Data from: Application of genomic offsets to inform freshwater fisheries management under climate change
Data files
Mar 13, 2026 version files 874.93 KB
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1-genomic-offset-calculations.R
17.53 KB
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2-donor-recipient-importance.R
4.63 KB
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Kokadapt_616snps_final.vcf
775.42 KB
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README.md
4.81 KB
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sample_metadata_final.csv
27.23 KB
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snp_metadata_final.csv
45.32 KB
Abstract
Genomic tools are becoming increasingly necessary for mitigating biodiversity loss and guiding management decisions in the context of climate change. Freshwater fish species are particularly susceptible to the impacts of changing environments, including kokanee, the resident form of sockeye salmon (Oncorhynchus nerka), which has already been negatively impacted by increases in extreme temperature throughout its distribution. A previous study using whole genome resequencing of wild kokanee stocks identified 1412 environmentally-associated SNPs and demonstrated genomic offset, a measure of climate vulnerability, to be significantly correlated with higher increases in extreme warm temperatures across much of the species’ range in western Canada. Here, we aimed to operationalize this information for fisheries management by first developing a Genotyping-in-Thousands by sequencing (GT-seq) panel populated exclusively with environment-associated SNPs. We then evaluated the robustness of the GT-seq panel relative to the signal in the whole genome resequencing baseline, and demonstrated a novel application of donor and recipient importance (DI/RI) analysis to inform recreational fisheries stocking decisions. We found that a reduced GT-seq panel of 616 SNPs exhibited a significant positive correlation with those calculated from the full set of 1412 SNPs across the climate change scenarios tested; similar results were obtained when adding new reference populations not included in the original whole genome resequencing baseline. The DI/RI analysis revealed clear spatial trends, with populations situated in the warmest regions of southern interior British Columbia (Canada) having the highest probability for successful translocations to different recipient locations to the north. Similarly, candidate recipient lakes for stocking at the centre of the distribution had higher recipient importance values than those located towards the eastern and western range peripheries. Although further refinement is required, pairing targeted genotyping with genomic offset and DI/RI predictions holds great promise for informing freshwater fisheries management moving forward.
GENERAL INFORMATION
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Title of Dataset: Data from: Application of genomic offsets to inform freshwater fisheries management under climate change
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Author Information:
Name: Anna Jacquemart Institution: The University of British Columbia, Kelowna, British Columbia, Canada Email: anna530@mail.ubc.ca Name: Dr Michael Russello Institution: The University of British Columbia, Kelowna, British Columbia, Canada Email: michael.russello@ubc.ca -
Date of data collection: 2010-2021
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Geographic location of data collection: Western North America (Canada; see metadata for specific location information)
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Funding sources that supported the collection of the data: Genome British Columbia, Natural Sciences and Engineering Research Council of Canada
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Citation: Jacquemart, Anna; Russello, Michael (2025), Data from: Application of genomic offsets to inform freshwater fisheries management under climate change, Dryad, Dataset, https://doi.org/10.5061/dryad.hdr7sqvwq
DRYAD FILES INCLUDE:
Kokadapt_616snps_final.vcf
VCF file containing SNP and genotypic information collected using for 298 tissue samples (including 6 technical replicates) at 616 SNPs
sample_metadata_final.csv
CSV file describing relevant sample metadata for all samples included in Kokadapt_616.vcf, including: sample ID, species, sex, sample material, collection date, location, location coordinates, whether or not the sample was retained in downstream analyses following quality control measures, and wheter or not the sample was a paired technical replicate.
snp_metadata_final.csv
CSV file describing relevant information about the SNPs genotyped in all samples, including: index, category, chromosome, chromosomal position and reference/alternate alleles.
CODE
1-genomic-offset-calculations.R
This file contains R scripts used for data analysis:
A. Gradient Forest modelling
i. input file "env_variables_past_future_wgs.txt" = past and future environmental variables data extracted from WorldClim for the populations included in the WGS baseline in this study
ii. input file "env_variables_pastfuture_comp.txt" = past and future environmental variables data extracted from WorldClim for the populations included in both the WGS baseline and GTseq in this study
iii. input file "env_variables_pastfuture_tatuk_duncan.txt" = past and future environmental variables data extracted from WorldClim for the populations included in the GTseq dataset in this study
iv. input file "wgs_panel_alleles_freq.txt" = allele frequency file for the WGS dataset described in the paper
v. input file "gtseq_panel_alleles_freq.txt" = allele frequency file for the GTseq dataset described in the paper
vi. input file "wgs_and_replacement_panel_alleles_freq.txt" = allele frequency file for the Replacement dataset described in the paper
input file "wgs_panel_alleles_Tatuk_Duncan_freq.txt" = allele frequency file for the dataset containing additioal populations described in the paper
B. Preparing WorldClim environmental data
i. input file "wc2.1_2.5m_bio/*.tif" = climate data extracted from WorldClim.org
ii. input file "wc2.1_2.5m_bioc_UKESM1-0-LL_ssp585_2041-2060.tif" = climate data extracted from WorldClim.org
iii. input file "wc2.1_2.5m_bioc_UKESM1-0-LL_ssp245_2041-2060.tif" = climate data extracted from WorldClim.org
C. Transform data using Gradient Forest model to extract genomic offset values
This script is run is RStudio Version 2025.05.1+513
Packages loaded in this script are "gradientForest", "dplyr", "tidyverse", "geodata", "raster", "sp", "rgdal", "data.table", "gtools", "colorRamps"
2-donor-recipient-importance.R
This file contains R scripts used for data analysis:
A. Gradient Forest modelling
i. input file "env_variables_past_future_wgs.txt" = past and future environmental variables data extracted from WorldClim for the populations included in the WGS baseline in this study
ii. input file "final_tatuk_duncan_freq.txt" = final allele frequency file for the dataset with additional populations described in the manuscript
B. Transform climate data with Gradient Forest model
i. input file "donor_env_var_past.txt" = past environmental variables data extracted from WorldClim for the populations included in the donor baseline in this study
ii. input file "rec_env_var_future.txt" = future environmental variables data extracted from WorldClim for the populations included in recipient database in this study
C. Calculate spatial offsets
D. Calculate (spatio-) temporal offsets
E. Donor and recipient importance
This script is run is RStudio Version 2025.05.1+513
Packages loaded in this script are "offsetEnsembleR", "gradientForest", "dplyr", "tidyverse", "geodata", "raster", "sp", "rgdal", "data.table", "gtools", "colorRamps"
This dataset of genotypic data of Kokanee salmon (Onchorynchus nerka) tissue (n=294; 288 individuals plus 6 technical replicates) samples at 616 single nucleotide polymorphisms (SNPs). Samples were genotyped using Genotyping-in-Thousands by sequencing (GTseq; Campbell et al. 2015) as modified by Schmidt et al. (2020).
