Harnessing the power of regional baselines for broad‐scale genetic stock identification: A multistage, integrated, and cost‐effective approach
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 comb...
Published in: | Evolutionary Applications |
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Online Access: | https://doi.org/10.1111/eva.13621 https://doaj.org/article/af10a402111247ca90646de491c9448b |
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ftdoajarticles:oai:doaj.org/article:af10a402111247ca90646de491c9448b 2024-09-15T17:59:40+00:00 Harnessing the power of regional baselines for broad‐scale genetic stock identification: A multistage, integrated, and cost‐effective approach Bobby Hsu Christopher Habicht 2024-02-01T00:00:00Z https://doi.org/10.1111/eva.13621 https://doaj.org/article/af10a402111247ca90646de491c9448b EN eng Wiley https://doi.org/10.1111/eva.13621 https://doaj.org/toc/1752-4571 1752-4571 doi:10.1111/eva.13621 https://doaj.org/article/af10a402111247ca90646de491c9448b Evolutionary Applications, Vol 17, Iss 2, Pp n/a-n/a (2024) Bayesian hierarchical modeling coast‐wide genetic baseline fishery management genetic stock identification mixed‐stock analysis two‐step approach Evolution QH359-425 article 2024 ftdoajarticles https://doi.org/10.1111/eva.13621 2024-08-05T17:49:57Z 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. Article in Journal/Newspaper Bering Sea Alaska Directory of Open Access Journals: DOAJ Articles Evolutionary Applications 17 2 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Bayesian hierarchical modeling coast‐wide genetic baseline fishery management genetic stock identification mixed‐stock analysis two‐step approach Evolution QH359-425 |
spellingShingle |
Bayesian hierarchical modeling coast‐wide genetic baseline fishery management genetic stock identification mixed‐stock analysis two‐step approach Evolution QH359-425 Bobby Hsu Christopher Habicht Harnessing the power of regional baselines for broad‐scale genetic stock identification: A multistage, integrated, and cost‐effective approach |
topic_facet |
Bayesian hierarchical modeling coast‐wide genetic baseline fishery management genetic stock identification mixed‐stock analysis two‐step approach Evolution QH359-425 |
description |
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. |
format |
Article in Journal/Newspaper |
author |
Bobby Hsu Christopher Habicht |
author_facet |
Bobby Hsu Christopher Habicht |
author_sort |
Bobby Hsu |
title |
Harnessing the power of regional baselines for broad‐scale genetic stock identification: A multistage, integrated, and cost‐effective approach |
title_short |
Harnessing the power of regional baselines for broad‐scale genetic stock identification: A multistage, integrated, and cost‐effective approach |
title_full |
Harnessing the power of regional baselines for broad‐scale genetic stock identification: A multistage, integrated, and cost‐effective approach |
title_fullStr |
Harnessing the power of regional baselines for broad‐scale genetic stock identification: A multistage, integrated, and cost‐effective approach |
title_full_unstemmed |
Harnessing the power of regional baselines for broad‐scale genetic stock identification: A multistage, integrated, and cost‐effective approach |
title_sort |
harnessing the power of regional baselines for broad‐scale genetic stock identification: a multistage, integrated, and cost‐effective approach |
publisher |
Wiley |
publishDate |
2024 |
url |
https://doi.org/10.1111/eva.13621 https://doaj.org/article/af10a402111247ca90646de491c9448b |
genre |
Bering Sea Alaska |
genre_facet |
Bering Sea Alaska |
op_source |
Evolutionary Applications, Vol 17, Iss 2, Pp n/a-n/a (2024) |
op_relation |
https://doi.org/10.1111/eva.13621 https://doaj.org/toc/1752-4571 1752-4571 doi:10.1111/eva.13621 https://doaj.org/article/af10a402111247ca90646de491c9448b |
op_doi |
https://doi.org/10.1111/eva.13621 |
container_title |
Evolutionary Applications |
container_volume |
17 |
container_issue |
2 |
_version_ |
1810436779997659136 |