An experiment was conducted to compare estimated genetic variance for maize doubled haploid (DH) with conventional twice-selfed (S2)-line hybrids. Starting with a 4-parent population, at least 160 lines were derived using both of these methods and crossed with two inbred testers. For both inbred testers, maize hybrid grain yield and stalk lodging had higher estimated genetic variances for DH than for S2. For one of the testers, estimated grain moisture genetic variance was higher for DH, but not for the other. The DH hybrid yield distributions on both testers were flatter and had more entries in tails compared with S2 distributions. With complete homozygosity of DH lines and the subsequent increased genetic variance among lines, the expected response to yield selection is higher for DH than for S2 line hybrids. Zea mays, L.) hybrids for testing and selection [Seed companies and other organizations such as CIMMYT  now commonly use doubled haploid (DH) lines to cross with inbred testers to form maize . The genetic variance VG in our case is variation from hybrid to hybrid after eliminating environmental and genotype-by-environment (G*E) influences, and VG can be estimated. Maize breeders generally select a subset of individual hybrids with best values of, for example, grain yield, to advance for further testing in another season. The true difference of the average of this selected group from the average of all hybrids or of check hybrids is considered the response to selection [There are theoretical advantages to having all genetic variance exhibited between homozygous DH lines rather than some variance left within lines, as is the case with those derived by selfing ,7. In thThere is a lack of reported estimates of genetic variances comparing maize DH-hybrids to conventionally derived hybrids based on data from actual field experiments, the results of which would be beneficial to scientists and maize breeders. Seitz  is one aA field experiment was conducted in Garst Seed Company in summer 1997 to compare the hybrid performance of maize doubled haploids with lines obtained by selfing twice (S2) from the same original 4-parent population. The objectives of this experiment were to estimate genetic variances for maize grain yield, grain moisture, and stalk lodging for the sets of DH-line and S2-line hybrids, to validate theory by testing whether DH-line genetic variance in hybrids is greater than that of S2-line hybrids, and to estimate response to selection in the first season of yield trials for the two methods of line derivation. This experiment provides estimates of the genetic variances from which response to selection for maize DH-hybrid yield can be inferred and illustrates DH superiority to conventional S2-line hybrids.Average values of yield, grain moisture, and stalk lodging for the maize doubled-haploid line hybrids and S2-line hybrids are provided in Genetic variance estimates are generally greater for DH- than S2-line hybrids . The varp = 0.05), although the DH hybrid genetic variance was numerically greater. For stalk lodging, ratio intervals were  and  for the L- and U-testers, respectively, implying that DH genetic variances are greater than for S2 hybrids.For our experiment and the grain moisture trait, the DH:S2 ratio confidence intervals were  and  for the L- and U-testers, respectively. Because the interval for the L tester contains 1, the DH and S2 hybrid genetic variances did not significantly differ , estimated as sg2/sp.Estimated response R = k*hation sp  (p. 189)p formula.Results of the calculations of R/k for first-year testing are contained in The impact of the increase in genetic variance and expected response for yield and grain moisture are illustrated in the frequency histograms below. The distributions clearly indicate that more superior hybrids are available in the initial year of testing from the set of DH-line hybrids than from the S2-line hybrids. For the L tester, six DH-line hybrids were above the 156 bu/A class vs. none for S2 , and forAnother interesting result from this experiment is that the correlation of yields between hybrids of the same line on each of the two testers was higher for the DH lines than for S2 lines. Observed yield correlation between L- and U-tester crosses for S2 lines was 0.37 and for DH lines was 0.61. This may be due to complete homozygosity and less within-line variance giving more consistent estimates of genetic effects for DH lines.Field experiment. The experiment used a 4-parent population from which DH lines and S2 lines were derived. The population started with 4 inbred lines, A, B, C, and D, which were crossed in pairs. Next, these F1 pairs were crossed with each other to get three sub-populations: (A × B) × (C × D), (A × C) × (B × D), and (A × D) × (B × C), which were blended together in a balanced bulk. Lines were derived from these 4-parent populations in about equal numbers for both methods (DH and S2). In the conventional method (S2), there were two generations of selfing, once in a nursery in Iowa, USA, and once in Hawaii. For the DH method, induction of maternal haploids and artificial chromosome doubling, as described by Geiger and Gordillo [The two sets of derived lines were crossed with each of two male tester inbreds, called L and U, to form four groups of hybrids: two testers, and two line-derivation methods (DH and S2). There were approximately 160 hybrids per group, and these were divided equally among four yield trial sets for the purpose of hybrid testing. Keeping hybrids of the same group within each of the sets was done to ensure conditions were as similar as possible to those of future yield trials, namely hybrids with the same tester, same line-derivation method (DH-line or S2), and evaluated in sets of about 40 or 50 hybrids. This experiment design does not allow good mean yield comparisons between line-derivation methods, but it does lead to providing variance estimates without having to contend with plot-to-plot competition effects from mixing line derivation methods.Each yield trial set was grown in a randomized complete block design with 2 reps. Hybrids from both derivation methods involving a given tester were grown at the same five locations in the Midwest US. The two testers differed in maturity, so the sets of hybrids on the earlier tester were not grown at all the same locations where the later maturing sets of hybrids were grown, but there were two locations in common to both testers. The S2 hybrids were grown in 50-entry sets, with 40 derived-line hybrids and 10 checks. DH hybrids were grown in 45-entry sets with 38 or 39 derived-line hybrids and 7 or 6 checks. For a given tester, six check hybrids were common to the DH and S2 sets. Two checks were common to all sets across both testers.Variables measured on each two-row, 1.5 × 6 m field plot of the yield trial experiments are final plant stand; grain yield corrected to 15.5% grain moisture, measured in bushels per acre (bu/A) and converted to metric tons per hectare by multiplying by 0.06278; grain moisture percentage at harvest; and stalk and root lodging and dropped ear counts, each expressed as a percentage of final stand. Too few plots had root lodging and dropped ears to report results here.Statistical analyses. Analyses were done for the traits grain yield, grain moisture, and stalk lodging to get hybrid least-squares means (LSmeans) over reps, estimate genetic variances using analysis of variance and restricted maximum likelihood (REML) [d (REML)  (pp 498–d (REML)  softwareAnalysis of variance was done for each of the testers by derivation method groups . Each group had 20 RCBD experiments (5 locations with 4 sets of hybrids each) analyzed with the model Y = Rep Hyb; to compute hybrid LSmeans for each of the traits measured. In the balanced data case, these are averages over reps. As example of the output, the U-S2 group had 50 hybrids in each set: 10 checks and 40 U/S2-line hybrids; from this analysis, there were 50 hybrid LSmeans for each set for a total of 200 hybrid means for each location. The 20 independent experiments for each tester-method group are considered 20 set-within-location environments. For each, we equated expected mean squares to observed values to solve for estimated genetic variances, then averaged these 20 estimates to get an estimate of genetic variance for that tester-method group.We also estimated genetic variance components over all locations and sets using SAS Proc Mixed  and REML12/s22), distributed as F with d1 and d2 numerator and denominator degrees of freedom. A 95% confidence interval for this variance ratio is1,d2)F  12/s22),2σP, where k is intensity of selection, 2h is broad-sense heritability on an entry-means basis, and Pσ is phenotypic standard deviation on an entry-means basis. This formula and estimates of genetic variance allowed estimation of response to selection for each of the line-derivation methods. Correlations of yields of hybrids with the same line on each tester were done for the DH lines and for S2 lines. We combined data over sets and locations to get LSmeans for each hybrid in each tester-method group. Means from the L- and U-testers were paired, matched on the derived line. For each derivation method, correlation of L/line with U/line hybrids was done to indicate consistency of the line performance over the two testers: correlation 1 indicating the exact same spacing and ordering of lines for both testers and correlation 0 indicating a random ordering for the testers.Response was estimated for traits using the formula on page 189 of Falconer and McKay , R = kh2This experiment was designed to compare the performance of maize hybrids made from DH lines with hybrids using conventional S2-lines. A common 4-parent population was the breeding source for the lines created with the two derivation methods, DH and S2. All derived lines were crossed with two inbred testers, and about 160 hybrids were grown in 5 locations for each tester by line-derivation method combination.p = 0.05) higher estimated genetic variances for the DH method than for the S2 method. For one of the inbred testers, estimated yield genetic variance was 2.54 as large for DH as for conventional S2-derived lines, and for the other tester 1.72 times as large. Seitz [The main difference in DH- and S2-line hybrid performance was in estimated genetic variances. For both inbred testers, maize hybrid grain yield and stalk lodging had significantly (Higher genetic variance for DH lines results in a change in the shape of distributions, with fewer maize hybrids in the middle and more in the tails of the DH-hybrid distributions. Expected response from first-year yield trials is, therefore, greater for DH-line hybrids than for conventional S2-line hybrids.This work provides evidence of positive differences in hybrid performance in yield trials for DH lines versus S2 lines derived from the same 4-parent population. Results from this experiment demonstrate empirically the theoretical breeding advantage of a higher genetic variance associated with DH lines contrasted to lines derived from conventional selfing.


The evolutionary forces that gave rise to the exceptional plant species richness of the Cape Floristic Region (CFR) have also likely played a role at the intraspecific level (i.e. plant populations)—and thereby generating shared phylogeographic patterns among taxa. Here we test whether plant populations in the CFR exhibit phylogeographic breaks across the boundaries between Centres of Endemism (CoEs). The boundaries between CoEs  represent a spatial proxy for the evolutionary diversifying drivers acting on plant taxa in the CFR.The CFR, located along the southern Cape of South Africa.Published phylogeographic literature were compiled and spatial patterns of genetic divergence re-analysed to assess the frequency at which CFR plant taxa exhibit phylogeographic breaks either (1) across or (2) within CoE boundaries. Population pairs from each study were compared across and within CoEs and scored as either exhibiting a phylogeographic break or not.2 = 886, p < 0.001) and fewer breaks within CoEs  than would be expected if there was equal probability of a genetic break occurring across CoE boundaries.Phylogeographic breaks in Cape plants were found to occur across the boundaries of CoEs more often than not. Significantly more population pairs exhibited phylogeographic breaks across CoE boundaries (506 of the 540, χThe evolutionary forces that have produced and maintained the exceptional plant diversity in the CFR appear to have operated at the population level, producing similar patterns of phylogeographic structuring of plant lineages regardless of life history or taxonomy. This tendency for Cape plants to exhibit shared patterns of spatially structured genetic diversity that match the distribution of endemic taxa may assist CFR phylogeographers to streamline sampling efforts and test novel hypotheses pertaining to the distribution of genetic diversity among CFR plant taxa. Additionally, the resolution at which CoEs are mapped should be refined, which may provide a valuable tool for future conservation planning and the development of precautionary guidelines for the translocation of genetic material during species reintroductions and commercial cultivation of Cape endemic crops. Thus, to answer the question ‘Do Centres of Endemism provide a spatial context for predicting and preserving plant phylogeographic patterns in the Cape Floristic Region, South Africa?’—yes, CoEs do appear to be an important tool for Cape phylogeographers. However, the data is limited and more plant phylogeography work is needed in the CFR. Understanding spatial patterns of biodiversity is necessary to protect ecosystems, biotic communities, and species of high conservation importance. Unfortunately, the underlying patterns of intraspecific genetic diversity are rarely given equal attention . RelyingThe CFR, located on the southern tip of southern Africa, is a winter rainfall region with exceptional plant species richness and endemism . Many ofErica (Protea (Restio (Tetraria (Protea repens (L.) L. (Proteaceae) (Restio capensis (L.) H.P. Linder and C.R. Hardy (Restionaceae)   and Restonaceae) , environonaceae)  as an imAssuming that a regional biota has (largely) experienced the same broad evolutionary pressures , patterns of phylogeographic structuring are likely to be shared among species  of This pattern of decreased size and increased abundance of CoEs in the western CFR reflects the distribution of species richness in the Cape. The number of localised endemic taxa and species diversity is greatest in the west and gradually decreases as one moves east; this trend is referred to as Levyn’s Law . A broad definition of phylogeography was adopted at this stage due to the general lack of intraspecific phylogeographic literature focusing on CFR plant taxa and any study including a phylogenetic analysis in relation to a mapped geographic distribution of the samples analysed was included (i.e. species-level phylogenetic studies). Thus, a preliminary total of 17 studies covering intra- and interspecific phylogeographic investigations were identified. Five of the studies were, however, excluded from the investigation, three due to potential data reproducibility issues of RAPD and ISSR molecular markers (DOI 10.6084/m9.figshare.11370468.v1) and included a range of molecular marker types   . In caseFrom the phylogenetic or phylogeographic analyses reported in each study, it was determined whether population pairs exhibited genetic divergence. Populations were assigned to a genetic group based on membership to a clade or sub-clade as reported in their respective studies. Population pairs occurring across adjacent CoE boundaries were then evaluated to determine if they belonged to the same, or different genetic groups. Each population pair was scored as either: (a) representing an phylogeographic break across the CoE boundary (hereafter referred to as a ‘CoE phylogeographic break’), or (b) representing no phylogeographic break across the CoE boundary (hereafter referred to as ‘inter-CoE homogeneity’). In addition, genetic divergence between population pairs occurring within CoEs was examined for each study using the same approach, with each possible population pair within a CoE scored as either exhibiting a phylogeographic break or homogeneity. The sample distributions of a subset of the studies used to explore phylogeographic breaks across CoEs are shown in p-value based on 10,000 replicates were used to determine whether the final population scoring deviated from random assignment with equal probability of population pairs exhibiting a CoE phylogenetic break or homogeneity. Tests were performed using the base package in R (v3.5.1) (Chi-squared tests with simulated (v3.5.1) .The final data set consisted of 540 population pairs compared across a total of 33 boundaries between adjacent CoEs. Eight genera were included in the analysis, with an average of 10  CoE boundaries compared per genus. The number of population pairs compared across adjacent CoEs ranged from one (six studies) to seven (two studies) with an average of 3  CoE boundaries compared per study. Sampling in five of the studies included in the CoE analysis were limited to the western CFR, while only one was limited to the eastern CFR and the remainders sampled across the western and eastern CFR.x2 = 886, p < 0.001). Eight populations exhibited CoE uncertainty and were subsequently combined with the populations pairs that exhibited inter-CoE homogeneity, resulting in a total of 34 population pairs exhibiting inter-CoE homogeneity. The number of cases of phylogeographic breaks, uncertainty of CoE membership and inter-CoE homogeneity for each of the CoE boundaries examined are shown in More populations pairs (506 of 540) exhibited phylogenetic breaks across CoE boundaries that would be expected if there was equal probability of assignment , which x2 = 300, p < 0.001) and shown in Within CoE divergence comparisons were conducted on a total of 619 population pairs occurring within 11 CoEs. Of these, 525 exhibited intra-CoE homogeneity (i.e. 94 pairs contained intra-CoE divergence), more than expected than if there was equal probability of assignment (n = 540) of paired populations found on either side of a CoE boundary whereas only 15.2% (n = 619) were found within CoEs.Thus, inter-population divergence was detected in 93.7%  was developed in 2003 , prior tThe description of conservation priorities using insights gained from phylogeographic analysis has been applied to biodiverse regions elsewhere in the world. These include setting conservation priorities for the: mammal fauna of the Amazon ; lizard In addition to delimiting areas for conserving extant habitat, due to the high numbers of locally endemic plant taxa (8,900 endemic species occur in the broader Cape area, With no general guidelines in place to direct the redistribution of genetic material in the CFR, it is unlikely that the potential for geographically structured genetic lineages is considered during translocation activities. However, with investigations into genetic issues associated with the translocation of endemic plants only recently emerging in the literature , it is cThe data sets used in this study were not developed with the intention of answering the questions posed here, and there is great potential to refine this work in future studies. Finer resolution mapping units should be used to generate CoEs. Be it with environmental layers that represent putative barriers to dispersal, modelled distributions of endemic plant taxa, which in itself includes environmental data, or more fine resolution locality data. Regardless, more refined edges between CoEs should be developed to better reflect natural boundaries between species distributions and evolutionary processes. From these refined CoEs, hypothesis-driven sampling of populations can be conducted to test for phylogeographic breaks across CoEs, facilitating explorations into the evolutionary processes that have generated the observed patterns of phylogeographic structuring and diversity in the CFR flora. Additionally, this study highlights the lack of available phylogeographic work in the CFR. Future studies should focus on exploring novel genera and widespread taxa, which will help to fill the current gaps in our phylogeographic knowledge of Cape plants.Despite the limited data available for the current study, the overwhelming tendency for phylogeographic breaks to occur across CoE boundaries highlights an important aspect of diversity in the CFR—intraspecific genetic divergence has likely been driven by the same forces that have generated the exceptional floristic diversity of this region. CoEs do, therefore, provide a spatial context for predicting and preserving plant phylogeographic patterns in the CFR. These findings highlight the potential value of CoEs for: developing phylogeographic research, aiding future conservation planning, and sampling wild genetic resources. Furthermore, policy should ensure that CoEs be adopted as precautionary spatial limits for the translocation of genetic lineages for rehabilitation and commercial cultivation, until data specific to the species concerned is made available.


Estimating the genetic diversity and structures, both within and among chicken breeds, is critical for the identification and conservation of valuable genetic resources. In chickens, microsatellite (MS) marker polymorphisms have previously been widely used to evaluate these distinctions. Our objective was to analyze the genetic diversity and relationships among 22 chicken breeds in Asia based on allelic frequencies.We used 469 genomic DNA samples from 22 chicken breeds from eight Asian countries  and three imported breeds . Their genetic diversity and phylogenetic relationships were analyzed using 20 MS markers.Hexp, 0.718±0.027) and polymorphism information content . Additionally, the observed heterozygosity (Hobs) was highest in LCH (0.690±0.039), whereas WLG showed the lowest Hexp (0.372±0.055), Hobs (0.384±0.019), and PIC (0.325±0.049). Nei’s DA genetic distance was the closest between VTN and VNH (0.086), and farthest between KNG and MGN (0.503). Principal coordinate analysis showed similar results to the phylogenetic analysis, and three axes explained 56.2% of the variance . STRUCTURE analysis revealed that the 22 chicken breeds should be divided into 20 clusters, based on the highest ΔK value (46.92).In total, 193 alleles were observed across all 20 MS markers, and the number of alleles ranged from 3 (MCW0103) to 20 (LEI0192) with a mean of 9.7 overall. The NPS breed had the highest expected heterozygosity (This study provides a basis for future genetic variation studies and the development of conservation strategies for 22 chicken breeds in Asia. Prior to the Convention on Biological Diversity  in 1992, genetic resources had been recognized as common global resources, however, after the CBD, they began to be regarded not as common, but as the individual resources of each respective country. In 2007, the Interlaken Declaration was adopted by the United Nations Food and Agriculture Organization, which suggested that each country should preserve their own animal genetic resources and promote the sustainable use of local breeds [Animal genetic diversity is a source of raw genetic material that can be utilized to improve breeds and adapt livestock populations to changing environments and demands. Thus, acquiring information on animal genetic diversity is essential to design strategies for their sustainable management ,4. In AsTo identify genetic uniqueness, many countries have evaluated the genetic diversity and relatedness of local breeds using DNA markers such as microsatellites (MS), mitochondrial DNA (mtDNA), copy number variation, and single nucleotide polymorphism (SNP) –11. In rAlthough many studies have analyzed the genetic diversity and phylogenetic relatedness of chickens using MS markers, they have been limited, as their samples generally only come from breeds of their respective country or of a few countries –22. The A total of 469 chickens, belonging to 19 different chicken breeds from eight different countries and three imported breeds were used in this study. The 22 chicken breeds included six Korean native breeds , four Laotian , three Indonesian , two Vietnamese , one Kyrgyzstani (KGPS), one Mongolian (MGN), one Nepalese (NPS), one Sri Lankan (SBC), and three imported . Detailed information on these breeds can be found in 2. The polymerase chain reaction (PCR) reactions were performed in a total reaction volume of 20 μL containing 2 μL of template DNA, and 0.4 to 2.6 μL (2 pmol/μL) of primer based on the multiplex combinations. The initial denaturation was performed at 95°C for 5 min, followed by 35 cycles of 60 s at 95°C, 45 s of annealing at 58°C to 62°C based on the multiplex combination, 60 s of extension at 72°C, a final extension at 72°C for 30 min, and then cooling to 4°C.Ten MS markers were selected from among the International Society for Animal Genetics / Food and Agriculture Organization of the United Nations (ISAG/FAO) recommended markers. Another Ten MS markers were selected based on their high heterozygosity in the Ark database website . The information for the twenty MS markers used in this study is available in After PCR amplification, the genotyping reaction mixtures were made using 1 μL of the PCR products, 10 μL of Hi-Di Formamide , and the GeneScan 500 LIZ Size Standard  mixture. The genotyping reaction mixture was denatured for 10 min at 95°C and then immediately placed in ice. Electrophoresis was performed using capillary arrays in an ABI PRISM 3130xl Genetic Analyzer . The allele sizes were determined using GeneMapper Software 5  and was analyzed statistically.Hexp), observed heterozygosity (Hobs), and polymorphism information content (PIC) values for each of the chicken breeds across the 20 loci were calculated using the MS Tool Kit [A genetic distances between breeds were calculated using the DISPAN software [The allele frequencies, the number of alleles, expected heterozygosity , Hobs (0.382±0.046), and PIC (0.272±0.027), were all found at the MCW0103 locus, whereas the highest values of Hexp (0.714±0.021) and PIC (0.640±0.022) were found at the ADL0176 locus, and the highest value of Hobs (0.735±0.034) was found at the MCW0193 locus. Moreover, MCW0103 showed low polymorphism levels in previous studies [Hexp≥0.6 were highly informative for genetic analysis. Our study demonstrated that 13 of the 20 MS markers were highly informative for discrimination analysis, and would be appropriate for the analysis of the 22 chicken breeds.To obtain insight into the genetic diversity and population structures, the Tool kit . From thf Hexp 0.8±0.036,  studies  reportedHexp (0.372±0.055), Hobs (0.384±0.019) and MGN showed the lowest PIC (0.306±0.048), while NPS showed the highest Hexp (0.718±0.027) and PIC (0.663±0.030). Correspondingly, although the population size was small, LCH had the highest Hobs (0.690±0.039). Overall, the diversity of the imported breeds was lower than that of local chicken breeds in 8 countries, except for MGN. This is because the imported breeds have been strongly selected for their performance characteristics and breeding purpose  for many decades. In our study, the genetic diversity of the Korean native chicken (KNC) breeds was lower than that of the other Asian chicken breeds tested. The reason for this may be that five KNC breeds  were restored in 2008 by NIAS, according to the genetic fixation of their different feather colors [Furthermore, the genetic diversity parameters of the 22 chicken breeds were calculated using the MS Tool Kit . The mea, Hobs 0.4±0.019 aA genetic distance between pairs of breeds, for all 22 chicken breeds, using the DISPAN program (A genetic distance (To further investigate the genetic divergences among the breeds using the 20 MS marker allele frequencies, we estimated Nei’s D program ; the shodistance . In our We conducted clustering analysis using Bayesian clustering, which provided more accurate estimates of relatedness of the breeds . AccordiTo assess the relatedness of breeds, we carried out PCoA analysis using the allele frequencies of the 20 MS markers . These rIn conclusion, the genetic diversity of the KNC and imported breeds was lower than that of the other Asian chicken breeds, whereas their genetic uniformity was higher, except in the MGN, KGPS, and LCH. Therefore, selection for the specific characteristics of Asian chicken breeds used in this study is necessary to increase their genetic uniformity. Moreover, estimating the genetic diversity between the 22 Asian chicken breeds is the first step in a strategic plan for the genetic characterization and conservation of these breeds. Although the sample size of some of the breeds was small, our findings are meaningful in that the study was conducted using various breeds from different countries. Additionally, this study may be useful as an initial guide for defining conservation objectives, for designing future investigations of genetic variation, and for developing conservation strategies for 22 Asian chicken breeds. However, further research is required to elucidate the specific reasons for the genetic differences between the Asian breeds and the native Korean and imported breeds.


Extensive knowledge on the genetic characterization of marine organisms has been assembled, mainly concerning the spatial distribution and structuring of populations. Temporal monitoring assesses not only the stability in genetic composition but also its trajectory over time, providing critical information for the accurate forecast of changes in genetic diversity of marine populations, particularly important for both fisheries and endangered species management. We assessed fluctuations in genetic composition among different sampling periods in the western Portuguese shore in three fish species.Diplodus sargus, sand smelt Atherina presbyter and shanny Lipophrys pholis were chosen, because of their genetic patterns in distinct ecological environments, insight into historical and contemporary factors influencing population effective size (Ne), and degree of commercial exploitation. Samples were obtained near Lisbon between 2003 and 2014 and screened for genetic variation with mitochondrial and nuclear markers. Analyses included genealogies, genetic diversities, temporal structures and contemporary Ne.White seabream Ne/generation when compared to the other species, which, in turn, revealed no evidence of genetic drift for most study periods. These results highlight the fact that temporal variations in genetic pool composition should be considered when evaluating the population structure of fish species with long distance dispersal, which are more vulnerable to recruitment fluctuations.For mtDNA no temporal structure was detected, while for nDNA significant differences were recorded between some sampling periods for the shanny and the sand smelt. Haplotype networks revealed deep genealogies, with various levels of diversification. The shanny revealed a smaller  Over the last four decades, we have assembled extensive knowledge on the genetic characterization of marine organisms, mainly concerning the spatial distribution and structuring of populations e.g., . With thNe—effective population size, one of the most important parameters in both conservation and evolutionary biology  have bem method , heterozm method , sibshipm method , moleculm method ) and twoquencies ).Ne has been the Temporal Method ; the sand-smelt Atherina presbyter Cuvier 1829 (Pisces: Atherinidae); and the shanny Lipophrys pholis L. 1758 (Pisces: Blenniidae).Genetic monitoring through time, specifically, the temporal method, has been successfully employed to evaluate the temporal stability and estimate rluccius , the broo trutta  and the s morhua . In the Although they are not threatened—the three species are listed as least concerned—their population trend is unknown, and they can be locally vulnerable due to various reasons. The white-seabream is a commercially important species throughout European shores, wide-spread and locally abundant  and, altNe estimation in populations with overlapping generations.These target species provide crucial insight into historical and present factors influencing effective population sizes and genetic patterns in distinct ecological environments and degrees of commercial interest. In this study, we assessed the changes in genetic composition in different points in time in the western Portuguese shore, using a mitochondrial and a nuclear marker (chosen due to their extensive use in the past decade). The specific objectives were: (1) to compare the inter-annual variation in genetic composition and structure of coastal species with contrasting traits, and (2) to test the temporal model and its potential for Diplodus sargus is a coastal species in the north-eastern Atlantic ranging from Senegal to the Bay of Biscay, including the archipelagos of Canaries, Madeira and Azores, Mediterranean and Black Sea , suggesting genetic isolation over large geographic distances  and Fonte-da-Telha  (L. pholis) and beach channels (D. sargus and A. presbyter). A small sample of fin clip was collected, preserved in 96° ethanol and deposited in ISPA-IU/MARE collections. All sampling and handling of fish were conducted according to established animal welfare guidelines  and following the relevant legislation, as none of the sampled species are endangered or protected in Portugal.Individuals of  9°11′W)  between  9°11′W) . They ar 9°11′W) . Juvenilhttp://www.stabvida.net) and GATC .Total genomic DNA was extracted from about 20 mg of tissue with REDEXtract-N-mp kit (Sigma-Aldrich) following manufacturer’s instructions. Polymerase Chain Reaction (PCR) amplification was performed for two fragments, the mitochondrial control region (CR) and an intron of the nuclear S7 ribosomal protein gene (S7), with the primer pairs: LPro1 (5′-ACT CTC ACC CCT AGC TCC CAA AG-3′) and HDL1 (5′-CCT GAA GTA GGA ACC AGA TGC CAG-3′) (CR for the three species) , S7RPEX1http://www.codoncode.com/index.htm) and aligned with Clustal X2 . Additional sequences from previous work  was performed with GenAlEx 6.5  within iversity  and to c χ2 test  was used χ2 test  was usedAlEx 6.5  to visuaAlEx 6.5 . The samAlEx 6.5 .Ne) and genetic drift (Fs) were estimated using TempoFs (Fs′ (genetic drift corrected for sampling plan) and Ne per generation. For this approach, we used sampling plan II  . Differences among haplotypes corresponded to 70 polymorphic sites  (For  indels) . For the indels) . For the indels) . A total indels)  was estiD. sargus included specimens collected in 2009, 2011 and 2014 (outgroup weight: 0.071). In this network, most of the haplotypes were not arranged in a star-like pattern, and some of the branches reached a maximum of 46 mutational steps, and 18 steps from the ancestral haplotype. For A. presbyter, the network built with the CR dataset presented several star-like patterns around haplotypes, including the haplotype inferred as ancestral (outgroup weight: 0.051), which comprised individuals from every sampling period. The network was dominated by two haplotypes shared by most sampling periods. In this network, also deep but with less diversification, some of the haplotypes differed 16 mutational steps from ancestor. For L. pholis, the CR network displayed several haplotypes, including the estimated ancestor, in the centre of star-like patterns. This ancestral haplotype included specimens collected in the three sampling periods (outgroup weight: 0.053), and some haplotypes differed as many as 17 mutations , χ2 = 4.342 (p = 0.362) and χ2 = 3.022 (p = 0.388), for D. sargus, A. presbyter and L. pholis respectively. The CR allelic richness and private allelic richness yielded distinct results for the three species, with no pattern for the seabream, increasing over sampling time in the shanny, and decreasing in the sand-smelt . Comparisons between periods were not significant in D. sargus and A. presbyter. However, for L. pholis, significant differentiation was detected between 2013 and 2014 samples . No species showed marked patterns of temporal genetic structure in CR and, therefore, the PCoA was not performed.Global genetic differentiation among sampling years was not significant for CR in any of the analysed species—AMOVA results , which resulted in much higher Ne/generation (∼inf).Estimates of effective population size based on the CR of the three species are given on D. sargus, 302 sequences of S7 were obtained with 308bp , comprising 99 haplotypes and 50 polymorphic sites   in A. presbyter resulted in a 201bp fragment, corresponding to 35 haplotypes (For  indels) . Only 12plotypes . A totalplotypes . The fraplotypes  was estiplotypes , and forplotypes .D. sargus, A. presbyter and L. pholis, respectively.The S7 haplotype networks of the three species revealed shallower genealogies comparing to the CR . The netD. sargus . Salicru tests yielded significant differences for the haplotype diversity between sampling year for both A. presbyter  and L. pholis . Similarly, the Markov chain tests yielded significant deviations from the HWE for some years of the sand-smelt and shanny datasets (p < 0.001) (The S7 genetic diversity indices of the three species are summarized in < 0.001) .FST = 0.003, p = 0.204). Pairwise FST comparisons were also non-significant. A different pattern was recovered for the S7 of the sand-smelt and the shanny, as significant global genetic differentiation was yielded . For A. presbyter significant differences were found between 2005 and every other period (p < 0.001). For L. pholis pairwise FST was significant between 2003 and the more recent sampling times (p < 0.001). The PCoA corroborated these patterns of temporal genetic structure, with the first axis explaining 94% and 99% of the observed variance, for the S7 of A. presbyter and L. pholis, respectively, and suggesting two groups associated with older vs recent sampling periods   .Genypterus capensis .L. pholis, notwithstanding the absence of temporal genetic structure.Another important finding was the stability across years in these three ecologically distinct species, reinforcing the preliminary work by Lipohrys pholis lays fewer eggs and has a longer planktonic larval stage when compared to both A. presbyter and D. sargus. Therefore, while its male-guarding behaviour can promote larval retention near rocky coasts, this is easily disrupted by atypical or severe events, when larvae may be transported very large distances if storm conditions prevail. Thus, the shanny’s higher differentiation  might be closely related with its greater vulnerability to recruitment fluctuations. Conversely, the white seabream revealed a greater degree of temporal stability for both molecular markers. In contrast, allozyme studies in D. sargus in the Mediterranean found rapid genetic change within a population, assumed to be driven by genetic drift . Conversely, the Ne/generation values for these species were very high (∼inf)   . Genetich (∼inf) , as founS. solea  and G. m. morhua    might beter (S7) .Ne. Not only is this method considered the least biased for species with overlapping generations  e.g., , which fvulgaris  and Labrbergylta ).Ne/generation when compared to the other species but revealed no evidence of genetic drift for most study periods. These results clearly underline that temporal variation in gene pool composition should be considered when evaluating population structure of long larval dispersion fish species, which are more vulnerable to recruitment fluctuations. Comparison between the commercially and non-commercially explored species yielded no conclusive results.Genetic monitoring through time seeks to disentangle life-trait patterns affecting marine organisms, while deepening our ability to forecast changes in both genetic composition and diversity. This study assessed fluctuations in genetic composition among different sampling periods in the western Portuguese shore using a comparative approach in three species with distinct ecological and life-history characteristics. No temporal structure was detected for the mitochondrial marker, while for nDNA significant differences were recorded among some sampling periods for the shanny and the sand-smelt. The haplotype networks revealed deep genealogies, and one of our major findings was the repeated appearance of previously inferred haplotypes. The shanny revealed a smaller Ne  to circumvent difficulties in short-term prediction of Ne . Future Ne , as rece10.7717/peerj.9098/supp-1Data S1Click here for additional data file.


Clonorchis sinensis, and is often associated with a malignant form of bile duct cancer (cholangiocarcinoma). Although some aspects of the epidemiology of clonorchiasis are understood, little is known about the genetics of C. sinensis populations. Here, we conducted a comprehensive genetic exploration of C. sinensis from endemic geographic regions using complete mitochondrial protein gene sets. Genomic DNA samples from C. sinensis individuals (n = 183) collected from cats and dogs in China  as well as from rats infected with metacercariae from cyprinid fish from the Russian Far East (Primorsky Krai region) were deep sequenced using the BGISEQ-500 platform. Informatic analyses of mitochondrial protein gene data sets revealed marked genetic variation within C. sinensis; significant variation was identified within and among individual worms from distinct geographical locations. No clear affiliation with a particular location or host species was evident, suggesting a high rate of dispersal of the parasite across endemic regions. The present work provides a foundation for future biological, epidemiological and ecological studies using mitochondrial protein gene data sets, which could aid in elucidating associations between particular C. sinensis genotypes/haplotypes and the pathogenesis or severity of clonorchiasis and its complications (including cholangiocarcinoma) in humans.Clonorchiasis is a neglected tropical disease caused by the Chinese liver fluke,  Clonorchis sinensis, which can induce malignant bile duct cancer (cholangiocarcinoma). Little precise information is available on the biology, epidemiology and population genetics of C. sinensis. For this reason, we explored here the genetic composition of C. sinensis populations in distinct endemic areas in China and Russia. Using a deep sequencing-informatic approach, we revealed marked mitochondrial genetic variation within and between individuals and populations of C. sinensis, with no particular affiliation with geographic or host origin. These molecular findings and the methodology established should underpin future genetic studies of C. sinensis causing human clonorchiasis and associated complications (cancer) as well as transmission patterns in endemic regions.Clonorchiasis is an important neglected tropical disease caused by the Chinese liver fluke,  Clonorchis sinensis . Chronic infection with this fluke can induce cholangiocarcinoma in humans–a malignant and fatal form of bile duct cancer )ternally –70. Althternally , 71, 72 agonimus , 74, and elegans , 75, whi elegans . Althougificity; , 67). Fuificity;  to eluciquencing  to eluciC. sinensis, which might reflect an ‘adaptive capacity’ in this species, particularly for factors linked to the establishment of infection. It is generally accepted that the adaptive capacity of a species is reflected in the levels of genetic diversity within populations, as a high level of variation provides a mechanism to respond to different selection pressures in the environment; such variation might be reflected in phenotypic variation. Phenotypic traits of particular interest in parasites, relevant to understanding parasite biology and potentially parasitic diseases, include differences in morphology, immune modulation or evasion, host preference or affiliation, sensitivity to drugs, virulence or pathogenicity, and/or adaptation to geographical regions with particular climatic conditions or host spectra. Investigating the impact that genetic variation within C. sinensis populations has on such traits relies on comprehensive genetic data and relevant biological, ecological, epidemiological or clinical information available upon sample collection. Our current data sets enabled us to investigate whether a genetic composition of adult C. sinensis populations correlated with geographical and/or known naturally infected definitive host species (cat or dog), as the worm isolates were obtained from disparate northern and southern zones in which C. sinensis is endemic [In the present study, numerous fixed nucleotide variations were detected between some individuals of  endemic .C. sinensis is similar across the distribution range of this species, which would suggest high levels of ongoing gene-flow between and among populations, probably due to the migration of humans and/or domestic or wild canids or felids, and possibly the transport of intermediate hosts or infected food products. This is concerning in the sense that, if new, evolutionarily advantaged phenotypes emerged , there would be a marked potential for such variants to be spread across the distribution range of C. sinensis; it might also facilitate and enhance parasite transmission or dissemination in endemic or newly established foci of infection. It is also interesting to note that the apparent endemic hot-spots for C. sinensis in the ‘north’ and ‘south’ might differ in their epidemiology  linked to climatic conditions , known to have a significant impact on the success of trematode transmission, presence, prevalence and biology of intermediate host species, such as higher temperatures enhancing snail growth, activity and survival [C. sinensis in these regions and what impact high human migration rates might have on these possible local adaptations  remain C. sinensis from cats and dogs appears to be indicative of a plasticity in adaptation to definitive host species. Not only were the samples obtained from the two host species genetically similar, remarkably, several of them were genetically identical in their mitochondrial protein-encoding sequences , in addition to cats and dogs [C. sinensis in humans to establish whether there is a genetic differentiation between isolates from people and other host species, and whether particular genotypes/haplotypes have an association with the pathogenesis, the severity of disease and complications, such as cancer, in humans. Such an endeavour would be reliant on obtaining worms during surgery, which might be feasible in endemic regions, such as southern China , and the Russian Far East. Alternatively, eggs from human faecal samples could be collected during epidemiological studies and subjected to genetic analysis–preferably at an individual egg level. Given the knowledge gaps regarding the genetic composition of C. sinensis in human populations, finding unique variants of C. sinensis could have significant implications for transmission and control . It seeC. sinensis in the present study and the establishment of an integrated deep-sequencing-informatic-phylogenetic approach provide a solid foundation to tackle these research questions or areas. Clear proof-of-principle of this technological approach also opens the door to large-scale and comparative population genetic and molecular epidemiological analyses using whole mitochondrial and nuclear genomic data sets.Clearly, the finding of marked genetic variability within S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S1 FigClonorchis sinensis individuals studied here and four published mitochondrial genomes  employing Bayesian (BI) and maximum likehood (ML) methods. Specimen numbers are indicated , haplotJ381664; ), haplotJ381664; ), and haJ381664; ). Cluste(TIF)Click here for additional data file.


Claviceps spp.  on florets, producing dark spur‐like sclerotia on spikes that are toxic to humans and animals, leading to detrimental impacts on agriculture and economy due to the downgrading of cereal grains, import–export barriers, reduced yield, and ecological concerns. At least seven phylogenetic lineages (phylogenetic species) were identified within the premolecular concept of C. purpurea s.l. (sensu lato) in agricultural areas and vicinities in Canada and the Western United States. Claviceps purpurea s.s (sensu stricto) remained as the most prevalent species with a wide host range, including cereal crops, native, invasive, and weedy grasses. The knowledge on genetic diversity and distribution of C. purpurea s.s. in North America is lacking. The objective of the present study was to shed light on genetic differentiation and evolution of the natural populations of C. purpurea s.s. Multilocus DNA sequences of samples from Canada and the Western USA were analyzed using a phylogenetic network approach, and population demographic parameters were investigated. Results showed that three distinct genetically subdivided populations exist, and the subdivision is not correlated with geographic or host differentiations. Potential intrinsic mechanisms that might play roles in leading to the cessation of gene flows among the subpopulations, that is, mating and/or vegetative incompatibility, genomic adaptation, were discussed. The neutrality of two house‐keeping genes that are widely used for DNA barcoding, that is, translation elongation factor 1‐α (TEF1‐α) and RNA polymerase II second largest subunit (RPB2), was challenged and discussed.The ergot diseases of agricultural and nonagricultural grasses are caused by the infection of  Claviceps purpurea, that infects wide range of agricultural and nonagricultural grasses, was subdivided in nature. The population structure shed light on the underline mechanisms that might have led to the cessation of gene flow among subdivided populations. House‐keeping gene markers were not neutral in certain circumstances.Regardless of the frequent sexuality and multiple modes of dispersal, the population of the toxigenic plant pathogen,  Host specificity studies under controlled conditions showed virulence variations between the C. purpurea s.l. isolates  released from ascostromata developed from overwintered sclerotia. While the infection of late flowering plants is primarily caused by secondary inocula in the form of asexual conidia, which is immersed in honeydew that oozes from florets and is transmitted by insect vectors, rain splash, or direct head‐to‐head contacts , Manitoba (MB), Ontario (ON), Quebec (QC), Saskatchewan (SK), and Colorado (CO), supplemented with occasional samples from British Columbia (BC), Montana (MT), Newfoundland (NL), Nova Scotia (NS), and Wyoming (WY). Sclerotia from the same host and location (<100 m2) were pooled as one sample, as such a total of 303 samples were obtained. Axenic fungal cultures were isolated and purified for 252 samples; other 51 samples were as sclerotia without pure culture  following the manufacturer's manual. All fungal isolates, sources (culture or sclerotium), hosts, and locations were provided in Table RPB2) using Claviceps specific forward primer TTTCGTGGTATTGTTCGCAGA  using EF1‐983F and EF1‐2218R  using easE996f and easE1895r, and ergot alkaloid chanoclavine I aldehyde oxidoreductase (easA) using easA547f and easA867r , and missing loci were treated as gaps. To confirm the identities of all samples, the concatenated alignment was appended with the 24 reference sequences of 14 related species, and subjected to a phylogenetic analysis using PAUP* 4.0b10  were available in BEASTv2.5, and therefore, we set GTR for TEF1‐α, and HKY for the other three genes . Other priors were set as default, 10,000,000 generations, sampling frequency 1,000, burn‐in 10%, and link trees. Resulting phylogenies were visualized using DensiTree v 2.0.0  via AMOVA between subpopulations (genetic clusters), PCoA, Mantel, and spacial autocorrelation analyses.Genetic differentiation between subpopulations  was further tested by the analysis of molecular variance  and the principle coordinate analysis (PCoA) in GenAlEx 6.5 ; 2. Eastern Canada ; and 3. Western US  ; (b) Noncrop Triticeae , and (c) Crop Triticeae . The two haplotypes on Avena (Aveneae) and other 22 haplotypes were not included in the analyses because their host species belonged to distant taxa and small sample size. Genetic differentiation among these three host groups was examined through AMOVA, PCoA, and pairwise genetic differentiation, ΦPT statistics, and compared with the estimates from genetic clusters.Besides geographic isolation, host specialization is considered another major force driving population subdivision in plant pathogen populations , Ne , I (Shannon's information index = −1* ∑ (Pi * Ln (Pi))), h (haploid genetic diversity = 1‐∑Pi2), where Pi is the frequency of the ith allele for the population; ∑Pi2 is the sum of the squared population allele frequencies. Nucleotide diversity (π), mutation rate (θ), recombination parameter (R), linkage disequilibrium, and neutrality were tested in DnaSP v6. The recombination parameters include R per gene , R per adjacent sites (Ra), and minimum recombination events (Rm). The parameter for linkage disequilibrium was the Kelly's ZnS statistic measuring the overall association between polymorphic sites , and ZZ (= ZnS − Za)  as a clade with a 94% bootstrapping support , haplotype, and nucleotide diversities , than three other genes. TEF1‐α was the least variable ; easE showed nonsignificant deviation based on Tajima's D, however, significant at 0.02 critical level based on Fu & Li' D and F; TEF1‐α and RPB2 showed significant departure from neutral based on all parameters at various critical levels . Overall, Fu & Li's statistics based on coalescence simulation, as expected, showed more sensitive than Tajima's D , 66% (29/44), and 62% (58/94), while relatively fewer private haplotypes 32% (9/28) were observed for easA , with very limited gene flow  was close to 0.5 and the PCoA did not ascertain geographic separation, indicating subpopulations were not strongly differentiated and likely belong to the same population , was much lower (0.036 versus 0.445), and gene flow level (Nm = 6.27) was much higher than among three genetic clusters. The differentiation among three host groups was not statistically significant and had a higher level of gene flow . Pairwise comparisons between Western U.S. and Canadian regions  were also statistically significant; however, the level of the differentiation as estimated by ΦPH values and Nei's D was an order of magnitude lower than those between genetic clusters  and was not significant between crops and each grass tribe  , however, p‐value = .010, indicating significant nonrandom distribution of haplotypes  Figure . However) Figure . In all d Figure . Mantel s Figure . Bromeae3.4Ne = 1.13 than GC2 (1.11) and GC3 (1.09), highest allelic diversity measured as Shannon's information index I = 0.144, and haploid genetic diversity h = 0.083. GC3 had highest total allele number (Na = 1.66), rare (frequency < 5%), and private allele frequencies , while GC3 had higher mutation rates per site and per sequence . Neutrality tests based on Tajima's D, Fu, and Li's D* and F* all suggested GC3 was under significant selective pressure. GC1 was under detectable pressure based on Fu and Li's D* , but not based on Tajima's D and Fu and Li's F*. GC2 was not under selection based on all parameters  were lower than the three genetic clusters independently. These parameters were the highest in GC2 , further supporting population subdivision. Combination of GC1 and GC2 resulted in lower values of Rg (109) and Ra (0.0465) compared with each individual genetic clusters . The same pattern was shown when GC1 and GC3 were combined (Table g (82) and Ra (0.035) were resulted, which is consistent with the result of PCoA analyses showing the incomplete separation between GC2 and GC3  were higher than the individual GCs (9–14). A likely explanation could be that Rm was affected by the sample sizes in that the combined population had a larger sample size (N = 85–156 samples) than three GCs . The estimates of LD parameters, Zns, Za, ZZ, Wall's B, and Q, were very low  suggesting frequent recombination in GCs and among GCs in general. In comparison with individual GCs, the lower values of combined populations, that is, GC1 with GC2, and GC1 with GC3, could suggest the lower recombination during selective sweeping between GC1 and GC2 and between GC1 and GC3  in Canada and western USA using multilocus genotyping. The results from the network analyses , higher rare, and private alleles and showed the deviation from neutrality, indicating GC3 is likely experiencing recent fast adaptation under selective pressure. The separation of three genetic clusters seems consistent with what was found in Oregon and Washington using simple sequence repeat markers  and LD  within and between the GCs , after chill treatment and incubation for 8–10 weeks, produced tiny buds on sclerotia, and then, these buds stopped growing up to normal stromata  and limited gene flow between the genetic clusters  that is likely maintained by high recombination rates and transposon‐mediated gene duplication, but the high recombination rate is also likely influencing the overall trend of purifying selection across the genome  were the results of a recombination event. It is possible that the combination of positive selection on secondary metabolite genes with purifying selection across the rest of the genome has resulted in a more specific, as yet undetected, niche adaptation followed by population stabilization that has resulted in the observed patterns in geographic and host overlap of the three GCs found in North America.The genomes of the members in three GCs may also be evolving and adapting to other environmental pressures, resulting in subpopulations remaining separated. A recent comparative genomic analysis of the genus 4.1easA was not significantly deviating from neutral, while the other three genes all showed significant deviation at varied critical levels  because it is common that genes involving secondary metabolite production are under selective pressure ; Formal analysis (lead); Funding acquisition ; Investigation ; Methodology (supporting); Project administration (lead); Resources (supporting); Supervision (lead); Validation ; Visualization ; Writing‐original draft (lead); Writing‐review & editing . Parivash Shoukouhi: Data curation (lead); Investigation ; Methodology (lead); Resources (supporting); Supervision (supporting); Writing‐review & editing (supporting). Kassandra Bisson: Data curation (lead); Investigation ; Methodology ; Resources (supporting); Writing‐review & editing (supporting). Stephen A. Wyka: Data curation (lead); Investigation ; Methodology ; Resources ; Writing‐review & editing . Kirk D. Broders: Data curation (supporting); Funding acquisition ; Project administration ; Supervision ; Validation ; Writing‐original draft (supporting); Writing‐review & editing . Jim G. Menzies: Data curation (supporting); Funding acquisition (supporting); Investigation ; Methodology ; Project administration (supporting); Resources (lead); Supervision ; Writing‐review & editing (supporting).Fig S1Click here for additional data file.Fig S2Click here for additional data file.Table S1Click here for additional data file.Table S2Click here for additional data file.


Rivers are fascinating ecosystems in which the eco‐evolutionary dynamics of organisms are constrained by particular features, and biologists have developed a wealth of knowledge about freshwater biodiversity patterns. Over the last 10 years, our group used a holistic approach to contribute to this knowledge by focusing on the causes and consequences of intraspecific diversity in rivers. We conducted empirical works on temperate permanent rivers from southern France, and we broadened the scope of our findings using experiments, meta‐analyses, and simulations. We demonstrated that intraspecific (genetic) diversity follows a spatial pattern (downstream increase in diversity) that is repeatable across taxa (from plants to vertebrates) and river systems. This pattern can result from interactive processes that we teased apart using appropriate simulation approaches. We further experimentally showed that intraspecific diversity matters for the functioning of river ecosystems. It indeed affects not only community dynamics, but also key ecosystem functions such as litter degradation. This means that losing intraspecific diversity in rivers can yield major ecological effects. Our work on the impact of multiple human stressors on intraspecific diversity revealed that—in the studied river systems—stocking of domestic (fish) strains strongly and consistently alters natural spatial patterns of diversity. It also highlighted the need for specific analytical tools to tease apart spurious from actual relationships in the wild. Finally, we developed original conservation strategies at the basin scale based on the systematic conservation planning framework that appeared pertinent for preserving intraspecific diversity in rivers. We identified several important research avenues that should further facilitate our understanding of patterns of local adaptation in rivers, the identification of processes sustaining intraspecific biodiversity–ecosystem function relationships, and the setting of reliable conservation plans. They have been central to the development of human societies: They aggregate humans and have set the development of most villages and cities around the world. Rivers indeed provide essential resources and services for our well‐being, and have always been used by humans as colonization pathways Solomon, . Rivers Rivers are unique ecosystems whose functioning is hardly comparable to any other ecosystem, making them scientifically intriguing. Their spatial arrangement into dendritic arborescence comparable to the hierarchical branching of trees  have shaped large‐scale  patterns of species diversity  have comparatively been much less studied in riverscapes and are rarely the target of global conservation policies  patterns along geographical or environmental gradients at both the intra‐ and interspecific levels Lawton, . The latVellend, . From a In riverscapes, spatial biodiversity patterns have long been studied, notably at the metacommunity level  sampled in two rivers from the Garonne–Dordogne River basin mainly differing by their levels of anthropogenic fragmentation . We imputed such spatial patterns to downstream‐biased asymmetric gene flow due to unidirectional water flow , hence highlighting the potential analytical consequences of river‐specific genetic diversity patterns and underlying processes in the context of demographic change inferences.Multispecific genetic diversity assessment constitutes one of the pillars on which our group has built its research over the last decade  sampled at 92 sites spread over the entire Garonne–Dordogne River basin  when looking at linear relationships between allelic richness and riverine distance from the outlet. Despite this apparent congruence in patterns of intraspecific genetic diversity, we demonstrated that the distribution of genetic diversity in these species was actually idiosyncratic when described using finer spatial models taking into account the topology of the river network  situated in intermediate sections of the dendritic network  displayed higher allelic richness than low‐connected demes situated at network extremities . Inversely, we demonstrated that mean Fst values were higher in low‐connected demes compared with highly connected demes. We imputed these patterns to the proneness of highly connected demes to receive genetically distinct alleles originating from upstream/downstream isolated demes. These results mirrored those from Carrara et al.  can be reached by many potential means . Using machine learning algorithms , hence limiting the uncertainty due to the equifinality problem. For instance, using a subset of studies from the meta‐analysis described above, we demonstrated that the past colonization history of populations was the most likely process to explain most observed patterns of DIGD , we were able to detect a loss of genetic diversity associated with this demographic decline. The overall L. burdigalensis metapopulation was losing rare alleles and was becoming more inbred, and a genetic bottleneck was emerging over time. In agreement with previous simulation studies, we also found that all indices of genetic diversity were not equally sensitive to demographic changes over time  must also be assessed to reveal the effects of evolutionary processes acting at different spatial or temporal scales or that cannot be revealed with neutral markers only  framework has provided a theoretical foundation for explaining relationships that can be observed between intraspecific genetic diversity and species diversity  that evolved in the presence of predators display specific functional traits  compared with populations that evolved in the absence of predators  dedicated to understanding spatial variations (and possible covariations) in genetic and phenotypic diversity . Genetic diversity and phenotypic diversity were poorly correlated, and the underlying determinants (at least for phenotypic diversity) were not common across the two species. This suggests that neutral genetic and phenotypic diversity should—in our case study—be considered as independent markers of intraspecific diversity. Interestingly, and although the two species are sympatric and display close ecological requirements, we found contrasting species‐specific phenotypic responses to the abiotic environment. In G. occitaniae, we evidenced trait–environment relationships suggesting adaptation or adjustment to local conditions. On the contrary, we did not find any trait–environment relationship in P. phoxinus, which could suggest a more "opportunistic" bet‐hedging‐like strategy to cope with environmental variation , we evidenced that functional traits such as body mass, risk‐taking behavior, and metabolic and excretion rates varied among P. phoxinus populations occupying rivers differing in their environmental characteristics .A subset of 3.2.2SGDC; Vellend & Geber, SGDCs depend upon species characteristics, but empirical studies testing this prediction are scarce, notably because they require a multispecific genetic sampling for both generalities and peculiarities to be identified. Building on a large field survey conducted at the Garonne–Dordogne basin , we investigated the relationship between genetic diversity estimated within four parapatric fish species and fish species diversity while accounting for local environmental conditions  strongly affected the size and abundance of preys consumed by P. phoxinus. It is noteworthy that intraspecific diversity and warming acted on ecological dynamics through different mechanisms. Indeed, while intraspecific diversity in fish phenotypes mainly mediated trophic interactions, temperature acted on other ecosystem functions such as litter decomposition rate. We finally demonstrated that the ecological consequences of intraspecific diversity were strong enough to alter the fitness of subsequent generations, leading to indirect trans‐generational effects of intraspecific diversity. By comparing the eco‐evolutionary consequences of intraspecific diversity to those of an indisputable environmental driver (temperature), we confirmed that intraspecific diversity really influences ecological dynamics beyond the population level.Understanding the relative importance of intraspecific diversity and environmental heterogeneity in shaping ecological processes is important to predict how natural and human‐mediated losses in intraspecific diversity affect ecological dynamics at the ecosystem level , environmental variation, and ecosystem functioning. We believe that—although much work remains to be done—next generations should bring together major disciplinary fields such as ecosystem ecology, functional ecology, evolutionary ecology, and molecular ecology to generate a holistic framework of the ecological and evolutionary dynamics of river ecosystems. Such a general framework should be accompanied by complementary studies in the wild. We still poorly know whether intraspecific diversity matters in stochastic natural settings in which environmental variations are not controlled. Hence, novel empirical surveys measuring simultaneously intraspecific diversity, environmental variability, and ecosystem functions have to be performed , but their development is accelerating worldwide in response to the growing demand for non‐fossil energy  in two French rivers , allowing an absolute and independent assessment of the individual effects of obstacles on connectivity  after barrier creation, while allowing valid comparisons among species and obstacles of different ages. The computation of the CINDEX requires a minimum amount of fieldwork and genotypic data, and solves some of the difficulties inherent to the study of artificial fragmentation in river systems. This makes the CINDEX a promising tool for riverscape connectivity restoration.Mitigating the negative aftermaths of fragmentation is of crude importance. It is thus essential for environmental managers to have access to precise estimates of the impact of weirs and dams on riverscape functional connectivity. Although the indirect monitoring of functional connectivity using molecular data constitutes a promising approach, it is still plagued with several constraints. For instance, the temporal inertia in the establishment of genetic differentiation after barrier creation makes it particularly difficult to compare the impact of obstacles differing in age or in the effective size of the populations they separate. We developed a standardized index of genetic connectivity (4.2.3P. phoxinus and G. occitaniae) from two distinct rivers, we designed a generalizable analytical framework based on an AIC‐based model selection coupling the creation of meaningful thematic predictors using principal component analyses and the filtering of thematic predictors at three different steps through selection criteria based on commonality analyses  can interact to affect patterns of genetic diversity. Nonetheless, robust analytical procedures are needed to handle the complexity of environmental and biological data collected in rivers . Multicollinearity among predictors is indeed likely to hamper the interpretation of multiple regression results, increasing the risk of drawing erroneous conclusions. Several approaches may be considered such as the creation of orthogonal synthetic predictors using principal component analyses or the use of commonality analyses  for conservation purposes received little attention until the early 2010s. Further, genetic criteria have long been ignored in large‐scale freshwater biodiversity reserve designs  from the Garonne–Dordogne River basin  are common in the Garonne–Dordogne River basin, whereas two are rare endemic species of particular conservation interest . We used a systematic conservation planning optimization tool . Performing such a task will be challenging and will require (a) defining sound conservation targets for each biodiversity level, (b) developing unifying frameworks accounting for variation across biodiversity metrics and scales  and concepts from various fields of ecology and evolution ; an approach directly influenced by Louis Bernatchez' works . We beliBox 1Like most anglers, I am passionate about freshwaters and fish since I am 6–7 years old. I have spent so many hours in the water that it was natural for me to seek for a job that was about freshwaters and fish. Fortunately, in my entourage, a scientist (Dr. Pierre Joly from Lyon University in France) sent me a letter to inform me very thoroughly about the different academic possibilities to accomplish that dream. At that time I was 13 years old, and it was during the past century, when the web was disconnected and when people communicated by writing letters . This is by far the most important letter I have ever read in my life. I perfectly remember that he was finishing this letter by mentioning that the PhD thesis was the best route  for people that are passionate. From that day, I knew that it was the route to follow, and that I just had to be patient (and lucky). And this is the route I followed, and this is on that route that I met Louis. I met Louis at the most important and dangerous part of that route; the PhD project. And this is where I have been lucky. Actually, at this moment (in 2003) I met Dr. Julian J. Dodson, another scientist that I cannot really dissociate from Louis. They both trusted me sufficiently to make me disperse from France to Québec City. And they offered me the PhD project that I was dreaming of since I am 13 years old, something about competitive interactions between native salmon and introduced trout . Moreover, they gave me the opportunity to develop a side project merging behavioral ecology and molecular ecology, a discipline that I discovered as a Master student and that was making a lot of scientific sense for me. These two personalities brought me a lot (as a Scientist and as a Human), but today is Louis's day so I'll not speak further about the memorable lab' parties I had with Julian and his team. Louis was inspiring for me for two main reasons. The first one is his incredible enthusiasm for research ideas. I remember leaving his office full of positive energy and more than happy about the ideas we discussed. He always had the good words  that make you stronger and that give you the feeling that you are not stupid and that you can go ahead. Louis is what we can call “a big name”, and being so positive and enthusiastic with young researchers is not only rare for someone like that, but also highly fortifying. The second reason is that at a moment of my scientific life , I had doubts. I had doubts about my role as a scientist in our society. And this is by looking back to my years with Louis that I chased my doubts and that I took clear decisions about what I wanted to be –as a Scientist. While I was finishing my PhD thesis (running the last hundreds of meters at the end of this long route), Louis was developing the field of evolutionary applications by launching the journal of the same name (Bernatchez & Tseng, None declared.


Anagallis foemina L. [syn. Lysimachia foemina (Mill.) U. Manns & Anderb.] is an annual, segetal weed from the family Primulaceae, recognized as a very rare and endangered species in many European countries. The rare occurrence of this species is associated with the specificity of the habitats in which it occurs. Knowledge of genetic diversity within and between rare species populations is a crucial step for investigating the causes of extinction as well as developing effective conservation strategies. The current study undertakes the assessment of the genetic variation and population structure of Anagallis foemina L. specimens collected in south-eastern Poland, Volhynian Polesie and West Volhynian Upland based on inter-simple sequence repeats (ISSR) polymorphism. Twenty ISSR primers amplified 374 DNA fragments, of which 79% were polymorphic. The polymorphic information content values ranged from 0.230 to 0.430 with an average of 0.344. An average genetic similarity calculated based on Dice algorithm between all analysed samples was 0.635 (0.28–1.00). The AMOVA study found a significant difference  between Anagallis L. genotypes gathered in Volhynian Polesie (VP) and West Volhynian Upland (VU). Analysis indicated, that 89% of the variation existed among groups and 11% within groups. UPGMA analyses grouped A. foemina samples into 2 clearly separated clusters. The plants of the same geographic origin were grouped together. Principal coordinates analysis (PCoA) as well as STRUCTURE also grouped samples in consistence with the collection site, indicating close genetic affinity of plants from the same location. The observed results are typical for fragmented and isolated populations of rare species. Isolation of a small population leads to a decrease in internal genetic variation and to an increase of variation among them. In that case, the conservation of populations from each regional cluster is important to preserve biodiversity. Primulaceae) included six genera: Lysimachia, Anagallis, Trientalis, Glaux, Asterolinon, and Pelletiera. However, based on cpDNA as well as rDNA sequence studies  occur in Mediterranean region or in Europe, including A. arvensis, a weed that has a more widespread global distribution  U. Manns & Anderb.] and A. foemina [syn. Lysimachia foemina (Mill.) U. Manns & Anderb]. The first is common in segetal and ruderal communities throughout the country and increases in numbers  method developed to identify DNA polymorphism in the proximity of microsatellites. Invaluable information on the basis of this studies regarding endangered species protection, not only in Poland, but also in European countries is expected.Thus far, molecular analysis of Anagallis foemina Mill.  is a species with an erect, branched stem, growing up to 20 cm tall. Its dark green leaves, 5–20 mm long, are narrow-ovate, margin usually without glands, pointed. The upper leaves are often narrower than the lower ones and placed opposite on the stem. The flowers are 8 mm in diameter, five-petal, blue, red at the base. The petals of the crown do not overlap each other deeply, they have a few, non-glandular cilia 6 × 3.5 mm. Flower stalks are set individually in the leaf axils, during flowering, they are shorter or as long as the supporting leaves. It blooms and produces seeds from May to October. The fruit is a spherical, multi-seed bag  were collected. Species affiliation was made through general characters for the species and the location. From each site, leaves were picked from 5 randomly selected plants. The exact geographical position of the sites was determined using a GPS device  from sites located in the fields of south-eastern Poland  and purified with Anti-Inhibitor Kit (A&A Biotechnology). DNA integrity and quality were evaluated by electrophoresis on 1.5% agarose gel. The DNA concentration was determined with NanoDrop2000 spectrophotometry and normalized to 100 ng/μL.Taq DNA polymerase, 4 mM MgCl2, 0.4 mM of each dNTP) (Thermo Fisher Scientific) and 0.6 nM of primer . To establish molecular weight of amplification products, Gene Ruler™ 100 bp Plus DNA Ladder was used. Fragments were visualized under UV transilluminator and photographed.i frequency of the amplified allele (band present)fi) frequency of the null allele (band absent) was calculated according to Roldán-Ruiz et al. , b as a Relationships among all examined genotypes was estimated using the Dice algorithm  and clusThe population structure was investigated using a Bayesian approach in the program STRUCTURE 2.3.4  were polymorphic. In average, a single primer initialized synthesis of 19 fragments (range 9–30). ISSR primer sr35 triggered amplification of the highest number of polymorphic bands (30), whereas sr36—the lowest (9). The rates of informative polymorphic bands varied from 50% (sr56) to 100% (sr42). PIC score ranged from 0.230 (sr56) to 0.430 (sr42) with an average of 0.344 (Table A. arvensis (0.435). Simultaneously, Dice index value between A. foemina from Volhynian Polesie and A. arvensis reached 0.283. Pairwise Mantel test showed statistically significant correlation between geographic and genetic distance of all analysed genotypes .An average genetic similarity calculated based on Dice algorithm between all analysed samples was 0.635 (0.28–1.00) (data not shown). Genetic similarity indices estimated within Volhynian Polesie (VP) and West Volhynian Upland samples were very high and reached 1.000 and 0.904, respectively  between Anagallis L. genotypes gathered in Volhynian Polesie (VP) and West Volhynian Upland (VU), Poland. Analysis indicated, that 89% of the variation existed among groups and 11% within groups.The AMOVA study found a significant difference . Number of private bands for this group was 163. Much lower value (0.08%) was observed for Anagallis from Volhynian Polesie. There were 105 bands unique to this group of genotypes . Second cluster encompasses A. foemina from West Volhynian Upland (VU). Dendrogram shows that A. arvensis (VP_r) is distinct from both of these groups and is located separately. Principal coordinate analysis  represents first group, second group is composed of A. foemina from West Volhynian Upland (VU). A. arvensis is recognized as a distinct gene pool  and West Volhynian Upland (VU) revealed significant difference  between genepools derived from these locations. Simultaneously, within groups variation was low (11%) with genetic similarity from 0.904 to 1.000. It indicates a very low diversity of populations, especially Volhynian Polesie derived, and can be an effect of population fragmentation as well as the genetic isolation. Consistent results were obtained by Brütting et al. (A. foemina within population diversity was on a very low level (16%). Greater diversity of molecular variance was observed between the populations studied and this is also in agreement with our results. The strong differentiation of A. foemina populations between geographical regions indicates that current gene flow is very low. Daco et al. (Gladiolus palustris, found that although most of the population of G. palustris in the regions retained significant genetic variation, genetic diversity is likely to decrease in small populations due to genetic drift.Genetic similarity analysis based on Dice algorithm, as well as AMOVA study of g et al.  who founo et al.  analyzinA. foemina samples into 2 clearly separated clusters based on places of their origin, with A. arvensis on the edge of dendrogram. PCoA results confirmed the clustering analyses. However the genetic similarity between A. foemina populations from Volhynian Polesie and West Volhynian Upland was even lower than genetic similarity of West Volhynian Upland samples to A. arvensis. It can indicate that genetic diversity of endangered A. foemina species is high and artificial crossing or introduction plants from isolated geographically regions can strengthen the probability of species survival.UPGMA analyses grouped A low level of genetic diversity is usually found in small, isolated populations (Jacquemyn et al. More and more species are threatened, including agricultural weeds. Isolation of a small population leads to a decrease in internal genetic variation, which in turn contributes to a decrease in immunity and adaptability to environmental changes, a decrease in reproduction rate and increased mortality. As a consequence of fragmentation, genetic differentiation among populations and the loss of genetic diversity is expected to increase with time. This becomes the reason for the extinction of the local plants populations (Zawko et al.


Capture and long‐distance translocation of cleaner fish to control lice infestations on marine salmonid farms has the potential to influence wild populations via overexploitation in source regions, and introgression in recipient regions. Knowledge of population genetic structure is therefore required. We studied the genetic structure of ballan wrasse, a phenotypically diverse and extensively used cleaner fish, from 18 locations in Norway and Sweden, and from Galicia, Spain, using 82 SNP markers. We detected two very distinct genetic groups in Scandinavia, northwestern and southeastern. These groups were split by a stretch of sandy beaches in southwest Norway, representing a habitat discontinuity for this rocky shore associated benthic egg‐laying species. Wrasse from Galicia were highly differentiated from all Scandinavian locations, but more similar to northwestern than southeastern locations. Distinct genetic differences were observed between sympatric spotty and plain phenotypes in Galicia, but not in Scandinavia. The mechanisms underlying the geographic patterns between phenotypes are discussed, but not identified. We conclude that extensive aquaculture‐mediated translocation of ballan wrasse from Sweden and southern Norway to western and middle Norway has the potential to mix genetically distinct populations. These results question the sustainability of the current cleaner fish practice. The genetics of ballan wrasse, a phenotypically diverse and extensively used cleaner fish, was studied along the Scandinavian coastline and in Galicia, Spain. A distinct genetic break was detected in Scandinavia, dividing northwestern and southeastern clusters. Additionally, distinct genetic differences were observed between sympatric spotted and plain phenotypes in Galicia, but not in Scandinavia. As suchCyclopterus lumpus L., 1758) and four species of wrasse: goldsinny , corkwing , rock cook , and ballan  , which in turn increases the likelihood of developing population structure  to investigate the population genetic structure of ballan wrasse in Scandinavia, including comparative analysis with Galician populations, and (b) to investigate the relationship between genetic and phenotypic variation in this species in both Scandinavia and Galicia. In order to achieve these objectives, we genotyped 1,058 ballan wrasse from Scandinavia and Galicia in Spain, using a newly developed SNP panel and existing microsatellite markers.22.1A total of 1,058 ballan wrasse were sampled from 16 locations in Norway, two in Sweden and one in Galicia, Spain Figure  in orderScandinavian fish were caught using fyke nets or pots. Fish were killed and thereafter sampled by trained individuals in accordance with EU Directive 2010/63/EU and national legislations. Galician samples were obtained from a local fish market in A Coruña . All fin clips were stored in 96% ethanol for later analysis.2.2https://www.ncbi.nlm.nih.gov/assembly/GCF_900080235.1/) using BWA  and aligned to the genome assembly available from NCBI  and followed by iPLEX (Agena Bioscience™) Extension Reaction, with extension primer (UEP) mix according to multiplex . SpectroCHIP arrays were analyzed using a MALDI‐TOF Mass Spectrometer  that assigns genotypes to sample fragments according to mass. The results were called and control checked in TyperAnalyzer v.4.1.83 (Agena Bioscience™), and all genotypes with a mass height below 0.4 were filtered.N = 513) were also genotyped for nineteen microsatellite loci . The 500LIZ size standard (Applied Biosystems) was used to accurately determine the size of the fragments and allelic variation. Fragments were analyzed with the software GeneMapper v5 (Applied Biosystems).In addition to genotyping the entire dataset with SNPs as described above, a subset of the samples  Microsa2.4Prior to any statistical analysis, extensive data quality checks were performed on the SNPs to ensure good clustering and low levels of missing data (<30%). Twenty‐four of the SNPs did not pass the quality checks, leaving the final dataset to consist of 82 SNPs. Individuals missing ≥50% of genotype calls were excluded from the analysis.FIS) were computed using the packages adegenet and hierfstat  of Evanno, Regnaut, and Goudet  in adeg Figures . Additioces Nei,  were calces Nei, .HZAR  axis both based on the panel of 82 SNPs and to the allele frequency of every individual locus to determine the position, width, and shape of clines over the total geographic distance. The reference cline was built using STRUCTURE Q‐score for the total dataset and, in both cases, the best cline model was decided upon AIC scores. Clines were considered significantly displaced if the two log‐likelihood unit support limits of the cline center did not overlap with the STRUCTURE Q‐score (Qb = 1 − Qs).A geographic cline analysis was conducted of the Scandinavian samples using the R package FST and STRUCTURE conducted using the R packages adegenet and ade4  analysis was computed for the entire Scandinavian dataset as well as within the clusters detected by pairwise 3The final dataset consisted of 1,025 individuals from 19 locations genotyped for 82 SNPs, and a subset of 513 individuals from 8 locations genotyped for 19 microsatellites. In addition, 325 individuals from five locations were also phenotype sorted (spotty and plain). It is important to note that microsatellites were only genotyped to provide supplementary information on genetic variation in some of the samples, that is, number of alleles and allelic richness.Out of the 82 SNP loci, five deviated from HWE, and eight displayed pairwise LD. Only one locus deviated in these parameters in more than one sampling location; that is, BaWr40 departed from HWE in Flødevigen and Flatanger, and thus, all loci were kept in all subsequent analyses. Overall observed heterozygosity was similar across sampling locations for SNPs Table , with vaNA) and allelic richness (AR) per sample in Scandinavia ranged from 69 to 99 (mean = 84) and 3.8 to 4.0 (mean = 4.0), respectively. In Galicia, values were considerably higher , thus showing greater genetic diversity in southern latitudes. This was also reflected in the higher estimate of heterozygosity for the microsatellites in the Galician sample (0.620) versus the values in Scandinavia .For microsatellites Table , the tot3.1FST analysis, STRUCTURE analysis, and DAPC were conducted both with and without outliers and because only marginal differences in the results were observed between the two sets of loci  across the 18 locations in Scandinavia (between northwestern [NW] and southeastern [SE]) and four between the phenotypic groups in Galicia . Pairwise i Figure , all mar3.2FST values ranging from 0.001 to 0.041 for the 82 SNPs (Table N = 22) that showed weak but nonsignificant differentiation (pairwise FST = 0.001–0.007) toward the sampling locations in the SE cluster. Within the two clusters, much smaller and nonsignificant pairwise FST values were observed, ranging from 0.001 to 0.009. Between samples from Scandinavia and Galicia, large significant genetic differentiation was observed, with all pairwise FST values ranging from 0.106 to 0.148 (Table FST = 0.139–0.148), than between samples within the NW Scandinavian cluster and Galicia (pairwise FST = 0.106–0.144).Genetic differentiation among the Scandinavian samples was small to moderate with pairwise Ps Table . Two disPs Table . An exce48 Table . SlightlSix SNPs showed a low or very low frequency of one allele in all samples in Scandinavia yet a high or very high frequency of the same allele in the Galician samples Figure . SeveralFST/1−FST) increased with increasing distance through the entire Scandinavian study area   for each of the phenotype sorted samples, only plain and spotty Galician phenotypes showed distinct clustering when genotyped for SNPs Figure . For Sca4This study presents the most extensive analysis of population genetic structure in ballan wrasse thus far. We identified two distinct genetic clusters splitting populations in NW and SE Scandinavia and discovered very large genetic differences between all Scandinavian and Galician samples. The split in Scandinavia was located in southwestern Norway across a region that also includes sandy beaches, representing a habitat discontinuity for this species. Finally, sympatric plain and spotty phenotypes were genetically distinct in Galicia, but not in Scandinavia.4.1Laminaria hyperborea . This sA result that should be noted is linked to the sample from Årdal in the NW cluster. This sample was not genetically distinct from any of the other Scandinavian sampling locations. Whether this is caused by Årdal being a very small sample , close to the center of the break between the two main genetic clusters, or whether this is due to aquaculture practice is not known and cannot be determined based on the current dataset. Future studies would benefit from including multiple other samples from areas close to the genetic break, and new markers designed to distinguish between the genetic clusters identified here and potential hybridization between them.4.2The majority of the North Sea was covered by an ice sheet during the last glacial maximum . Therefore, this aquaculture practice requires revision in light of our data. Studies of other species of wrasse subjected to the same aquaculture‐driven harvest and translocation regimes have indicated escapes and potential hybridization between translocated individuals and local populations ; Formal analysis (lead); Investigation (lead); Visualization (lead); Writing‐original draft (lead); Writing‐review & editing (lead). María Quintela: Formal analysis (supporting); Resources (supporting); Supervision (supporting); Writing‐review & editing (supporting). Ellika Faust: Resources (supporting); Writing‐review & editing (supporting). Kim T. Halvorsen: Resources (supporting); Writing‐review & editing (supporting). François Besnier: Formal analysis (supporting); Writing‐review & editing (supporting). Eeva Jansson: Investigation (supporting); Resources (supporting); Writing‐review & editing (supporting). Geir Dahle: Data curation (supporting); Formal analysis (supporting); Writing‐review & editing (supporting). Halvor Knutsen: Funding acquisition (supporting); Writing‐review & editing (supporting). Carl André: Funding acquisition (supporting); Resources (supporting); Writing‐review & editing (supporting). Arild Folkvord: Supervision (supporting); Writing‐review & editing (supporting). Kevin A. Glover: Conceptualization (lead); Project administration (lead); Supervision (lead); Writing‐original draft (supporting); Writing‐review & editing (supporting).Appendix S1Click here for additional data file.