Bayesian inference from the conditional genetic stock identification model

Moran, Benjamin M.; Anderson, Eric C.

doi:10.1139/cjfas-2018-0016

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The NOAA IR serves as an archival repository of NOAA-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by NOAA or funded partners. As a repository, the NOAA IR retains documents in their original published format to ensure public access to scientific information.

i

Bayesian inference from the conditional genetic stock identification model

2019
By Moran, Benjamin M. ; Anderson, Eric C.
Source: Canadian Journal of Fisheries and Aquatic Sciences, 76(4), 551-560

[PDF-298.68 KB]

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Journal Title:

Canadian Journal of Fisheries and Aquatic Sciences
Personal Author:

Moran, Benjamin M. ; Anderson, Eric C.

Moran, Benjamin M. ; Anderson, Eric C. Less -
NOAA Program & Office:

NMFS (National Marine Fisheries Service) ; SWFSC (Southwest Fisheries Science Center)

NMFS (National Marine Fisheries Service) ; SWFSC (Southwest Fisheries Science Center) Less -
Description:

Genetic stock identification (GSI) estimates stock proportions and individual assignments through comparison of genetic markers with reference populations. It is used widely in anadromous fisheries to estimate the impact of oceanic harvest on riverine populations. Here, we provide a formal, explicit description of Bayesian inference in the conditional GSI model, documenting an approach that has been widely used in the last 5 years, but not formally described until now. Subsequently, we describe a novel cross-validation method that permits accurate prediction of GSI accuracy when making Bayesian inference from the conditional GSI model. We use cross-validation and simulation of genetic data to confirm the occurrence of a bias in reporting-unit proportions recently reported in Hasselman et al. (2016) . Then, we introduce a novel parametric bootstrap approach to reduce this bias, and we demonstrate the efficacy of our correction. Our methods have been implemented as a user-friendly R package, rubias, which makes use of Rcpp for computational efficiency. We predict rubias will be widely useful for GSI of fish populations.
Source:

Canadian Journal of Fisheries and Aquatic Sciences, 76(4), 551-560
DOI:

https://doi.org/10.1139/cjfas-2018-0016
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0706-652X;1205-7533;
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pdf
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Canadian Science Publishing
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Journal Article
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urn:sha-512:53c19d97be8b9d81df6cf5208aa84a83ee147e01ceda8a0f2b767ecb60a7ad9af0a980f0dde3e828e14f3e977f4ea3e843c0f36edfee92288940ea52289fdcf4
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Bayesian inference from the conditional genetic stock identification model

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