DOI: 10.5061/dryad.qbzkh18qk
title: Data from: harnessing the power of regional baselines for broad-scale genetic stock identification: a multistage, integrated, and cost-effective approach

description: In mixed-stock fishery analyses, genetic stock identification (GSI)
 estimates the contribution of each population to a mixture and is
 typically conducted at a regional scale using genetic baselines specific
 to the stocks expected in that region. Often these regional baselines
 cannot be combined to produce broader geographical baselines due to
 non-overlapping populations and genetic markers. In cases where the
 mixture contains stocks spanning across a wide area, a broad-scale
 baseline is created, but often at the cost of resolution. Here, we
 introduce a new GSI method to harness the resolution capabilities of
 baselines developed for regional applications in the analysis of mixtures
 containing individuals from a broad geographic range. This method employs
 a multistage framework that allows disparate baselines to be used in a
 single integrated process that produces estimates along with the
 propagated errors from each stage. All individuals in the mixture sample
 are required to be genotyped for all genetic markers in the baselines used
 by this model, but the baselines do not require overlap in genetic markers
 or populations representing the broad-scale or regional baselines. We
 demonstrate our integrated multistage GSI model using a synthesized data
 set made up of Chinook salmon, Oncorhynchus tshawytscha, from the North
 Bering Sea of Alaska. The data set is designed to be run using R package,
 Ms.GSI, and it does not represent the composition of the real fishery. The
 results show an improved accuracy for estimates using an integrated
 multistage framework, compared to the conventional framework of using
 separate hierarchical steps. The integrated multistage framework allows
 GSI of a wide geographic area without first developing a large scale,
 high-resolution genetic baseline or dividing a mixture sample into smaller
 regions beforehand. This approach is more cost-effective than updating
 range-wide baselines with all regionally important markers.
description: We used existing genetic baseline to simulate synthetic mixture
 for this analysis. See article for details.
description: # Harnessing the power of regional baselines for broad-scale genetic stock
 identification: A multistage, integrated, and cost-effective approach
 ## Description of the data
 and file structure * `mix_ayk_205.rda` is the mixture containing 205
 individuals in the format of the Gene Conservation Lab. Columns 1 and 2
 are identifications of each individual. "SillySource" column is
 the unique individual ID. "SILLY_CODE" is the location of the
 collection. SillySource and SILLY_CODE are known as "indiv" and
 "collection" in R package *rubias* format, respectively. Columns
 3 and on identify the loci and their alleles for each individual. Missing
 genotypes are represented by "NA." * `base_ayk10.rda` is the
 broad-scale baseline, and it is in the same format as the mixture file. *
 `base_yukon380_5grps.rda` is the regional baseline in the same format as
 the mixture and broad-scale baseline files. * `ayk_pops60_5grps.rda` is a
 table containing information for the broad-scale populations.
 "collection" column contains the name of the population.
 "repunit" contains the names of assigned reporting unit.
 "grpvec" contains the corresponding numbers for the reporting
 units. * `yukon_pops43_4grps.rda` is a table containing information for
 the regional populations. It has the same format as the table for the
 broad-scale populations * `msgsi_supp_data.r` is a file containing R code
 to run the supplemental data using R package `Ms.GSI`.