Atlantic herring in International Council for Exploration of the Sea (ICES) Divisions 6.a, 7.b–c comprises at least three populations, distinguished by temporal and spatial differences in spawning, which have until recently been managed as two stocks defined by geographical delineators. Outside of spawning the populations form mixed aggregations, which are the subject of acoustic surveys. The inability to distinguish the populations has prevented the development of separate survey indices and separate stock assessments. A panel of 45 single-nucleotide polymorphisms, derived from whole-genome sequencing, were used to genotype 3480 baseline spawning samples (2014–2021). A temporally stable baseline comprising 2316 herring from populations known to inhabit Division 6.a was used to develop a genetic assignment method, with a self-assignment accuracy greater than 90%. The long-term temporal stability of the assignment model was validated by assigning archive (2003–2004) baseline samples  with a high level of accuracy. Assignment of non-baseline samples  from Divisions 6.a, 7.b–c indicated previously unrecognized levels of mixing of populations outside of the spawning season. The genetic markers and assignment models presented constitute a ‘toolbox’ that can be used for the assignment of herring caught in mixed survey and commercial catches in Division 6.a into their population of origin with a high level of accuracy. HoweverWhile delineation by predefined area may be convenient for management and regulation purposes, accurately assessing the status, biomass and sustainable exploitation rates of mixed ‘stocks’ is inherently difficult if not impossible, as they do not correspond to biological units. Fisheries-dependent and independent data may be confounded, which may mask changes in the abundance of individual populations and lead to biased estimates of population abundance and overexploitation of smaller populations . It is tClupea harengus Linnaeus, 1758) populations using a wide variety of different techniques, including life-history characteristics, morphometric and meristic characters of whole bodies and otoliths, parasite analyses, physical tagging and genetic approaches (see [Downs component), which are largely managed as one unit [6aN_Aut (6.a.N); 6aS ; Irish Sea (7.a north of 52°30′ N) and Celtic Sea  (6aN_Aut herring spawn in autumn (September/October) off Cape Wrath on the north coast of Scotland, the 6aS herring spawn in winter (November–February) primarily off the coast of Donegal in the northwest of Ireland, Irish Sea herring spawn in autumn (September/October) mainly on the Douglas Bank east of the Isle of Man in the Irish Sea, and Celtic Sea herring spawn in winter (November–February) off the south coast of Ireland. Several groups of spring spawning (February–May) herring are also known to occur in the Minch (6aN_Sp), Clyde and Milford Haven, though these are not currently assessed and are believed to be small populations (see review in [There is a long history of research into the characterization of Atlantic herring (hes (see ). While hes  and the6aN_Aut stock is separated from the North Sea autumn spawning stock by the 4° west line of longitude, despite there being no biological evidence that these represent different populations . Withineview in . Herring6aN_Aut, 6aS, 6aN_Sp) and possibly from adjacent populations (Irish Sea and Celtic Sea) are believed to form mixed aggregations on common feeding grounds in Division 6.a during summer [Adult herring from different populations, both within Division 6.a . SVM motructure  and can ssignPOP  has receca 10 million single-nucleotide polymorphisms (SNPs) identified by Han et al. [ca 800 SNPs, associated with ecological adaptation to different geographical areas and spawning conditions, were shown to be able to discriminate all the sampled populations of herring  from across the species distribution. The populations around Ireland and Britain are the southernmost ecomorphs of herring in the Northeast Atlantic and are genetically distinct from the other adjacent Northeast Atlantic herring populations, including Norwegian spring spawning herring to the north and the Baltic herring to the east, from which they can be discriminated with a small subset of 12 independent loci [6aN_Sp) and the Clyde; the 6aN_Aut and North Sea autumn spawning herring, which were indistinguishable from each other, thus supporting the conclusion these stocks are most likely a single population; the 6aS herring, which were differentiated from all populations but more closely related to the southern group consisting of Celtic Sea, Irish Sea and Downs herring, which had the lowest level of genetic differentiation between them. Identification of the primary genome level differences between the herring populations around Ireland and Britain offers the potential to develop a genetic assignment method for discriminating and resolving the outstanding issues of separating mixed survey and commercial catches in ICES Divisions 6.a, 7.b–c.Recent studies using whole-genome sequencing (WGS) approaches have comprehensively clarified the genetic population structure of Atlantic herring across its distribution and have illustrated that herring populations show a strong population structure inferred to be associated with a high level of local ecological adaptation ,35,38. G (i)et al. [validate a small ‘toolbox’ of genetic markers, from those identified by Han et al. , that co (ii)develop a temporally stable genetic baseline dataset by collecting and analysing multiple years of spawning baseline samples from each population; and (iii)develop, test and validate an assignment model for the genetic assignment of individuals of unknown origin collected in Divisions 6.a, 7.b–c back to their population of origin.The aims of the current study were therefore to. 2. 2.1et al. [3 piece of tissue was excised from the dorsal musculature of each specimen, taking care to avoid skin and scales, and stored in absolute ethanol at 4°C. Archive fin clips were also available from the spawning baseline samples collected during the WESTHER project 2003–2004 . Extracted DNA was stored in 96-well PCR plates at −20°C until ready for genotyping.Samples of herring were collected from the catches of fisheries surveys and commercial fishing operations, between 2014 and 2021 in the core ICES Divisions 6.a, 7.b–c area and on the adjacent populations where possible. Each fish was measured for total length (to the 0.5 cm below), total body weight to the nearest 1 g and assessed for sex and maturity. Samples processed by Marine Scotland Science were maturity staged using the 9-point scale, those processed by the Irish Marine Institute were maturity staged using the 8-point scale, and samples processed by the Wageningen University and Research on behalf of the Dutch Pelagic Freezer Trawler Association were maturity staged using the ICES 6-point scale . All matet al. . A 0.5 c. 2.2The SNPs used in the current study  were identified during the GENSINC project  and were derived from the analyses of WGS of pooled samples from herring populations across the species distribution, which was undertaken to study the biological significance of the genetic variants underlying ecological adaptation in the Atlantic herring ,35,38,39. 2.3et al. [http://eu.idtdna.com/calc/analyzer).The majority of samples were genotyped using a genotyping by sequencing approach  describeet al. . In shoret al.  in Geneiet al.  with optet al. . A set oet al. [Amplification and barcoding reactions were carried out using a two-step PCR as described in Farrell et al. ,41. In set al. . Reads wet al.  based onSNP genotyping was automated by using a modified Perl script from the genotyping-in-thousands by sequencing (GT-seq) approach , which cIdentiSNP genotyping assay chemistry, which uses target-specific primers and universal hydrolysis probes. Following an endpoint PCR reaction, different genotypes were detected using a fluorescence reader. Concordance between the two genotyping methods was confirmed by genotyping a subset of samples from each of the target populations (n = 24 per population) and confirming that the same genotypes were called with each method (data not shown).Genotyping of the majority of samples collected from quarter three 2019 to 2021 was undertaken by a commercial provider; IdentiGEN, Dublin, Ireland, using their proprietary . 2.4It should be noted that the aim of the current study was not to undertake an exhaustive population genetics and demographic study of the herring populations around Ireland and Britain but was to develop a genetic-based method to separate the herring caught in putatively mixed survey and commercial catches in ICES Divisions 6.a, 7.b–c into their population of origin. The analytical approaches followed were tailored to this specific task. The limited number of genetic markers used in the current study were high graded to maximize the power of discrimination between the core Division 6.a populations and in some instances comprised multiple SNPs from a small number of loci. Therefore, the dataset may not be suitable for conventional population genetic analyses and as such some of the analyses presented (e.g. estimation of fixation indices) were for exploratory purposes only.full baseline dataset (see Results) were tested with Genepop 4.2 using default settings [STF with 1000 bootstrap replications and 10 000 permutations. In all cases with multiple tests, significance levels were adjusted using the sequential Bonferroni technique [STF results and to explore the relationships between the different samples, principal coordinate analysis (PCoA) using the covariance standardized method was conducted in GenAlEx 6.51b2 [Deviations from HWE, linkage disequilibrium (LD) and excess and deficiency of heterozygotes in the settings . Microsasettings  was usedechnique . In ordex 6.51b2 .adegenet [6aS, Celtic Sea, Irish Sea, 6aN_Aut, 6aN_Sp, North Sea, Downs) which represented the putative populations in the study area. Following further analyses (see Results), a reduced 6a baseline dataset consisting only of samples from groups that are confirmed as being present in Division 6.a, i.e. 6aS, 6aN_Aut and 6aN_Sp, was analysed using DAPC. In this instance, DAPC was conducted as before with prior definition of group membership (Approach 1) and also following a second approach (Approach 2) using the find.clusters function to infer genetic clusters. This function transforms the data using PCA, then runs the K-means algorithm (function kmeans from the stats package) with increasing values of K and computes Bayesian information criterion to assess the best supported model.Discriminant analysis of principal components (DAPC), from the R package adegenet ,52, is a. 2.5assignPOP [assignPOP uses Monte Carlo cross-validation (assign.MC) to divide the baseline data into a training dataset and test dataset. The assignment model is developed with the training dataset and subsequently tested with the independent test dataset, which avoids introducing ‘high-grading bias’ , which involves randomly dividing the individuals from each population into K groups and then using one group from each population as test individuals and the remaining K − 1 groups as the training individuals. Assignment tests are performed until every group and hence individual is tested, resulting in K tests. assignPOP has a number of classification model options including the SVM model from the R package e1071 [assignPOP trained on the 6a baseline dataset as the constituent populations had the highest level of discrimination between them with the current marker panel. As per the DAPC analyses, the assignment model was developed using two different approaches with, in this case, each approach conducted at two hierarchical levels.The R package ssignPOP , which pas’ (see ). As thege e1071 . Based oApproach 1 used the 6a baseline dataset with the predefined 6aS, 6aN_Aut and 6aN_Sp population groups. Approach 2 was initially independent of the assumptions of prior populations and instead used the output of the K-means clustering analyses of the 6a baseline dataset to identify different baseline assignment clusters. In the cases where multiple clusters represented a single assumed population of origin, these clusters were combined. The assignment approaches were conducted at two hierarchical levels based on the power to discriminate the different groups in the DAPC analyses. In Level 1, the winter and spring spawning herring (6aS and 6aN_Sp) were combined and tested against the autumn spawning herring (6aN_Aut). In Level 2, the combined winter and spring spawning group was split, and the individual groups tested against each other.xvalDapc function in adegenet. Exploratory analyses were conducted in assignPOP to determine the optimum model and kernel for the assignment model, and the tune, tune.control and best.svm functions in R package e1071 [K-fold cross-validation to estimate membership probability. In order to avoid unbalanced sample sizes among the baseline groups, the number of individuals in the training sets was specified and was limited by the number of individuals in the smallest group. Level 1 assignments were tested with 200, 400, 600 and 800 individuals in the training set, while the Approach 1-Level 2 assignment was tested with 50 and 75 individuals and Approach 2-Level 2 assignment with 100, 150 and 200 individuals. Both Monte Carlo and K-fold cross-validation were performed using 25%, 50%, 75% and 100% of the highest STF loci (loci.sample=‘fst’), and all tests were conducted with 100 iterations.In order to avoid over-fitting the model and to objectively determine the optimum number of PCs to be used in both assignment approaches, DAPC cross-validation was conducted with the ge e1071  were useloci.sample=‘random’) rather than highest STF loci and were run with 20–100% of loci in 10% intervals. All other parameters were the same as the previous runs.An important consideration when developing the assignment model was to determine how many genetic markers were required for accurate assignment using either of the approaches and at either of the levels. This enabled the threshold for missing data of unknown samples to be set with a robust basis without compromising the integrity of the assignments. In order to do this, the Monte Carlo cross-validation analyses were run again with random sampling of loci , were also collected during the study. These non-baseline samples were used to further test the assignment model and also to provide an exploratory analysis of potential mixing of populations within Divisions 6.a, 7.b–c. The samples were assigned as per the WESTHER samples, however. In this instance, the samples were divided by quarter for plotting using . 3. 3.1full baseline dataset. The resulting contemporary full baseline dataset contained 64 samples, comprising a total of 3480 herring . In total, 92 contemporary samples were collected  were retained in a separate . 3.26aN_Sp_18b (10/45 SNPs), 6aS_17d (11/45), 6aS_17e (12/45) and 6aS_19c (6/45). Samples 6aS_17d and 6aS_17e also displayed indications of a significant heterozygote deficiency in 8 and 13 loci, respectively, which was probably the result of some of the 6aS samples containing a mixture of early and later spawning components (see pairwise STF results). Samples 6aN_Sp_18b, 6aS_19c and DWN_18 displayed indications of significant heterozygote excess at 10, 7 and 7 loci, respectively. The significant indications of LD were in keeping with the loci already identified  from Han et al. [full baseline dataset for further analyses.There were no significant patterns of deviation from HWE, heterozygote deficiency or heterozygote excess at the locus level (45 SNPs). At the population level, significant deviations from HWE were observed in samples n et al. . All marSTF  indicated proportionately higher STFs and significant differentiation between samples collected from the different putative populations except between the 6aN_Aut and North Sea populations, which displayed little if any significant genetic differentiation among or between the temporal samples. There was no significant differentiation between the temporal samples from the Irish Sea or Downs population, and there was little if any significant genetic differentiation between the temporal samples from the Celtic Sea and 6aN_Sp. There were some indications of differentiation between the temporal samples from 6aS, with some of the samples collected in January and February  showing a low level of differentiation from the other samples. The PCoA of the pairwise STF results enabled a clearer interpretation and illustrated the clustering of samples within and between putative populations , which represented the putative populations in the study area. The DAPC and MSA analyses were run again on the pooled samples. Pairwise multi-locus STF analyses of the pooled full baseline dataset indicated significant differentiation between all baseline population groups (6aN_Aut and North Sea groups. The level of differentiation between these groups (STF = 0.016) was lower than the average differentiation (0.032) between all of the samples within the 6aN_Aut pool . The highest level of differentiation was between the 6aN_Sp group and the other groups. There was also a very low level of differentiation between the Downs group and the Celtic Sea and Irish Sea groups, while the Downs group had a high level of differentiation from the North Sea group.The DAPC results supported the previous indications of temporal stability within each of the putative population areas with samples from the same putative populations clustering together . Therefore, the temporal samples were combined to form seven groups  and also as those observed in Han et al. [6aS and the 6aN_Aut groups, though some outliers were evident. The 6aS and 6aN_Sp groups were discriminated primarily on the secondary axis (18%). These groups partially overlapped, indicating a lower potential to accurately discriminate between them. The 6aS, Celtic Sea and Irish Sea groups overlapped, indicating that the current marker panel cannot be used to distinguish these groups with a high level of accuracy. Therefore, the Celtic Sea and Irish Sea groups were removed from the baseline data and excluded from further analyses. There is no evidence of significant numbers of herring from these groups being present in Division 6.a [6aN_Aut and North Sea groups. There is currently no evidence to support the assertion that the North Sea autumn spawning herring comprise a different population to the 6aN_Aut herring [North Sea samples were removed from further analyses. The Downs group was confirmed to be distinct from the North Sea group though it could not be reliably discriminated from the Celtic Sea and Irish Sea groups with the current panel of markers and, as such, the Downs group was removed from further analyses. The resulting reduced 6a baseline dataset consisted only of the 43 samples from populations that are confirmed as being present in Division 6.a i.e. 6aS, 6aN_Aut and 6aN_Sp .The DAPC results indicated the same pattern of structure as the analyses a and alsn et al.  based onry axis 1%. These ry axis 1%. These 6a baseline dataset indicated that six clusters were the optimum number to provide the most accurate division of the samples based on their assumed population of origin . DAPC of the clustered 6a baseline dataset indicated clear division between the clusters with minimal overlap (c), suggesting that an SVM model-based assignment using this approach would have a high accuracy. The majority of the 6aN_Aut individuals were represented by the combined Clusters_1 + 3 + 5, and the majority of the 6aS individuals by the combined Clusters_4 + 6 . This was particularly notable for the 6aS/6aN_Sp group in Level 1, where there was a significant drop in assignment accuracy and an increase in the number of outliers below 50% of loci. This indicated that at least 23 of the 45 loci were required for accurate assignment at this level. Ideally over 60% (27 loci) should be genotyped at this level to ensure assignment accuracy. The Approach 1-Level 2 assignment was not very sensitive to the number of loci, and there was little difference in the accuracy of assignment down to 20% of the loci. The Approach 2-Level 1 assignments had a similar pattern of sensitivity to the number of loci as the Approach 1 assignments indicating that a minimum of 60% of loci should be genotyped at this level to ensure assignment accuracy. The Approach 2-Level 2 assignments had a higher level of accuracy than the Approach 1-Level 2 assignments and again were less sensitive to the number of markers than the Level 1 assignments.The . 3.4Level 1 and Level 2 assignments according to the assignment decision table (6aN_Aut samples from 2003 (6aN_03) and 2004 (6aN_04) displayed near-perfect assignment to the 6aN_Aut groups, with only one individual in each of the years being misassigned to the 6aS groups (6aS WESTHER sample (6aS_03) was more uncertain, with a quarter of individuals misassigned to the 6aN_Aut groups . The 2004 6aS sample (6aS_04) had a higher level of correct assignment (greater than 80%). The assignment of the 2004 6aN_Sp (6aN_Sp_04) sample indicated perfect assignment, at Level 1 in both approaches, to the 6aS/6aN_Sp groups. As expected, the Approach 1-Level 2 assignment displayed a high rate of mis-assignment (53%) to the 6aS group and a high rate of below-threshold individuals (37%), which could not be confidently split below the level of 6aS/6aN_Sp. The Approach 2-Level 2 assignment provided a more robust assignment with 67% of individuals assigned to A2L26aS/6aN_Sp. It should be noted that the 6aN_Sp_04 WESTHER sample did not fulfil the criteria of baseline samples as defined in the current study, as 27% of the individuals were maturity stage 2 individuals .The final assignment call of each individual in the archive WESTHER samples was based on a combination of the on table . The assApproach 1-Level 1 and 2 but only for Level 1 in Approach 2 . The assignment probabilities for the 2003 6aS individuals were more variable at Level 1 for Approach 1 and 2, highlighting a degree of uncertainty around the assignment of some of the individuals; however, the Level 2 assignments all had a probability greater than 0.9 . The 2004 6aS individuals also showed some uncertainty at Level 1 and at Approach 1-Level 2. The Approach 2-Level 2 assignments all had a probability greater than 0.9. The individuals of the 6aN_Sp_04 sample had the highest proportion of unassigned individuals at the higher thresholds for Approach 1-Level 2. The Approach 2-Level 2 assignments all had a probability greater than 0.9.The average proportion of unassigned individuals in the WESTHER samples increased with increasing assignment threshold for proach 2 . Analysi. 3.56aN_Sp_19a and 6aN_Sp_19b) were dominated by resting (Stage 5) individuals, and the assignments indicated a significant proportion of 6aN_Aut individuals, though assignments to both the 6aS and 6aS/6aN_Sp groups were also evident . One haul to the north of the Hebrides (6aN_Sp_19d) comprised primarily maturing (Stage 2) and spawning (Stage 3) herring that assigned to the 6aS and 6aS/6aN_Sp groups. In the south Minch area, there was a significant proportion of immature juvenile (Stage 1) individuals in one sample (6aN_Sp_19 h), which assigned primarily to 6aS. The other two hauls in this area (6aN_Sp_19f and 6aN_Sp_19 g) also had the majority of assignments to the 6aS/6aN_Sp groups.The quarter 1 non-baseline samples from Division 6.a.N came primarily from the Scottish West Coast International Bottom Trawl Survey and comprised a number of small samples of herring of mixed maturity stages (6aS_19b), one from Lough Swilly (6aS_19a) and two from Galway Bay (6aS_18b and 6aS_18c)  fish. The assignment of both samples was quite similar with the majority of individuals assigned to 6aS and most of the remaining samples to 6aS/6aN_Sp. The Galway Bay samples were caught later in quarter 1 and 2 and had similar maturity staging with primarily Stage 4 and Stage 5 individuals. The assignments of the two samples were also quite consistent with each other, with the majority of individuals assigned to 6aS.The quarter 1 and 2 samples from Irish coastal waters were collected from by-catch of commercial vessels and comprised four samples, one sample from Lough Foyle  primarily comprised Stage 1 individuals, which assigned mainly to 6aS and 6aS/6aN_Sp. The two samples from northwest of Cape Wrath (6aN_18b and 6aN_19e) were composed of mainly Stage 2 individuals and comprised a mix of 6aN_Aut, 6aS and 6aS/6aN_Sp. The two samples from July (6aN_19f and 6aN_19 g), caught west of the Hebrides and north of Scotland, had similar maturities with mainly Stage 2 individuals and a smaller proportion of Stage 5 fish. The majority of individuals in the west of Hebrides sample were assigned to 6aS. Conversely, the north of Scotland sample, which was caught adjacent to the 4° W line of longitude, contained a significant proportion of 6aN_Aut fish in addition to significant proportions of 6aS and 6aS/6aN_Sp individuals.The quarter 3 non-baseline samples all came from Division 6.a.N and comprised samples from acoustic survey and monitoring fishery catches see ,55,56). ,56. 55,5Stage 1 to Stage 4, yet the assignments were relatively consistent across all of the samples . The Division 7.b sample (6aS_19d) contained Stage 1 and Stage 2 fish and the assignments followed the same pattern as the Division 6.a.S samples. The three Lough Foyle samples were collected in three different years and had markedly different maturity stages, with one sample (6aS_17f) dominated by Stage 3 fish, one sample (6aS_18e) predominately Stage 2 and the third sample (6aS_20b) a mixture of Stages 1–4 fish .The quarter 4 non-baseline samples all came from Divisions 6.a.S, 7.b and Lough Foyle and comprised samples from monitoring fishery catches and by-catch. The nine samples, caught over 5 years, contained a wide range of maturity stages, from Approach 1-Level 1 and in particular for Approach 1-Level 2  showed differences between the individual samples. The non-baseline quarter 1 samples from Division 6.a.N displayed the highest level of unassigned individuals, particularly at the Approach 1-Level 2 assignment. The Approach 2 assignment of the majority of samples resulted in a lower incidence of unassigned individuals at all threshold levels.Similar to the WESTHER samples, the average proportion of unassigned individuals increased significantly with increasing assignment threshold for -Level 2 . This wa. 4The genetic markers and assignment methods presented in the current study constitute a ‘tool box’ that can be used for the assignment of herring caught in mixed survey and commercial catches in Division 6.a into their population of origin with a high level of accuracy (greater than 90%). This will enable the population assignment of commercial catch and acoustic survey (e.g. MSHAS) samples, which will facilitate the development of separate stock assessments for the populations in this area.Level 1 than the groups in Level 2. In Approach 1-Level 2, there was a weakness in the model in discriminating between the 6aN_Sp samples and the quarter 1 late-spawning 6aS samples. This was due in part to the small number of samples of 6aN_Sp in particular and potentially also to the genetic markers used not being optimized for distinguishing between these groups. There are inherent difficulties in sampling these groups for which no specific fishery currently exists and which spawn in areas that are subject to unfavourable weather conditions for sampling at the time of spawning. Further sampling of both groups is required though, and efforts are also under way to conduct WGS on representative samples from these groups to identify more informative markers. In any case, Approach 2 mitigated this issue by combining them into a single group (A2L2 (Cluster_2)6aS/6aN_Sp) for assignment purposes, which resulted in higher classification success and lower rate of unassigned individuals. In this case, the majority of 6aS fish could be separated from the other two populations with a high level of accuracy and only the minority of 6aS fish were left in an unsorted mix with 6aN_Sp. This is considered preferable to the outcome of Approach 1, as for the purposes of splitting catch and survey samples, it is better to be able to assign some level of grouping rather than have a high abundance of unassigned individuals. Mixed categories such as A2L26aS/6aN_Sp can be acknowledged in the overall abundance estimates but retained in a separate category that is not allocated to a single stock, which can also act as a precautionary buffer for any undetected mis-assignments. In each assignment approach, at least 60% of the 45 markers were required to ensure accurate self-assignment, which indicates that there is a level of redundancy built into the panel of markers as was expected given that the markers are distributed among 14 loci. This redundancy is an advantage when analysing unknown samples as it allows up to 40% missing data in the genotypes of individuals. Missing genotypes may occur when analysing suboptimal quality samples collected from commercial catches or when analysing older archive samples, which have not been stored under optimum conditions.Both assignment approaches had a high level of self-assignment accuracy, though it was notable that there was a higher power to discriminate between the groups in 6aN_Aut WESTHER samples assigned near perfectly to the 6aN_Aut groups, the assignments of the 2003 and 2004 6aS WESTHER samples were not as confident. There were a significant number of mis-assignments to 6aN_Aut groups, particularly in the 2003 6aS sample. This spawning sample was collected in October which is earlier than any of the contemporary samples in the 6a baseline dataset, and it is possible that these misassigned individuals shared some genetic similarities related to spawning time with the 6aN_Aut autumn spawning herring. Historically, autumn spawning herring were abundant in Division 6.a.S and particularly in Division 7.b where they supported local fisheries ; howeve6aN_Aut though this was mostly in keeping with the expected error rate of the assignment model, which was higher for 6aS and 6aS/6aN_Sp than for 6aN_Aut. The samples from Lough Foyle were shown to be genetically and biologically the same as the 6aS samples underlining the inappropriateness of the existing classification of Lough Foyle as part of the 6aN_Aut stock.The assignment of the non-baseline samples also provided an additional layer of validation of the assignment model and an interesting exploratory analysis of potential mixing of the different populations in Division 6.a. The Division 6.a.S and 7.b sample assignments were relatively consistent across all quarters indicating stability in the composition of herring shoals in the area. In all samples, a minority proportion of individuals were assigned to 6aS and mixed 6aS/6aN_Sp herring comprised a varying but significant proportion of all samples and were far in excess of the expected error rate. The assignment of the juvenile samples from the Minch primarily to 6aS, 6aN_Sp, 6aS/6aN_Sp instead of 6aN_Aut was not unexpected given the existing knowledge about the larval drift in the area and the lack of differentiation between the 6aN_Aut and North Sea autumn spawning herring. It is well documented that the larvae of autumn spawning herring off the northwest of Scotland are carried in easterly flowing currents and spend their juvenile phase in the North Sea [6aN_Aut herring indicate a need for ongoing monitoring of survey and commercial catches in this area as any future fisheries in this area will probably be mixed stock fisheries. The presence of 6aN_Sp herring in the samples was also of interest as this population used to be the dominant population in the region but was reported to have collapsed in the 1950s [6aS herring so no conclusions can be drawn about their relative abundance, but further efforts should be directed towards improving the sampling of this population. The two samples collected in July west of the Hebrides and North of Scotland also offer some insight into the future assignments of the MSHAS samples that are collected during this period. The west of Hebrides sample comprised primarily 6aS individuals with a smaller proportion of 6aS/6aN_Sp fish  indicated a significant degree of mixing of different populations. orth Sea –61. The he 1950s . Despitehe 1950s . However_Sp fish , while t6aS, 6aN_Aut and 6aN_Sp) and does not include adjacent populations. The initial genetic analyses of the full baseline dataset in the current study and those in Han et al. [One weakness of the assignment model in the current study is that it is solely based on the populations empirically proven to occur within Division 6.a . Theref6aN_Aut herring and the North Sea autumn spawning herring suggests that the 4° W line of longitude is also inappropriate as a stock delineator. Though this has been recognized since its inception, as Saville & Bailey [the dividing line between VIa and the North Sea (sub-area IV) at 4° W longitude was not chosen on any criterion of herring stock differentiation but for convenience in statistics collection’. Further, the current study has demonstrated that 6aS herring may be found up to at least the 4°W line of longitude and Farrell et al. [The current study has also highlighted some of the potential stock identification issues that are apparent with the North Sea herring. The lack of differentiation between the & Bailey  noted, ‘l et al.  demonstrl et al.  and withl et al. . What isl et al. ). TherefassignPOP in the current study was favoured over traditional methods that rely on genotypic frequency distribution, as it was not constrained by underlying assumptions of HWE and linkage equilibrium. Further, it enabled a transparent and reproducible approach that could be clearly understood by non-geneticists, which is important if genetic stock identification methods are to be accepted and widely implemented as part of standard fisheries data collection protocols. Though not used in the current study, assignPOP also allows for the use of non-genetic markers either as stand-alone assignment models or in combined models with genetic data. This option may be attractive to institutes that have long time series of morphometric and meristic-based stock identification data as the transition to genetic-based methods can be made easier with direct comparison of the different datasets that are capable of being conducted within the same analyses.Implementation of a universal marker panel would also necessitate the development and implementation of a standard assignment approach across multiple jurisdictions. The use of the SVM model in The SNP panel deployed in the current study was composed of adaptive markers that are known to be under diversifying selection and proven to be associated with local ecological adaptation ,35. GeneSalmo salar [Oncorhynchus kisutch [To date, there are few examples of genetic stock assignment being used for the regular assignment of survey or catch data of marine fish into population of origin for the purposes of stock assessment. These methods have primarily been used for one-off studies, that at best have been used to inform management, but few have been developed for regular monitoring and data collection ,19. Genemo salar  and spec kisutch . In thes6aS herring. The apparent lack of differentiation between the 6aN_Aut herring and the North Sea autumn spawning herring also raises the question of whether it is appropriate to conduct a stand-alone assessment on the 6aN_Aut herring or whether it should be combined with the North Sea autumn spawning herring assessment, though it is beyond the remit of the current study to make this recommendation. What is clear is that the results of the current study have improved the capacity to delineate, survey and assess the herring stocks in Division 6.a, and there is a need now to translate this into improved management.If implemented as part of regular data collection on the MSHAS, the genetic stock identification method in the current study will enable the splitting of the survey indices into their constituent Division 6.a populations, which has not previously been possible. As a result, it will be possible to develop a separate stock assessment for the Division 6.a.S, 7.b–c stock. Although it should be noted that as there were no spawning herring observed or sampled in Divisions 7.b and 7.c, it was not possible to test the assumption that the herring that spawn in these divisions are the same population as the


Acanthophlebia cruentata of Aotearoa, New Zealand, is widespread in streams of North Island but absent from the cooler South Island. Mayfly nymphs are restricted to streams but adults fly, facilitating gene flow among catchments. We detected higher genetic diversity at lower latitudes of this mayfly’s range compared to most of its distribution, concordant with predictions of limited forest cover in New Zealand during Pleistocene glacial periods. A signature of recent range expansion was observed in the higher latitudes. Despite initial observation suggesting mayfly size correlated with latitude, we found sex, elevation and sampling date were significant predictors of size, and some size variation is also explained by three regional groups based on haplotype distribution.Patterns of variation within a widespread species can provide evidence of population history. Adjacent stable populations with gene flow show clinal genetic divergence forming a pattern of isolation by distance. Populations that have grown due to an increase in potential habitat such as forest cover since the last glacial maximum will have low genetic variation showing patterns of range expansion. The mayfly Acanthophlebia cruentata of Aotearoa, New Zealand, is widespread in Te Ika-a-Māui North Island streams, but has never been collected from South Island despite land connection during the last glacial maximum. Population structure of this mayfly might reflect re-colonisation after volcanic eruptions in North Island c1800 years ago, climate cycling or conceal older, cryptic diversity. We collected population samples from 33 locations to estimate levels of population genetic diversity and to document phenotypic variation. Relatively low intraspecific haplotype divergence was recorded among mitochondrial cytb sequences from 492 individuals, but these resolved three geographic-haplotype regions . We detected a signature of isolation by distance at low latitudes (north) but evidence of recent population growth in the west and east. We did not detect an effect of volcanic eruptions but infer range expansion into higher latitudes from a common ancestor during the last glacial period. As judged from wing length, both sexes of adult mayflies were larger at higher elevation and we found that haplotype region was also a significant predictor of Acanthophlebia cruentata size. This suggests that our mitochondrial marker is concordant with nuclear genetic differences that might be explained by founder effect during range expansion.The mayfly  Aotearoa, New Zealand, hosts a rich freshwater invertebrate fauna with high levels of endemism . The disAcanthophlebia cruentata [Aprionyx in Africa, Hapsiphlebia in South America, Atalophlebia in Australia and Papposa in New Caledonia [Acanthophlebia [A. cruentata is abundant and occurs widely [A. cruentata, but he noted it was to be found “commonly in all the streams in the vicinity of Wellington”. It is present on offshore islands including Gt Barrier, Little Barrier and Kapiti but has never been recorded in South Island. The nymphs mostly inhabit moderate speed, hard bottomed streams with abundant overhead vegetation, and upstream native forest cover is important in determining the relative abundance of nymphs [The New Zealand mayflies (Ephemeroptera) comprise 57 described species, all of which are found only within the archipelago and include three endemic families . Of thesruentata , a monotaledonia , but a mophlebia . The evos widely ,13, beins widely ). Hudsons widely  did not f nymphs .A. cruentata is suggestive of a specialised ecology, and the short-lived adults with vestigial mouthparts [A. cruentata throughout North Island required dispersal and colonisation capacity as southern North Island is geologically young [Coloburiscus humeralis) indicates this opportunity for range expansion was realised by many species.The lack of near relatives of uthparts  generalluthparts , yet thely young . Howeverly young . In contly young  and insely young ,22, and A. cruentata might also have been influenced by processes that altered stream physicochemistry. Indeed, reduced abundance and extirpation in streams modified by anthropogenic pasture development supports this inference [A. cruentata range and diversity [Clitarchus hookeri that displays the classic pattern of geographic parthenogenesis [Acanthophlebia and forest streams today (cf. streams in open pasture situations) applied during the LGM  were used for whole genomic DNA extraction using a salting-out method . Tissue A. cruentata [2, 0.5 U Taq), treated to 36 cycles of 94 °C for 30 s, 54 °C for 30 s, 72 °C for 90 s with an initial denaturation of 94 °C for 2 min and a final extension at 72 °C for 5 min. We also generated sequences for a portion of the mtDNA cytochrome oxidase subunit 1 (cox1) for a subsample of Acanthophlebia cruentata to provide comparative ‘DNA-barcode’ information, using PCR primers LCO1490 and HCO2198 [Mitochondrial DNA sequences were obtained using primers that target a portion of the cytochrome-b gene (cytb) homologous with published data for ruentata . Polymerruentata  in 10 or HCO2198 .maps package [DNA sequences were checked for ambiguities, translated and aligned in Geneious . We iden package  and evol package  implemen package . During  package . Howeveradegenet [We used spatial Principal Component Analysis , implemeadegenet  to visuaadegenet  for AMOVRange expansion is expected to leave a signature of increasingly divergent allelic frequencies and reduced genetic variation at the leading edge ,49. SoutRange expansion is usually associated with population growth and therefore we predicted a signature of southward demographic expansion if this mayfly species was restricted to higher latitudes in Northland during the LGM. We calculated neutrality statistics and population size changes for the entire intraspecific sample and for regional subsets . We looked for evidence of demographic expansion and population size constancy considering the frequency of pairwise genetic distances among haplotypes ,52.ST [ST with linear distances between sampling sites, obtained by triangulation of New Zealand Map Grid coordinates, and used a mantel test implemented in R with the package vegan [Genetic structure resulting from gene flow rather than range expansion would leave a signature of isolation by distance (IBD). To estimate population differentiation we calculated population sample pairwise ΦST  with Arlge vegan  to deterAcanthophlebia cruentata from 33 sites across the species range to document intraspecific phenotypic variation, using forewing length as a proxy for individual size [We compared 1064 adult ual size . A singlual size . Samplinggplot2 [lme4 [lmerTest [Analysis used R with the package ggplot2  to plot t2 [lme4  and lmerlmerTest . Wing lelmerTest  so the cAcanthophlebia cruentata individuals, from 33 population samples spanning the known range from North Cape to Wellington, North Island Aotearoa–New Zealand  had individuals with a haplotype very similar to the common haplotype , Coromandel Peninsular  and Waikato . More than 50% of all specimens from which we obtained cytb data (254/492) had one of two common haplotypes (#16 and #27), while more than three quarters of all haplotypes (85.7%) were recorded in just one or two individuals . One or Interpolation of the spatial distribution of the cytb genetic variation using two global sPCA axes revealed three regions with distinct genetic diversity: north, west and east . An imagGenetic diversity as measured by the number of mtDNA haplotypes per individual within population samples (h) was generally higher in the north and northwest. For example, samples of 12 individuals from Whangarei (WG) and Waikato (TA) each had seven or eight haplotypes, while some similar sized samples from the east had a single haplotype  was lowest at the southern limit of this mayfly’s range . Populatp < 0.05, Fu & Li’s D* −7.18167 sig p < 0.02, Fu & Lis F* −5.624 sig p < 0.02, Fu’s Fs −98.926), which, under the assumption of functional neutrality of the marker, is a signature of rapid recent population increase. However, when subdivided into regional population sample groups  whereas the West  and East  were  were . ViewingST) also indicated regional-specific processes. A significant correlation between geographic and genetic distances was found when all population samples were analysed (p = < 10−6) but with a mediocre coefficient (r = 0.4146). However, we found strong statistical support (p = 0.00004) and a higher correlation coefficient (r = 0.7164) within the northern population samples  or no signal of IBD . In combination with the demographic analyses we infer longer standing population structure in the north, but recent, rapid population expansion elsewhere that is consistent with mismatch distributions.Analysis seeking evidence of isolation by distance (IBD) using pairwise population sample differentiation , Taupo (OH), East Cape , Manawatu-Wellington  and Northland  provided DNA sequence from a fragment at the 5′-end of cox1. An alignment of 585 bp contained 27 variable sites and we identified 34 distinct haplotypes (GenBank ON442277–ON442310). Among those haplotypes the average Jukes-Cantor distance was 0.00728 (0.73%) and the maximum was 0.014 (1.4%).Acanthophlebia cruentata mitochondrial haplotypes was low . Applying substitution rates inferred for insect intraspecific mtDNA molecular evolution of 0.0285–0.0792 substitutions per site per million years [A. cruentata existed approximately 38,000–50,000 years ago. Though by no means a robust time calibration, this coincides with the last glacial maximum [Mean DNA sequence divergence within on years ,61 to th maximum  and is c maximum .n = 533) compared to females  but different mean sizes across the species range  from 33 sites were measured and analysed taking into account latitude, elevation, instar and time of sampling, and lineages defined from cytb sequence data . Densityes range . Data frAfter stepwise removal of non-significant effects the final model included the random location effect (55% of variance), quadratic elevation, and haplotype region . Male wiThe results of model fitting and simplification  were simAcanthophlebia might have contained cryptic taxa reflective of a long history of diversification in New Zealand, but we did not find that. Instead we found low genetic divergence within Acanthophlebia cruentata sampled throughout its range in North Island, New Zealand; from North Cape in the Far North to Wellington in the south, Taranaki in the west and East Cape. Within the large sample of A. cruentata mean sequence divergence was less than 1% , which is at the lower end of the intraspecific range (1.2–1.4%) reported for New Zealand mayflies, stoneflies and caddisflies [Arachnocolous phillipsi) to 3.9% (Zephlebia versicolor). This contrasts with much higher sequence divergence within a number of endemic terrestrial insects in the same landscape; average mtDNA distances of ~8% in the wētā Hemideina crassidens [Hemiandrus pallitarsus [The monotypic genus disflies . Other Ndisflies ), but unassidens  and Hemilitarsus  suggest Acanthophlebia cruentata but a signature of population expansion in the south. Despite low divergence among the cytb sequences the haplotype diversity revealed a pattern of regionally-restricted variants delimitating three sections of the North Island. Population samples north of Whangaparāoa contained region-exclusive haplotypes and relatively high nucleotide diversity per location. In contrast, to the south, genetic diversity was lower and dominated by two common haplotypes. Here, population samples fell into east and west groups, each comprising a common haplotype and its derivatives. This pattern of haplotype distribution is typical of recent range expansion scenarios. Mayflies are habitat sensitive aquatic stream invertebrates whose populations are likely to be responsive to geophysical processes that abruptly alter local conditions ) and so Acanthophlebia cruentata reflects changing habitat opportunities during Pleistocene climate cycling, and we found that the level of genetic diversity indicated a common ancestor about 38,000–50,000 years ago. This suggests climate during the last (Otiran) ice age  reduced population size of this mayfly, and the impact may date to the coldest phases associated with ice advances since ~65,000 years ago [Overall we found a pattern of population expansion ,68 in ceears ago . All putears ago . SimilarHemideina thoracica with a concordant decline in nucleotide diversity (π) southwards [Clitarchus hookeri [Lasicrhynchus barbicornis [Kikihia cutora but three mtDNA clades were identified with distributions that closely match the north, west, east locations of the genetic groups within A. cruentata [Acanthophlebia cruentata than these other endemic insects. Given the possibility that the time of population expansion for these different species was not concordant we cannot conclude that the processes involved were the same .Acanthophlebia cruentata, with smaller wings observed from warmer streams, but after accounting for sex and elevation, our models found no support for an effect of latitude. Adult size, measured here as forewing length, is greater in females and they also show stronger responses to other predictors of wing length than males. Wing length was greater at higher elevation as expected from the thermal equilibria hypothesis [Acanthophlebia wing length even in competition with strong proxies for temperature and after accounting for sampling location effects. This significant effect of haplotype region in our data, hinting at a genetic difference between Acanthophlebia cruentata in the north, west and east, might be explained by founder effect on functional genes [Intraspecific size variation in stream insects generally correlates with sex and water temperature associated with latitude and elevation, with smaller winged, small bodied adults (of each sex) at warmer (northern) latitudes and lower elevation ,74. Thispothesis , but uneal genes . Acanthophlebia cruentata has, in the context of endemic insects of Aotearoa–New Zealand, shallow lineage diversity. Nevertheless, we are able to identify from the distribution of genetic variation a spatial pattern of late Pleistocene age. We find signal for two, parallel, range expansions within the mtDNA data and an indication that adult mayfly size is linked to distinct, though shallow, regional haplotype clusters. Changing habitat availability associated with Pleistocene climate cycling provides the parsimonious explanation for this shallow population history but it is plausible that the Acanthophlebia lineage itself has a shallow ancestry in Aotearoa–New Zealand. The mayfly


Chrysanthemoides monilifera ssp. rotundata (bitou bush) from South Africa, into eastern Australia (EAU), and Western Australia (WAU). We determined genetic diversity and population structure across the native and introduced ranges and compared hypothesized invasion scenarios using Bayesian modeling. We detected considerable genetic structure in the native range, as well as differentiation between populations in the native and introduced range. Phylogenetic analysis showed the introduced samples to be most closely related to the southern‐most native populations, although Bayesian analysis inferred introduction from a ghost population. We detected strong genetic bottlenecks during the founding of both the EAU and WAU populations. It is likely that the WAU population was introduced from EAU, possibly involving an unsampled ghost population. The number of private alleles and polymorphic SNPs successively decreased from South Africa to EAU to WAU, although heterozygosity remained high. That bitou bush remains an invasion threat in EAU, despite reduced genetic diversity, provides a cautionary biosecurity message regarding the risk of introduction of potentially invasive species via shipping routes.Many plants exchanged in the global redistribution of species in the last 200 years, particularly between South Africa and Australia, have become threatening invasive species in their introduced range. Refining our understanding of the genetic diversity and population structure of native and alien populations, introduction pathways, propagule pressure, naturalization, and initial spread, can transform the effectiveness of management and prevention of further introductions. We used 20,221 single nucleotide polymorphisms to reconstruct the invasion of a coastal shrub,  Bitou bush is a coastal shrub, native to Southern Africa, and invasive in Australia. We used single nucleotide polymorphisms to reconstruct the invasion history of bitou bush into eastern and Western Australia. We found strong genetic structure in the native range, and detected bottlenecks through the introduction process. Our results highlight the risk of introduction of potentially invasive species via shipping routes, even from a small number of propagules. The risA critical component of improving management is understanding early invasion stages, including introduction pathways, naturalization, and initial spread  revealed a stepwise invasion history, starting with an early introduction into Western Europe from two source populations, which then served as a genetic bridgehead for invasions into Chile and then California, followed by evolution of increased plant size in the Californian population  T.Norl. (bitou bush). Bitou bush is a shrub native to coastal subtropical regions in the Eastern Cape and KwaZulu Natal provinces of South Africa. The plant was first recorded in Australia in 1908 near the port city of Newcastle, New South Wales (NSW), hypothesized to have been inadvertently introduced in dry ship's ballast  have been introduced into Australia  from South Africa  were sampled during 2017–2018 and used as outgroups in genetic analyses. All leaf material was desiccated immediately after sampling with silica gel beads  with minor modifications for semi‐succulent leaves .A modified version of the ddRADseq protocol  were calculated using stackspopulations. Heterozygosity was calculated using vcftools.SNPs identified in the bitou‐pisifera and bitou‐pisifera‐boneseed groups were filtered using 2.5raxml 8.2.11  using the default simple prior and convergence criterion. We selected the optimal value of K using faststructurechooseK and the method of Puechmaille  native range south , (2) native range central , (3) native range north , and two from Australia: (4) eastern Australia  and (5) Western Australia  axes. It was not necessary to use a held out set of test summary statistics, because we used an ABC random forest approach implemented in the R package abcrf 1.8 , were selected to be tested in a final analysis of the invasion of EAU. The best scenario for the introduction into EAU was used in all further analyses, which included the WAU population. Two preliminary analyses conducted as above identified the best scenarios for the origin of the WAU population considering either an invasion out of South Africa, or an invasion involving the EAU population. The most highly supported scenarios were then selected as above and compared in a final analysis. For the best scenario identified in this final analysis of all populations, 1 million additional simulated datasets were generated with diyabc to use for model checking and parameter estimation. Model checking was conducted with diyabc using 10,000 sets of summary statistics (1%) that were compared with the observed values. To prevent overfitting, an additional model checking analysis was carried out using the set of summary statistics that had not been used for the additional scenario selection step for the final scenario. The posterior distributions of the parameters were estimated by using local linear regression on the 1% of the simulated data closest to our observed data set, with a logit transformation of the parameter values.We used the software DIYABC 2.1.0  compared to a free model that optimally fits the data.We used two methods to estimate the ploidy‐level and genome size of bitou bush. Sample ploidy was inferred based on allele frequencies at biallelic sites using Solanum lycopersicum L. cultivar “Stupické polní rané”  and together  with a razor blade in 400 μl of chilled LB‐01 extraction buffer  and 22.3 × (SD = 9.5 ×) for the bitou‐pisifera dataset. After filtering, the number of samples was reduced to 138 and the number of SNPs to 20,221 (average MAF = 0.192).3.2faststructure analysis of the entire bitou‐boneseed‐pisifera dataset reached an optimal marginal likelihood based on chooseK and the method of Puechmaille  and EAU (ФST = 0.189) populations. The WAU population maintained a high level of population differentiation from EAU (ФST = 0.131), indicating limited, if any, gene flow between populations. The genetic structuring between South Africa and Australia, in addition to the structuring within Australia, was supported by fine scale population ФST comparisons  and EAU (FIS = −0.023) populations, with a slight excess of heterozygotes over the expected number in both regions. Increased inbreeding was inferred in the South African populations (FIS = 0.278), likely because we are pooling multiple genetically differentiated populations in this calculation. The reduction of private alleles in the Australian populations relative to the South African populations provides evidence of a genetic bottleneck occurring, although most measures of genetic diversity have not been affected.The South African populations collectively contained the highest number of private alleles, with large reductions of private alleles observed in the Australian populations Table . Of the 3.4faststructure analysis. The source of the EAU population was once again inferred as a ghost population  and phylogenetic analyses. Within South Africa, we detected three distinct genetic clusters in bitou bush of northern, central, and southern populations through faststructure analysis. The same clusters were detected with PCA, but with the northern cluster further subdivided into samples from Durban and samples from the populations further north. Each South African population formed a clade in phylogenetic analysis, with groupings consistent with the faststructure and PCA analyses. Within each of the Australian regions there was little, if any, geographic structure. A lack of genetic structure is likely the result of relatively recent population expansion from a single introduction and/or high ongoing gene flow. Both explanations would be consistent with the intentional planting of material sourced from a limited number of plants for dune stabilization across a broad geographic range  and tetraploid (2C DNA content of 2.97–5.41 pg) species  from the Port of Durban toward the northern end of the native range, to meet climate matching criteria with bitou infested areas of EAU and on the assumption that Durban was the most likely port of origin (Scott unpublished observations). While C. germana is a successful agent, many others have failed ; formal analysis (supporting); investigation (lead); project administration ; writing – original draft (lead); writing – review and editing (supporting). Armin Scheben: Data curation ; formal analysis (lead); methodology (supporting); software (lead); writing – original draft (supporting); writing – review and editing (supporting). John K. Scott: Conceptualization (lead); funding acquisition (supporting); methodology (supporting); supervision (supporting); writing – original draft (supporting); writing – review and editing (supporting). Bruce L. Webber: Conceptualization (supporting); funding acquisition (lead); methodology (supporting); supervision (supporting); writing – original draft (supporting); writing – review and editing (supporting). Kathryn L. Batchelor: Investigation (supporting); project administration (supporting). Anita Severn‐Ellis: Investigation (supporting); methodology (supporting); writing – original draft (supporting). Ben Gooden: Conceptualization (supporting); investigation (supporting); writing – original draft (supporting); writing – review and editing (supporting). Karen Bell: Conceptualization (supporting); data curation (lead); funding acquisition (supporting); investigation (supporting); methodology (lead); project administration (supporting); supervision (lead); writing – original draft (supporting); writing – review and editing (lead).The authors declare no conflict of interest.Appendix S1Click here for additional data file.


Ursus arctos L.) population in Greece, we estimated the census (Nc) and effective (Ne) population size as well as the genetic status of brown bear sub-populations in three National Parks (NP): Prespa (MBPNP), Pindos (PINDNP), and Rhodopi (RMNP). The Prespa and Pindos sub-populations are located in western Greece and the Rhodopi population is located in eastern Greece. We extracted DNA from 472 hair samples and amplified through PCR 10 microsatellite loci. In total, 257 of 472 samples (54.5%) were genotyped for 6–10 microsatellite loci. Genetic analysis revealed that the Ne was 35, 118, and 61 individuals in MBPNP, PINDNP, and RMNP, respectively, while high levels of inbreeding were found in Prespa and Rhodopi but not in Pindos. Moreover, analysis of genetic structure showed that the Pindos population is genetically distinct, whereas Prespa and Rhodopi show mutual overlaps. Finally, we found a notable gene flow from Prespa to Rhodopi (10.19%) and from Rhodopi to Prespa (14.96%). Therefore, targeted actions for the conservation of the bears that live in the abovementioned areas must be undertaken, in order to ensure the species’ viability and to preserve the corridors that allow connectivity between the bear sub-populations in Greece.In order to optimize the appropriate conservation actions for the brown bear ( Ursus arctos, L.) is one of the most widespread large carnivores with a distribution covering [2 of suitable habitat. Animal populations living at the edge of their range, such as brown bears in the Hellenic peninsula, are important biological units as they can provide critical information on the evolutionary processes involved in the adaptation to local environments [The brown bear  has become available for the demographic study of species ,13,14,15(a)The population structure  through the estimation of the census (Nc) and effective (Ne) population size and testing for signatures of past bottlenecks.(b)The genetic variability of the brown bears in Greece and possible differentiation between the eastern and the two western sub-populations,(c)Evidence of possible connectivity and migration of bears between the abovementioned study areas.Based on the above, in this study we focused on the genetic analysis of 10 microsatellite loci using DNA received from hair samples of brown bears living in three regions in Greece: Prespa National Park (MBPNP), Pindos National Park (PINDNP), and Rhodopi National Park (RMNP). The aim of the study was to obtain the following information:The results of the research will indicate the viability status of the surveyed populations and are expected to have a direct impact on the species’ conservation and management policies and actions in the studied areas. Moreover, these data are crucial parameters that will lead to a mid- and long-term conservation planning and will help to establish a balanced coexistence between humans and brown bears in the Prespa, Pindos, and Rhodopi national parks.The samples were collected from 2020 till 2021 from a network of 569 hair traps in total installed in the areas of Prespa (MBPNP) (51), Pindos (PINDNP) 262), and Rhodopi (RMNP) 256) national parks in the frame of the LIFE “ARCPROM” project (LIFE18NAT/GR/00768), with implementation from 2019–2024. Non-invasive genetic sampling was based on the ubiquitous marking and rubbing behaviour of bears on wooden poles of the telephone and electricity network (power poles) in Greece [6 nationa2, and RhEach tuft of hair on a set of barbs was considered a sample. Hair samples were collected without contact to human skin; they were placed in uniquely numbered paper envelopes labelled with the exact location, coordinates, date, and height of collection and then stored at −20 °C in Ziplock bags with silica gel until being analyzed. For all the collected samples, DNA extraction was performed using the DNA Mini kit  following the manufacturer’s instructions.DNA was amplified for 10 commonly used microsatellite loci: G10H, Mu26, G1D, G10X, G1A, G10P, G10C, Mu59, G10L, Mu50, as well as ZFX and the SRY gene ,28,29. G2, and 400 µM of each dNTP), 3 μL DNA, 1 p/μL for each primer and RNase-Free Water. The amplification conditions used were denaturation at 94 °C for 5 min, 40 cycles at 94 °C for 30 s, primer hybridization at 58 °C for 45 s, and elongation at 72 °C for 1 min. The final elongation was performed at 72 °C for 8 min. Thermal cycling was performed using an MJ Research  PTC-200 thermocycler with 96-well “gold” blocks. Because of the low quantity of DNA, the samples were not electrophorized after the DNA extraction process.Given that co-amplification reduced the success rates for hair samples, the microsatellite loci were not coamplified, but we performed separate PCR assays for each microsatellite locus in a 15 μL total volume reaction using 7.5 μL HotStarTaq Master Mix  on a QIAxcel Advanced System , according to the manufacturer’s instructions. To our knowledge it is the first time that QIAxcel is used for microsatellite analysis in brown bears see .In order to increase the success rate and the accuracy of the procedure we performed separate PCR assays for each microsatellite locus and did not co-amplify the loci. The identification of the exact length was conducted through capillary electrophoresis, after each successful PCR. To minimize the genotyping errors in the final data set, PCR assays were performed twice for all samples, and each assay always included a negative control (blank). Subsequently, capillary electrophoresis was performed for each successful PCR product, only when the negative control (blank) was clear (absence of DNA product). Some samples were identified as mixed samples (samples with hair from >1 bear) by evidence of ≥3 alleles at ≥1 locus . In the ®, Waltham, MA, USA), which allows the process of 1–5 different loci at the same time. This method was used in 10 samples to verify the size of the bands in each locus and to ascertain the accuracy of QIAxcel capillary electrophoresis , given the sample size (n), under the assumption of mutation-drift equilibrium [In order to identify possible siblings in the samples, Gimlet software was used . Moreoveilibrium , and MICilibrium ,41.NeEstimator v1.3.  was used for estimating contemporary effective population size (Ne) using multi-locus diploid genotypes from population samples, using the linkage disequilibrium option . In ordeThe structure of the population was studied using STRUCTURE 2.3.4 software , using a parameter set of a 50,000 burn-in period and 50,000 replications, combined with the Admixture Model . The value of α was the same for all populations and the program uses a Uniform Prior . AdditioAn attempt was made to isolate DNA from all samples, followed by the application of PCR protocols for the 10 microsatellite loci as well as ZFX and SRY genes. Regarding hair samples, the quality and quantity of the hair roots determines the outcome of the microsatellite loci amplification . In the Based on their complete genotype for the 10 microsatellite loci, a total of 53, 65, and 77 unique individuals were identified in Prespa, Pindos, and Rhodopi NP, respectively. Moreover, regarding the sex ratio, in Prespa males were 2.5 times more numerous than the females ; in Pindos males were 2 times more numerous than the females ; and in Rhodopi males were 6 times more numerous than the females . The complete genotype and gender of all samples are shown in Microsatellite data of the 10 microsatellite loci revealed abundant genetic diversity in the population from Prespa NP area. The average observed heterozygosity (Ho) was 0.42 (range 0.09–0.83), and the average estimated heterozygosity (He) was 0.73 (range 0.57–0.90) for the 53 unique individuals, respectively . The PICp < 0.001). In our analysis all loci seem to have a Fis value higher than 0.15 . A Fis value higher than 0.15 indicates that high inbreeding levels occur in the population [ID-sib > 0.05, indicating a moderate presence of siblings and relatives.We estimated that the 5 loci had significant deviations from the HWE , and the average He was 0.65 (range 0.38–0.86). The PIC  at each p < 0.001), as shown in ID-sib > 0.05, indicating a moderate presence of siblings and relatives.Seven loci had significant deviations from HWE , and the average He was 0.72 (range 0.52–0.90). The PIC  at each p < 0.001)  . Regardip-value of the Wilcoxon test is equal to 0.72168, 0.99854, and 0.94727 for Prespa, Pindos, and Rhodopi NP, respectively). Therefore, in the three studied sub-populations, no evidence of a significant bottleneck was detected. The L-shaped distributions of the alleles’ frequencies, which are shown in We used the stepwise mutation model (SMM) , which iRegarding the population of Prespa, we found that Ne = 35  and that Nc = 191 . Moreover, the mean arrest/sample ratio was 1.11 since 48 of the 53 samples were obtained once, four samples were obtained twice, and one sample was obtained three times. In Pindos NP, we found that Ne = 118  and Nc = 202 . The mean arrest/sample ratio was 1.18, since 56 of the 77 genotypes were obtained only from one sample. On the contrary, six of the 77 samples were obtained twice, and three samples were obtained three times.Finally, in Rhodopi we found that Ne = 61  and Nc = 92 . In Rhodopi, the mean arrest/sample ratio was 1.73 since 7 of the 121 samples were obtained once, 42 were obtained twice, 18 were obtained three times, 4 samples were obtained four times, and 7 samples were obtained seven times.The basic results of genetic analysis of the three areas used in this study are summarized in To resolve these findings, we estimated the recent migration rate between populations and therefore the gene flow . The dirTo our knowledge, this is the first study that includes bears from the overall area of Prespa National Park (MBPNP), but there are studies that have conducted genetic analysis of bears from regions close or part of MBPNP  ,9,59. BaAs shown in By using genetic data from hair samples collected exclusively from power poles, we obtained population estimates despite fairly low capture and recapture rates (in Pindos the mean arrest/sample ratio was 1.18). Based on these data we found that complete genotypes of the 10 microsatellite loci are informative (PIC > 0.5) and they show high genetic diversity of brown bears in Pindos NP.Regarding the 65 unique bears found in Pindos, our findings show that they are characterized by relatively high genetic diversity and low values for Fis in the majority of loci. This declares a low inbreeding level of the population, which agrees with some of the previous studies ,26 that Specifically, our study  estimateNon-invasive genetics of the brown bear sub-populations of Rhodopi have been conducted in the past ,9. HowevRegarding the inbreeding values, a high Fis ( >0.15) implies a considerable degree of inbreeding ,57, whicIn addition, only in the Rhodopi population and regarding sex ratio, we found a larger difference between the number of male and female bears, since the males were six times more numerous than the females. It is known that male bears are more likely to rub on trees and electricity poles, in order to mark their home range, especially during the breeding season, and this behaviour leads to a certain degree of uneven hair sampling between the two genders ,62. ThisNon-invasive sampling has many advantages regarding the collection of genetic material from large carnivores . On the The growing interest in microsatellite genotyping, combined with non-invasive genetic sampling, has led to the necessity of managing these data. Analysis of our genetic data showed high genetic diversity in all populations, high levels of inbreeding in Prespa and Rhodopi, and lower values of inbreeding in Pindos NP. Moreover, no sign of a significant bottleneck was detected in any of our sub-populations. Based on our genetic data, we showed that our three sub-populations can be successfully distinguished in two clusters with a clear distinction between the western (Pindos and Prespa NP) and eastern (Rhodopi) populations . TowardsID_SIB analysis for the three populations, the majority of loci under investigation showed moderate evidence for excessive presence of siblings or relatives in the sample [Based on the Fst values, the population of Prespa seems to show a tendency of breeding more with the population of Rhodopi than with the population of Pindos, while the population of Rhodopi seems to be genetically distinguished from Pindos. Fst values close to 0 (or negative) mean that the examined populations have high levels of breeding and values > 0.05 indicate genetic isolation between populations, which means that the populations are not currently breeding with each other . In the e sample ,71. Nevee sample . This coe sample . RegardiWe conclude that the overall census population size and the total effective size of brown bears in the three studied areas is 485 and 214 individuals, respectively, which agrees with the findings of Pylidis et al. who found a total of Nc = 499 and Ne = 198 individuals in the same areas. However, we found an increased value of inbreeding in Prespa NP and Rhodopi NP, a high Nc/Ne ratio in Prespa, and a relative high male/female ratio in Rhodopi. These findings show that, at this moment, the sub-populations of Rhodopi and Prespa are more vulnerable compared to the population of Pindos. Therefore, the estimation and characterization of the population size in all the study areas should be continued on a long-term basis through the analysis of a larger number of specimens from the same regions. To ensure the viability of the species in these areas, it is necessary to take immediate and appropriately targeted actions for the conservation and management of these populations. This will provide safer answers regarding both population density and genetic variability, based on extended non-invasive sampling and monitoring programs. Finally, the above results also highlight the need to conserve the corridors that allow the connectivity of the bear sub-populations over large distances and geographic scales in the fragmented landscape context of Greece and the Balkans.


Pharmacogenomics in Neglected Populations”, Asiimwe and Pirmohamed present an excellent review article about the importance of accounting for ethnic diversity in warfarin pharmacogenomics. The authors highlighted the lack of proper representation in existing pharmacogenetic studies of warfarin, emphasizing on the argument that health disparities could be exacerbated when genetic polymorphisms and algorithms are not suitable for populations with ethno-geographic particularities. Consequently, the authors examined the extent to which individual races and ethnicities are represented adequately in the existing body of pharmacogenomic evidence and postulated that the prediction power of these pharmacogenetic-guided warfarin dosing algorithms will depend on the availability of data from all ethnic groups representing the genomic diversity of the entire population.In this Research Topic titled “Characterization of ADME gene variation in Colombian population by exome sequencing”, Fonseca-Mendoza et al. provided relevant whole-exome sequencing (WES) data to illustrate the genetic variability of 35 ADME pharmacogenes among Colombians as well as their genetically-inferred admixture for the first time. Unfortunately, the vast majority of published studies have been conducted on individuals of largely European ancestry, standing out the problem of the missing diversity in human genetic studies. Indeed, there is a paucity of relevant genomic data from studies on underrepresented groups. It is noteworthy that this study revolved around the importance of partially closing that knowledge gap by characterizing the pharmacogenomic profile and the major sources of genetic variability in this neglected population. Strikingly, they found that about nine out of 10 individuals from this population have at least one actionable pharmacogenetic variant in highly polymorphic ADME-related genes , with significant allele frequency differences detected when compared to other Latinx and the global population. In their analysis, the authors also identified multiple rare missense variants, which were 2.1 times more frequent than common variants in this population and were predicted as potentially deleterious. It is important to bear in mind that the field is shifting the attention toward low-to-moderate frequency variants, which are more likely to be population specific. Accordingly, there will no longer be a need to rely on the extrapolation of findings from one population to another. Finally, this study further evidences the utility of WES for a more comprehensive assessment of pharmacogenomics and its clinical implementation.On the other hand, in a very much-needed original research article titled “Leitch et al. collaborated with an original article in which they highlighted the healthcare disparities for rural and tribal populations. These populations do not have easy access to the pharmacogenetic tests and novel strategies need to be developed to improve assistance in these areas. Interviews were conducted with 45 healthcare professionals, administrative staff, and informatics professionals at three different sites serving underserved and neglected populations in Montana. Participants described the need for improvements related to infrastructure, personnel, and funding to implement pharmacogenetic tests. Participants from tribal communities emphasized the historical misuse and abuse of genetic data from peoples as a barrier for implementation. Participants recognized the importance of pharmacogenetics for improvement of treatments for patients with limited access to follow-up services in remote areas. The costs related to the test was also pointed as a barrier and as a reason that could exacerbate inequities in care. On the other hand, the telehealth service was valued by the participants as a way to deliver pharmacogenetic consults with the ability to connect providers and patients to resources and expertise relevant to driving successful pharmacogenetic implementation. The results show the importance of developing strategies to facilitate the access to pharmacogenetic tests for neglected populations in order to reduce the inequities.Tomaz et al. evaluated the nicotine metabolite ratio (NMR) in 185 Brazilian smoking patients. The individuals were classified into slow metabolizers (NMR <0.31) and normal or fast metabolizers (NMR ≥0.31). Thirty percent (30%) of the patients were classified as slow metabolizers while 70% were considered normal/fast metabolizers. Authors also showed that there was an association between the number of cigarettes smoked per day and metabolite averages. The study described categories of metabolizers in Brazilian patients under pharmacological treatments and can contribute to the personalization of smoking cessation treatments, improving clinical outcomes.Panda et al. analyzed 1,029 genomes from Indian individuals, part of the IndiGen initiative, and reported great differences in several variants when comparing to 1,000 Genomes samples. The authors analyzed non-synonymous variants from 545 druggable kinase genes and their protein sequences. A subset of 217 variants were used for structural analysis and critical differences were found in the strength of the binding caused by variants present in Indian populations. This article also showed how common variants present in Indian populations are related to structural protein function in molecules with pharmacogenomic interest. They suggest that the healthcare system may be “Population or Individual Specific Drug System” instead of “One Size Fits for All” by using these information, and, additionally, they suggest an ADR database to understand the risk of therapies in Indian populations.In this context, we emphasize, with this Research Topic, that efforts are being performed in order to increase pharmacogenomic evidence about neglected populations. These studies are important to define the pharmacogenomic patterns of admixed/neglected populations, in order to increase their access to suitable precision medicine. However, we also reaffirm that more studies are needed to create a better understanding of their allele frequencies, new variants (and their relation to protein function), and clinical applications to achieve adequate personalized medicine for these populations.


The Silk Road, a historical network of interlinking trade routes across the Afro-Eurasian landmass, was of great importance to the transport of peoples, goods, and ideas between the East and the West. Although its main use was for importing silk from China, traders moving in the opposite direction carried to Central China jewelry, glassware, and other exotic goods from the Mediterranean, jade from Khotan, and horses and furs from the nomads of the Steppe. In historical records, communication between China and Central Asia has been unbroken ever since the opening of the Silk Road in the Han Dynasty. However, relics unearthed from archaeological sites indicate that communication between people along the Silk Road began during the Bronze Age. The Silk Road brought together the achievements of the different peoples of Eurasia to advance the Old World as a whole.Ethnic groups with different religions, cultures and customs inhabited the Ancient Silk Road and experienced complex histories. Patterns in genetic variation between individuals can tell us about the population history of these groups. In recent years, using relatively direct means of studying ancient samples through osseous material, alongside indirect means of analyzing the genomes of modern populations, demographic history—migrations, expansions and colonizing events - have been progressively revealed in numerous genetic studies of early human populations. However, until the present, the origins of the populations along the Ancient Silk Road and relationships have been examined in far less detail.Kairov et al.; Allen et al.; Guarino-Vignon et al.) or microarray technologies , or various kinds of markers, including mtDNA , Y chromosome , forensic STRs , SNPs , and InDels data  For example, a novel 6-dye direct and multiplex PCR-CE-based typing system has now been validated and could be considered as a reliable tool for human identification and intercontinental population differentiation In this special editorial, we collected 15 genetic investigations involving the populations living along or related to the Silk Road from ancient times to the present day. These studies approach academic and public Research Topic  of the targeted populations through genome-wide sequencing .The eastern end of the Silk Road lies on the middle reaches of the Yellow River. The Shimao site in Shaanxi Province is an important Neolithic archaeological culture in this area. To further reconstruct the genetic structure of Silk Road-related populations, it is necessary to understand the genetic compositions of such early local populations as these. The Shimao population showed a mostly local origin and showed a maternal affinity with Taosi site, a Longshan culture population in the middle Yellow River valley . The former site dates back to the Han Dynasty (118BCE-191CE) and the latter to the Wei - Jin Dynasties and Sui and Tang Dynasties (220CE- 907CE). For the two sites, from the paternal Y-chromosome perspective, all male individuals showed the Sino-Tibetan speaking origin of Yellow River-related populations, while Foyemiao samples demonstrated a higher proportion of an Altaic speaking and North Eurasian ancestral component, alongside a small proportion of southern East Asian ancestry. From the maternal perspective, female Heishuiguo individuals showed a northeast Asian origin and revealed a sex-biased migration from the middle and lower reaches of Yellow River to Hexi corridor. This was consistent with evidence in historical records, especially unearthed slips  and was an integral component of the eastern section of Silk Road. Heishuiguo site and Foyemiao site lie in the central and western portion of the Hexi corridor, respectively  Another site, Shichengzi, a Han Dynasty agricultural garrison, also located in ancient Xinjiang, has revealed to us genetic makeup of a Frontier population in early imperial China . Archaeogenomics at Shichengzi has revealed two subgroups with East Asian origin and Northeast Asian origin, respectively, occupying a single burial space. Interestingly, stable isotope analysis showed that dietary patterns among site inhabitants could be split among agro-pastoral and agricultural groups. Considering ancient DNA and stable isotope evidence together, it has been argued that Northeast Asian origins of Altaic pastoralists and East Asian origins of Han agriculturalists lived together in the Shichengzi military outpost.Population admixture also can be observed in the Western Regions (present Xinjiang), where individuals at the Xiabandi reveal genetic flow from Central Asian populations and thus the influence of Middle and Late Bronze Age steppe pastoral cultures , Kazakh , Hui , Dongxiang , Bonan , Yugur , Salar , Mongol , and Manchu  groups have been investigated using genetic markers and/or genome sequencing at the population level. Generally speaking, population from the same language group exhibit a closer genetic affinity. Furthermore, the Turkic-speaking population from China and Kazakhstan both present a closer genetic relationship to Central Asian populations, while Hui and Tungusic-speaking populations had a significant admixture with Han Chinese. Mongolian’ mitochondrial genomes show a dominant East Asian related ancestry, an outcome of Bronze Age events and Mongol Empire expansion along the Silk Road.The Altaic language family is divided into Mongolic, Tungusic and Turkic language groups. Altaic speaking nomads played an important role in shaping the northern steppe Silk Road. However, their history is often only recorded sporadically in ancient writings of surrounding civilizations. In this special edition, the genetic history of Uyghur .In addition to the northern steppe Silk Road, the Tea and Horse Ancient Road or South Silk Road were another part of the ancient Silk Road. Beginning at approximately 1200 years ago (Tang Dynasty), the Tea and Horse Ancient Road emerged as a famous caravan road system for tea, salt and horse trading in Southwest China. Guizhou Province, located near the Tea and Horse Ancient Road has been documented as a critical depository of substantial sociocultural, genetic, and linguistic diversity for studying southern Silk Road related populations. The southern Han constitute the majority ethnic group within Guizhou. Guarino-Vignon et al.). The use of recent aboriginal paleogenomes, combined with the genomes of related modern humans, will help us to understand further details of the genetic history of regional populations.At the western end of the Silk Road, Guarino-Vignon et al. found that a bronze age individual from Oxus Civilization  at the Ulug-depe site in Turkmenistan, shared genetic affinity with a local BMAC population, and further revealed that modern Central Asian Indo-Iranian–speaking populations primarily harbored ancient BMAC related ancestry (In summary, this special edition focuses on the population history along the Silk Road, covering the eastern limits of the Silk Road, the Western Regions, the northern steppe, and southern Tea and Horse Ancient Road, and western end. The genetic diversity and population structure of modern populations in these regions are dissected using ancient forensic markers. Moreover, combined with modern human DNA data, ancient DNA researches discuss the formation of targeted populations in greater detail. In future work, fine temporal-spatial scales using enlarged sample sizes should be considered when outlining changes in population dynamics along the ancient Silk Road.