DOI: 10.5061/dryad.rfj6q57kb
title: Atlantic herring population baseline (genotypes) for genetic stock identification

description: Sustainable fisheries management is important for the continued harvest of
 the world’s marine resources, especially as they are increasingly
 challenged by a range of climatic and anthropogenic factors. One of the
 pillars of sustainable fisheries management is the accurate identification
 of the biological units, i.e., populations. Here, we developed and
 implemented a genetic baseline for Atlantic herring harvested in the
 Norwegian offshore fisheries to investigate the validity of the current
 management boundaries. This was achieved by genotyping &gt;15,000
 herring from the northern European seas, including samples of all the
 known populations in the region, with a panel of population-informative
 SNPs mined from existing genomic resources. The final genetic baseline
 consisted of ~1,000 herring from 12 genetically distinct populations. We
 thereafter used the baseline to investigate mixed catches from the North
 and Norwegian Seas, revealing that each management area consisted of
 multiple populations, as previously suspected. However, substantial
 numbers (up to 50% or more within a sample) of herring were found outside
 of their expected management areas, e.g., North Sea autumn-spawning
 herring north of 62°N (average = 19.2%), Norwegian spring-spawning herring
 south of 62°N (average = 13.5%), and western Baltic spring-spawning
 herring outside their assumed distribution area in the North Sea (average
 = 20.0%). Based upon these extensive observations, we conclude that the
 assessment and management areas currently in place for herring in this
 region need adjustments to reflect the populations present. Furthermore,
 we suggest that for migratory species, such as herring, a paradigm shift
 from using static geographic stock boundaries towards spatial dynamic
 boundaries is needed to meet the requirements of future sustainable
 management regimes.
description: DNA was extracted from all herring (N = 18,138) in 96-well plates
 using the Qiagen DNeasy 96 Blood & Tissue Kit or by Beckman
 Coulter DNAdvance – Genomic DNA Isolation Kit on a Biomek i5 Automated
 Workstation following manufacturer’s instructions (Beckman Coulter 2021;
 Qiagen 2016). A panel of SNPs distinguishing between
 populations assumed to occur in the Norwegian Sea and North Sea was
 established from mining genomic data from the herring genome (Han et al.
 2020; Pettersson et al. 2019) following a similar set of criteria as
 described in Bekkevold <em>et al.</em> (2023). It should be
 noted that the 6a spring-spawning herring (Sp-6a) samples were added after
 the SNP panel was designed and as such the panel may not be optimized to
 discriminate this population from other populations. Otherwise, all
 baseline populations were considered during the SNP panel development. In
 short, SNPs were selected based on sequence data (Han et al. 2020) and
 their inferences about genomic regions influenced by selective sweeps, as
 well as association with characteristics such as spawning time, salinity,
 and geography. Additional markers identified by Han <em>et
 al.</em> (2020) to discriminate populations along the Norwegian
 coast and inside the fjords were also included. The final panel consisted
 of 60 SNPs (Suppl. Table 1) covering 20 chromosomes all associated with
 selective outlier regions from Han <em>et al.</em> (2020). In
 contrast to Bekkevold <em>et al.</em> (2023), which focused on
 establishing a SNP panel for population differentiation primarily in the
 North Sea-Baltic Sea transition area, the SNPs chosen here were selected
 to primarily differentiate populations likely present in the North Sea,
 Norwegian Sea and along the Norwegian coast. Therefore, the number of SNPs
 to discriminate among populations within the Baltic Sea, e.g., southern
 vs. northern central Baltic herring or WBSS from inner Danish water vs.
 WBSS from Rügen (Bekkevold <em>et al.</em>, 2023), was reduced
 in the present panel to only identify the overall groups. One out of the
 60 SNPs displayed linkage with the sex determination gene (Rafati et al.
 2020), <em>i.e.</em> it was not informative on
 population/stock level, and was thus not included in the following
 baseline analysis. For the final panel, primers and unextended primers
 (UEP) were organized originally in 4 multiplexes but later in 3
 multiplexes using the Assay Design software (Agena BioscienceÔ) for high
 throughput genotyping on an Agena MassARRAYÒ iPLEXÒ Platform (Agena
 Bioscience n.d.). We investigated how many individuals from the baseline
 samples would be removed by choosing three levels of acceptance of missing
 data (5.2%, 9.2% and 25%). Finally, a compromise threshold was decided at
 25% to prevent depleting samples with few individuals. Along all the
 process of baseline building, efforts were made to balance the number of
 individuals per sample to avoid overrepresentation of populations.
 However, it is worth mentioning that in the final baseline, consisting of
 genotypes from 1,098 individuals, only 9 of the individuals
 (<em>i.e.,</em> &lt;1%) reached the maximum of 25% missing
 data whereas 416 individuals (38%) displayed ≤5.2% missing data.
description: # Atlantic herring population baseline (genotypes) for genetic stock
 identification
 [https://doi.org/10.5061/dryad.rfj6q57kb](https://doi.org/10.5061/dryad.rfj6q57kb) ## Description of the data and file structure Genetic samples of known herring populations were collected to establish a genetic baseline for population identification. This baseline was used to assign unknown individuals from mixed-populations samples to the population of its origin. ### Files and variables #### File: Pub\_station.xlsx **Description:** Overview of mixed-population samples ##### Variables * Year: collecting year * serialnumber: ID for the sample/haul * Year_serial: Unique identifier for each sample/haul * Type: Defines the type of the samples (commercial vs. scientific). In the case of scientific samples, the survey is provided: HERAS = HERring Acoustic Survey; IESNS = International Ecosystem Survey in the Nordic Seas; IESSNS = International Ecosystem Summer Survey in the Nordic Seas * longitudestart: Longitude of the sample/haul * latitudestart: Latitude of the sample/haul * No: Number of individuals per sample/haul #### File: Pub\_Supplementary\_Table\_1.xlsx **Description:** Information about the  59 SNPs used to establish the baseline. For each SNP (locus), diploid genotypes, i.e., two alleles per locus, are provided where A = Adenine, C = Cytosin, G = Guanine, and T = Thymine. ##### Variables * Chromosome: Chromosome of the herring genome where the SNP/locus is located * Position: Exact position of the SPN/locus (bp) * SNP-primer-ID: ID of the SNP/locus used in the genotypes datasets * Number genotypes: Number of genotypes for the SNP/locus * Splits: Information about the splitting characteristic of the SNP/locus * Flanking region: Flanking region needed to produces primers to extract the specific SNP/locus #### File: Pub\_genotypes\_baseline.xlsx **Description:** Genotypes of the baseline individuals. The dataset includes 59 SNPs used to establish the baseline. For each SNP (locus), diploid genotypes, i.e., two alleles per locus, are provided where A = Adenine, C = Cytosin, G = Guanine, and T = Thymine. Missing genotypes or missing variables are marked with "n/a" referring to "not available". The file includes 2 separate sheets: "Final Baseline samples" and "All Baseline samples". The first sheets includes only individuals used for establishing the final baseline presented in the paper. Whereas the second sheet, includes all baseline individuals considers during the process of establishing the final baseline. Population abbreviations are explained in the table below, as well as which sampling sites represent the final baseline samples. The data file *Pub_Supplementary_Table_1.xlsx* contains information about the specific genotypes (SNPs).  | **Sampling site** | **Pop\_short** | Baseline | | :----------------------------- | :------------- | :------------------------------ | | Gulf of Riga | BASH-GR | BASH | | Bornholm Basin  | BASH-CB | BASH | | Western Baltic Sea (Rügen) | BASH-RU | BASH | | Vistula Lagoon (Gdańsk) | CBSS-GD | CBSS | | Gulf of Finland | CBSS-GF | CBSS | | Southern Bight | Downs | Downs | | North Sea | NSAS | NSAS | | Norwegian Sea | NSS | NSS | | Kattegat | WBSS-KA | WBSS | | Ringkøbing Fjord | WBSS-RF | WBSS | | Western Baltic Sea (Rügen) | WBSS-RU | WBSS | | Skagerrak East (Öckerö)  | WBSS-SKE | WBSS-SK | | Skagerrak West (Høvåg) | WBSS-SKW | WBSS-SK | | The Minch, West of Scotland | Sp-6aN | Sp-6a | | Donegal | Sp-6aS | Sp-6a | | Rossfjordvannet | ROSSFJ | Pacific-Hybrids | | Balsfjorden | BALSFJ | Pacific-Hybrids | | Gloppenfjorden | GLOPFJ | Local-Fjords | | Lindåspollen | Lindas | Local-Fjords | | Lustrafjorden | LUFJ | Local-Fjords | | Sognefjorden | SOGNEFJ | Local-Fjords | | Trondheimsfjorden | THF | THF | | North Atlantic (Faroe Islands) | FASH | NASS | | North Atlantic (Iceland) | ISSH | NASS | | North Atlantic (Lofoten) | NASH | NASS | | Kirkefjorden | KIRKFJ | Removed from the final baseline | | Austefjorden | AUFJ | Removed from the final baseline | | Dalsfjorden | DAFJ | Removed from the final baseline | | Gursken | GURS | Removed from the final baseline | | Hjørundfjorden | HJFJ | Removed from the final baseline | | Romsdalsfjorden | ROMSFJ | Removed from the final baseline | | Sykkylven | SYKK | Removed from the final baseline | | Volda | VOL | Removed from the final baseline | | NSS-Strandfjorden | NSSSF | Removed from the final baseline | ##### Variables * Baseline: Abbreviation of the genetic population used in the Baseline * Unifier: A unique ID for each individual  * Pop_short: Abbreviation for the actual spawning site * UherIMR03: genotype * UherIMR04: genotype * UherIMR06: genotype * UherIMR07: genotype * UherIMR08: genotype * UherIMR09: genotype * UherIMR13: genotype * UherIMR14: genotype * UherIMR15: genotype * UherIMR18: genotype * UherIMR21: genotype * UherIMR22: genotype * UherIMR24: genotype * UherIMR27: genotype * UherIMR28: genotype * UherIMR32: genotype * UherIMR33: genotype * UherIMR34: genotype * UherIMR35: genotype * Uher_10_008: genotype * Uher_100_037: genotype * Uher_101_038: genotype * Uher_122_151: genotype * Uher_14_010: genotype * Uher_148_057: genotype * Uher_1523_141: genotype * Uher_185: genotype * Uher_188: genotype * Uher_189: genotype * Uher_192: genotype * Uher_2123_144: genotype * Uher_222: genotype * Uher_224: genotype * Uher_241_077: genotype * Uher_244: genotype * Uher_252: genotype * Uher_256: genotype * Uher_259: genotype * Uher_274: genotype * Uher_29_014: genotype * Uher_291: genotype * Uher_294: genotype * Uher_325: genotype * Uher_330: genotype * Uher_333: genotype * Uher_337: genotype * Uher_342: genotype * Uher_343: genotype * Uher_346: genotype * Uher_346_096: genotype * Uher_347: genotype * Uher_349: genotype * Uher_356: genotype * Uher_4386_147: genotype * Uher_46_024: genotype * Uher_5_007: genotype * Uher_899_128: genotype * Uher_958_131: genotype * UherC12_15859613: genotype #### File: Pub\_genotypes\_mixed.csv **Description:** Genotypes of the individuals from mixed-popultion samples. These individuals have been assigned to their population of origin using the established baseline. This dataset also includes 59 SNPs which have been used to establish the baseline. For each SNP (locus), diploid genotypes, i.e., two alleles per locus, are provided where A = Adenine, C = Cytosin, G = Guanine, and T = Thymine. Missing genotypes or missing variables are marked with "n/a" referring to "not available". ##### Variables * Unifier: A unique ID for each individual * Pop_short: Abbreviation for the survey and year * Year: collecting year * serialnumber: ID for the sample/haul * Year_serial: Unique identifier for each sample/haul * longitudestart: Longitude of the sample/haul * latitudestart: Latitude of the sample/haul * Type: Defines the type of the samples (commercial vs. scientific), and which survey it belongs to * UherIMR03: genotype * UherIMR04: genotype * UherIMR06: genotype * UherIMR07: genotype * UherIMR08: genotype * UherIMR09: genotype * UherIMR13: genotype * UherIMR14: genotype * UherIMR15: genotype * UherIMR18: genotype * UherIMR21: genotype * UherIMR22: genotype * UherIMR24: genotype * UherIMR27: genotype * UherIMR28: genotype * UherIMR32: genotype * UherIMR33: genotype * UherIMR34: genotype * UherIMR35: genotype * Uher_10_008: genotype * Uher_100_037: genotype * Uher_101_038: genotype * Uher_122_151: genotype * Uher_14_010: genotype * Uher_148_057: genotype * Uher_1523_141: genotype * Uher_185: genotype * Uher_188: genotype * Uher_189: genotype * Uher_192: genotype * Uher_2123_144: genotype * Uher_222: genotype * Uher_224: genotype * Uher_241_077: genotype * Uher_244: genotype * Uher_252: genotype * Uher_256: genotype * Uher_259: genotype * Uher_274: genotype * Uher_29_014: genotype * Uher_291: genotype * Uher_294: genotype * Uher_325: genotype * Uher_330: genotype * Uher_333: genotype * Uher_337: genotype * Uher_342: genotype * Uher_343: genotype * Uher_346: genotype * Uher_346_096: genotype * Uher_347: genotype * Uher_349: genotype * Uher_356: genotype * Uher_4386_147: genotype * Uher_46_024: genotype * Uher_5_007: genotype * Uher_899_128: genotype * Uher_958_131: genotype * UherC12_15859613: genotype ## Access information Other publicly accessible locations of the data: * Not applicable Data was derived from the following sources: * Not applicable