PST) which largely exceeds neutral genetic differentiation (FST) in bill size. Our results indicate an important role for salinity, forest structure, and resource availability in maintaining phenotypic divergence of Mangrove Warblers through natural selection. Our findings add to the growing body of literature identifying the processes involved in phenotypic differentiation along environmental gradients in the face of gene flow.Despite the enormous advances in genetics, links between phenotypes and genotypes have been made for only a few nonmodel organisms. However, such links can be essential to understand mechanisms of ecological speciation. The Costa Rican endemic Mangrove Warbler subspecies provides an excellent subject to study differentiation with gene flow, as it is distributed along a strong precipitation gradient on the Pacific coast with no strong geographic barriers to isolate populations. Mangrove Warbler populations could be subject to divergent selection driven by precipitation, which influences soil salinity levels, which in turn influences forest structure and food resources. We used single nucleotide polymorphisms (SNPs) and morphological traits to examine the balance between neutral genetic and phenotypic divergence to determine whether selection has acted on traits and genes with functions related to specific environmental variables. We present evidence showing: (a) associations between environmental variables and SNPs, identifying candidate genes related to bill morphology (BMP) and osmoregulation, (b) absence of population genetic structure in neutrally evolving markers, (c) divergence in bill size across the precipitation gradient, and (d) strong phenotypic differentiation ( We used single nucleotide polymorphisms (SNPs) and morphological traits to examine the balance between neutral genetic and phenotypic divergence to determine whether selection has acted on traits and genes with functions related to specific environmental variables in the endemic subspecies of Mangrove Warbler in Costa Rica along an environmental gradient The environmental gradient of the Pacific coast of Costa Rica, then, has the potential to influence the divergence of traits involved in physiology, foraging, and possibly behavior of this insectivorous habitat specialist bird.The Pacific coast of Costa Rica presents a strong precipitation and salinity gradient in which yearly rainfall varies between 1,000 mm in the north and ~6,000 mm in the south creating a salinity gradient that has a strong influence on mangrove forest structure Figure . SeveralFor example, insects accumulate excess salt in their exoskeleton in high salinity environments Bradley, . Salt reSetophaga petechia aeurolea) in spite of their strong geographic isolation  There are no strong geographic barriers  to isolate the Mangrove Warbler populations along the gradient and (b) high levels of gene flow have been previously documented among populations of the Galapagos and Coco's island warbler (FST), (c) estimate levels of phenotypic divergence along the environmental gradient (phenotypic structure: PST), and (d) determine the balance between genetic and phenotypic divergence.Studies that investigate the role of environmental gradients on adaptive variation in birds largely focus on divergence along elevational gradients . For every individual captured, we measured body mass, bill length (from the nares to the tip) and height (top and bottom bill at the nostrils point), tarsus , flattened wing cord length (bend of the wing to the tip of the longest primary feathers), and tail length (base of the tail to the tip of the longest feathers). These morphological traits were measured with a caliper (±0.005) except for body weight which was measured with a 100 g (±0.01) analog scale. These traits were chosen because they are expected to respond to ecological differences in habitat . The nine populations chosen along the environmental gradient were selected based on their accessibility. Although we considered including Atlantic populations in our sampling, we were not able to find any Mangrove Warbler individual at the Caribbean during the time of our study. Using digitized maps, we calculated the Euclidean and coastal distances among sampling populations. Euclidean distance refers to the straight line between sites (so might involve flying over water) while coastal distance is the distance of shoreline between two sites along the coast (assuming birds would not fly over water). Sampling sites were between 15 and 345 km apart using Euclidean distance and between 15 and 2,584 km apart using coastal distance.2.2R > 0.88 among the four variables), and we considered that precipitation was the principal factor affecting conditions along the gradient , consequently, we used only annual precipitation (BIO12) to describe the environmental gradient and for analysis of our morphological and genotypic data  . We first extracted DNA from the blood samples using the Qiagen PureGene DNA Isolation kit. DNA extractions were quantified using a NanoDrop ND8000 spectrophotometer and a Qubit 2.0 fluorometer with the DNA HS assay kit (Life Technologies) and checked for DNA quality on an agarose gel to select samples with appropriate DNA concentration (>20 ng/µl) and quality. From the 115 individuals collected and for which we had morphological data, we only succeeded in extracting DNA, constructing libraries, and obtaining reliable genetic data from 68 of them Table , using EM = 5), a minimum stack depth of three (m = 3) among reads with potentially variable sequences  of 20 and a maximum stack depth of 100. This step removed SNPs genotyped with too low coverage (m < 20) to be accurately called as well as SNPs genotyped with too high coverage (m > 100) that might reflect repetitive regions. Then, we retained SNPs genotyped in at least 80% of the individuals and 80% of the sampling locations and excluded markers showing heterozygosity >0.50 within samples  at more than 60% of the locations. SNPs with more than 30% missing data were also eliminated. Finally, we removed SNPs with very low frequency (MAF < 0.05), as these can create biases in quantifying genetic connectivity and should be removed when inferring demographic processes  and identified outlier SNPs across the nine populations based on the Q‐threshold of 0.05. Second, we detected outlier SNPs with BAYESCAN v. 2.1  among populations . We used only average bill length for the phenotype–genotype association, as bill length and bill height were highly related . p‐Values from all five runs for the two independent genomewide association tests were combined and adjusted for multiple tests using a false discovery rate correction of 0.05. We used the default parameters recommended for long runs to achieve convergence of the MCMC  using the entire set of loci to identify outlier loci and their correlation to environmental variables  and aligned the sequences to the reference genome of Zebra finch (Taeniopygia_guttata‐3.2.4 reference Annotation Release 103 NCBI (https://blast.ncbi.nlm.nih.gov/Blast.cgi), using the basic alignment search tool (Blast) from NCBI to align the sequences. We used “nr/nt” database and with setting parameters max target sequences to 100, expect thresholds to 10, word size to 28 and max matches in a query to 0. We considered a locus homologous if the e‐value returned was smaller than 1.0 e−10. We determined whether there was any gene within 25 kb upstream or downstream of each candidate SNP to focus on genes likely to be within the same linkage group as our SNP . We ran ADMIXTURE ver. 1.22  error values were computed for each k using a fivefold cross‐validation procedure.We used ADMIXTURE to evaluate population structure with different numbers of hypothetical populations  in the package ADEGENET 2.0.1 , we correlated coastal and precipitation distances to pairwise FST values using a multiple matrix regression implemented in the ECODIST package . Finally, we fitted a smooth surface using precipitation (BIO12) and salinity (ppm) as dependent variables and the morphological PC as coordinates for fitting. The regressions and principal component analysis were performed in R, and for fitting the smooth surface, we used ordisurf function in the VEGAN v.2.0‐10 package  to phenotypic differentiation (PST). To calculate PST, we first estimated within‐population and among‐population variance using a linear mixed model with only intercept as a fixed effect and populations as random effects. We then used the within‐population variance as the residual variance and between‐population variance as the variance of the random effect. Confidence intervals of the within‐ and between‐population variances were estimated using 1,000 parametric bootstrap replicates  from the original loci set: 14 SNPs were found exclusively with the BayeScEnv approach, 11 SNPs were found exclusively with LFMM, and 12 SNPs were found with both methods  between genotype and precipitation were upstream of genes with known function in avian morphology and osmoregulation , the first axis was positively related to Bio4 and the second and third axis were positively related with Bio1, Bio 12, and Bio 15  to 0.057 between two populations in different habitats (at the drier and wetter ends of the gradient), but none of the pairwise FST values were significant (p > .05). The mean pairwise FST was 0.015 ± 0.09  for reliable estimation of migration rates, which suggests ongoing gene flow among S. p. xanthotera populations .Pairwise 09 Table . The FS1 Figure . MultiplWe found high congruence between the lack of support of population structure from ADMIXTURE and the clustering pattern by PCA, which suggested no distinct population clustering Figures  and S2. 3.3F = 90.5, p < .001; salinity: F = 48.9, p < .001) and explained a considerable amount of variability in the dependent variable . The fitting of the smooth surface on the phenotypic data showed that changes in the environment were mostly related to changes in bill morphology which was consistent with the individual analyses performed  was higher than FST in two (bill height and bill length) of the six traits evaluated  is substantially higher than genetic differentiation (FST). All these evidence points to the hypothesis that divergent natural selection at the gradient's extremes may be strong enough to counteract the homogenizing effect of gene flow potentially promoting initial steps of ecological speciation.Using an integrative approach, we found that Mangrove Warbler populations along the precipitation/salinity gradient of the Pacific coast of Costa Rica maintain significant phenotypic divergence despite the absence of genetic structure across most of the genome, suggesting high gene flow among populations. According to the four objectives of our study, our results show that (a) genes associated with bill growth and osmoregulatory pathways are associated with precipitation, (b) there is extremely low genetic structure in neutrally evolving loci, (c) morphological traits (bill size) change significantly along the gradient, and (d) bill phenotypic differentiation  along an ecological gradient in African rain forests, despite relatively high rates of gene flow among populations. Badyaev et al. (Carpodacus mexicanus) that live in desert and urban areas interbreed freely but, have significant divergence in bill traits linked to differences in foraging resources among habitats.The strong phenotypic divergence observed along the gradient contrasts with the lack of genetic structure. Similar results have been obtained in marine organisms where high dispersal distances lead to high levels of gene flow  which may trigger ecological speciation (Caro, Caycedo‐Rosales, Bowie, Slabbekoorn, & Cadena, PST/FST comparisons, we cannot argue that selection influences tarsus length, wing length, and body size. However, we did find a trend in which tarsus and wing length decreased along with precipitation. Both traits have been previously reported to respond significantly to environmental changes in other bird species (Milá et al., Based on our 5Setophaga petechia species complex has abundant geographic intraspecific variation based on plumage color and pattern (Browning, aestiva group, eighteen in the petechia group, and sixteen in the erithachorides group. Such high variability might indicate that there is incipient diversification and our study suggests that such differentiation might be caused by environmental variability. Further studies in other groups within S. petechia can lead to a better understanding the early stages of the formation of biological diversity in a group in which numerous populations could potentially constitute incipient or full biological species.Our findings highlight the importance of understanding both phenotypic and genetic variation along with their links, when examining population differentiation processes in populations which are not geographic isolated. The None declared.T.C.‐P. conceived the project, collected and analyzed most of the data, and wrote the manuscript; J.P.G. contributed with data analysis and manuscript writing. R.K. and M.M.M. assisted in study design and manuscript writing. J.U.‐M contributed to build RAD‐Seq libraries. R.B. contributed with analyses of genomic data and manuscript writing. 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. Click here for additional data file. Click here for additional data file.


Salmo trutta): one naturally sympatric set of populations and another pair of populations introduced to a common environment. We validate our approach by comparing the results to those from markers previously used to describe the populations  and from mapping the Pool‐seq data to a reference genome of the closely related Atlantic salmon . We find that genomic differentiation (FST) between the two introduced populations exceeds that of the naturally sympatric populations , in concordance with estimates from the previously used SNPs. The same level of population divergence is found for the two genome assemblies, but estimates of average nucleotide diversity differ , although the relationships between population values are largely consistent. This discrepancy might be attributed to biases when mapping to a haploid condensed assembly made of highly fragmented read data compared to using a high‐quality reference assembly from a divergent species. We conclude that the Pool‐seq‐only approach can be suitable for detecting and quantifying genome‐wide population differentiation, and for comparing genomic diversity in populations of nonmodel species where reference genomes are lacking.Developing genomic insights is challenging in nonmodel species for which resources are often scarce and prohibitively costly. Here, we explore the potential of a recently established approach using Pool‐seq data to generate a de novo genome assembly for mining exons, upon which Pool‐seq data are used to estimate population divergence and diversity. We do this for two pairs of sympatric populations of brown trout  populations are qualitatively similar to those found from a previously used SNP panel and highly concordant to estimates from mapping our data to the publicly available Atlantic salmon  reference genome. The Pool‐seq only approach is deemed suitable for detecting population differentiation in nonmodel species for which reference genomes are lacking.We explore the potential of a newly established approach that relies on Pool‐seq data to generate de novo genome assemblies in nonmodel species upon which Pool‐seq data are used to estimate population divergence and diversity. Estimates of population differentiation between sympatric brown trout ( Pool‐seq data from populations are mapped against the final gene‐models‐only genome assembly which contains both protein‐coding and noncoding sequences, for estimation of population diversity and differentiation (akin to mapping RNA‐seq data against a de novo transcriptome of the same data). This method  which belongs to the family Salmonidae that is characterized by large genomes (c. 3 Gbp) with the added complexity of a whole‐genome duplication event that occurred roughly 90 million years ago (MYA) followed by subsequent, and ongoing, rediploidization , and grayling  over several decades  or the F1 generation for which population assignment (no hybrids) was possible using the AGP‐2 genotype were considered. A total of 41 individuals representing the P generation were available, and 59 F1 fish were randomly selected out of c. 700 available fish to provide n = 50 per population.We used n = 50 individuals per natural population collected during 2002–2007 and assigned to their respective population by Andersson, Jansson, et al.  with an additional RNase A treatment. DNA quality was assessed by visual inspection of DNA fragmentation on agarose gels and absorbance at 260/280. DNA with high molecular weight from each of 50 individuals per population was quantified using fluorometry  and pooled at equal concentrations to achieve 3 μg pooled genomic DNA in a volume in the range of 65–120 μl. The National Genomics Infrastructure (NGI), Uppsala, Sweden (Science for Life Laboratory), conducted the construction of PCR‐free paired‐end libraries with an average insert size of 350 bp (TruSeq) followed by sequencing using read length 150 bp on an Illumina HiSeq 2000 machine.2.4http://sourceforge.net/projects/bbmap/). Pool‐seq short‐read data from natural population A were used to generate a draft de novo genome assembly using CLC Genomics version 5.5.1 with default settings: k‐mer size 20, bubble size 50, and minimum contig length 200.Illumina short reads from the four population pools were cleaned for adapters and low‐quality bases using BBDuk implemented in BBTools version 37.31 (2.5https://www.ncbi.nlm.nih.gov/sra/SRX3421649%5Baccn%5D) from whole organism tissue of hatchery strain juveniles of undetermined sex  following default recommendations. Rcorrector version 2 . The resulting data were used as input for a transcriptome assembly using Trinity version 2.5.1 .We used publicly available RNA‐seq data  and default settings.Scaffolding involves joining contigs that belong to nonoverlapping regions of the same protein and thus reduces assembly fragmentation. The Pool‐seq de novo assembly was scaffolded using the 2.7S. trutta assembly and the Atlantic salmon genome , bwa mem available in bwa version 0.7.17  using the identify‐genomic‐indel‐regions.pl script in POPOOLATION2 version 1201 , which measures deviations from mutation–drift equilibrium at segregating sites due to selection or demographic events  and TD. Calculations were made for nonoverlapping 500‐bp windows across each of the assemblies, using a minor allele count of 2 for a SNP to be called and stringent depth filters for variant and invariant sites to be included . We identified the putative locations of these SNPs in the S. trutta assembly by blasting the sequence surrounding each SNP to the S. trutta assembly and retaining the best hits . For each location, we retrieved the corresponding FST value estimated in POPOOLATION2 per base pair for Pool‐seq data using BEDTools intersect version 2.25.0  of the proteins in the salar90 protein dataset were represented by nearly full‐length (>90%) sequences in our S. trutta protein dataset.RNA‐seq data from whole organism tissue of hatchery strain juveniles were used , and this number increased to 70,376 postscaffolding. A total of 87,191 unique, that is, single‐copy, gene models were identified in the final, collapsed S. trutta assembly. These gene models were located on in total 38,888 scaffolds with an N50 of 17,722 bp, and a total length of 446,412,000 bp (Table Protein‐based superscaffolding of the 50 Table . Before S. trutta assembly had 58% complete (45% single‐copy and 14% duplicated), 20% fragmented, and 22% missing orthologs. After superscaffolding, 71% of matches were complete , 12% fragmented, and 17% missing.Of the 4,584 Actinopterygian single‐copy orthologs, the initial 3.3S. trutta gene models as proper pairs and c. 50% of reads mapped as pairs against the S. salar reference , which is nearly 6 times the size of the S. trutta assembly, and its annotation contains 97,918 gene model predictions. Although reads had been filtered for mapping quality 20, average mapping quality was 11 for reads mapped to S. salar due to the large proportion of reads that did not map  of raw data were generated per population pool, corresponding to c. 9 M reads. After cleaning, 30% of reads (on average 2.8 M read pairs) mapped to the ap Table . The edi3.4S. trutta assembly  with the lowest value observed in introduced population II. Pairwise comparisons from Wilcoxon signed‐rank tests showed that all population pairs except that of introduced population I versus natural population A differed, to a degree that results in statistical significance. With the S. salar reference genome, average π¯ were considerably lower—between 0.97 and 1.14 ; all pairwise comparisons provided statistically significant differences among all population pairs.On average, 53,538 SNPs were called in POPOOLATION from quality‐filtered and subsampled mpileup files for each pool mapped against our constructed Appendix  and minoce Table . AlthougTD) were all below 0 ranging from −0.202 to −0.065 when mapping to S. trutta with the largest value observed in introduced population I . Pairwise comparisons indicate statistically significant differences between the populations except for between introduced population II and each of the natural populations. When mapping to S. salar, TD increases substantially, but the relationship with the largest value observed for introduced I remains  and statistically significant differences in TD distributions between both population pairs.Tajima's D values  than noncoding  regions , although the mean values differed only slightly for each pair  at the uppermost part of a water system, and where introduced fish originated from few parent fish caught in the wild and taken to a hatchery for production of fish for release.We found no strong indications of demographic perturbations or selection in the sampled populations, which agrees with our general knowledge of these populations. Average nucleotide diversity estimated from data mapped to the ns Table . This esFST between the two natural populations was the lowest of all pairwise comparisons, while the highest FST was found between the two introduced populations  are over 0.7 in all four populations and linear regression coefficients are well over 0.8 and close to 0.9 for each of the naturally sympatric populations. We suggest that observed differences  of frequency of the most common allele than the allele frequency estimates from Pool‐seq  when close to 0.5 but the opposite when the frequency of the most common allele is close to 1  in both pools and individual sequencing and use the same individuals in both.In contrast to assembly‐wide average comparisons of s Figure  are largs Figure , x‐axes q Figure , y‐axes FST from Pool‐seq data for both pairs of populations were much smaller than the estimates obtained from allozymes  estimates were lower when using the S. salar reference than when mapping pools to the S. trutta assembly  and the conserved regions are likely to be less variable. The regions with most sequence diversity, on the other hand, are likely to be those in the process of divergence between the species. Reads mapped to the S. trutta draft assembly are expected to align with higher probability to those regions and may thus be better at reflecting nucleotide diversity. Similarly, the TD values were larger when mapping to the S. salar reference compared to our S. trutta assembly. However, the relative estimates of both π and TD among pools are largely consistent for both references  and two pairs of populations from which we had previous population genetic information from allozymes and individually genotyped SNPs. We also mapped our pools to a high‐quality reference genome from Atlantic salmon , a closely related species, to compare to our Pool‐seq‐only results. We find high consistency in genome‐wide FST values between the two population pairs using the Pool‐seq‐only approach versus the S. salar reference. We find less consistency when comparing the genome‐wide Pool‐seq FST values to those obtained from 14 allozymes and those from c. 3,000 SNPs. In contrast, a high correlation in FST values and allele frequencies is observed when comparing the exact same SNPs in the pools versus those from previous individual genotyping. Estimates of nucleotide diversity and Tajima's D are higher when mapping to the Pool‐seq assembly versus when mapping to the S. salar reference. However, the relationships between values are largely consistent. We conclude that the Pool‐seq approach explored here is a cost‐effective way to gain basic population genomic insights in nonmodel species where a reference genome is lacking. The method is particularly suitable for exploring population divergence but might also be used to compare relative levels of genome‐wide diversity among populations.We explored a recently presented Pool‐seq‐only approach to generate a draft genome assembly for a nonmodel species. We then mapped Pool‐seq data to this assembly to estimate population genomic parameters. We used the brown trout (None declared.The biological research initiating this study was led by L.L., S.K., A.A., and N.R. They provided the questions and materials and designed the study guided by L.A., C.W., and C.J.R. S.K. analyzed the data with M.P.C.M., J.H., and V.E.K., supervised by C.W., V.E.K., and L.A. Graphs and statistical analyses were produced by S.K., assisted by A.A. S.K. also led the writing, with all authors contributing to and approving the submitted manuscript. The research was funded by grants to L.L. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Salmo trutta) populations and substantial reductions in numbers in many others. Higher altitude populations with no stocking influence, which are isolated above natural and artificial barriers and subjected to the greatest effect of acidification, exhibited the least intrapopulation genetic diversity . These, however, were characterised by the greatest interpopulation divergence (highest pairwise DEST 0.61 and FST 0.53 in contemporary samples) based on 16 microsatellite loci and are among the most differentiated S. trutta populations in NW‐Europe. Five lochs above impassable waterfalls, where S. trutta were thought to be extinct, are documented as having been stocked in the late 1980s or 1990s. All five lochs now support self‐sustaining S. trutta populations; three as a direct result of restoration stocking and two adjoining lochs largely arising from a small remnant wild population in one, but with some stocking input. The genetically unique Loch Grannoch S. trutta, which has been shown to have a heritable increased tolerance to acid conditions, was successfully used as a donor stock to restore populations in two acidic lochs. Loch Fleet S. trutta, which were re‐established from four separate donor sources in the late 1980s, showed differential contribution from these ancestors and a higher genetic diversity than all 17 natural loch populations examined in the area. Genetically distinct inlet and outlet spawning S. trutta populations were found in this loch. Three genetically distinct sympatric populations of S. trutta were identified in Loch Grannoch, most likely representing recruitment from the three main spawning rivers. A distinct genetic signature of Loch Leven S. trutta, the progenitor of many Scottish farm strains, facilitated detection of stocking with these strains. One artificially created loch was shown to have a population genetically very similar to Loch Leven S. trutta. In spite of recorded historical supplemental stocking with Loch Leven derived farm strains, much of the indigenous S. trutta genetic diversity in the area remains intact, aside from the effects of acidification induced bottlenecks. Overall genetic diversity and extant populations have been increased by allochthonous stocking.Anthropogenic acidification in SW‐Scotland, from the early 19th Century onwards, led to the extinction of several loch (lake) brown trout ( These range in size from <1 ha to Loch Doon at 820 ha , all of which were known previously to contain S. trutta , low calcium (1 mg l−1) and high labile aluminium concentration  to what extent has stocking with Loch Leven based farm strains resulted in introgression into natural populations; (d) if sympatric sub‐structuring occurs within any of the loch S. trutta stocks and how this has evolved; (e) key populations in south‐west Scotland of high conservation or scientific value.The diverse landscape ecology, water chemistry and anthropogenic influences, including stocking, of south‐west Scotland make it an important and ideal area for studying the effect of these factors on the population genetics of es JNCC, . The mai22.1S. trutta was undertaken in May 1987. This involved 300 fish with c. equal numbers of: Little Water of Fleet, the outflowing river below the impassable waterfall (age 1+ years wild S. trutta); Loch Dee, a large loch on the geographically adjacent Ken‐Dee catchment ; Solway Fish Farm (age composition similar to Loch Dee stock). The S. trutta were batch marked by fin clipping to allow identification of the three stocks. In July 1988, a second batch of 220 S. trutta, involving the same three stock types and essentially the same age distribution, was introduced. These were marked with individual tags .Improvements in water chemistry in the late 1980s and early 1990s indicated that conditions might be suitable for S. trutta is known to have been carried out in other lochs and rivers in the area. According to Sandison . Stocking with offspring of Riecawr S. trutta also took place in the River Girvan catchment .Some stocking with farm strain Sandison , Loch Ma2.2S. trutta specimens were taken from several geographically separated sites within each river to provide an overall river profile. In the case of Rivers Annan, Girvan and Loch Doon, specimens were taken from below and above natural barriers. Adult S. trutta were sacrificed by cranial blow as per standard angling practice while juveniles were killed with an overdose of MS‐222 anaesthetic. An adipose‐fin clip or piece of skeletal muscle was taken, either immediately or at the end of the fishing day and stored in 98% molecular‐grade ethanol. Lethal sampling was not considered to be a threat to the populations since most lochs and rivers appeared to have high densities of S. trutta, or angling was carried out within normal bag limits.Most specimens (age 1+ year and older) were obtained by angling, which was carried out with appropriate permissions and according to angling regulations. Juveniles were provided by authorised fishery professionals from rivers being electro‐fished as part of their ongoing survey activities. Juvenile S. trutta specimens were obtained, mainly in 2010, 2011 and 2012 (denoted as contemporary samples). Frozen S. trutta specimens from historical loch samples (1982–2002) were available at the Marine Scotland, Freshwater Laboratory Pitlochry. Samples were taken from 23 lochs, the main focus of the study and seven rivers in south‐west Scotland. In addition, samples were obtained from Loch Leven and from Howietoun farm, as the local Solway farm, previously used in the area for stocking including Loch Fleet, was no longer in operation.In total 2420 e.g., GRA82) where temporal samples were available, are given in Table e.g., GRA). For the Loch Fleet analyses only, 20 fry specimens were obtained from the only inlet river (Altiwhat River) and seven specimens from the outlet river (Little Water of Fleet) immediately below the loch . Samples were screened for 18 microsatellite marker loci  resolved in two multiplex reactions. These markers were chosen from the 38 loci characterised and optimised by Keenan et al. DNA screening and interpretation were carried out as detailed in McKeown et al.  estimates, sibling groups were reduced to a maximum of three individuals with the least amount of missing microsatellite data or in numerical sequence otherwise.Potential full sibling groups were identified by the maximum‐likelihood method implemented in the program COLONY 2.0.5.4  and expected heterozygosity (HE) were estimated using diveRsity  and private allelic richness (NPAR), the number of alleles or private alleles in a sample were estimated using the rarefaction method in HP‐RARE  and those from areas inaccessible to upstream migration as a result of barriers  it is above three adjacent barriers, which restrict upstream movement. Samples were also divided into two groups on the basis of the underlying geology of the loch area  were dirs Table . Note thFST  by randomising multi‐locus genotypes between pairs of samples with 1000 bootstrap permutations.Differentiation for all sample pairs was measured using Weir & Cockerham's ST Jost, , the latST Jost, , which oNAR, NPAR, HE, FST and DEST recalculated; i.e., these corrected genetic diversity measures were based on the identified pure clusters rather than the original geographically defined samples.To examine the possible effects of historical stocking on contemporary patterns of population genetic structuring, admixed individuals (identified as described below) were removed from samples of natural populations and corrected et al.,S. trutta population genetic structuring. In the first instance, the Bayesian clustering method implemented in the program STRUCTURE 2.3.4 , which ranged from 1 to 10 using the following variables: length of burn‐in period = 100,000; number of MCMC reps after burn‐in = 100,000; admixture model, allele frequencies correlated models with and without location priors. The ΔK ad hoc approach , to recover hierarchical relationships among population samples comparable to the STRUCTURE hierarchical analysis.The Bayesian analysis of population structure program  to identify admixed individuals is that the program also generates a P‐value for each individual. This provides a test statistic for simulated q‐values, which is the likelihood that a particular individual is indeed admixed  rather than a true member of the local population. Thus, individuals having P‐values ≤0.05 are considered as having significant evidence of admixture . In order to examine, in more detail, the potential effects of historical human mediated gene flow due to stocking, a group comprising the accessible parts of rivers and the four groups of samples representing the main catchments  were analysed independently. To check for possible supplemental stocking with farm‐reared S. trutta, LEV was included in each of these five sample sub‐sets. Given the known stocking history summarised in Section K was chosen by trial and error to be greater than the optimal number of clusters. Samples from individual lochs were examined separately using BAPS and STRUCTURE to determine if further population structuring was present beyond that seen in the overall and catchment analyses.BAPS was used to identify significantly admixed individuals within inferred populations by identifying the original samples or BAPS clusters from which each individual's alleles originate . BAPS was used in a similar way applying the trained clustering approach , neighbour‐joining (NJ) trees based on Nei's Ne) was estimated using: (a) the bias‐corrected version of the linkage disequilibrium (LD) method . For the historical samples, GRA94 and GRA02 did not differ significantly. However, GRA82 and GRA10‐12 were significantly different from both of these samples. NAR00‐02 and NEL01 were significantly different from their contemporary equivalents. All geographical sample pairs were significantly different with the exception of NEL11 and VAL and those involving samples from RDU, LDU and DEE (including two inflowing tributaries). The latter were pooled as a single DEE population sample for all but Ne analyses. However, NEL and VAL were not pooled, due to the significant difference between NEL01 and NEL11.While full sibs were observed in 69% of the samples examined, the actual number of full sib families in each case was small, with 73% of these consisting of two sibs only Table . Compari11 and DRY in which nine and eight loci deviated from HWE (Supporting Information Table 96 (five pairs); GIR (six pairs); NAR00‐02 (22 pairs); NEL01 (five pairs).Significant departures from Hardy‐Weinberg proportions, in more than two samples for an individual locus, were found for MHC1 (14 samples) and CA053293 (10 samples). MHC1 and CA053293 were thus removed and all other analyses conducted using the 16 remaining loci. Even after Bonferroni correction, most samples were found to display one to four loci deviating from HWE. The exceptions were NELNA) observed at the 16 loci varied from 26 (NEL01) to 164 (WOF), with a mean of 102 alleles (Table NAR) ranged from 2.98 (LGL) to 8.98 (GIR), with a mean of 5.8, while the NEL01 sample had a value of 1.63. Calculating genetic diversity based on samples from natural populations with admixed individuals  lower NAR (mean 5.3) in comparison to populations in accessible rivers (mean 8.6). No significant difference was found between the two groups of natural loch populations based on granite or sedimentary geology  and positively correlated with loch area  and total catchment area . No significant correlation was found between NAR and the minimum‐recorded pH for a loch or with the 2010–12 angling catch .The number of alleles observed per locus ranged from two One102‐a) to 42 (CA048828) with a mean of 16. The total number of alleles , which was not significant (95% C.I. –0.003 to 0.012). Values between the various river population samples accessible to anadromous S. trutta were also within the lower end of the scale and ranged from 0.024 (95% C.I. 0.01–0.043) for PAL vs. WOF to 0.063 (95% C.I. 0.028–0.104) for ANN vs. GIR. The value (0.021) between the two most distant (c. 400 km sea distance) river samples, ANN vs. RDO, was not significantly different from zero (95% C.I. –0.005 to 0.055). Conversely, samples GRA and DEE, from lochs separated by a river distance of c.15 km without any barrier preventing unidirectional GRA to DEE gene flow, showed a DEST value of 0.137 (95% C.I. 0.115–0.162). The highest values in multiple comparisons of contemporary natural populations were those involving COR, EYE, GRA, LGL and RGL, with the highest being comparisons related to LGL . Pairwise DEST values involving GRA, ENO and NAR, including temporal samples, were either very low or not significant . DEST involving LEV and the accessible rivers  ranged from 0.059 (95% C.I 0.034–0.087) for GIR to 0.109 (95% C.I. 0.075–0.156) for ANN. As expected, FST values were of lower magnitude but were highly correlated with DEST . Some estimates found to be statistically significant for DEST were not significant for FST  although heterogeneity G‐tests for allelic frequency differences concurred with DEST‐tests (data not shown).The lowest S. trutta population structuring yielded similar results. Only the results of the STRUCTURE hierarchical analyses are shown  was placed in group 2. Without a location prior, VAL fell into group 1 (mean q = 0.52). BAPS analysis assigns both VAL and NEL to the same group even without spatial information. Also, VAL and NEL11 were not significantly different in allelic frequencies and DEST value (see above). No other STRUCTURE differences were seen with and without location prior information. At the next level, group 2 subdivided into two groups. The first, which comprised samples from the River Cree system and the upper River Ken‐Dee, split into two further clusters representing these two river systems;  and . The second larger group then splits hierarchically into clusters and individual population samples, with the exception of LDO and RIE. Notably DEE and FLE form a single cluster at this level. INV, SHI, MAN, WOF, PAL, GIR, BRE, RDO ANN, LEV and HOW initially form a single group, with INV plus SHI and BRE then splitting off. The remainder split into the accessible rivers  and LEV related samples (LEV–MAN–HOW). GRA split into three groups and FLE into two. The unrooted Nei's genetic DA NJ tree  but with low frequencies in DEE (0.9%), LDO (1.3%) and RIE (5%). GRA admixture involved VAL (33.3%) and NEL11 , LDO .3% and R9%), LDO .3% and Rly Table  the maxi11 and FLE12 samples analysed separately indicated an optimal K = 4. With a fixed K of 2, as seen in the overall STRUCTURE analysis  and FST of 0.028 (95% C.I. 0.038–0.054) estimates between the two main groups were significant. The analyses of FLE11 and FLE12 as a single sample resulted in similar results for assignment of individuals in the FLE11 specimens, but with more differences for the FLE12 specimens. This analysis, however, allowed the determination of homologous groups in the two samples. Percentages of individuals belonging to different groups in the FLE11 and FLE12 samples are shown in Table i.e., by including them with the FLE11 and FLE12 loch samples) resulted in all seven juveniles from the outlet river being assigned to FLE1 (outlet group). For the inlet river specimens, all but three were assigned to FLE2 (inlet group) when included with the FLE11 sample and all were assigned to FLE2 when analysed in conjunction with the FLE12 sample. Of the FLE12 group 1 September sub‐sample of S. trutta, 28 (90%) were captured in the southern (outlet) sector of the loch while three (10%) were obtained in the northern (inlet) sector of the loch. Conversely for FLE12 group 2 fish, eight (29%) were captured in the southern sector and 20 (71%) in the northern sector. Thus, there is a significant departure from random distribution for both groups (χ2P < 0.001 and <0.02 respectively).In independent analyses of samples from individual lochs only FLE and GRA indicated further sub‐structuring. BAPS analyses of FLEDEST estimates of FLE with potential ancestors, which were lowest for FLE vs. DEE (mean 0.055) for both FLE1 and FLE2 (Supporting Information Table The USEPOPINFO model in STRUCTURE indicated that the highest proportion of genes in both FLE1 (58.9%) and FLE2 (50.6%) was from DEE Table . GRA con10‐12 at a frequency of 0.643, is the only haplotype that was private to any of the four putative FLE ancestors ; 0.047 (95% C.I. 0.019–0.053) and 0.023 (95% C.I. 0.014–0.033); FST 0.027 (95% C.I. 0.013–0.045); 0.031 (95% C.I. 0.02–0.045) and 0.005 (95% C.I. 0.001–0.011). Overall, these three groups formed 28%, 37% and 35% of the sample. There was significant heterogeneity in the distribution of the three groups within the loch with GRA12 group 1 being present at a greater frequency than either GRA12 2 or GRA12 3 in the northern half of the loch (χ2P < 0.02). The latter two groups were not significantly different in their distribution within the loch.STRUCTURE and BAPS analysis of GRANe) estimates for the linkage disequilibrium and temporal methods are only given for a minimum allele frequency of 0.01 . In many cases where sibship estimates are <50, the LD method also indicated a similarly low Ne. With the two methods, Ne estimates for GRA82 were 13 and 19 respectively, the lowest estimates for any of the natural populations. The combined sibship and LD harmonic means over two consecutive samples for GRA82, GRA94, GRA02 and GRA10‐12 mirror the temporal estimates involving those years . FLE11 and FLE12 combined showed a harmonic mean Ne estimate with the LD and sibship methods of 101, with the individual years being 146 and 78. FLE1 and FLE2 for FLE11 and FLE12 combined showed harmonic mean estimates respectively of 19 and 120 with the individual years for FLE1 being 75 and 11 and for FLE2 being 168 and 93. Ne estimates based on samples from DRY, ENO96, EYE, HAR, HOW, LGL, NAR00‐02, NAR12, NEL11 and VAL were low (≤68) with both LD and sibship methods.Effective population size  had become extinct as a result of acidification  ratios of 0.06–0.26, determined for an upland Swedish lake  may have been of the order of 50 to 300 fish in 1982.Netting surveys and environmental information suggested that five loch Ne based estimate of a substantially reduced population in Grannoch, angling records also indicate substantially reduced numbers. Thus, the annual Grannoch S. trutta catch was approximately 1000 fish in 1940 but this declined steadily to <100 in the early 1970s, even with greatly increased fishing effort  and Long Glenhead) are from the lochs where catches were absent or very low in 1978–79.As well as this NAR with altitude in the natural loch populations probably reflects that the higher altitude populations are all above impassable waterfalls, which prevent upstream gene flow restoring lost gene diversity. In addition, they are in the upper regions of their catchments with no higher populations from which downstream gene flow could occur. Previous studies on salmonids have also shown a decrease in genetic diversity from downstream to upstream  with populations in other parts of that catchment  was carried out in Lochs Neldricken and Valley, angling in Loch Valley in 1996 resulted in several individual S. trutta from 500 to 900 g . Since the largest age 2+ years S. trutta captured in Loch Enoch in 1996 was only 207 g  connecting river, this ancestor must have been in Loch Neldricken. This is supported by the fact that S. trutta were first caught (1996) in the semi‐enclosed bay of Loch Valley into which the river from Loch Neldricken flows and only later (1999 on) in the main part of Loch Valley . It would appear then that a small population survived in Loch Neldricken, or its afferent rivers and when environmental conditions improved this population expanded rapidly and colonised Valley. The limited influence of GRA S. trutta in Lochs Neldricken and Valley contemporary populations is likely due to the number of Grannoch S. trutta stocked being small relative to the recovered natural populations. Several salmonid studies have shown that wild prior residents have a competitive advantage over new arrivals especially when the latter are relatively few in number and were hatchery reared . Sib and Ne estimates indicated that relatively few parents were used to produce the juveniles for stocking Loch Enoch, with the current population showing reduced genetic diversity compared with its Loch Grannoch S. trutta ancestor. Similarly, the Loch Narroch S. trutta population showed reduced genetic diversity relative to the Loch Enoch population. Guidelines for setting up hatchery stocks suggest the use of a minimum of 50 males and 50 females to adequately represent the genetic diversity of the original stock  , together with Loch Grannoch, have contributed to this successful restoration. Loch Dee was clearly the largest overall contributor to the current Loch Fleet stock even though this loch is in a different catchment. Similar to Loch Fleet, Loch Dee has a river–lake migratory stock with spawning occurring in both the inlet rivers and particularly in a major tributary  and thus their genetic propensity for anadromy  in an Alaskan lake. In addition, the pH below the waterfall in the Little Water of Fleet was considerably higher than in Loch Fleet .Several studies have shown a decrease in admixture over time in 4.4i.e., genetically distinct and thus reproductively isolated sympatric S. trutta populations) was found only in Lochs Fleet and Grannoch. Two main genetically distinct populations were found in Loch Fleet with an inlet spawning population and an outlet spawning one, the latter comprising three sub‐populations. Reproductive isolation due to inlet  and outlet  spawning occurs in other lakes, e.g., Lough Melvin, Ireland  cannot be separated from differentiation post establishment, which could have occurred as a result of genetic drift due to low  Rusello  found evk et al.  have shoS.trutta . Elsewhere such segregation has been shown to result in selection for reproductive isolation with consequent phenotypic and genetic divergence . Two genetically distinct populations in Lakes Trollsvattnet, Sweden , which would be supported by other important biological features of the loch as well as the unique S. trutta populations.From a S. trutta and its management. Although it is often assumed that small populations with low genetic variability have low adaptive potential, Prodöhl et al.  genetic distance (DA) for Salmo trutta population inferred samples. Percentage bootstrap support shown at nodes. Colours refer to catchment colours as per Figure on Nei's 983 geneton Nei's 983 genetClick here for additional data file.FIGURE S3. Neighbour joining unrooted tree based on Nei's et al. (DA) for Salmo trutta population inferred contemporary samples. Based on natural south‐west Scotland populations only with admixed individuals removed. Percentage bootstrap support shown at nodes. Colours refer to catchment colours as per Figure s et al.  genetic Click here for additional data file.FIGURE S4. Number (n) of Salmo trutta caught per hour by anglers fishing at Round Loch of Glenhead 1989–2011.Click here for additional data file.FIGURE S5. Number (n) of Salmo trutta caught per hour by anglers fishing at Loch Valley 1996 to 2011.Click here for additional data file.TABLE S1. Background information on lochs from which Salmo trutta were sampled.Click here for additional data file.TABLE S2. Summary population sample statistics per locus and overall loci for Salmo trutta population samples.Click here for additional data file.TABLE S3.DEST and FST pairwise values between STRUCTURE or Bayesian analysis of population structure program  inferred populations. In each case, estimates are shown using a colour heat map  below diagonal . Values in parentheses above diagonal indicate 95% CI for estimates. , non‐significant pairwise comparisons are highlighted in yellow. n.b. Natural populations have had admixed individuals removed. GRA1, 2 and 3, and FLE1 and 2 are the separate populations identified in these lochs with in the former only the GRA10‐12 sample being used in the computation.Click here for additional data file.


Astacus astacus) populations is becoming particularly important since the number of individuals is rapidly declining across the distribution range of the species in Europe. Five crayfish populations in northwestern Poland have been constantly monitored for two decades. However, the genetic structure of these populations has not been analysed, although this information is important to devise effective conservation strategies.Conservation of noble crayfish .Microsatellite results analysed with AMOVA showed that the diversity between populations corresponds to 18% of total variability, which was confirmed by similar results obtained using AFLP. Additionally, significant genetic diversity was revealed by high average FIS) suggest a low number of mating individuals, a possible consequence of the phenomenon known as genetic bottleneck. However, additional comprehensive analyses are needed to assess the genetic structure, origin and vulnerability of the remaining populations of noble crayfish in the Bytów Lakeland of northwestern Poland, which have high conservation value and are particularly important as a live genetic bank for breeding and restitution purposes.The invariably negative inbreeding coefficients (F Astacus astacus L.) populations reflect those occurring in the native range of the species and have proved to be especially dynamic over the past 120 years. It is well accepted that noble crayfish populations are highly vulnerable to habitat loss, to alien species competing for food and space, and particularly to pathogens such as the water mould Aphanomyces astaci as the causative agent of the fatal crayfish plague , are one of the most informative genetic markers. SSR are highly polymorphic, abundant and evenly distributed throughout genomes . This feMost studies on crayfish genetic diversity only used one type of genetic marker, such as allozymes , ITS linThe aim of the present study was to characterize noble crayfish populations in the Bytów Lakeland of northwestern Poland by using microsatellite and AFLP markers. This information was to serve as a basis for conservation measures for noble crayfish, complementing the ongoing monitoring of crayfish populations, including restituted populations, the selection of new water bodies suitable for restitution, the identification of genotypes for restocking to minimize impact on natural populations, and the improvement of restitution and breeding methods.A. astacus) specimens were collected from each of five lakes located in the Bytów Lakeland of northwestern Poland: Lakes Graniczne (Gr), Babinki (Ba), Biwakowe (Bi), Sęki (Se) and Kwisno (Kw). All lakes were sampled by scuba diving during the autumn of 2014 , 10.5 µL water, 0.5 µL (0.2 mM) forward primer, 0.5 µL (0.2 mM) reverse primer and 1 µL DNA template. Nine SSR regions  were amplified according to the methodology described by Kobreveiv et al. . A fluorescent marker dye , D2, D3 or D4 was attached to the 5′end of sense primers (For) from each pair. PCR products were mixed with the DNA Size Standard Kit –400  and separated by capillary electrophoresis using a CEQ 8000 automatic sequencer (Beckman Coulter). Briefly, DNA isolates (400 ng) were digested with two restriction enzymes, Eco RI (5U) and Mse I (5U) , at 37 °C for 3 h. Next, oligonucleotide adapters were ligated for 16 h at 20 °C using T4 ligase (1U) in T4 buffer (1x) (New England Biolabs). The products of this reaction were diluted 1:10 and used as a template for the following pre-amplification step. Three microliters of diluted template DNA were then added to a PCR reaction mix  containing Taq DNA Polymerase (1U), buffer B (1X), dNTPs (10 mM), and primers E01 and M01 at a concentration of 10 mM each. Next, one microliter of the diluted 20-fold preamplified template was added to the PCR reaction mix to a final volume of 20 µL containing 10 µL of the FastStart PCR Master Mix (Roche Diagnostics), 8.2 µL of water, 0.2 µL of 10 mM 5′ fluorescently labelled (WellRED dye) EcoRI primer and unlabelled 0.2 µL of 10 mM MseI primer, each carrying three selective nucleotides , allelic richness (AR), and Nei’s distance DA (A) matrix was used to construct an UPGMA dendrogram and to test population grouping by bootstrapping analysis  applying the PHYLIP software package ver. 3.695  software ver. 4.05  was usedT values , observetance DA . The samr. 3.695 . Tests fr. 3.695 , using Fp-values . Sequentp-values  were app package . Hierarc package . To inve package  was usedo method  using tho method . The popo method  softwareFragment data were transferred to a binary (1/0) data matrix using the software GenomeLab (Beckman Coulter). The data matrix was then imported to FAMD ver. 1.25   to estimO) was 0.470 and ranged from 0.05 for Aas2 to 1.00 for Aas766 and Aas7. The average expected heterozygosity (HE) per locus was 0.380 and ranged from 0.109 for Aas2 to 0.655 for Aas1198. Twenty population-specific alleles  were identified  ranged from 0.422 for the Bi and Se populations to 0.506 for the Gr and 0.511 for the Ba population, whereas the expected heterozygosity (HE) ranged from 0.293 for the Se to 0.448 for the Gr population  among the five analysed crayfish populations was relatively high (FST = 0.183). Pairwise estimates of FST ranged from 0.038 (between the Gr and Kw populations) to 0.348 (between the Bi and Se populations)  . PCoA ofulations . Global ulations . The Se ulations . The bess  were observed in the Gr population . The lowFST = 0.211; The analysis of genetic diversity among and within natural populations of noble crayfish in northwestern Poland was conducted using two types of genetic markers, SSR and AFLP. Both revealed significant genetic differentiation in the five analysed populations. One of the most important factors influencing genetic diversity of the crayfish populations from the Bytów Lakeland is a diverse topography shaped by the action of glaciers . Lakes lFIS coefficient (heterozygote excess) of –0.285, a low allelic richness value (2.77) and a statistically significant deviation from the HWE in the genotype distribution , Finland  and Estonia (Laugi). In contrast to the populations studied here, all mean FIS values calculated for populations in those countries were positive . Lake Graniczne has a permanent connection to the Pokrzywna River, a left-bank tributary of the Wieprza River. Therefore, it is conceivable that populations could move between the two lakes in the past and thus interbreed. However, over the years, Lake Kwisno and the Wieprza River became disconnected and a subpopulation could emerge as a result of a founder effect, a mechanism that has been widely described also for other species  suggests common ancestors, further supporting the above conclusions derived from the use of other methods  between the investigated populations was greatest between Lake Biwakowe and the lakes of the Brda catchment. This outcome is most likely the result of geographic isolation, which has probably existed since the Bytów Lakeland was formed when the glacier receded after the last glaciation  among the five populations  and the highest value for German (4.2) populations. The populations of the Bytów Lakeland investigated here had an intermediate value (3.3). Similarly, the average HO or HE values of 0.470 and 0.380, respectively, in the present study ranged between the highest and lowest average values for the German  and Finnish  populations. Strong within-country genetic differentiation of noble crayfish was observed in Estonia, probably due to limited gene fiow among populations in different catchments as well as to genetic drift   showed tResearch in the Bytów Lakeland over the past 20 years has shown that noble crayfish may be an indicator of aquatic habitat quality . NumbersA. astacus in norhwestern Poland. Given their diversity, the last populations of noble crayfish in the Bytów Lakeland analysed in the present study have great value as a live genetic bank for such breeding and restitution purposes. As the results presented here show, genetic analyses of the populations are also valuable to inform about the genetic diversity, post-glacial re-colonisation routes and population structure of noble crayfish populations in Europe. However, additional comprehensive analyses are needed to assess the genetic structure, origin and vulnerability of the remaining natural populations in norhwestern Poland.The five crayfish populations investigated in the present study have been constantly monitored for two decades. Results of these monitoring efforts indicate that conservation of the populations requires active protection measures such as restocking with offspring obtained especially through aquaculture breeding programmes of crayfish with known genetic make-up. DNA markers facilitated the genetic characterisation of selected remaining natural populations of 10.7717/peerj.7301/supp-1Dataset S1Raw data for microsatellites (SSR) obtained for 5 populations of noble crayfish. BI –Biwakowe Lake, KW –Kwisno Lake, GR –Graniczne Lake, SE –Sęki Lake, BA –Babinki LakeClick here for additional data file.


Oncorhynchus clarkii) are one of the most widely distributed freshwater fish species in western North America exhibiting substantial phenotypic and genetic variability; however, fish stocking practices have translocated populations outside of their native range and may have obscured intraspecific boundaries. This study focuses on cutthroat trout populations representing three distinct evolutionary clades that are found intermixed within a contact zone between the Bonneville and upper Snake River watersheds in the western United States. We used mitochondrial and microsatellite genetic data, as well as historical stocking records, to evaluate whether populations of cutthroat trout in the contact zone are native or are introduced. We found significant genetic differentiation and fine-scale genetic population structure that was organized primarily by watershed boundaries. While we detected increased genetic diversity in some areas in close proximity to the greatest number of stocking events, the highly organized population structure both within and between areas of the contact zone indicates that the populations are native to the watersheds. Intermixing of distinct evolutionary lineages of cutthroat trout appears to be the result of historical connections between paleodrainages. Our analyses provide a context for understanding how genetic data can be used to assess the status of populations as native or introduced.Species with large geographic distributions often exhibit complex patterns of diversity that can be further complicated by human activities. Cutthroat trout ( Natural geological processes may have a substantial influence on population structure and gene flow by altering the landscape through volcanism, glaciation, mountain building, and plate tectonics ,2. SimilIn freshwater ecosystems, natural features commonly isolate populations because many aquatic animals cannot move around physical barriers that extend across the land-water interface . As a reOncorhynchus clarkii) is one of the most widespread freshwater fish species native to western North America and is also a popular sport fish that has been propagated and translocated from relatively few hatchery stocks [In addition to historical geographic features, contemporary processes have been instrumental in shaping the genetic population structure of fish species through various human activities ,25,26. Iy stocks –30. Cutty stocks ,32, and y stocks . In westy stocks –38. As ry stocks ,40. Furty stocks ,42. WithO. c. utah) and Yellowstone cutthroat trout (O. c. bouvieri) are two subspecies whose range is defined by a watershed boundary separating the upper Snake River from the adjacent Bonneville Basin within the Great Basin region of the western U.S. [Bonneville cutthroat trout  and subsequently recolonized using cutthroat trout from neighboring Eight Mile Canyon Creek . We included Six Mile Creek as a method for comparison describing the genetic population structure of a known translocated population of cutthroat trout.To describe genetic variation of cutthroat trout in the contact zone, we collected tissue samples from individuals representing 30 populations within the Bonneville Basin and upper Snake River . We haphIn the laboratory, genomic DNA was extracted from fin clips using the ZR Genomic DNA tissue extraction kit  following the manufacturer’s protocol. Fish collection procedures were approved by the Institutional Animal Care and Use Committee at Idaho State University (protocol number 0403509). Fish collection permits were provided under the authority of the Idaho Department of Fish and Game or the Utah Division of Wildlife Resources.xl automated sequencer.We examined the diversity and geographic distribution of cutthroat trout lineages by comparing mitochondrial (mtDNA) haplotypes from all study populations. Mitochondrial DNA was sequenced for 10 individuals from each of the 30 populations (n = 300) for the NADH dehydrogenase subunit 2 gene (ND2). Amplification by polymerase chain reaction (PCR) used the sequencing primers NDintF6 and NDVarR ,56. PCR O. c. pleuriticus), greenback cutthroat trout (O. c. stomias), Rio Grande cutthroat trout , coastal cutthroat trout (O. c. clarkii), Lahontan (O. c. henshawi), and westslope cutthroat trout (O. c. lewisi), as well as a rainbow trout haplotype used as an outgroup. These supplemental ND2 sequences of cutthroat trout and rainbow trout were obtained from the National Center for Biotechnology Information, GenBank database (http://www.ncbi.nlm.nih.gov/genbank). Phylogenetic trees were constructed using the Tamura-Nei substitution model with invariant sites based on jModeltest [Sequences were edited and aligned to a reference cutthroat trout sequence using Sequencer v.4.9 software and Mega v. 6 . We estiodeltest  results.odeltest  and editodeltest ). Mitochodeltest ).xl automated sequencer. We subsequently used GeneMapper software (ver. 3.7) to genotype every individual at each locus. All peaks were verified manually to ensure accuracy.Estimates of genetic population structure, organization, and diversity were compared using nuclear microsatellite loci. All individuals from each population  were genST) with Microsatellite Analyzer , FSTAT r. 4.05; ), and ArGeographic structuring of genetic data was visualized both at the population level as well as at the watershed level, using a neighbor-joining tree and population assignment tests. If cutthroat trout populations have a natural distribution history, the neighbor-joining tree and clustering should group by watershed and migration should be between neighboring populations. Alternatively, the absence of geographic structure or migration events between watersheds would indicate a significant influence of non-native introductions. For the neighbor-joining tree, we estimated genetic distance using Cavalli-Sforza chord distance  and consST) and geographic distance (km). To test for associations of genetic data between and within watersheds, we used a principal component analysis (PCA) and an AMOVA to determine if the geographic distribution of microsatellite alleles was primarily organized by watershed boundaries or if they were intermixed across the contact zone. The placement of populations on the principal components axis was based on the similarities across all microsatellite allele sizes. PCA scores were calculated in R statistical software and the average PCA score per population was used to compare population association among locations sampled. For the AMOVA, we examined microsatellite diversity as partitioned among watersheds, among populations within watersheds, and within populations as implemented in Arlequin.To examine the degree of geographic structuring and isolation among populations, we compared stream distance and genetic distance between population pairs and tested for associations of genetic data. Stream distance was measured between sampling locations to estimate geographic distance between populations using ArcMap (version 10.3) and the Spatial Tools for the Analysis of River Systems (STARS) extension . Geographttps://idfg.idaho.gov/fish/stocking). Stocking records from the Snake, Southeast, and Magic Valley regions were compiled by waterbody name for all years available in the database (1967–2016) and used to assign the site of translocation within upper Snake River and northern Bonneville watersheds. While most of the study area is covered by the Idaho database, a small portion of the Snake River watershed occurs in the northern extent of Utah  for 300 cutthroat trout representing 30 populations, and yielded 18 different haplotypes . The numPhylogenetic comparisons using the 18 different haplotypes identified in this study and including representative samples from other cutthroat trout subspecies as well as rainbow trout, revealed two distinct clades within the contact zone. . This brGeographic structuring of haplotype distribution in the contact zone was evident within and among watersheds. All populations from the Malad River watershed were associated with the Great Basin clade . While mST) indicate significant differentiation between all 30 population pairs. The lowest genetic differentiation occurred between neighboring populations, Fish Creek and Pebble Creek (FST = 0.045) and was highest between two Raft River populations, Six Mile Creek and Wildcat Creek (FST = 0.67). Across all populations, average FST was 0.28 . While some migration was detected, most of these events occurred between neighboring populations . Not surBayesian cluster analyses of the 30 trout populations suggested the most likely number of clusters, based on the data, was K = 5 or K = 18 . ResultsThe degree of geographic structuring and isolation among populations is further supported by comparisons of geographic distances and genetic distance between population pairs. Across all populations sampled, isolation by distance tests reveal that a significant proportion of the genetic variation between populations is explained by geographic distance , positively correlated to the number of stocking events , and negatively related to the number of fish stocked .Spatial analyses of stocking data and genetic data indicated significant associations between some variables or a combination of variables and not others. Simple correlational analysis of the number of mtDNA haplotypes detected at a sampling location was weakly and negatively related to distance to the nearest stocking location  as was the number of stocking events , but not the number of fish stocked . The number of alleles detected at a sampling location was negatively related to distance to nearest stocking location in multiple regression analysis , positively related to the number of stocking events , and not related to the number of fish stocked analysis . Heterozygosity was negatively related to distance to nearest stocking location , and not significantly related to the number of stocking events , or the number of fish stocked .Microsatellite data also revealed similar associations of genetic diversity with measures of stocking history. Allelic richness was negatively correlated with distance to nearest stocking location . In the populations we sampled, most were far from stocking locations in headwater streams and typically isolated by movement barriers, making it difficult for hatchery fish to interact with these native populations.Extensive stocking of nonnative fishes has occurred in ecosystems worldwide and can have significant effects on native biodiversity through competition, predation and disease transmission –103. WheThe correlation between genetic diversity and the proximity to stocking was not unexpected. The introduction of individuals into a population should add genetic variation ,114 and Differentiation and geographically organized genetic population structure appears common among non-anadromous trout populations and is especially prominent among interior cutthroat trout subspecies. Past studies of resident trout populations have revealed similar findings to our results, with strong genetic differentiation hierarchically organized within and between watersheds and less differentiation at smaller spatial scales ,119,120.The exchange of aquatic taxa between neighboring watersheds illustrates how the gain and loss of natural connections over time can create species distribution patterns that do not adhere to watershed boundaries. In particular, changes in climate and hydrological conditions affect the extent and degree of connectivity between populations of aquatic organisms ,16. BecaCutthroat trout are a species of conservation concern and efforts to improve their status have focused on removing non-native competitors or predators, restoring habitat, and by reintroduction programs –127. As Natural pathways of dispersal appear to be a significant factor influencing the distribution of cutthroat trout in the Snake-Bonneville contact zone. Cutthroat trout populations have diversified into three phylogenetic clades intermixed in the Snake River and adjacent Bonneville Basin. Historical events appear to have shaped the distribution of these evolutionary lineages through geographic connections and isolation. However, human-mediated translocations of cutthroat trout into neighboring populations may have also influenced the distribution of genetic diversity by facilitating gene flow near stocking locations. Mitochondrial DNA data support historical aquatic connections that allowed dispersal of cutthroat trout into the upper Snake River watershed through the Bonneville Flood and headwater transfer with ancient Lake Bonneville. Microsatellite evidence identifies contemporary gene flow and migration that is primarily within watersheds and influenced by stream distance. While extensive stocking has occurred within the watersheds, these events appear to have had minimal influence on the natural distribution of populations sampled. Our study illustrates how genetic data can be used to identify native or introduced populations in a contact zone. Importantly, this information will help identify where historical connections may have existed and allow mangers to prioritize populations of conservation concern.S1 TablePopulation ID’s are defined by (XLSX)Click here for additional data file.S2 Tablehttps://idfg.idaho.gov/fish/stocking) for the years 1967 to 2016.Stocking records of cutthroat trout obtained from the Idaho Department of Fish and Game historical stocking records database ((CSV)Click here for additional data file.S1 DatasetHaplotype ID's are defined by (TXT)Click here for additional data file.S2 DatasetPopulation ID's are defined by (XLSX)Click here for additional data file.


Cryptomeria japonica D. Don with genomic prediction for traits of growth, wood properties and male fecundity. Among the assessed populations, genetic characteristics including the extent of linkage disequilibrium (LD) and genetic structure differed and these differences are considered to be due to differences in genetic background. Through population-independent GWAS, several significant SNPs found close to the regions associated with each of these traits and shared in common across the populations were identified. The accuracies of genomic predictions were dependent on the traits and populations and reflected the genetic architecture of traits and genetic characteristics. Prediction accuracies using SNPs selected based on GWAS results were similar to those using all SNPs for several combinations of traits and populations. We discussed the application of genome-wide studies for C. japonica improvement.A genome-wide association study (GWAS) was conducted on more than 30,000 single nucleotide polymorphisms (SNPs) in unrelated first-generation plus tree genotypes from three populations of Japanese cedar  Elucidating genetic control of various objective traits of forest trees enables increased economic efficiency of forestry, improved quality of forest products and provides direction for meeting societal expectations regarding environmental issues. Due to the long time to maturation for forest tree species, evaluation of traits and breeding and phenotyping strategies are cost- and time-prohibitive, while genome-wide studies including genome-wide association study (GWAS) and genomic selection (GS) strategies are innovative, attractive and effective methodologies . With thFor rapid improvement of various objective traits that impact social needs, it is effective to construct genome-wide studies based on populations consisting of genetically diverse or unrelated individuals. Predicting breeding values of unrelated individuals is exactly required in the most promising applications of GS . HoweverCryptomeria japonica D. Don), which is a member of the Cupressaceae botanical family, is an endemic species to Japan and is one of the most important forestry species in the country, accounting for 44% of the plantation area in Japan. The improvement program for the species was started in 1957, and 3,670 phenotypically superior first-generation plus trees were selected from artificial and natural forests throughout Japan and were clonally conserved  and genotyping platforms  to perform GWAS and genomic prediction for several traits, and (3) to clarify the relationship between the results of genomic prediction and the population structure. The targeted traits in this study were growth, wood properties and male fecundity. Further, we discuss future applications of genome-wide studies in C. japonica based on this study.In the present study, we reveal the potential of genome-wide studies in a coniferous species using unrelated  program . Using tSupplementary Table S1). Plus trees in the Kanto breeding region were selected from two populations, N-Kanto and S-Kanto, which are located in northern-inland and southern-coastal areas of the Kanto breeding region, respectively . The regions from where these populations originated showed the same cluster in STRUCTURE analysis with K = 2 in the population genetics study based on the core collection of plus trees using 1000s of SNPs  were selected for sampling in this study : GSE95616] or Axiom_Cj_70K_ver. 2.0  arrays  arrays . SNP dat) arrays  in R 3.3) arrays .C. japonica F2 family based on 6,629 markers including SNPs used in this study and simple sequence repeat markers. Here, we used the map information for the 6,455 SNPs that remained following our SNP selection process described above. We calculated the intensity of LD (r2) between the mapped SNPs within the same linkage group (LG). LD was calculated the “LD” function in the “genetics” package in R (age in R .HE) and observed (HO) heterozygosities using the “basicStats” function of the “diveRsity” package in R (K), the cross entropy criterion that was calculated by the snmf function with “entropy = TRUE” option with K = 1–8, was used. In order to evaluate the magnitude of admixture in each genotype for each population, we proposed the effective number of clusters per genotype (NQ) using the following equationWe calculated the average expected  for all tested populations together by the “prcomp” function in R.where In this study, we assessed three important traits for genomic prediction, growth traits , wood properties (wood stiffness and density) and reproductive traits  using clonal propagated plus tree individuals.Growth traits were evaluated at age 10 years using plot mean data obtained at clonal test sites. Almost all clonal test sites had incomplete random block design with three replications. Dozens of genotypes were planted at each test site, and there were several overlaps of genotypes between the test sites. Each replication had multiple plots, and >10 individuals of each genotype were planted within each plot. For the analysis of growth traits, we used growth data obtained from 137 and 132 clonal test sites in Kanto and Kyushu regions, respectively. Genetic values were calculated as phenotype data of growth traits for genomic prediction using a linear mixed model based on the BLUP (best linear unbiased prediction) method using ASReml 3 software . The following mixed linear model was used:yijk is a plot mean value of the kth genotype at the jth block within ith test site; μ is the overall mean; (SC)ik is the interaction effect of the ith test site and kth genotype; and eijk is the residual. The fixed effects included μ, Si and S(B)ij, and the others were random effects. The parameter Ck was assumed to be the genetic value of height or DBH.where Wood stiffness and density were evaluated by stress wave velocity measured with TreeSonic Microsecond Timer  or Fakopp Microsecond Timer  and by penetration depth with Pilodyn 6J Forest  according to the procedure described by 3) on three shoots in early July of the same year, the index (1: less – 5: much) for strobili quantity on each individual tree was recorded by multiple observers in December, and the average from the three shoots was taken as a measure of male fecundity for each individual. Genotypes from N-Kanto and S-Kanto populations were assessed together at a clonal garden in FTBC, and those from the Kyushu population were assessed at a clonal garden at the Kyushu Regional Breeding Office; clonal replication was in the range of 2–3 at both sites. As the clonal value, we averaged the scores from individuals of each genotype. The data of genotypes in N-Kanto and S-Kanto were the same as reported in For evaluation of male fecundity, male strobili quantity index was measured according to the procedure described by Supplementary Table S2. Based on the dataset, significance of genetic effects for the objective traits was confirmed by F test or a likelihood ratio test according to D1,2, was calculated using the following equation:The N-Kanto and S-Kanto populations were evaluated together for genotype traits and compared with those in the Kyushu population because individuals from N-Kanto and S-Kanto were planted together. The numbers of analyzed data and genotypes for each trait within each breeding region are shown in D1,2 follows a chi-square distribution with 1 degree of freedom. The genetic effects were statistically significant for all assessed traits in this study (Supplementary Table S2). The genotypes used for GWAS and genomic prediction (Supplementary Table S1) were all included in the phenotypically evaluated genotypes in Supplementary Table S2.where log Supplementary Figure S2); the values were set to 2, 6 and 5 in the N- and S-Kanto and Kyushu populations, respectively. We used a false discovery rate (FDR) < 0.1 or -log10(P) > 3 as criteria for statistically significant GWAS results. We calculated q-values for FDR using the “p.adjust” function in R. For estimation of unmapped SNP positions which showed -log10(P) > 3 in GWAS, we conducted LD calculation with the 6,455 mapped SNPs. If the highest r2 was more than 0.6, the unmapped SNP position was assumed as being equal to that of the paired SNP. We independently performed GWAS for each population and then detected significant SNPs at similar map positions (<10 cM) across the populations for identifying commonly significant genome regions. Arabidopsis homologs were used as searches for significant loci within the TAIR10 database using BLASTN with an E-value cutoff of 1E-5.We carried out GWAS for each population and each trait using all 32,036 SNPs in the “GWAS” function of the rrBLUP package in R. In the function, the “K” option, which specifies the covariance between genotypes, was set with a kinship matrix between genotypes calculated by the “A.mat” function of the rrBLUP package and the “n.PC” option, which specifies the number of principal components (PC) to include as fixed effects, was determined by the variances of PC scores based on the PC analysis (PCA) for each population (We performed genomic prediction for each population and each trait using three methods: genomic best linear unbiased prediction (GBLUP), BayesB and Random Forest (RF). GBLUP was performed by using the “kin.BLUP” function of the rrBLUP package of R. BayesB was performed by using the “BGLR” function of the “BGLR” package of R  with a 110(P) of GWAS; (ii) exclude all the SNPs positioned ± d from the selected SNP ; (iii) if there are still SNPs to select for, move to step i; (iv) if all SNPs are excluded or selected, select the SNP which shows the highest -log10(P) and the distance more than d = d/2 from already excluded ones to select; (v) move to step ii, and carry out until all SNPs that are located in different positions are selected; (vi) if all SNPs that are located different positions from the selected SNPs, pick up the SNPs in the order of higher values of –log10(P). The other was the semi-random selection procedure as follows: (i) assign scores  randomly and uniquely for all SNPs; (ii) assuming the assigned scores as –log10(P), conduct the GWAS-based selection procedure from step i. Following this procedure enables a limitation on the inclusion of markers in the same region  based on the mapped SNPs within the same LGs decayed rapidly within several centimorgans for all populations, and the decay was more rapid in the N-Kanto and S-Kanto populations than in the Kyushu population . The average r2 values showed a similar low level in all populations, ranging from 0.056 to 0.063 (Table 1). The average ratio of significant SNP pairs (p < 0.01) was higher in Kyushu (16.9%) than in N-Kanto (8.6%) and S-Kanto (7.8%), as shown in Table 1. On the other hand, based on SNPs within the same isotigs, which were assembled ESTs, SNP pairs in which LDs extended over 6 kb were observed in all populations . The average values of r2 (0.393–0.395) and the ratio of significant SNP pairs (82.5–86.8) were much higher than those based on the mapped SNPs, but values were similar within the populations (Table 1). The distribution of LDs between mapped SNPs within LG2 for each population is shown in Figure 2. In LG2, pairs showing higher level of LD  were observed only close to the diagonal line in all populations, i.e., SNP pairs located in the vicinity. In the Kyushu population, SNP pairs showing r2 > 0.1 were more frequently observed relatively far away from the diagonal line than in the N-Kanto and S-Kanto populations. Data for other LGs showed similar trends as was observed in LG2 .The extent of LDs , and ancestry analysis for the genotypes from the all populations at this value show four clusters in each population but in different proportions . The N-Kanto and S-Kanto populations mainly consisted of three clusters . On the other hand, the Kyushu population consisted of clusters represented by yellow, green and red colors, and the blue cluster was a relatively minor one. The mean value of NQ for the genotypes in Kyushu was relatively smaller  than that in the N-Kanto (1.83) and S-Kanto (1.80) populations.Figure 4 shows a scatter plot of the first and second principal components  from the PCA result. The genotypes from the N-Kanto and S-Kanto populations showed overlapping and similar distributions. On the other hand, genotypes from the Kyushu population showed a wider ranging distribution with partial overlap in the distributions with those from the two Kanto populations.10(P) > 3 were observed in the GWAS results . Out of 466 significant SNPs, only two loci that showed FDR < 0.1 were detected (Supplementary Table S3); each one SNP was observed for height in the N-Kanto and for wood stiffness in Kyushu populations, respectively. The map positions of the two SNPs were not accurately determined because these were not mapped by r2 with paired SNPs were ≤ 0.6 (Supplementary Table S3).Throughout all traits in the three populations, significant markers in the criterion based on –logTable 2 shows significant SNPs (–log10(P) > 3) located close (<10 cM) to the position across the three populations. In total, 13 SNPs were detected, i.e., at ∼131–141 cM in LG5 for DBH, at ∼52–56 cM in LG5 for wood stiffness, and at ∼81–84 cM in LG7 for male fecundity. Particularly for male fecundity, the map position (84.71 cM in LG7) was consistent in the three populations. These loci were matched with Arabidopsis homologs by a BLASTN search (Table 2).Table 3). For height, RF showed the highest prediction accuracies in N-Kanto (0.267) and Kyushu (0.468), whereas in S-Kanto the highest accuracy was obtained with BayesB and the value was close to zero (–0.026). For DBH, similar but slightly better results were observed compared to those for height; RF showed the highest accuracies in N-Kanto (0.299) and Kyushu (0.523), but in S-Kanto, BayesB again showed the highest accuracy but the value was close to zero (0.033). For wood stiffness, GBLUP and RF showed higher accuracies in S-Kanto (0.313) and Kyushu (0.531), respectively; in N-Kanto, the highest accuracy was obtained with BayesB and the value was close to zero (–0.099). In terms of wood density, GBLUP and RF showed higher accuracies in N-Kanto (0.247) and Kyushu (0.193), respectively; in S-Kanto, the highest accuracy was obtained for RF but the value was close to zero (–0.076). For male fecundity, GBLUP had higher accuracies for all populations .We compared genomic prediction accuracies among the three models, GBLUP, BayesB and RF  as a function of the number of SNPs selected by GWAS-based selection or semi-random selection procedures. For all traits, except for male fecundity, and all populations, prediction accuracies with SNPs of GWAS-based selection were higher than with semi-random selection. Based on results of the GWAS-based selection, high accuracies that were not significantly different than the highest ones (p > 0.05) were observed with relatively small numbers of SNPs (≤500) for height in S-Kanto and Kyushu, DBH in S-Kanto and Kyushu, wood stiffness in N-Kanto and Kyushu, and wood density in S-Kanto. For male fecundity, prediction accuracies with a small number of SNPs in the semi-random selection were higher than for GWAS-based selection, and similar transitions of accuracies with larger samplings of SNPs (≥500) were observed for both selection procedures in all populations. Particularly in the S-Kanto population, high accuracy using 2,500 SNPs selected by the semi-random selection procedure was observed for male fecundity. Prediction accuracies for height and DBH in N-Kanto, wood stiffness in S-Kanto, wood density in N-Kanto and Kyushu, and male fecundity in N-Kanto and Kyushu were significantly higher at all SNPs  than that at the other sampling levels.C. japonica, LDs at the LG level rapidly decayed in all populations; however, LDs in the Kyushu population were slightly higher than that in the others. As C. japonica plus trees because the isotigs consisted of coding regions.For the first-generation plus tree populations of NQ) than in the other two populations. Additionally, the genotypes from the Kyushu population showed a wider range distribution than did those from the two Kanto populations in the PCA. From these results, characteristics in the Kyushu population were different from the other populations, in which the distribution of weak but positive LDs throughout the chromosomes, smaller NQ and higher genetic diversity were observed. On the other hand, differences between the two Kanto populations were slight compared to the differences between the Kyushu population and the two Kanto populations.The results of the genetic structure based on ancestral analysis showed that individuals in the Kyushu population represented a less effective number of clusters per individual  for some traits in the Kyushu population and for male fecundity in the N-Kanto population. Among the populations, the Kyushu population showed slightly higher LD, corresponding to the order of prediction accuracies. The extent of LD is one of the key factors for genomic prediction because it is based on the LD between markers and causal QTLs  prediction based on LD between markers and QTL; (2) prediction based on genomic relationships arising from population structure . In C. jsal QTLs . AdditioC. japonica based on clonal abilities, which would be assumed as the TBV of genotypes. Therefore, the prediction models constructed in this study could be generally considered to be applicable to other individuals within the same population, although dependent on traits or populations. Namely, the models would allow to predict the traits of other first-generation plus trees which had not been phenotyped and to select additional superior trees from natural or artificial forests. By phenotyping such selected individuals in future, it would be possible to verify the effectiveness of the prediction models for general GS applications.In terms of successfully applying GS models, moderate to high prediction accuracy is required. In this study, we observed moderate prediction accuracies for DBH and wood stiffness in the Kyushu population and male fecundity in the N-Kanto and Kyushu populations. Further, for general applications of GS, prediction of breeding values for individuals from the same population but not particularly closely related to the training individuals, or ‘unrelated’ individuals, is required  and are C. japonica. Advanced genome sequencing of the species would enable us to develop more high-density SNPs in the near future.In GS application, high genotyping cost would be another problem. Reducing the SNP number is an effective way to cut genotyping cost. Pre-selecting SNPs could be crucial for improving the quality of genomic predictions . In thisC. japonica breeding (currently in the second generation) is relatively delayed compared with other coniferous species, such as Pinus taeda (in the third generation; (Pinus radiata (in the third generation; C. japonica. Genome-wide studies might have potential to accelerate the breeding cycle of this species. In a simulation study of GS for C. japonica, although prediction accuracies of models constructed in an early generation decreased with each generation, genetic gains were maintained for several generations (C. japonica.Since the progress of eration;  and Pinuerations . Therefoerations . These ferations . In thisC. japonica using multiple populations of unrelated first-generation plus tree genotypes. Among the assessed populations, the genetic characteristics including the extent of LD and genetic structure were different, considering that such characteristics are tied to population histories. We demonstrated the possibility of detection significantly contributing to genomic regions for traits by GWAS using multiple populations. In addition, we showed the basic potential of genomic prediction and empirically revealed the genetic characteristics as determinative factors of prediction accuracy using multiple populations. Furthermore, we demonstrated an effective strategy of SNP selection for genomic prediction using the GWAS results. Our present study suggests the potential of GWAS and GS for improvement of the species.This is the first empirical study of both GWAS and genomic prediction in YH and AW conceptualized the present research. EF, KM, TI, MTs, and MK did phenotyping. TH generated genotyping data. YH, EF, KM, KT, and MiTa conducted data analysis. MaT provided technical assistance with data analysis. YH wrote 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.