Dothistroma resistance (DO_R). The average narrow-sense heritabilities were 0.11, 0.21 and 0.30 while the average broad-sense heritabilities were 0.27, 0.34 and 0.40 for DBH, height and Dothistroma resistance, respectively. Dothistroma resistance was assessed as the percentage of needles that were not affected by Dothistroma needle blight. G×E interactions were analysed using an approximate reduced factor analytic model. Apparent G×E interactions were estimated for DBH, height and Dothistroma resistance. Estimates of G×E interactions and their standard errors were strongly influenced by the level of connectivity between trials, in terms of common clones and common parents. When there was sufficient connectivity between trials , a high level of G×E interaction was found for DBH and height but not for Dothistroma resistance. In two simulated clonal trials planted in two environments, low connectivity between environments resulted in a lower estimated genetic correlation between environments with an increased standard error. These results suggest that the number of clones in common between clonal trials is a key factor for inclusion in future experimental designs for estimating G×E interaction. When designing clonal trials for use in multiple environments for accurately estimating the level of G×E, if the resource for creating connectivity between environments is limited, at least 30% of the clones need to be in common between environments.Twenty-eight clonal trials of radiata pine planted across Australia and New Zealand were used to investigate genetic variation and genotype by environment (G×E) interaction for diameter-at-breast-height (DBH), height and  Pinus radiata) is a fast-growing pine species, native to the Central Coast of California and both Guadalupe Island and Cedros Island of Mexico [Radiata pine (f Mexico . Its timf Mexico . There af Mexico . Radiataf Mexico . The Newf Mexico . Currentwww.rpbc.co.nz). Compared with unimproved seedlots, a GFPlus 25 seedlot had 25% increase in realized genetic gain for total standing volume at age 30 years, and each unit increase in GF Plus rating was associated with a 1.51% increase in volume growth rate [Forest tree breeding programmes have played an important role in improving forest productivity around the world. The radiata pine breeding programmes in New Zealand and Australia began in the 1950s and initially focused on growth, form and health traits, later extending to wood quality traits . Radiatawth rate .Dothistroma resistance are important selection traits for radiata pine in New Zealand and Australia [Dothistroma septosporum (Dorog.) M. Morelet [Growth and ustralia . Dothist Morelet , is a se Morelet –16. It o Morelet . This di Morelet . On an i Morelet .Dothistroma pathogen [In order to produce improved forest tree germplasm, genotypes are usually tested across a wide range of environments. Genotype by environment interactions (G×E) have been observed in a number of forest tree species and can be problematic for breeding programmes ,20. G×E pathogen .The ability to realise expected genetic gains in the forest depends on the accuracy of estimated breeding values of selection candidates for the deployed population. G×E complicates the design and interpretation of trials across environments that are used for estimation of breeding values of selected genotypes intended for deployment, whether as seed orchard progeny, or tested clones. It reduces the profitability of the forest venture to the extent that additional investment in a more ‘fine-grained’ series of screening trials may be justified. Breeders interested in optimising gains in deployed tree stocks often address G×E either by selecting for stable genotypes that are not sensitive to environmental changes, or selecting genotypes for specific environments in order to maximise genetic gain on specific sites, or site types . In New Dothistroma resistance in radiata pine. The connectivity between these clonal trials was realized through sharing common parents or clones between trials at different levels. The secoand objective of this study was to investigate the relationship between the level of G×E and the connectivity between trials. Further the clonal testing used in this study allowed us to examine the effectiveness of these types of trials for estimation of G×E.Genetic correlation between environments has been used as a measure of G×E in plants and livestock. The accurate estimation of genetic correlation between environments depends on the connectivity or relationship shared between individuals planted in two or more environments. As a first objective, this study used clonal trials planted in New Zealand and Australia to estimate the level of G×E for growth and Dothistroma resistance (DO_R). DBH was assessed in centimeters at breast height . Total tree height was assessed in metres. Infection by Dothistroma needle blight on individual trees was assessed as the percentage of the total needles present on the tree that was diseased [Nine single-tree-plot clonal trials (S01-S09) used in this study were from planted forest estates across southern Australia and 19 single-tree-plot clonal trials (S10-S28) were from planted forest estates across New Zealand . Three tdiseased . Scoringy is a vector of trait phenotypic observations, b is a vector of fixed effects , u is the vector of random additive genetic effects, d is a vector of random non-additive genetic effects, and e is a vector of random residual effects. X, Za, and Zd are known incidence matrices relating the observations to effects of b, u, and c, respectively. Experimental features of replicates, sets within replicates, row, block, and plot were fitted as random effects.Heritabilities and genetic correlations between trials were analysed using an individual-tree linear mixed model, implemented in ASReml-R :y=Xb+Zau have a normal distribution with u~N, where i, i and trial j, A is the numerator relationship matrix, t is the number of trials, ⊗ denotes the Kronecker product; the non-additive genetic effects d have a normal distribution with d~N, where i and I is the identity matrix; and the residual effects e have a normal distribution with e~N, where i. The FA2 model for the additive genetic effects of m genotypes in t trials can be modelled as u = (Λ⨂Im)f+δ [t× 2 matrix of loadings, Im is an identity matrix of dimension m×m, f is the 2m × 1 vector of scores and δ is the mt × 1 vector of genetic regression residuals. Var(u) = (Ψ)⨂ImΛΛ′+ with the assumption of var(f) = I2m, var(δ) = ψ⨂Im, where ψ is a t×t diagonal matrix with a variance  for each environment, and the vectors of random effects f and δ are mutually independent as a multivariate Gaussian distribution with zero means. The between environment genetic variance matrix is defined as Ga = (ΛΛ′+Ψ). Ga can be estimated with the REML algorithms as Dothistroma resistance were estimated using the mixed linear model (1) with heterogeneous genetic variances across trials for the additive genetic effects, the non-additive genetic effects and residual effects. The narrow-sense heritability for trial i f+δ ,41, wherA simulation was conducted to mimic the sizes of trials analysed  with sima), the residual effects (ε) and population mean (μ) as Eq , where ε are sampled from a multivariate normal distribution with ε~N, where Phenotype (y) was simulated with a simplified genetic model as the sum of the additive genetic effects (μ) as Eq . No domiu have a normal distribution with u~N, where e have a normal distribution with e~N, where rg) were estimated as Eq (4) [Additive genetic correlation between two simulated environments was estimated using the unstructured model in ASReml-R  and calcs Eq (4) ::rg=σaThe standard error of estimated genetic correlation was obtained from ASReml-R outputs. The deviation of the additive genetic correlation between environments from zero was tested using a two-tailed likelihood ratio test, by comparing Log Likelihood from models of either adding or dropping the term for the covariance between environments  . For hei and S20 .When the percentage of common clones tested between trials was above 25%, genetic correlation between these trials ranged from -0.08 to 1.00 . When thStandard errors of genetic correlations between trials tended to be higher when there was a lower number of clones or parents in common . When thEstimates of genetic correlation between environments and their standard errors were shown in This study has shown that the precise estimation of genetic correlations between trials depends on the levels of connectivity between trials. Apiolaza  found anThe simulation results clearly showed the same pattern, that is, that the lower the connectivity between environments the lower the estimate of genetic correlation and the higher the estimate of its standard error. In the simulation, a low level of G×E between two environments was simulated but the estimated genetic correlation showed a high level of G×E when the number of common clones between environments was below 100. These results suggest that the number of genetic entities in common between trials is a key factor in future experimental designs for estimating G×E interaction. Low connectivity via common genetic entities between trials might lead to underestimated genetic correlations and, therefore, an over-estimated level of G×E interaction. In other words, these results indicate that if there is insufficient connectivity between trials, they should not be used in to identify the level of G×E in radiata pine.The simulation results showed that a lower connectivity between environments led to a lower estimate of genetic correlation with a higher standard error, compared to the situation with a higher connectivity between trials in different environments. Moreover, there was a high percentage of cases where the estimate of genetic correlation was not significantly different from zero. If a genetic correlation estimate was not significantly different from zero, it can mean that genetic correlation between environments could not be reliably estimated with the given sample size and connectivity between environments. A trial designed with 50 families planted in two environments and used to estimate the level of G×E between the environments may be considered acceptable, since there is over a 90% chance that the genetic correlation between environments can be estimated. Therefore it is recommended that, infuture, for detection of the level of G×E for a trait in a trial it should have at least 50 families and 10 clones per family, where at least 6 clones per family are in common between environments when 20–30 families are in common and at least 4 clones per family are in common when 35 or more families are in common. This is equivalent to at least 30% of clones needing to be in common between environments, which was a similar threshold of connectivity found in G×E interactions are generated when differences exist between environments . It has Forest tree breeders are interested in having information of G×E patterns in the forest in order to make decisions on where to deploy genotypes to maximize genetic gain. There are two ways that tree breeders can obtain information to reveal the patterns for breeding and deployment. One easy way of identifying G×E interaction in tree breeding is to screen the performance of a set of genotypes across a set of commercially important environments. In this way, breeders can identify which genotypes are not sensitive to the environments, or alternatively, which genotypes are the best match to each of the environments. The second way is more complicated and requires long-term efforts to achieve the goal of increased overall genetic gains. It would require all of the unique environments within forest estates in a country or a region to be identified and G×E interaction patterns to be fully examined in trials andunderstood for any genotypes in a breeding population. The G×E patterns can be caused by either of, or a combination of, rank-change interactions or variance differences between these unique environments . IdentifGenetic variance in growth traits, expecially DBH, has been studied extensively in New Zealand and Australia. The average narrow-sense heritability for DBH varied from 0.03 to 0.88 among 38 studies, with a grand mean of 0.23 for all studies while the average estimate of broad-sense heritability was 0.39, or almost twice the average estimated narrow-sense heritability . Clonal G×E for growth traits has been extensively studied in New Zealand and Australia. Family × site interaction variance components for stem volume were highly significant, statistically and biologically, between two pumice soil sites and two phosphate-retentive clay soil sites . The corDothistroma resistance has been identified in a number of studies and was also apparent in the current study. The narrow-sense heritabilities reported in the current study were in the range reported in the published literature. Wilcox [Dothistroma resistance of 0.30 with strong additive genetic variance, indicating that straightforward selection of resistant trees would give predictable improved resistance in the offspring. Carson [Dothistroma resistance of 0.24 across four sites. A narrow-sense heritability estimate of 0.18 for Dothistroma resistance in radiata pine was estimated for a factorial cross which included six full-sib families [Dothistroma resistance in radiata pine. These estimates demonstrate that genetic gain can be achieved with selection on additive genetic variance.Significant genetic variation in . Wilcox  reported. Carson  reportedfamilies . In a sefamilies . Kennedyfamilies  reportedDothistroma resistance in the current study, but after considering the lack of connectivity between trials it is likely that the level of interaction is much less important than the genetic parameter estimates suggest. This is consistent with what is reported in the literature for G×E interaction in Dothistroma resistance in radiata pine. Shelbourne [Dothistroma resistance, indicating that seedlot rankings obtained on one site will be reliable for overall ranking of parents in radiata pine. The ratio of additive genetic variance × site interaction variance to additive genetic variance for Dothistroma resistance was found to be 0.57 in radiata pine [Dothistroma resistance may not be the best strategy. In contrast, high levels of G×E interactions have been reported for other diseases. A significant G×E interaction for needle retention in radiata pine was observed with an interaction variance to additive variance ratio of 0.86 [Pinus taeda), Li and Mckeand [Cronartium quercuum (Ber.) Miyabe ex. Shirai f. sp. Fusiforme) at 21 test locations in coastal Georgia, Florida, Alabama, and Mississippi, but only a few interacting families had regression coefficients significantly different from the expected value of 1. A low level of G×E for rust incidence was reported for loblolly pine planted in the Lower Coastal Plain of the Southeastern USA [Pinus elliotti), genetic correlations between sites were over 0.67 for fusiform rust resistance and G×E did not appear to be important in rust resistance in the United States [Dothistroma pini in New Zealand in a genetic diversity study [In the literature, G×E interactions for disease resistance have been reported in radiata pine and other species. An apparent level of G×E interaction was estimated for elbourne  suggesteelbourne  reportedata pine . This su of 0.86 . In lobl Mckeand  reportedd States . It is id States , whereasty study . This isty study .Dothistroma resistance of genetic material ranged from 28 months to 99 months. Ideally, the age of performance assessment across all trials should be at the same age. In forest tree breeding, resources required for establishing trials are always limited. Each year only a small number of trials are able to be established. Genetic analyses usually use trials established during a number of years or even all trials that were ever established [One limitation of this study was that the assessment age of DBH, height and ablished . The assablished . From onablished , suggestThe model used in the simulation was simplified to show the relationship between connectivity and the estimates of additive genetic correlation between environments. We found that lower connectivity between environments led to a lower estimate of additive genetic correlation with a higher estimate of standard error of the genetic correlation. Variances for dominance effects, epistatic effects and variance between ramets of the same clones were not considered in the simulation. The relationship between connectivity and the estimation of additive genetic correlation would likely be unchanged if these terms were included, but model complication would increase. An attempt was made to include the dominance effects in the simulation of phenotypes and in the estimation model. However, ASReml-R was sometimes not able to converge, which might suggest that the simulated population size was not large enough to estimate dominance effects. A large simulated population size may be needed to estimate dominance effects and an even larger population size may be needed if epistatic effects are included. In addition, in the future, simulations with various population sizes tested at more than two environments may be needed to investigate how many clones need to be in common between environments for obtaining reliable estimates of genetic correlation and G×E.G×E interaction appears to exist for growth traits in radiata pine genetic trials. However, this study suggests that the magnitude of G×E interactions found in radiata pine clonal trials was often overstated due to a number of factors, including particularly the lack of connectivity between environments, especially lack of connectivity with common genetic entities tested across multiple environments. The implication of the results for growth rate in the current study for radiata pine breeding in New Zealand and Australia is that a strategy does need to be developed to deal with G×E, either by selecting stable genotypes that show high performance across multiple environments or by selecting the best genotypes for each environment to maximize genetic gain in each specific environment. Given the substantially increased resources that would be required to achieve the latter, it is likely that the former is the best strategy, that is, selecting for the best set of stable genotypes. This is because the requisite increase in resources for estimating gain in specific environments to capture G×E could alternatively be used to increase gain from selecting stable genotypes more effectively. The results in the current study also suggest that connectivity between trials with common genetic entities that are tested across environments needs to be considered when designing trials and maximizing gain. When designing clonal trials with a population size similar to the trials used in this study in multiple environments to accurately estimate the level of G×E, and where resources for creating connectivity between environments are limited, at least 30% of clones need to be in common between environments.S1 FigLevel of G×E between trials is measured by the genetic correlation. The higher is the level of genetic correlation the lower the level of G×E between trials.(TIFF)Click here for additional data file.


There are a few well-known indigenous breeds of Chinese rabbits in Sichuan and Fujian provinces, for which the genetic diversity and population structure have been poorly investigated. In the present study, we successfully employed the restriction-site-associated DNA sequencing (RAD-seq) approach to comprehensively discover genome-wide SNPs of 104 rabbits from four Chinese indigenous breeds: 30 Sichuan White, 34 Tianfu Black, 32 Fujian Yellow and eight Fujian Black. A total of 7,055,440 SNPs were initially obtained, from which 113,973 high-confidence SNPs  were selected to study the genetic diversity and population structure. The mean polymorphism information content (PIC) and nucleotide diversity (π) of each breed slightly varied with ranging from 0.2000 to 0.2281 and from 0.2678 to 0.2902, respectively. On the whole, Fujian Yellow rabbits showed the highest genetic diversity, which was followed by Tianfu Black and Sichuan White rabbits. The principal component analysis (PCA) revealed that the four breeds were clearly distinguishable. Our results first reveal the genetic differences among these four rabbit breeds in the Sichuan and Fujian provinces and also provide a high-confidence set of genome-wide SNPs for Chinese indigenous rabbits that could be employed for gene linkage and association analyses in the future. Oryctolagus cuniculus) are one of the most recently domesticated animals with an estimated history of approximately 1,400 years [Rabbits  have become the most popular genetic markers for studying genetic diversity and population structure in wild and domestic animals. With rapid development of high-throughput sequencing techniques, restriction site-associated DNA sequencing (RAD-seq) provides a relatively cost-effective approach to obtain tens of thousands of genome-wide SNPs , 9. The The objective of the present study was to discover the genome-wide SNPs by RAD-seq approach and then investigate genetic diversity and population structure of the four Chinese rabbit breeds. In addition to providing a high-confidence set of genome-wide SNP markers that could be employed for gene linkage and association analyses, the revealed inter-breed genetic differences will help us for better establishing the conservation strategies of genetic diversity and crossbreeding systems in rabbit industry.All experimental protocols involved in this study were approved by the Institutional Animal Care and Use Committee of the College of Animal Science and Technology, Sichuan Agricultural University, Sichuan, China (No. DKYB20081003).Blood samples were randomly collected from 104 unrelated individuals from four indigenous breeds of Chinese rabbits , includiEcoRI , onto which the P1 adaptor was ligated. Subsequently, the samples were pooled, randomly sheared, and size-selected in sequential steps. After the second adapter (P2) was added, the DNA fragments of 300 to 500 bp in length were used to construct the sequencing libraries. Finally, the Illumina HiSeq2000 platform was employed to sequence the constructed libraries and generate 150 bp paired-end reads .RAD-seq sequencing libraries were constructed according to the recommended pipeline . Brieflyphred value < 5) more than 50% of the total length, (ii) reads containing adaptor sequences, and (iii) reads containing ambiguous bases more than 10% of its total length. This filtering step of reads was performed using the fastp tool (v0.19.5) [All the raw sequencing reads were first subjected to quality control by removing these low-quality reads, which were defined by any of three criteria: (i) reads containing low-quality bases (Qv0.19.5) , after whttp://broadinstitute.github.io/picard/), including the coordinate sorting and duplicate removing. Subsequently, the GATK software (v3.7) [All reads were mapped against the reference rabbit genome (OryCun2.0) using the BWA-MEM algorithm in BWA software v0.7.17)  with defe (v3.7)  was appl  with dee (v3.7) . AdditioHe), observed heterozygosity (Ho), private allele number (Ap), frequency of the most frequent allele (P), fixation index (FST), and inbreeding coefficient (FIS) for each breeds were computed using the ‘population’ program in Stacks (v2.2) [FST and FIS values were computed to analyze pairwise population differences among the four breeds. To evaluate the genetic relationship among all four breeds, a principal component analysis (PCA) was conducted with GCTA (v1.26.0) [First, we investigated the overall read depth and chromosomal distribution for all SNPs using the VCFtools . The nucs (v2.2) . The Pops (v2.2)  was utilv1.26.0)  after cov1.26.0) . All thev1.26.0)  from R pWe obtained 295 Gb of raw paired-end reads with an average of 2.83 Gb per sample, which ultimately produced 260 Gb of clean paired-end reads after the quality filtering . An averWe subsequently computed six indexes in relation to the intra-breed genetic diversity for every one of the four rabbit breeds . There wFST values showed low to moderate levels of genetic differentiation among the four rabbit breeds (FST = 0.0370) and between FB and SW (FST = 0.0504), respectively. The intra-population inbreeding coefficient of FIS ranged from -0.1109 (FY) to -0.0390 (TB). Furthermore, the PCA-based clustering first revealed that all the four breeds were clearly distinguishable Click here for additional data file.


Cucumis melo L., is an important horticultural crop with abundant morphological variability, but the genetic diversity and relationships within wild and cultivated melons remain unclear to date. In this study, thick-skinned (TC) (cultivated subspecies melo), thin-skinned (TN) (cultivated subspecies agrestis), and wild accessions were analyzed for genetic diversity and relationships using 36 microsatellite markers. A total of 314 alleles were detected with a mean allelic number of 8.72 and polymorphism information content of 0.67. Cluster analysis of the accessions resulted in four distinct clusters  broadly matching with the TC, TN, and wild groups. Cluster I contained only two Indian wild accessions. Cluster II was consisted of 49 South Asian accessions, 34 wild accessions, and 15 TN accessions. Cluster III was a typical TC group including 51 multiorigin TC accessions and one wild accession. The remaining 88 accessions, including 75 TN accessions, 6 wild accessions, and 7 TC accessions, formed the cluster IV, and all the TN and wild accessions in this cluster were from China. These findings were also confirmed by Principal component analysis and STRUCTURE analysis. The South Asian subspecies agrestis accessions, wild and cultivated, had close genetic relationships with a distinctive genetic background. Chinese wild melons showed closeness to cultivated subspecies agrestis landraces and could be a return from the indigenous cultivated melons. The AMOVA and pairwise F statistics (FST) presented genetic differentiation among the three groups, with the strongest differentiation (FST = 0.380) between TC and TN melons. These results offer overall information on genetic diversity and affiliations within a variety of melon germplasms and favor efficient organization and utilization of these resources for the current breeding purpose.Melon, Cucumis melo L., 2n=2x=24) is an economically important horticultural crop widely distributed in tropical and subtropical areas. This species is highly diverse in morphology, particularly for the fruits, leading to its multiple applications. In China, the sweet fruits of melon are conventionally consumed as a dessert  and some medium-size nonsweet fruits as vegetables , whereas some large-size nonsweet fruits are commonly used as animal fodder. Also, the abundant diversity in this species attracts a number of studies concerning phylogenetics and taxonomy [ C. melo proposed firstly by Pitrat [ C. melo into two subspecies on the basis of ovary pubescence, melo and agrestis. Mostly, subspecies melo bears comose ovaries and subspecies agrestis has ovaries with glabrous skins or short hairs. Subspecies melo, conventionally known as thick-skinned (TC) melon in China, is characterized by large or medium fruits and grown widely around the world, while subspecies agrestis, also called thin-skinned (TN) melon, carries smaller fruits and is limited in East Asia, especially in China [Melon  or the accessions from a certain region. Using different marker systems, several studies analyzed the genetic diversity of melon accessions including wild melons but with a limited accession size; most of the wild accessions were found to be more close to subspecies agrestis [ C. melo ensuring an exact examination from the analysis. The information from our results will favor dissecting the lineage relationship of various melon groups and promote the utilization of these diverse plant resources.Genetic diversity and relationships of studies –14, mostagrestis –17. In t agrestis accessions (TN and wild types) and 58 subspecies melo accessions and covered eight countries in the world including the major origin regions of melon, such as India, Iran, Turkey, and China. A majority of the accessions  were landraces and the remaining were commercial cultivars  and wild accessions . Among them, 88 were requested from the National Mid-term Genebank for watermelon and melon , 86 were from USDA-ARS National Plant Germplasm System, and the remaining 17 were from the Research Group of Watermelon and Melon at Henan Agricultural University.A total of 191 melon accessions were used in this study . Also, this screening took into account even distribution across the melon chromosomes. Finally, a set of 36 high-polymorphism SSR markers were obtained, with each chromosome containing three markers at the top, medium, and bottom of the chromosomes. As a result, 15 SSRs were coming from the report of Zhu et al. [ et al. [ et al. [Genomic DNA was extracted from young leaf samples of all the accessions using a CTAB procedure described by Doyle and Doyle . SSR maru et al. , 13 were[ et al. , and the[ et al. . Detaile et al. [Mspl marker, Sangon Biotech, Shanghai).PCR amplification was carried out in a Thermal Cycler (BIORAD C1000™) with the reaction system and amplification program being same to the report of Wang et al. . The amp et al. [All the markers were scored as codominant data according to the amplicon size. This resulted in a genotypic matrix that was used to calculate the genetic parameters with the software PowerMarker v3.51 , i.e., t et al. .To analyze the genetic diversity and relationship of the melon accessions, the genotypic data were imported into the software MEGA6  to constFST), genetic distance [Genetic differentiation among the different groups was measured by calculating pairwise F statistics  were calculated for the 36 markers estimated from the 191 melon accessions, as shown in momordica accessions) with the pairwise genetic distances (GDs) varying from 0.23 to 0.78 (mean = 0.63). Most of the accessions (32 wild accessions and 14 TN landraces) in this cluster derived from India and only three from Maldives, two wild accessions (No. 99 and 100) and one TN landrace (No. 77). Clearly, the wild and TN accessions in this cluster from the two adjacent countries had close lineages. Cluster III was a typical TC group , containing 51 TC accessions (subspecies melo) and one wild accession (No. 120). These accessions in this cluster were morphologically diverse belonging to at least five varietas suggested by Pitrat [ cantalupensis, reticulatus, inodorus, ameri, and chandalak, and also, their origins covered a wide geographical distribution . Although these subspecies melo landraces in cluster III derived from different regions, they had close genetic relationships and similar genetic backgrounds, probably implying their same origin. The wild accession in this cluster III (No. 120) was an exception since it was clustered together with the subspecies melo accessions, and therefore it could involve in gene exchange with the subspecies melo plants.With the SSR genotypic data, a NJ dendrogram  was cons agrestis accessions). The TN accessions in this cluster included 48 chinensis accessions, 13 conomon accessions, 7 makuwa accessions, 6 momordica accessions and one acidulous accession, most of which derived from China. Except for the TN accessions, cluster IV possessed 7 TC accessions  and 6 wild accessions . This fact indicated close affiliations of these accessions. The seven TC accessions were clustered into this cluster, perhaps due to the introgression of the subspecies agrestis lineages during their domestication processes. It should be noted that six wild accessions were also included in this cluster, in contrast to most of the wild accessions assigned to cluster II. Of these wild accessions, 4 were from China and the other two from Costa Rica and the US, which were markedly different from the wild accessions in clusters I and II with South Asian origin. A lower level of variation was observed for cluster IV with the GDs ranging from 0.18 to 0.72 (mean = 0.45), indicating a comparatively narrow genetic basis in this cluster.The remaining 88 accessions formed cluster IV that covered both the two subspecies , mainly representing by East Asian TN melons  and STRUCUTRE analysis, were used to offer an alternative view of the relationships within the accession collection. On the PCA dendrogram , all the melo and agrestis accessions.Since distinct divergences were found among the different groups, the six genetic parameters  were computed for each group to compare their diversity levels. As shown in FST and Nei's genetic distance  between the TC and TN groups. The minimum genetic distance (0.083) occurred between TC and wild groups. Of the 36 marker loci, 15 showed clear differentiation among the groups with the pairwise FST value of >0.15  , 33 and C. melo has a large number of morphotypes, cultivated and wild [ melo and agrestis. TN melons (subspecies agrestis group) are special morphotypes mainly distributed in East Asia, especially in central and eastern China, the important diversity center of subspecies agrestis in the world [ melo group), having a large number of commercial cultivars in the world, are the main cultivated forms. To utilize efficiently these melon resources in modern agriculture, we adopted 36 core microsatellite markers to examine the genetic diversity and relationships present within an Asian melon collection. Our assays detected 314 alleles (mean=8.72) with a mean PIC value of 0.67, showing a high level of variation in the melon genome. The mean allelic number and PIC value were much higher than those of available reports with the experimental materials being a certain group or from a certain region [ et al. [and wild ; cultivaand wild , 35. In he world , 35. Thin region –14. This C. melo have been frequently reported mainly focusing on cultivated accessions. These results showed that subspecies melo accessions were obviously distinguished from subspecies agrestis accessions [ conomon, chito, dudaim, and momordica, but far from American and European cantalupensis and inodorus [ agrestis [ agrestis landraces were clustered closely with the wild accessions showing their close genetic relationship, as a probable result of the frequent gene exchanges . From the records of USDA-ARS National Plant Germplasm System (Data not shown), these Indian subspecies agrestis accessions have similar morphological features to the wild species and are probably old indigenous landraces or semiwild forms. Since there are a variety of wild melons and semiwild forms or landraces in India, it is reasonable to assume that Indian subcontinent acts as the center of origin of this crop [ agrestis accessions (Chinese TN accessions) in group IV. The same finding was also described in our previous study [ agrestis. Therefore, East Asian subspecies agrestis melons probably originated in Indian subcontinent and were intensively domesticated in Central China, as the indigenous wild melons could not be their pioneers or ancestors. The similar view has been suggested in several studies that East Asian subspecies agrestis melons benefited from Indian introduction  and were domesticated in Huang-Huai-Hai plain in China [To date, genetic diversity and relationships incessions , 36–39, inodorus , 15–17. us study . A view us study  is that in China , 41.FST = 0.380); such a high level of differentiation could be supported by a fact that TC and TN accessions belong to two different subspecies, melo and agrestis [ C. melo have been reported in several studies [ melo (TC melons) were initially considered to originate from Africa [ melo possesses 11 varietas and is richer in morphological diversity than subspecies agrestis (containing 5 varietas), as also evidenced from our result  with stability and even distribution across the genome. The present study revealed an existence of distinct population structure in 191 melon accessions. Indian wild accessions, revealing a close relationship to the local subspecies


Morphological predictors that can be measured on live fish and that correlate with real slaughter yields hence remain a possible alternative. To quantify the power of morphological predictors for genetic improvement of yields, we estimated genetic parameters of slaughter yields and various predictors in 3-year-old common carp reared communally under semi-intensive pond conditions. The experimental stock was established by a partial factorial design of 20 dams and 40 sires, and 1553 progenies were assigned to their parents using 12 microsatellites. Slaughter yields were highly heritable  and strongly genetically correlated with each other (rg = 0.96). To create morphological predictors, external  and internal measurements (ultrasound imagery) were recorded and combined by multiple linear regression to predict slaughter yields. The accuracy of the phenotypic prediction was high for headless carcass yield (R2 = 0.63) and intermediate for filet yield (R2 = 0.49). Interestingly, heritability of predicted slaughter yields (0.48–0.63) was higher than that of the real yields to predict, and had high genetic correlations with the real yields (rg = 0.84–0.88). In addition, both predicted yields were highly phenotypically and genetically correlated with each other (0.95 for both), suggesting that using predicted headless carcass yield in a breeding program would be a good way to also improve filet yield. Besides, two individual predictors (P1 and P2) included in the prediction models and two simple internal measurements (E4 and E23) exhibited intermediate to high heritability estimates (h2 = 0.34 – 0.72) and significant genetic correlations to the slaughter yields (rg = |0.39 – 0.83|). The results show that there is a solid potential for genetic improvement of slaughter yields by selecting for predictor traits recorded on live breeding candidates of common carp.Common carp is a major aquaculture species worldwide, commonly sold alive but also as processed headless carcass or filets. However, recording of processing yields is impossible on live breeding candidates, and alternatives for genetic improvement are either sib selection based on slaughtered fish, or indirect selection on correlated traits recorded  Cyprinus carpio and C. rubrofuscus) is highly important freshwater fish species for world aquaculture, with an annual production exceeding 4,000,000 tons  indicated that filet yield can be specifically improved in a selection program  determine morphological predictors by external  and internal measurements (ultrasound imagery) that can be combined by linear regression to predict slaughter yields (headless carcass and filet yields) in common carp, (ii) estimate genetic parameters of slaughter yields and their predictors, (iii) predict and compare the potential genetic gain based on hypothetical mass selection, sib selection and indirect selection based on the predictors of slaughter yields.The methodological protocol of the current study was approved by the expert committee of the Institutional Animal Care and Use Committee (IACUC) of the University of South Bohemia in České Budějovice (USB), Faculty of Fisheries and Protection of Waters (FFPW) in Vodňany according to the law on the protection of animals against cruelty . To enhance animal welfare and decrease suffering during all fish handling, the fish were anesthetized using 2-phenoxyethanol for each live trait recording, and humanely euthanized (humane endpoint) for final recording of slaughter traits. The main author of study owns the certificate (CZ 01704) giving capacity to conduct and manage experiments involving animals according to section 15d paragraph 3 of Act no. 246/1992 Coll.-1) to the prepared nursery ponds at the Klatovy fish farm. Since then, the families were reared communally in ponds. The families were reared in various pond sizes depending on age of fish and annual period (0.2–4 ha) under semi-intensive pond management based on natural food and supplementary feeding  served three times a week. At fish age of 1-year, a random sample of 3000 fish from the best pond  was anesthetized with 2-phenoxyethanol (0.5 ml per 1 l of water) and individually PIT-tagged and fin-clipped for parentage assignment. After the second growing period and the second overwintering, the fish were harvested and the data were collected for a related study about the genetic potential of overwintering performance in common carp  in České Budějovice, Faculty of Fisheries and Protection of Waters (FFPW) in Vodňany, Czech Republic. Amur mirror carp (AM), Vodňany line, recently certified as a new Czech common carp breed , was chomon carp . At markmon carp .5 ∗ BW/SL, relative body height: RelBH = BH/SL, and relative head length: RelHL = HL/SL. After biometric recordings, each fish was processed and the following body portions were weighed (to nearest 0.5 g): head, left filet, viscera, gonads , left filet skin, half carcass, left filet ribs + trimmings, fins, and scales. The weight of slaughter body parts and vertebral axis was created by combining the previous body portions: headless carcass weight [hl-CarssW = left filet + left skin + left ribs and trimmings + half carcass], filet weight with skin [FiletW = (left filet + left filet skin) ∗ 2] and vertebral axis weight: [AxisW = half carcass - (left filet + left skin + left ribs and trimmings)]. The percent slaughter yields were calculated as follows: headless carcass yield % [% hl-Carss = (hl-CarssW/BW) ∗100], and filet yield [% Fil = (left filet + left skin) ∗ 2/BW ∗ 100]. In addition, sex effect of analyzed traits was calculated using one-way ANOVA and HSD Tukey test at p = 0.05. Finally, to obtain alternative trait definitions to ratio-based traits, the natural logarithm was calculated for the weight of each slaughter body part and regressed on the logarithm of body weight to obtain growth-independent allometry residuals that fix the bias of ratio traits . Total length (TL), standard length (SL), body length (BL), head length (HL), body height (BH), and body width (BWI) were measured to the nearest 0.1 mm with an in-house electronic ruler, and body weight (BW) was weighed to the nearest 0.1 g with an electronic scale. For external phenotypic measurements, fish were photographed (left side) using a digital camera (CANON EOS 1000D). Internal measurements were recorded by ultrasound tomography . The total muscle fat content (% Fat) was recorded using a Fish Fatmeter FM 692 , using calibration option ‘CARP – 1.’ The phenotype for % Fat is expressed as the mean of four repeated measurements  taken on the left side of the fish performed as guided by the manufacturer’s guideline. In addition, selected yield-related biometric indicators were calculated as follows: Fulton’s condition factor: FC = 10o traits  and probo traits . Thus, fFigure 1). Furthermore, vertical blue lines were added into each image in order to facilitate the manual positioning of landmarks on the surface of the fish. We also checked if the magnification of camera between each working day was unchanged  and B  and calculated with the formula: To quantify the shape of body, head and lateral line, a total of 20 coordinates of morphological points were digitized using ImageJ with the Point Picker plugin  that allThe internal measurements were collected using ultrasound imagery . Four muscular thicknesses from anterior (E4), intermediate , and posterior (E6) muscles and one internal depth of the body cavity (E23) were measured, at the same position described by The association of the variation in carp morphology to the variation in real processing yields was analyzed using the MorphoJ software . This meR2 and F-value. The models were used to calculate the predicted yield values for each fish that are termed as Mod_hl-Carss for headless carcass yield and Mod_Fil for filet yield.A multiple linear regression using the reg.best function of the FactoMineR of R software package was performed using the external morphology descriptors, ultrasound measurements and fat meter value as independent variables and the Logr_hl-Carss and Logr_Fil as dependent variables. The best prediction model identification corresponds to those with the highest K subsets (here K = 20), the analysis is performed on the data of the K-1 subsets (training sets) and validated on the data of the remainder of the dataset . Then the coefficient of determination of the cross validation (R2CV) was calculated.Models were validated by cross validation method using the crossval function of the bootstrap package in R software . Such anThe fin tissues of the 60 parents and 2035 offspring (sampled after the second growing period) were placed into 96 well plates and sent to LABOGENA-DNA, the French laboratory for livestock genotyping . Parentage assignment was based on the analysis of 12 microsatellite loci and performed using the AccurAssign software, applying a maximum-likelihood method . The parh2), phenotypic and genetic correlations  were estimated using DMU statistical software  and α is the vector of random additive genetic effects , and 𝜀 is the vector of random residual effects. The additive  genetic effects were assumed to follow N, with G the genetic (co) variance matrix between traits and A the numerator relationship matrix relating all animals in the pedigree, while the residual effects were assumed to follow N, R the residual (co) variance matrix between traits and I an appropriate identity matrix. In the first step, an additional random effect common to dams  was included in the model. However, this effect was negligible for all traits, and thus it was not included in the final model.where VA) divided by the total phenotypic variance (VP), h2= VA/VP. A model with maximum three traits at a time was used to obtain convergence for genetic correlations. However, when condition factor (FC) was calculated, convergence could not be obtained, and thus the genetic correlations between FC and other traits were obtained from a bivariate analysis of such traits. The likelihood ratio test (LRT) was used for comparing the goodness of fit of two models . The animal additive genetic effect (and thus the associated heritability estimate) was considered significant when the difference in -2Log-likelihood was higher than the threshold value for p < 0.05 of a χ2 distribution with 1 degree of freedom (rg|-|1.96 × S.E.| was higher than zero (two-tailed hypothesis).Heritabilities were estimated using a univariate model, and were calculated as the ratio of additive genetic variance ( freedom . GeneticG) per generation for filet yield were calculated using the equations of GMS = i h2 σP, where i is the selection intensity and h2 and σP are the heritability and phenotypic standard deviation of the trait under selection, respectively. The genetic progress of FSS was estimated by n is the number of slaughtered sibs sampled per family (n = 10 sibs), r is the genetic correlation between sibs (r = 0.5 for full sibs) and t is the phenotypic intra class correlation (t = rh2). The predicted genetic gain through indirect selection criteria was calculated by ΔGIS = i × h1 × h2 × rg × σP2, where ΔGIS is the estimated genetic gain on the target trait, h1 and h2 are the square roots of heritability of the indirect selection trait (on which selection is applied) and of the target trait, respectively, rg is the genetic correlation estimated between the indirect trait and the target trait and σP2 is the phenotypic standard deviation of the target trait. As genetic gains for filets were calculated in log units, the real genetic progress was scaled back to the percent body weight units by multiplying ΔG by the real mean filet yield in the present experimental stock. The selection intensities were set up of 10 and 30%, with 10 sibs per family in FSS as the most reasonable values related to a potential carp selection program.Expected genetic gains  could be assigned to a single parental pair, 84 (4.1%) had two possible parent pairs and were considered unassigned, 28 (1.4%) could not be assigned to any parent pair and 23 (1.1%) had DNA quality problems and thus no exploitable genotype. Out of the 1901 uniquely assigned fish, 1622 were still alive at the time of final sampling, and of those 1553 had adequate phenotypes after removal of outliers.The 1553 fish used in the analysis originated from 197 out of the possible 200 full-sib families. The number of progeny per sire varied from 14 to 79, the average was 39. The number of progeny per dam varied from 25 to 128, the average was 78. The sexes were distributed equally .Table 1. Sex effect was significant and % Fat, RelBH and both yields were higher for females than for males. Yields of headless carcass (66%) and filets (50%) were higher than usual in common carp, probably due to the specific experimental processing which was different from the commercial one but reflected better the biological characteristics of traits.Mean, standard deviation, differences between sexes  and minimum and maximum values of yield-related traits and slaughter yields are listed in A positive allometry (regression coefficient >1 in log-log plots) was observed for filet weight, viscera weight and skin weight, showing that heavier fish have proportionally heavier filet, viscera and skin than smaller fish  included into two prediction equations, and their R2 and Fisher test values (F) are shown in Table 2. Logr_hl-Carss could be predicted with a model combining three individual predictors : the ratio of head area to total body area (P1), the ratio of abdominal filet thickness to height between the lateral line and the aligned ventral point (P2), and the ratio of caudal part area to ventral part area (P3). Mod_hl-Carss explains 63% (R2CV = 0.624) of total phenotypic variance in Logr_hl-Carss. Logr_Fil was best predicted by the model using the same predictors as for Logr_hl-Carss  and in addition body weight (P4) and % Fat (P5). Mod_Fil explains 49% (R2CV = 0.489) of total phenotypic variance of Logr_Fil.The set of best morphological predictors  and maximal (1.00) for traits associated to body shape .Heritability estimates of yield-related phenotypes, slaughter yields (Logr) and model-predicted (Mod) slaughter yields are given in Table 3).Logr slaughter yields had higher heritability than the commonly used percentage yields . Heritabilities of internal measurements were moderate to high .Heritabilities obtained for allometric log-log residuals of the weights of different body portions to body weight were low for vertebral axis (0.04) and ribs (0.18), which are very prone to measurement errors, and moderate to high 0.31 – 0.62) for the other body parts  and to both predicted slaughter yields . Oppositely, % Fat was positively associated to Logr and Mod slaughter yields (range 0.25–0.56). To ensure that the positive relationships of % Fat with Logr and Mod slaughter yields were not generated by the relation of % Fat with BW, the analysis was also run using BW as a covariate for % Fat. With such a model, the genetic correlations become more positive (rg = 0.40–0.68). Body shape traits  were highly correlated to each other (0.78–0.96) but differed in their relation to slaughter yields. Both FC and RelBH were only slightly negatively and mostly insignificantly correlated to yield traits. Oppositely, RelHL was intermediately negatively associated to Logr and Mod slaughter yields (rg = -0.47-0.64).The genetic correlations between yield-related phenotypes, Logr and Mod slaughter yields are listed in rg = 0.96), similarly as in case of predicted slaughter yields (rg = 0.95).Logr slaughter yields (Logr_hl-Carss and Logr_hl-Carss) were highly correlated to each other (rg = 0.84–0.88), suggesting a good possibility of using predicted yields as indirect selection criterion.Interestingly real and predicted slaughter yields were highly associated (Table 4. P1 and P4 showed low to moderate negative genetic associations to Logr slaughter yields (-0.35 – -0.57). Oppositely, P2 (0.76 – 0.83), P3 (0.29 – 0.34), and P5 (0.25 – 0.27) were positively correlated to Logr slaughter yields. Hence, individual predictors might be also used in non-invasive genetic improvement of slaughter yields.The genetic correlations of the predictors that composed the predictive models with Logr slaughter yields are presented in in vivo selection for improved yields.The internal measurements E23, E4, E5, E6, E8 achieved negative relationship to the Logr slaughter yields (-0.03 – -0.61), only E8 was in slightly positive relation to Logr yields (Supplementary Table rg Logr_LFil = -0.49), viscera yield (rg Logr_Viscera = -0.40), % Fat (rg = -0.46), ribs yield (rg Logr_Ribs = -0.65) and fins yield (rg Logr_Fins = -0.50) implying a tradeoff of investing in reproduction compared to somatic growth and reserves.The correlations between Logr body portion yields, BW and % Fat are presented in Supplementary Table Table 5. Absolute genetic gains for the hypothetical mass selection on real filet yield were 0.70 and 0.46% per generation when 10 and 30% selection intensities were applied, respectively. Genetic gain for FSS with 10 sibs selected per family with the 10 and 30% selection pressure was slightly lower (0.61 and 0.40%) than for mass selection. Estimated genetic gains achieved by indirect selection on the predictor Mod_Fil were 0.66% for 10% selection intensity and 0.43% for 30% which is better than FSS and only slightly lower than direct mass selection on filet yield (which is not possible in practice). Genetic gains ranged from 0.15 to 0.52% for the single predictors used in the models, and from 0.21 to 0.51% for best two internal measurements.Expected genetic gains using various selection schemes are listed in The present study provided important results relative to the possibility to genetically improve processing yields in common carp: (i) we found high heritability estimates of real and predicted slaughter yields showing a solid potential for their genetic improvement; (ii) high positive genetic correlations were observed between the real and the predicted yields, showing that the latter might be used as non-invasive selection criteria; (iii) expected genetic gain achieved by indirect selection on the predicted yields were higher than those obtained by sib selection that is traditionally applied for improvement of traits needing destructive recording. Thus, we showed that selection of common carp for improved slaughter yields should be feasible, even in a simple breeding program using indirect selection criteria.In this study, sex of the fish had a significant effect on some traits, including slaughter yields. Conversely, BW was found to be independent of sex. However, females had significantly greater relative head length, muscular fat and both yields. This is in accordance with the previous studies performed on common carp in Central European conditions ,b, 2007.Heritability estimates of yield-related traits and body morphology were high and in the upper range when compared to previous studies done on the different batches of common carp  showing h2 = 0.28–0.36; The slaughter yields in fish are commonly calculated as a ratio between the given processed body part weight and body weight. However, ratio traits are often biased by growth allometry that is common between body portions and body weight , and ratWe observed a strong genetic correlation between Logr_hl-Carss and Logr_Fil (0.96 ± 0.02). Likewise, rg = -0.35). On the contrary, a high positive genetic correlations of body weight and slaughter yields (0.73–0.74) were found previously in common carp . A strong genetic relationship of % Fat to percent yields (0.66–0.76) was reported earlier in common carp  as well as RelBH (rg = 0.83). Uncontrolled selection for a smaller relative head length may thus lead to a limitation of gains in growth and faster change to an oblong-like body shape that may be less favorable for some carp consumer buying whole fish on the traditional market.The relative head length (RelHL) was moderately negatively correlated to both yields (-0.47 – -0.53) implying that selection for lower RelHL could be an indirect selection criterion for increased yields in common carp. This is in agreement with R2 = 0.63), and intermediate for Logr_Fil (R2 = 0.49). Such prediction of slaughter yields, combining external and internal measurements, was recently performed on rainbow trout (R2 = 0.38 for headless carcass yield in R2 = 0.02 – 0.18 for filet and 0.27 – 0.41 for carcass yield in h2 = 0.46–0.50), and also higher when compared to other studies in which inheritance of predicted yields were estimated . The accuracy of phenotypic prediction was high for Logr_hl-Carss  and % Fat (P5) (0.63–0.68). In the recent studies  with negative association to yields. So, selection on lower value of P1 would lead to higher yields as smaller head is related to higher yields (rg = -0.52–0.57) similar to RelHL discussed above. P2 was the ratio between ultrasound measurement of abdominal thickness (E8) and external belly height measured between landmarks 8 and 9 in 2D, and was highly positively associated to yields (rg = 0.76–0.83). A similar relation occurs in rainbow trout  and high for BW  might be used as alternative indirect criteria in accordance to their high heritability and intermediately high genetic correlations to yields  would produce slightly lower genetic improvement than hypothetical mass selection (MS) applied on filet yields in both selection intensities. Still, sib selection might be effectively applied in genetic improvement of common carp yields. In addition, FSS method could be practically performed on real headless carcass yield, which is easier to be measured and less prone to measurement errors than filet yield, but has a simultaneous favorable effect on filet yields due to the high genetic correlation between both (0.96). On the other hand, sib selection utilizes only between-family genetic variation without exploiting genetic variation within families .in vivo could overcome limitations from sib selection mentioned above and give an even better response compared to FSS (expected genetic gain was 0.43–0.66% for indirect filet yield improvement). Alternatively, simple internal measurements (E4 and E23) or individual predictors P1 and P2 might be used as traits for indirect genetic improvement of yields (0.21–0.52%) as it was also suggested by On the other hand, indirect selection using Mod filet yields (or the simpler Mod_hl-Carss) recorded Nevertheless, it must be stressed that using Logr yields values that were used for derivation of best predictors and Mod yields might slightly overestimate the potential genetic gains. This was visible when simulation selection analysis was run in accordance with in vivo morphological predictors. In addition, both real and predicted headless carcass yield might be used as an efficient surrogate  for filet yield improvement through sib or indirect selection. Besides, two internal ultrasound measurements and two individual predictors could be also alternatively used as traits for indirect selection in genetic improvement of slaughter yields in common carp. As predictors are combining several sources of information, further information on the resulting breeding accuracies and realized genetic gains would be valuable in the future to quantify the expected progress. Furthermore, validation of best predictors would be necessary before their transfer to other carp breeds, strains, lines, and size categories.In the present study, model-predicted slaughter yields in common carp were highly heritable and strongly genetically associated to highly hereditary real yields, expressed as log-log residuals. The results show potential for genetic improvement of processing yields through selective breeding, also by using https://osf.io/vfnbs/).The dataset underlying our findings is fully available in the public data repository (OSF: MP, DG, and MK shared on establishing and on-growing the experimental stock, PIT tagging, and fin clipping the fish. PH and MP provided the methodology and equipment. MP, JB, MV, AV, JZ, DG, AB, and MK shared on final trait recordings. JZ digitized the morphological points. AK introduced MP to the quantitative genetic analysis. JB carried out the phenotypic prediction of slaughter yields. LG performed the DNA extractions and parentage assignment. MP and MV estimated the genetic parameters. All authors contributed to drafting the manuscript and approved the final 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. The handling Editor declared a past co-authorship with several of the authors MP, MV, AB, PH, and MK.


Excirolana hirsuticauda along 1900 km of the southeast Pacific coast, across 30°S.There is a biogeographic break located at 30°S in the southeast Pacific, in a coastal area of strong environmental discontinuities. Several marine benthic taxa with restricted dispersal have a coincident phylogeographic break at 30°S, indicating that genetic structure is moulded by life history traits that limit gene flow and thereby promote divergence and speciation. In order to evaluate intraspecific divergence at this biogeographic break, we investigated the genetic and morphological variation of the directly developing beach isopod COI sequences and microsatellite data both identified a strong discontinuity between populations of E. hirsuticauda to the north and south of 30°S, and a second weaker phylogeographic break at approximately 35°S. The three genetic groups were evidenced by different past demographic and genetic diversity signatures, and were also clearly distinguished with microsatellite data clustering. The COI sequences established that the genetic divergence of E. hirsuticauda at 30°S started earlier than divergence at 35°. Additionally, the three groups have different past demographic signatures, with probable demographic expansion occurring earlier in the southern group (south of 35°S), associated with Pleistocene interglacial periods. Interestingly, body length, multivariate morphometric analyses, and the morphology of a fertilization-related morphological character in males, the appendix masculina, reinforced the three genetic groups detected with genetic data.The COI sequences, microsatellite data, and morphology was concordant and showed two geographic areas in which divergence was promoted at differing historical periods. Variation in the appendix masculina of males has probably promoted reproductive isolation. This variation together with gene flow restrictions promoted by life history traits, small body size, oceanographic discontinuities and sandy-beach habitat continuity, likely influenced species divergence at 30°S in the southeast Pacific coast. The degree of genetic and morphological differentiation of populations to the north and south of 30°S suggests that E. hirsuticauda harbours intraspecific divergence consistent with reproductive isolation and an advanced stage of speciation. The speciation process within E. hirsuticauda has been shaped by both restrictions to gene flow and a prezygotic reproductive barrier.The degree of divergence of The online version of this article (10.1186/s12862-019-1442-z) contains supplementary material, which is available to authorized users. Biogeographic breaks are areas of shifts in species composition and represent the limits of biogeographic regions. These discontinuities are often located in areas with prominent topographical features or steep environmental discontinuities. Along the southeast Pacific coast, biogeography of marine communities is characterized by two major biogeographic breaks. The southern break is located at 42°S and corresponds to the northern limit of the Magellan Province that is dominated by sub-Antarctic species and is characterized by a topography of fjords, islands and channels, with strong influence of rivers and glaciers . Also ocTo the north of 42°S the coast is mainly linear and under the influence of the HCS , 2. The The HCS is an eastern boundary system with coastal wind-driven upwelling . Wind, uA common pattern along biogeographic breaks is a concordant genetic discontinuity, or phylogeographic break, of intraspecific lineages , 14. PhyOrchestoidea tuberculata ). refugia –64. GeneE. hirsuticauda can also rapidly recolonize sandy beaches [Species from sandy beaches such as cirolanid isopods may locally disappear after major changes in beach morphology and height, but some species, including  beaches . Environ beaches , 58 may  beaches , furtherE. hirsuticauda to the north and south of 30°S, with reciprocal monophyly and lack of gene flow, suggests an advanced stage of the speciation process occurring at a biogeographic break. The morphological trait that explained most of the divergence at 30°S was the appendix masculina of males. Isopods have internal insemination and sperm transfer is often mediated by the appendix masculina, a structure of the pleopod that shows great variation between species [E. hirsuticauda populations showed significant divergence in the relative length (and not the absolute length) of the projection of the appendix masculina in relation to the rest of the appendix, which should lead  to a prezygotic reproductive isolation, similar as reported for E. braziliensis [appendix masculina of E. hirsuticauda clearly distinguishes and allows the correct assignment of individuals from the north and south of the 30°S biogeographic break. Additionally, the appendix masculina also allows identification of linages separated at 35°S albeit with less divergence than at 30°S. There is consistency in the degree of genetic and morphological divergence in each of the two discontinuities detected, at 30°S and 35°S.The degree of genetic and morphological divergence of  species . Detecteiliensis  and otheiliensis , 70). Diiliensis , 72. HerE. hirsuticauda led to concordant signals of divergence in genetic diversity and morphology, specifically of a trait related to reproductive isolation, at two geographic areas with differing time since onset of the divergence. This study highlights that in E. hirsuticauda, divergence in the appendix masculina of males, morphometry and genetic diversity co-vary at the same latitudes. Additionally, it exemplifies the result of divergence across biogeographic breaks that can shape species diversity by restricting gene flow. Restricted gene flow resulting from the combination of small body size, mode of development without a pelagic larval stage, oceanographic and habitat discontinuities have likely shaped the divergence processes in E. hirsuticauda along the Humboldt Current System. The evolution of a morphological trait that could be a reproductive barrier suggests that even in the absence of ecological or habitat constrains to gene flow, reproductive isolation would persist. Thus, both restricted gene flow and the prezygotic reproductive barrier were likely factors that promoted speciation within E. hirsuticauda.The evolutionary history of E. hirsuticauda were collected from each of 14 localities between 25°S and 42°S , after rarefaction to the minimum sample size of N = 20, was estimated using CONTRIB 1.4 [A median-joining haplotype network was constructed using NETWORK 4.6 . Number of segregating sites, number of haplotypes, nucleotide diversities, Tajima’s D  and Fu &D [F  were calD [F . HaplotyTRIB 1.4 .6 generations sampled every 1000 generations. For each group, we performed separately a JModelTest v2 [http://beast.community/tracer). Tracer v 1.6 was used to depict the Bayesian skyline plots. For this, we used the median values and corresponding 95% HDP (highest density probabilities) confidence intervals for effective population size (Ne) dynamics trough time and the time of the most recent common ancestor (tMRCA) of the demographic expansion for each group.To detect historical population expansions, mismatch frequency distributions of the number of nucleotide differences between pairs of sequences and Raggedness index were performed in Arlequin 3.5  and BayelTest v2  in orderST values were estimated using ARLEQUIN 3.5. ARLEQUIN 3.5 was used to perform AMOVA using a priori groups detected by previous analyses using the pairwise difference as distance method with 10,000 permutations.Pairwise Φ8 MCMC iterations sampling every 100 generations, with a burn-in period of the first 25%. According to the authors’ recommendation, we assumed that the mutation rate is under a Hasegawa-Kishino-Yano (HKY) model. After achieving convergence in the M-mode, we used the simulated genealogies using the L-Mode (Load Tree mode) to calculate the log maximum-likelihood and credibility intervals (95% under HPD) estimates for migration parameters using a Likelihood Ratio Test (LRT). Splitting times were rescaled into years (t/μ) and effective rate of migration (Θx/ mx)/2)  using a mutational rate of 2% per Myr [To estimate the splitting time (t) of the detected genetic groups and the degree of isolation of each group (migration patterns), we used the isolation-with-migration model implemented in the software IMa2 , 85. For per Myr , 87.E. hirsuticauda from eight localities. Amplicons were run on an automated sequencer and allele sizes were scored using GENEMAKER 1.80 (SoftGenetics). MICRO-CHECKER 2.2.3 [For microsatellite DNA analyses, eight primer pairs  were useER 2.2.3  was usedER 2.2.3 .FIS ; estima9.3.2 [,  implemens method  by compaE. hirsuticauda increased with latitude [At least 200 individuals were collected at each of seven beaches between 26.99°S and 42.66°S. Since a recent study had confirmed that body sizes of latitude , herein E. braziliensis Weinberg & Starczak. Especially since the appendages involved in sperm transfer might vary among populations , 19 measures of body morphology , we, weE. br2 = 0.78) [Nested ANOVA was used to evaluate changes in body length, using sites nested within three regions. The regions were defined with a multivariate classification tree , 100 tha = 0.78) , definin2) was used to determine differences in shape among groups. Analyses were carried out using the software PAST [Principal component analysis (PCA) of morphological variables was used to evaluate geographic differences in morphology, accounting for allometric effects on body size using Thorpe’s method , 102. Hoare PAST . We examare PAST .PERMANOVA , 105 anaAdditional file 1:COI haplotype frequencies of the 128 haplotypes of Excirolana hirsuticauda. (XLSX 44 kb)Raw Additional file 2:ST values for COI mtDNA of Excirolana hirsuticauda among 14 sites. (DOCX 79 kb)Pairwise population ΦAdditional file 3:Excirolana hirsuticauda using IMa2 with COI sequences. (DOCX 714 kb)Marginal posterior probability distributions of migration rates and time since splitting between groups of Additional file 4:COI sequences of Excirolana hirsuticauda using BEAST. (DOCX 183 kb)Demographic reconstructions in Additional file 5:Excirolana hirsuticauda in GeneAlex format. (CSV 22 kb)Microsatellite data of Additional file 6:Excirolana hirsuticauda. (DOCX 80 kb)Frequency of null alleles per locus and population for microsatellite loci of Additional file 7:Locus-by-locus probability of deviation of Hardy-Weinberg Equilibrium after Bonferroni correction. (DOCX 72 kb)Additional file 8:Excirolana hirsuticauda. (DOCX 117 kb)Summary statistics for microsatellite loci of Additional file 9:FIS values for microsatellite loci of Excirolana hirsuticauda. (DOCX 69 kb)Additional file 10:FST of Excirolana hirsuticauda. (DOCX 61 kb)Microsatellite pairwise population Additional file 11:Data of samples used for body length and morphometric analysis. (DOCX 62 kb)Additional file 12:Excirolana hirsuticauda. (DOCX 137 kb)Body length range distribution of Additional file 13:Eigenvectors of morphometric analysis. (DOCX 97 kb)Additional file 14:Connectivity of sandy beaches across the study area based on beach length and beach separation. (DOCX 190 kb)Additional file 15:Excirolana hirsuticauda. (DOCX 89 kb)List of the 19 morphometric measurements analysed in males of Additional file 16:Excirolana hirsuticauda. (DOCX 106 kb)Multivariate classification tree based on morphological traits of


Salvelinus fontinalis) populations residing in the Upper Hudson River watershed of New York State by investigating the spatial genetic structure of over 350 fish collected from 14 different sampling locations encompassing three river systems. Population genetic analyses of microsatellite data suggest that fish in the area exhibit varying degrees of introgression from nearby State‐directed supplementation activities. Levels of introgression in these populations correlate with water‐way distance to stocking sites, although genetic population structure at the level of individual tributaries as well as their larger, parent river systems is also detectable and is dictated by migration and influenced by habitat connectivity. These findings represent a significant contribution to the current literature surrounding Brook Trout migration and dispersal, especially as it relates to larger interconnected systems. This work also suggests that stocking activities may have far‐reaching consequences that are not directly limited to the immediate area where stocking occurs. The framework and data presented here may aid in the development of other local aquatic species‐focused conservation plans that incorporate molecular tools to answer complex questions regarding diversity mapping, and genetically important conservation units.As anthropogenic impacts accelerate changes to landscapes across the globe, understanding how genetic population structure is influenced by habitat features and dispersal is key to preserving evolutionary potential at the species level. Furthermore, knowledge of these interactions is essential to identifying potential constraints on local adaptation and for the development of effective management strategies. We examined these issues in Brook Trout ( We assessed population structure in the area by employing current techniques for landscape genetics, and compared measures of effective population size and genetic variation throughout the region, to elucidate site‐by‐site differences in reproductive success and genetic diversity. We then conclude this study by discussing the management implications of this work as well as how the methods used here may help to inform local conservation strategies related to species‐based conservation planning, and the identification of well‐defined genetic management units where managers and municipalities may have been historically hesitant to embrace molecular techniques.22.1n = 337) were taken from Brook Trout in the Upper Hudson River drainage . These populations were specifically bred for stocking purposes by the New York State Department of Environmental Conservation and are commonly used for supplementation throughout the study area, but are stocked under different circumstances. The Domestic strain and Temiscamie/Domestic F1 hybrid fish are stocked throughout the region regardless of the presence of wild reproducing Brook Trout populations, to enhance recreational angling. The three heritage strains included in the analysis  are specifically stocked in waters that have been reclaimed , and waters where Brook Trout were previously extirpated due to acidification, but where evidence of recovery has warranted their reintroduction, while pure Temiscamie strain fish are only used to create the F1 hybrids, and do not see stocking in the study region.Upper caudal fin clippings . Thirteen autosomal microsatellite loci were genotyped using the same procedures for all individuals. Polymerase chain reaction (PCR) was used to amplify loci using primer pairs created specifically for Brook Trout  and expected (HE) heterozygosity, were carried out using the ARLEQUIN 3.5 software package. In addition, the inbreeding coefficient  were calculated across all sampling locations while correcting for differences in sample size using the FSTAT 2.9.3.2 software package  with 95% parametric confidence intervals were also calculated using NeEstimator v2.01 , using Weir and Cockerham's unbiased estimator of FST (theta) using the FSTAT 2.9.3.2 software package.Summary statistics, including observed  associated with stocked strains across sampling locations. In order to assess the number of distinct groupings across all of the scenarios tested, we used the Evanno method  plateaus across increasing K  , both exhibiting estimates associated with stocked strains at levels more than twice that of any other sampling location examined in this study. We again ran the analysis with a burn‐in step of 250,000 Markov Chain Monte Carlo (MCMC) iterations, followed by 500,000 MCMC iterations with five replicates for each K‐value, but this time assuming K = 1 through K = 15 to examine population structure across the wild‐collected sample set with an initial alpha value of 0.08, based on the number of putative populations in the data set. The same downstream analysis was applied to determine the number of distinct populations. In addition to cluster analysis, we examined isolation by distance (the relationship between genetic differentiation and water‐way distance) between our sampling areas. FST values previously produced using the program FSTAT were incorporated into Rousset's equation . Tests for linkage disequilibrium resulted in three significant pairwise occurrences out of 1,092 pairwise tests: two between loci from Snyder Brook and one between loci from Unnamed brook 1 .Results of neutrality testing using the LOSITAN workbench suggested that all loci were selectively neutral and therefore suitable for further analysis , whereas some of the lowest estimates of genetic diversity were attributed to both Gulf Brook  and Slide Brook . Measures of FIS for Upper Hudson River localities all exhibited estimates slightly less than zero, whereas all other sampling areas, with the exception of Unnamed Brook 1 and Shanty Bottom Brook, exhibited FIS measures slightly elevated from zero. Tests to determine the presence of heterozygote excesses or deficits produced no significant results. Measures of effective population size varied widely between sampling locales. Estimates of effective population size were lowest for the Blue Ledge Tributary and Vanderwhacker Brook , whereas the highest measures of effective populations size were attributed to Unnamed Brook 1 and the combined Dix‐Elk Tributaries .Genetic diversity indices did show variation between sampling locales, but estimates were generally in the same range across the putative populations sampled . The lowest estimated FST value was found between Durgin Brook and Snyder Brook (FST = 0.012), although this measure was followed closely by comparisons between Durgin Brook and Slide Brook (FST = 0.024) as well as Durgin Brook and Unnamed Brook 1 (FST = 0.026). Overall, sampling sites within the Boreas river system exhibited the lowest pairwise FST values calculated (mean FST =0.037). The highest FST values were exhibited between Huntley Pond Inlet and Slide Brook (FST = 0.184) followed by Huntley Pond Inlet and Gulf Brook (FST = 0.179).K and Ln(K) measures produced by STRUCTURE HARVESTER for the runs that included the stocked strains, all sampling locations were identified as genetically distinct from the supplementation strains (K = 6). Bar plots produced by STRUCTURE and processed with CLUMPAK, which examined each sampling site individually with these strains, suggested that the many of the sampled individuals show comparatively low estimates of introgression from long‐term stocking in the area, exhibiting minimal signs of admixture associated with the New York State stocked strains compared to fish sampled from Huntley Pond Inlet and the Blue Ledge Tributary , greater than twice that of any other sample site examined; these individuals were therefore removed from downstream analyses that examined genetic structure between wild fish, to exclude possible bias associated with human‐mediated translocations. Gulf Brook and Snyder Brook exhibited the lowest level of ancestry associated with the New York stocked strains . All other populations exhibited mean hatchery ancestry at levels that ranged anywhere between 3% and 35% , with the majority of individuals falling in the 3%–10% range . Beta regression analysis suggested a significant relationship between hatchery ancestry and water‐way distance to the nearest stocking site  measure produced conflicting results . These results indicate weak population structure at the level of the individual river system and sampling sites, with substantial mixing between individuals at sampling sites both within and between river systems. The isolation‐by‐distance plot suggests a statistically significant linear correlation between genetic differentiation and water‐way distance, although this correlation is somewhat weak, with water‐way distance explaining approximately seven percent of the variation in genetic differentiation between sample sites  to each other, the Δs Figure .M) was detected to some extent across all sampling sites, it was estimated to be the highest within the Boreas River system (M = 16%–24%). Estimates of the number of nonmigrants (NM) for each sampling site within this region were also comparatively low (NM = 68%–74%). The number of nonmigrants exhibited by the Gulf Brook populations was the highest (NM = 89%), while both Shanty Bottom Brook and Calamity Brook produced similarly high measures . Migration estimates were generally much lower between river systems than within, consistent with the admixture patterns produced by STRUCTURE.Migration estimates produced by BAYESASS mirror the results of the STRUCTURE analysis Figure . While o4Understanding how anthropogenic influences have affected the natural genetic structure of communities across the landscape is key to making responsible decisions about the effective management of those species, especially if they are in a state of decline  exhibit varying degrees of introgression from state‐based stocking activities, which shows a significant positive relationship with proximity to stocking sites; (b) exhibit weak genetic population structure at the level of individual tributaries as well as the larger river systems where they are found; (c) are experiencing ongoing migration at least partially influenced by geographic distance; and (d) demonstrate comparatively similar measures of genetic diversity but varied measures of effective population size, based on sampling location.When comparing the genetic structure of fish collected on a site‐by‐site basis with the strains currently stocked by the NYSDEC, it is apparent that although introgression from stocked fish is minimal in most cases, it is present and is in discernable amounts throughout the study area. In addition, admixture estimates produced using the program STRUCTURE attributed this introgression almost entirely to Domestic strain fish, Temiscamie strain fish, or in most cases some combination of the two. This is an important finding because it suggests that the Adirondack “heritage” strains used for stocking in the region are not readily interbreeding with wild fish stocks. Nevertheless, the Temiscamie/Domestic F1 hybrid fish is the primary fish used for supplementation on top of wild reproducing fish in this region, so whether this introgression is a product of a selective advantage over the other stocked strains, or based on circumstantial stocking practices is ultimately unclear. Unfortunately, comprehensive, historical stocking data for this region were not readily available, and supplemental stocking has been occurring in this watershed for decades, so any link between stocking intensity and introgression would be speculative. Nevertheless, we were able to detect a significant relationship between the level of introgression from stocked fish and water‐way distance to known stocking sites; although this model did not explicitly explain the level of introgression for all individuals sampled, these results do suggest distance from stocking sites may be one of many factors contributing to the level of hybridization observed. When taking all of these findings into context, the introgressive hybridization elucidated here has substantial implications from a management standpoint, especially if the goal of stocking is to provide opportunities for anglers while limiting genetic impacts on native, naturally reproducing populations. The two populations that showed the highest levels of putative hatchery ancestry (Huntley Pond Inlet and the Blue Ledge Tributary) are also in very close proximity to each other, as well as Huntley Pond, a reclaimed trout pond, which is supplemented with Temiscamie/Domestic hybrids on an annual basis NYSDEC, . These rOur findings also suggest that when an area is stocked, the stocked fish have the potential to move into, and mix with, fish in neighboring tributaries. None of the fish that exhibited hatchery ancestry in this study were determined to be purebred stocked fish; all stocked ancestry was attributed to admixed individuals. This was a surprising finding, for which the reasons are unclear. The main stems of the Boreas, Schroon, and Upper Hudson rivers are all regularly stocked with hatchery‐bred fish from both State and local municipalities NYSDEC, . DespiteAdmixture estimates and estimates of migration for the relatively nonintrogressed wild‐caught samples are analogous and suggest ongoing migration both within and to a lesser extent between river systems. This is an important finding, as relatively little work has been done with this species to determine levels of migration in such a network  were relatively similar across all sampling sites, while estimates of effective population size (Ne) varied widely. The comparatively large effective population size of the combined Dix‐Elk tributaries (Ne = 254.3) may be a result of sampling across a comparatively wider range , but the comparatively large effective population size for Unnamed Brook 1 and Durgin Brook (Ne = 315.3 and 114.8 respectively), taken into consideration with the migration estimates for these areas, suggests that these tributaries  may play a central role in reproduction and dispersal throughout the system. In addition, confidence intervals for effective population size estimates across all sample sites were large in most cases, with upper limits in the infinity range for many areas, suggesting open populations not restricted to the sampled stream stretch.Genetic diversity indices (As human activities continue to transform habitats across the Northeastern United States, identifying and understanding how hydrology affects dispersal and gene flow are critical to preserving an aquatic species’ ability to adapt. In addition, understanding how human‐mediated transference of species affects native assemblages is paramount to preserving natural genetic population structure, especially that of indigenous populations that possess local adaptations. In this study, we utilized genetic markers to assess intraspecific interactions and migration in a complex, wide‐ranging system, where a traditional radio‐tag or mark and recapture study would have been logistically challenging, potentially unable to answer all of the study questions, and likely more costly. There is currently a general consensus among researchers and fisheries managers regarding the need for species‐focused conservation plans, although the role of molecular applications in such plans continues to be disputed (Allendorf, Hohenlohe, & Luikart, The authors made the following contributions to the work presented here. S.B. conceived the project; S.B., J.W. developed the project; S.B., J.W. participated in fieldwork; S.B. performed all laboratory work from DNA extraction to genotyping; S.B. performed the statistical analysis with input from J.W.; S.B. wrote the paper with input from J.W.https://doi.org/10.5061/dryad.c68b045.GPS coordinates for all sampling locations and microsatellite genotype data from this study are freely available to all interested parties through the Dryad database.  Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.