Harnessing the power of regional baselines for broad‐scale genetic stock identification: A multistage, integrated, and cost‐effective approach

• Published in: Evolutionary Applications
• Main Authors: Bobby Hsu, Christopher Habicht
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2024
• Subjects: Bayesian hierarchical modeling; coast‐wide genetic baseline; fishery management; genetic stock identification; mixed‐stock analysis; two‐step approach; Evolution; QH359-425; Bering Sea; Alaska
• Online Access: https://doi.org/10.1111/eva.13621; https://doaj.org/article/af10a402111247ca90646de491c9448b

Abstract 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 the utility of our integrated multistage model using a synthesized data set made up of Chinook salmon, Oncorhynchus tshawytscha, from the North Bering Sea of Alaska. 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.