Poor-data and data-poor species stock assessment using a Bayesian hierarchical approach

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dc.contributorVirginia Techen
dc.contributor.authorJiao, Y.en
dc.contributor.authorCortes, E.en
dc.contributor.authorAndrews, K.en
dc.contributor.authorGuo, F.en
dc.contributor.departmentStatisticsen
dc.date.accessed2014-01-08en
dc.date.accessioned2014-01-10T20:07:54Zen
dc.date.available2014-01-10T20:07:54Zen
dc.date.issued2011-10en
dc.description.abstractAppropriate inference for stocks or species with low-quality data (poor data) or limited data (data poor) is extremely important. Hierarchical Bayesian methods are especially applicable to small-area, small-sample-size estimation problems because they allow poor-data species to borrow strength from species with good-quality data. We used a hammerhead shark complex as an example to investigate the advantages of using hierarchical Bayesian models in assessing the status of poor-data and data-poor exploited species. The hammerhead shark complex (Sphyrna spp.) along the Atlantic and Gulf of Mexico coasts of the United States is composed of three species: the scalloped hammerhead (S. lewini), the great hammerhead (S. mokarran), and the smooth hammerhead (S. zygaena) sharks. The scalloped hammerhead comprises 70-80% of the catch and has catch and relative abundance data of good quality, whereas great and smooth hammerheads have relative abundance indices that are both limited and of low quality presumably because of low stock density and limited sampling. Four hierarchical Bayesian state-space surplus production models were developed to simulate variability in population growth rates, carrying capacity, and catchability of the species. The results from the hierarchical Bayesian models were considerably more robust than those of the nonhierarchical models. The hierarchical Bayesian approach represents an intermediate strategy between traditional models that assume different population parameters for each species and those that assume all species share identical parameters. Use of the hierarchical Bayesian approach is suggested for future hammerhead shark stock assessments and for modeling fish complexes with species-specific data, because the poor-data species can borrow strength from the species with good data, making the estimation more stable and robust.en
dc.description.sponsorshipUSDA Cooperative State Research, Education and Extension Service 0210510en
dc.description.sponsorshipMARFINen
dc.description.sponsorshipNOAAen
dc.identifier.citationYan Jiao, Enric Cortés, Kate Andrews, and Feng Guo 2011. Poor-data and data-poor species stock assessment using a Bayesian hierarchical approach. Ecological Applications 21:2691–2708. http://dx.doi.org/10.1890/10-0526.1en
dc.identifier.doihttps://doi.org/10.1890/10-0526.1en
dc.identifier.issn1051-0761en
dc.identifier.urihttp://hdl.handle.net/10919/24795en
dc.identifier.urlhttp://www.esajournals.org/doi/pdf/10.1890/10-0526.1en
dc.language.isoen_USen
dc.publisherEcological Society of Americaen
dc.rightsIn Copyrighten
dc.rights.holderEcological Society of Americaen
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectBayesian hierarchical modelen
dc.subjectdata-poor assessmenten
dc.subjecthammerhead sharken
dc.subjectfish complexen
dc.subjectpopulation dynamicsen
dc.subjectsmall sample sizeen
dc.subjectdeveloping fisheriesen
dc.subjectpopulation-modelsen
dc.subjectdemographyen
dc.subjectgrowthen
dc.subjectuncertaintyen
dc.subjectsalmonen
dc.subjectsharken
dc.subjectmanagementen
dc.subjecttrendsen
dc.subjectEnvironmental Sciences & Ecologyen
dc.titlePoor-data and data-poor species stock assessment using a Bayesian hierarchical approachen
dc.title.serialEcological Applicationsen
dc.typeArticle - Refereeden

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