and discussed the results. AA, RV, HO, FS, DM, PL, and LB reviewed and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer MC declared a past co-authorship with one of the authors FS to the handling editor.


Genetic analysis is an important tool in understanding population structure, genetic diversity, and phylogenetics of endangered species likely to be affected by microevolution and anthropogenic factors. Western Ghats landscape is one of the identified biodiversity hotspots in India, and micro-evolutionary processes are observed in this landscape due to the presence of the gaps in the mountain ranges. Nilgiri tahr is endemic to and distributed in this landscape while very little is known about genetic characteristics, population structure and impact of these gaps on the species. In the present study, two different populations of Nilgiri tahr from the north (NPG) and south (SPG) of Palghat gap (PG) were studied using the cytochrome b gene  of mtDNA genome in the Western Ghats, India. Two variable sites were observed in the Cyt b fragment while the mean pairwise genetic distance between these two populations was 0.007. All the samples phylogenetically clustered in either north or south of PG. The presence of shallow divergence indicates the presence of suitable habitat in past which may have facilitated movement between NPG and SPG. A subsequent change in Paleo-climatic conditions and gradual formation of PG may have resulted in population diversification during the Pleistocene. Besides, Forensically Informative Nucleotide Sequence (FINS) observed would help in geo-assigning any individual from NPG or SPG to understand the likely influences on population demography due to poaching. Nilgiritragus hylocrius  and Anamalai Tiger Reserve (ATR) respectively in the Western Ghats . These pQ > 20) were considered for detecting mutation. Multiple sequence alignments were performed using the CLUSTAL W algorithm implemented in BIOEDIT version 7.0.5.33 . Total 310 bp long nucleotides were found suitable for the analysis after validating the sequences. Clean electropherograms and high-quality value  and Ovis ammon (π 0.0054). The explanation provided for the low genetic diversity in Ovis ammon was recent population divergence  suggests there is no sharing of genetic material (D and Fu’s Fs test were found to be positive (p<.05) in the combined data. This indicates strong population subdivision, loss of rare alleles or population bottleneck   shows the greater divergence time 56 Ma than these observed for these taxa in north and south of PG  regarding attachment surfaces of the muscles that control biting and mastication as a response to food plant characteristics. Due to lack of information on such anatomical variations in Nilgiri tahr, we believe that such difference may exist to harvest nutrients differently from plant species in these two populations of dry and moist regions of Western Ghats. Therefore, we suggest both the populations of the Nilgiri tahr should also be managed as a separate MU for in situ conservation plans as well as being considered as separate entities for ex situ conservation strategies. Besides, we also reiterate that thorough studies on anatomical differences, microsatellite and other mitochondrial markers are required to understand the extent of population divergence and gene flow in Nilgiri tahr populations across the Western Ghats.The mitochondrial marker (Cyt b) used in the current study suggests the presence of reciprocally monophyletic clades in Nilgiri populations of north and south of PG in the Western Ghats. The literature reveals that the northern parts have been relatively drier than the southern Western Ghats during the Pleistocene and acted as a refugium for the species (Sukumar et al.


Deschampsia antarctica Desv. can be found in diverse Antarctic habitats which may vary considerably in terms of environmental conditions and soil properties. As a result, the species is characterized by wide ecotypic variation in terms of both morphological and anatomical traits. The species is a unique example of an organism that can successfully colonize inhospitable regions due to its phenomenal ability to adapt to both the local mosaic of microhabitats and to general climatic fluctuations. For this reason, D. antarctica has been widely investigated in studies analyzing morphophysiological and biochemical responses to various abiotic stresses . However, there is little evidence to indicate whether the observed polymorphism is accompanied by the corresponding genetic variation. In the present study, retrotransposon‐based iPBS markers were used to trace the genetic variation of D. antarctica collected in nine sites of the Arctowski oasis on King George Island (Western Antarctic). The genotyping of 165 individuals from nine populations with seven iPBS primers revealed 125 amplification products, 15 of which (12%) were polymorphic, with an average of 5.6% polymorphic fragments per population. Only one of the polymorphic fragments, observed in population 6, was represented as a private band. The analyzed specimens were characterized by low genetic diversity  and high population differentiation (FST = 0.4874). An analysis of Fu's FS statistics and mismatch distribution in most populations  revealed demographic/spatial expansion, whereas significant traces of reduction in effective population size were found in three populations . The iPBS markers revealed genetic polymorphism of D. antarctica, which could be attributed to the mobilization of random transposable elements, unique features of reproductive biology, and/or geographic location of the examined populations. Deschampsia antarctica is among the two angiosperms considered as a native for the Antarctic. The retrotransposon‐based iPBS markers applied in the study revealed low genetic variation of the species accompanied by surprisingly high population differentiation. Seven iPBS primers were used individually in a polymerase chain reaction (PCR) according to the protocol described by Kalendar et al. , percentage of polymorphic bands (P), Shannon's Information Index (I), unbiased expected heterozygosity (uHe), and Nei's genetic distance (ND)  based on ND values in GenAlEx 6.5. The genetic structure of the studied populations was inferred based on Bayesian model‐based clustering method implemented in STRUCTURE ver. 2.3.4. , achieving linkage equilibrium within clusters. We performed 10 replicate runs for each K, ranging from 1 to 9, 500, 000 Markov Chain Monte Carlo repetitions and a burn‐in period of 500, 000. During the analysis, admixture model was used without any prior information on the original populations. To determine the optimal number of clusters, an ad hoc statistic ΔK was used  which corresponds to a scaled allele frequency variance, where the variance is measured among contemporaneous populations  or absent (0) across genotypes and treated as single dominant loci. Based on the obtained binary matrix of amplification products Table , the folDN) Nei, . The genD. antarctica populations were also estimated. Spatial genetic structure was investigated by testing the significance of isolation by distance (IBD) in the Mantel test with 9,999 permutations of the relationship between the matrix of pairwise FST/(1−FST) and the matrix of log‐transformed geographic distances between populations  were calculated between the studied sampling sites based on the previously described data concerning general soil properties and nutrient content  to test the mutation–drift hypothesis against the bottleneck hypothesis . The highest number of 24 bands was revealed by primer 2,253, and the lowest number of amplification products (14) was obtained for primers 2,374 and 2,376. An average of 17.86 bands was obtained per primer. Fifteen of the identified loci (12%) were polymorphic , as well as Gst‐B (0.4349). The θ(III) estimate, though lower in value, has reasonably similar magnitude (0.3703).The results of AMOVA revealed that 51% of the identified genetic variation occurred between individuals within populations, whereas the remaining 49% was attributed to variation among populations . The projection of the analyzed populations on the first two axes is shown in Figure Nei's genetic distance was calculated to estimate genetic differentiation between ns Table . This paD. antarctica populations Bayesian model‐based clustering method was also applied, and the optimal number of clusters was estimated with the ΔK method  was characterized by the highest proportion of membership of each predefined population in the third cluster , which suggests that geographic distance did not influence genetic structure. The IBE Mantel test comparing geographic distance with pairwise environmental distance  did not reveal significant correlations, either. However, when environmental variables were analyzed separately, a significant correlation was found between geographic distance versus. the content of P and N–NO3  , significant traces of bottleneck effect were noted in populations 1 and 5 based on the results of all three tests , and in population 3 based on the results of the Standardized Differences test and the Wilcoxon test, but the el Table .4Antarctic hairgrass is one of the most intriguing plant species in the world whose unique morphological and physiological features enable survival in extreme environments and colonization of the remote and inhospitable areas of the Maritime Antarctic. The species has large disjunctive distribution from north Patagonia in South America (around 38°S) to the Antarctic Peninsula, with the southernmost known locality in Lazer Bay  polymorphic loci, and average polymorphism was determined at 5.6% (across loci and populations). Previous studies relied on iPBS markers demonstrated up to 97% polymorphism in Colobanthus quitensis)  were polymorphic, with 14% of polymorphic loci per population on average. The average values of He and I were determined at 0.040 and 0.061, respectively, which indicates that genetic diversity in C. quitensis was twice higher than that noted in D. antarctica in this study . The results of AMOVA revealed 84% of the polymorphisms within the studied populations of C. quitensis and only 51% within populations of D. antarctica. As a result, the hierarchical analysis of population structure produced clearly higher values of the F‐statistic for D. antarctica (FST = 0.4874) than C. quitensis (FST = 0.164). Our results are highly consistent with the observations made by van de Wouw et al. , the analyzed populations of D. antarctica are characterized by relatively high genetic differentiation  = 0.3703, results of STRUCTURE analysis which assigned the studied individuals into 6 clusters, and high population dispersal revealed by PCoA), which could be attributed to limited gene transfer between these populations. This is a surprising observation in view of the fact that the analyzed populations were sampled in close proximity. However, local differences in landform and climatic conditions, such as strong winds blowing mainly from one direction  and low genetic diversity  indicates that differences in nutrient content could be associated with population divergence. However, further tests are needed to fully determine whether the differences in the composition and nutrient content of soils in the Antarctic oasis  use plants, to build nests, which suggests that zoochory could play a role in the local dispersal of D. antarctica.The reproductive biology of a et al.  reportedFS statistic revealed a demographic expansion of most studied populations , whereas significant traces of reduced effective population size were noted only in three populations . The observed variations indicate that the studied D. antarctica populations had different demographic histories, which could be attributed to recurring adverse environmental conditions  or even recent local extinction–recolonization events. These processes could explain why bottlenecks were detected in only some of the analyzed populations. However, the difference between bottleneck and founder effects is difficult to identify with the use of dominant markers.Regardless of its dispersal mechanism, a newly established population often has a limited number of individuals, which reproduce mainly by self‐fertilization and/or vegetative propagation ; Data curation (lead); Formal analysis (lead); Investigation ; Methodology ; Resources (supporting); Supervision (lead); Validation ; Visualization (lead); Writing‐original draft (lead); Writing‐review & editing (lead). Katarzyna J. Chwedorzewska: Conceptualization ; Data curation (supporting); Formal analysis (supporting); Investigation (supporting); Methodology ; Resources ; Validation ; Visualization (supporting); Writing‐original draft ; Writing‐review & editing . Justyna Dulska: Data curation (supporting); Investigation ; Writing‐review & editing (supporting). Sylwia Milarska: Data curation (supporting); Investigation ; Writing‐review & editing (supporting). Irena Giełwanowska: Conceptualization ; Data curation (supporting); Formal analysis (supporting); Investigation (supporting); Methodology ; Resources ; Supervision ; Validation ; Visualization (supporting); Writing‐original draft ; Writing‐review & editing .Table S1Click here for additional data file.Table S2Click here for additional data file.


In addition, 389 genotypes representing village and commercial pigs from America, Europe, and Asia were accessed from a previous study and used to compare population clustering and relationships of South African pigs with global populations. Moderate heterozygosity levels, ranging from 0.204 for Warthogs to 0.371 for village pigs sampled from Capricorn municipality in Eastern Cape province of South Africa were observed. Principal Component Analysis of the South African pigs resulted in four distinct clusters of (i) Duroc; (ii) Vietnamese; (iii) Bush pig and Warthog and (iv) a cluster with the rest of the commercial (SA Large White and Landrace), village, Wild Boar and indigenous breeds of Koelbroek and Windsnyer. The clustering demonstrated alignment with genetic similarities, geographic location and production systems. The PCA with the global populations also resulted in four clusters that where populated with (i) all the village populations, wild boars, SA indigenous and the large white and landraces; (ii) Durocs (iii) Chinese and Vietnamese pigs and (iv) Warthog and Bush pig. K = 10 (The number of population units) was the most probable ADMIXTURE based clustering, which grouped animals according to their populations with the exception of the village pigs that showed presence of admixture. AMOVA reported 19.92%–98.62% of the genetic variation to be within populations. Sub structuring was observed between South African commercial populations as well as between Indigenous and commercial breeds. Population pairwise FST analysis showed genetic differentiation (P ≤ 0.05) between the village, commercial and wild populations. A per marker per population pairwise FST analysis revealed SNPs associated with QTLs for traits such as meat quality, cytoskeletal and muscle development, glucose metabolism processes and growth factors between both domestic populations as well as between wild and domestic breeds. Overall, the study provided a baseline understanding of porcine diversity and an important foundation for porcine genomics of South African populations.Genetic diversity is of great importance and a prerequisite for genetic improvement and conservation programs in pigs and other livestock populations. The present study provides a genome wide analysis of the genetic variability and population structure of pig populations from different production systems in South Africa relative to global populations. A total of 234 pigs sampled in South Africa and consisting of village ( Sus Scrofa domesticus) originated from the Sus scrofa, which is commonly known as the wild boar belonging to the Suidae family (Phacochoerus africanus (Common warthog), Potamochoerus larvatus (Bush pig) and Hylochoerus meinertzhageni (Giant Forest hog) some that are indigenous to Africa  which was first discovered at Cape Verde, Senegal is one of the three species found in Africa. The Cape Warthog (Phacochoerus aethiopicus) is now extinct due to the rinderpest epizootic of the 1860s (Phacochoerus delamerei) species was described in Somalia and later renamed Phacochoerus aethiopicus delamerei as it is similar to the Cape Warthog  and Oliver Reginald Tambo  districts in Eastern Cape province and Mopani  and Capricorn  districts in Limpopo province. Commercial pig breeds of Large White , South African Landrace  and Duroc  were sampled from commercial farmers in Limpopo province. Indigenous populations Kolbroek  and Windsnyer  were sampled from the Agricultural Research Council-Animal Production Institute in Pretoria, South Africa (n = 5) was sampled from the Johannesburg Zoo and represents a breed that is endangered in Vietnam, its country of origin but has been raised in a conservation zoo in South Africa. European Wild Boar (n = 4), Warthogs (n = 31), and Bush pigs (n = 3) were sampled as representatives of the wild pig populations. The European Wild Boar and Bush pigs were sampled from the surrounding villages in the North-West whilst the Warthog samples were collected from geographically separated National Parks from North-West (n = 4), Eastern Cape (n = 3), and Limpopo (n = 24). The distribution of the sampled individuals is illustrated in South African specimens were collected from a total of 234 samples from different production systems, representing village, intensively farmed populations in conservation units and free ranging populations. Village and non-descript pig populations were sampled from Alfred Nzo  was used to assess the quality and integrity of the DNA.DNA was extracted at the Agricultural Research Council-Biotechnology Platform from the ear tissue and hair samples using a commercially available Perkin Elmer Genomic DNA kit according to the manufacturer’s protocol. DNA concentration was quantified using the QubitPed (Pedigree file) and Map (SNP panel file) for use in downstream analysis.All 234 animals were genotyped using PorcineSNP60 v2 genotyping BeadChip  containing 62,163 SNPs with an average gap of 43.4 kb. Genotyping was done using the standard infinium assay at the ARC-Biotechnology Platform in South Africa. GenomeStudio version 2.0  was used to process the genotype data, including raw data normalization, clustering and genotype calling. A final custom report was created to be able to generate a Plink Sus Scrofa v10.2). Markers were then filtered to exclude SNPs located on the sex chromosomes. From this data set, Minor allele frequency (MAF) and deviation from Hardy–Weinberg equilibrium (HWE) were estimated per population for the 10 populations that excluded BSP, VIT, and WBO, which were left out due to small sample sizes. Additional quality control (QC) was also performed per population to remove SNPs with less than 85% call rate, MAF < 0.02 and HWE < 0.0001. The resultant filtered dataset was used to calculate observed (HO), and expected (HE) heterozygosities, inbreeding (FIS) and effective population size (Ne).Golden Helix SNP Variation Suite (SVS) version 8.5 was used to update the SNPs marker file  based on the pig genome assembly  and closely related individual [Identity By Descent (IBD) ≥ 0.45] to produce a filtered dataset used for population structure analysis using ADMIXTURE and Principle Component Analysis (PCA).Quality control was then performed overall population to remove SNPs with less than 85% call rate, MAF < 0.02 and HWE < 0.0001 and generate a dataset used for analysis of molecular variance (AMOVA) and HE and HO were calculated as measures of within population genetic variation using PLINK 1.07 (FIS) was calculated on Golden Helix SNP Variation Suite (SVS) version 8.5 . Effective population size (Ne) trends across generations were estimated based on a relationship between r2 (expected LD), Ne and C (recombination rate). SNeP software (Version 1.1) tool was used based on the following formula suggested by The MAF, INK 1.07 . In addiwhere:NT(t): Effective population size estimated t generations agoCt: Recombination rate t generations agor2adj: Linkage disequilibrium estimation adjusted for sampling biasnessα: a constant.The recombination rate was estimated by using the following formula proposed by The Bush pig, Vietnamese Potbelly and Wild Boar were excluded from the diversity within population analysis due to their small sample sizes. The few available samples were sampled from zoos and game reserves in the country where only few animals are often rescued and kept in conservation.FIS), among populations within group (FSC) and among groups (FCT) using ARLEQUIN v3.5  was used to determine the genetic variance within populations (UIN v3.5 . The popK) for the population. ADMIXTURE was run from K = 2 to K = 15. The number of potential genetic clusters (K) was tested from 1–15 to reassign each sample to its population of origin. The optimum K-value was that with the lowest cross-validation error value. Initially, all the 13 populations sampled from South Africa were included in the population structure analysis. After this the South African data set was merged to Porcine SNP60K genotype data from Principal Component Analysis (PCA) using SVS version 8.5  and the eigenvector method was used to determine population clustering. ADMIXTURE version 1.20  was usedFST values were estimated according to the formula of FST values, PCA and ADMIXTURE based clustering, FST analysis per marker was estimated between pairs of highly differentiated populations of the village populations, indigenous populations and commercial breeds as well as amongst highly differentiated commercial breeds and wild populations. To reduce noise, an FST averaged smooth value was used to identify genomic regions differentiating pairs of populations. Manhattan plots of per marker FST values between pairs of populations were plotted against chromosomal coordinates using the porcine assembly (Sus Scrofa 10.2). Highly differentiating SNPs (FST ≥ 0.8) were subsampled and genes associated with these SNPs searched using genome browse including their associations with known QTLs in the pig genome based on the Sus Scrofa 10.2 on Ensembl1.Population pairwise NSNP) remaining after QC per population and overall is presented in FST analysis. After LD and IBD pruning, 23,345 SNPs and 176 individuals were used for the population structure analysis.The percentage of polymorphic and number of SNPs (HO (0.188 ± 0.155) and Windsnyer the highest (0.385 ± 0.171). Expected heterozygosity values ranged from 0.204 ± 0.151 from Warthog to 0.371 ± 0.126 for Capricorn. The highest inbreeding coefficient (FIS) was for Warthog at 0.398 ± 0.475 while the Duroc had the lowest and slightly negative value of −0.067 ± 0.153. FIS values were positive for all village populations as well as Warthog suggesting some level of inbreeding within these populations. MAF was the highest in village population from Capricorn (0.264 ± 0.147) and the least in Warthog pigs (0.076 ± 0.109).Genetic diversity parameters among the 10 populations are summarized in Ne across all the populations across generations. The indigenous and commercial populations had higher effective population size compared to the village populations. The Kolbroek had the lowest effective population size 12 generations prior.FIS values ranging from 76.41 to 98.62%. Diversity within populations (FIS) in village populations from this study and those from FCT) was 62.35%. Diversity of South African commercial pigs was 76.41% within populations, 18.17% among populations within group and 5.42% among groups. When including the commercial breeds from FIS = 30.97%, FSC = 8.31% and CTF = 60.72%. High FCT (>60%) were observed in the category consisting of South African indigenous and Chinese indigenous (FCT = 70.08%) as well as that consisting of the South African Wild Boar and the worldwide Wild Boar (FCT = 73.58%).Genetic differentiation between populations is presented in Principal component one (PC1) and principal component two (PC2) explained approximately 30.7% and 11.8% of the total variation, respectively. The PCA of South African breeds yielded four main genetic clusters . The DurK = 2. Duroc separated from the rest of the populations at K = 3 followed by Vietnamese at K = 4. K = 4 clustered animals in the same way observed with PCA based clustering. Beyond K = 8, the genetic clusters of the commercial, indigenous, Asian and wild breeds are maintained whilst the added K is distributed within the village populations. K = 10 which was the optimal K  within South Africa and in global populations. The highest differentiation was found between Warthog and Duroc at FST = 0.481. Warthog and Kolbroek pigs showed the high differentiation at 0.468. All other populations had FST values above 0.282. The extent of differentiation between Warthog and all the other populations was high ranging from 0.312 (Warthog and Creole from Columbia) to 0.589 (Warthog and Vietnamese). Highest FST observed was between Vietnamese and Bush pig populations at 0.700  where considered breed differentiating and the associated SNPs were functionally annotated for genes within a 1 MB region. Fixed SNPs (STF = 1.0) where observed on chromosome 9 between Duroc and Warthog, on chromosome 12 between Koelbroek and Warthog and on chromosome 18 between Windsnyer and Warthog. For all the pairwise comparisons, 281 SNPs (FST ≥ 0.8) were detected (RPL18) and inflammatory response (IL17B and ARHGAP23) as illustrated in ADGRB3 was in close proximity of SNPs rs81353971, rs81353988, rs81353991, rs81297001, and rs81333295 that were of significant between Duroc and Warthog. Inflammatory response genes such as ARHGAP23 were associated with the significant SNPs observed between Koelbroek, Large White and Windsnyer populations. For reproduction traits, genes CD28, TCP11L2, TLK1, ATPB2, GPR137C, ZNF609, ARHGAP22, EPSTI1, GPR63, TCTE3, PTP4A2, ZSCAN20, CLU, and CACNA2D3 were observed within 14 significant SNPs on chromosomes 1, 2, 5, 6, 11, 14, and 15. Genes that had association with meat traits such as DLX1, BRPF1, CLPTM1, FANCD2, SEC13, FHL3, FSTL5, CEP135, EXOC1, FOXO1, ASTN2, MYO18B, PLXNA1, DNAH2, HECTD2, TMEM39B, TXLNA, CSMD2, COL16A1, SCARA3, ZFAND3, and PTPRD were also reported. Comparison with indigenous pigs showed genes that were associated with mastitis resistance  and hair follicle development (FOXN1). A total of eight SNPs associated with growth traits  were detected. PTPN3 gene associated with immune response was observed between indigenous and Wild Boar. Wild Boar and Duroc comparison resulted in genes associated with adaptation (HDAC1 and GNAI3).Per population, per marker pairwise detected  with onlThe Porcine SNP60K BeadChip was developed in 2009  and has The Porcine SNP60K BeadChip was designed using genomic resources from Western pig genomes  and hencoH = 0.358) compared to the South African Landrace (Ho = 0.372) and other breeds of the Duroc and Kolbroek. It must be noted that these previous studies used microsatellite markers that are highly polymorphic markers and cannot be compared to SNPs that are biallelic in nature. High gene diversity is therefore expected in microsatellites markers. However, results on genetic diversity from this study were comparable to other studies that used the Porcine SNP60K BeadChip in Chinese and Western pig populations  in the recent 12–22 generations for both commercial and indigenous populations are of concern  to FCT = 5.42  might be due to gene flow between different populations within a sub-populations. Moderate diversity within population  relative to elevated FCT in the same categories implies a higher genetic variation distributed among groups from different geographic locations. This genetic variation observed amongst groups of the South African and FCT = 62.35–73.58) is higher than the variation reported amongst Angora goats from South Africa, France and Argentina using 50K SNP BeadChip  was expected. The Duroc breed was created in the United States with pigs of several ancestries, including African pigs . StudiesThe distance of Vietnamese Potbelly population from the rest of the domestic pigs is clear evidence of independent domestication that took place between the European and Asian subspecies of the wild boar . The PCAK = 2 presented the first level of ancestry of the Suidae family representing Phacochoerus africanus (Warthog) and Potamochoerus larvatus (Bush pig) versus Sus scrofa (domesticated pigs including the Wild Boar) species (K = 2 to K = 7 is not surprising (K = 2) from the rest of the domestic pig population. The results for the village populations showed high levels of admixture and weak between population sub-structuring. As opposed to pigs from the commercial sector that practices the intensive production systems, pigs in the villages are farmed under semi-intensive of free-range production systems, which might explain the admixture observed in this study. There is considerable indiscriminate crossbreeding that is taking place in village populations  might be attributed to the (i) pressures of natural selection (ii) the separate histories of domestic and wild populations and (iii) the unique population dynamics of Warthogs that are known to live in clans of adult females, males and their offspring while maintaining minimal contacts with other clans  that are associated with reproduction  and growth  characteristics  were linked to the Warthog vs. Kolbroek populations. Growth traits were associated with five of the SNPs between Warthog vs. Kolbroek. Indigenous Kolbroek are reported to be smaller in size when compared to commercial breeds such as Large White (P > 0.05) between the Kolbroek and Large White populations that had higher fat percentages when compared to the other commercial breeds . When it comes to the value and taste of the pork meat, intramuscular fat is an important characteristic because meat that is high in IMF tends to be juicy and tender . The genformance . Heat stformance .PTPN22 gene on chromosome 4 has a regulatory effect on T- and B- cell activation in immune response  in cattle .NH collected samples, analyzed the data, and wrote the draft manuscript. FM, PS, and ED designed the experiment and sourced funding. KH analyzed the genomic data for the experiment. FM, PS, and ED coordinated the conduct of the study and writing of manuscript and revisions. All authors read and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The first step towards the protection and valorization of the genetic resources of a country is their definition. Although Ecuadorian zootechnical species are very diverse, they are scarcely characterized, and hence the efforts towards their protection are not as fruitful as they could be. The present paper approaches the biometric characterization of the Creole hen population in Ecuador through the study of sexual dimorphism and the differentiation of an agroecologically-based structured population using fourteen zoometric measures as differentiation criteria. Highlands region provinces of Cotopaxi and Tungurahua were the most zoometrically distant from the rest. However, Morona Santiago province population in the Amazonian region slightly differs from those in Guayas, Chimborazo and Bolívar in the Coastal and Highlands regions, respectively. The dual-purpose nature of Ecuadorian Creole hen resources enables the implementation of breeding programs that seek meeting a wider scope of public demands, through the definition of the agroecologically-based breed differentiated production of local hen eggs and meat.p < 0.05), especially for length of head, beak, neck, dorsum, tarsus, thigh, leg, and middle finger. Then, individuals were biometrically clustered into populations after a stepwise canonical discriminant analysis (CDA) computing interpopulation Mahalanobis distances. Agroecologically-based structured populations were identified when zoometrical criteria were used to classify the animals. Cotopaxi and Tungurahua provinces were reported to be the most distant from the rest, with a slight differentiation of the Morona Santiago province population from those in Guayas, Chimborazo and Bolívar. Conclusively, Ecuadorian Creole hens were higher than longer contrasting light hen breeds, which favors their dual-purpose aptitude. Hence, the development of selection programs aimed at Ecuadorian differentiated entity of production of eggs and meat across agro-ecological areas is feasible.Despite Ecuador having a wide biodiversity of zootechnical species, characterization studies of these genetic resources are scarce. The objective of this research was to perform the biometric characterization of the Creole hen population through 14 quantitative zoometric measures. We evaluated 207 hens and 37 roosters from Ecuador’s three agro-ecological regions: the Sierra (highlands) region ; the Costa  region (Guayas); and the Oriente Amazonian region (Morona Santiago). Sexual dimorphism was assessed using one-way analysis of variance (ANOVA). Body dimensions were generally significantly higher for males ( Gallus gallus), the most likely ancestor of these avian populations p < 0.0001).The absolute values of Mahalanobis’ distances between the local populations of the six provinces involved in the analysis are shown in 2), defined as the square of the distance between the measures of the standardized values of Z, was used to verify whether there were significant differences between the provinces. Thus, the greater the value of the distance, the greater the distance between the means of the provinces considered as well [Mahalanobis’ distance analysis is based on the analysis of generalized squared Euclidean distances adjusted for unequal variances. The Mahalanobis distance , being more appropriate to suit the requirements for meat production and characteristic of carnic breeds . TanzaniSimilarly, males of the Ching’wekwe ecotype and females of the same ecotype and female of the Morogo ecotype presented similar values to those of the provinces of Guayas and Tungurahua, but much higher than those of the province of Cotopaxi. Likewise, this value was similar to that reported by Nigerian birds , which mSimilarly, larger sizes were recorded for breeds such as the Partridged Penedesenca and Blonde Empordanesa with an average of 8 cm, which may be because the birds studied also come from four different eco-climates, homologous to those of the areas considered in this research.For birds from India , averageShort tarsi have been identified with a greater ability to escape from predators , hence, Canonical discriminant analysis suggests that the variables perimeter of the leg (legc), metatarsus tarsus length (metl) and middle finger phalanx length (mfpl) were the ones that had the greatest discriminatory capacity between provinces. The results revealed the presence of wide ranges of variation within and among Creole hens in Ecuador. However, four large population blocs could be identified, namely Cotopaxi, Tungurahua, Morona Santiago, and that comprising the populations of Bolivar, Chimborazo and Guayas, a fact that could be attributed to the different conditions found across the various agroecological zones in the country, ethnic groups handling these resources and cultural implications that they have, along with the huge migration events suffered by these resources when facing natural and/or man-made challenges.Toalombo, et al.  identifiThe most likely reason for which Morona Santiago, Bolívar, Chimborazo, and Guayas populations present a mixed structure which does not permit their complete segregation from one another may have its basis on the fact that poultry farmers in this province are prone to preserve their birds, avoiding the introduction of individuals from external populations, despite the fact that poultry production has already been developed in their areas.The provinces of Morona Santiago, Bolívar, and Chimborazo are nearby to each other, sin contrast to the province of Guayas, located in the Costa Region. However, there is a provincial road that joins Chimborazo (Sierra) and Guayas (Costa). It should be noted that agricultural fairs are likely to take place along the road connecting both provinces. These events act as exchange centers of genetic material, which is mainly performed with minor species such as birds, given the considerable ease to transport such resources. The same would happen with Morona Santiago, which is located in the Ecuadorian Amazon, but near Chimborazo, which is geographically located in the center of the country. The provinces in question are characterized by their agricultural and livestock background, such that 13% of the population is engaged in poultry production activity; hence, it could be assumed that the populations maintain the genetics of their fowl over time.The connection with Guayas, as stated by the General Secretariat of the Andean Community in 2009, lays on the fact that during the seventies, there was a reduction in domestic agricultural-livestock production, which led to the migration of the inhabitants of Nabuzo-Penipe (Chimborazo) to the coast of Ecuador. This social movement implied people carried easily-used animal species such as hens along with them. Complementarily, as a result of the eruptions of the Tungurahua volcano, constant since the late 1990s , the CanThe results revealed the presence of wide ranges of variation within and among Creole hens in Ecuador. However, four large population blocs could be identified, namely Cotopaxi, Tungurahua, Morona Santiago, and that comprising the populations of Bolivar, Chimborazo, and Guayas, a fact that could be attributed to the different conditions found across the various agroecological zones in the country, the ethnic groups handling these resources and cultural implications that they have, along with the huge migration events suffered by these resources when facing natural and/or man-made challenges. This addresses the high number of opportunities to implement programs for the promotion, conservation, and genetic improvement of these local resources, through their selection and crossing after their definition and characterization, since their inherent resistance and adaptation to the different environmental conditions allow the definition of technical and scientific strategies to exploit their productive potential. In addition, the local nature of these resources should be highlighted as intangible ancestral value in the field of food sovereignty, and should be inserted within the productive policies at a governmental level, as has already been done with other species, with the aim of improving rural livelihoods and meeting the growing demand for poultry products.