ParkinsonCanadaV1, Hal, Corpus, bibRecord, 000002

Estimation of gillnet efficiency and selectivity across multiple sampling units: A hierarchical Bayesian analysis using mark-recapture data

Identifieur interne : 000002 ( Hal/Corpus ); précédent : 000001; suivant : 000003

Estimation of gillnet efficiency and selectivity across multiple sampling units: A hierarchical Bayesian analysis using mark-recapture data

Auteurs : P. J. Askey ; J. R. Post ; E. A. Parkinson ; Etienne Rivot ; A. J. Paul ; P. A. Biro

Source :

Fisheries Research [ 0165-7836 ] ; 2007.

RBID : Hal:hal-01453589

Descripteurs français

mix :
- amérique du nord, canada, lac et étang, oncorhynchus mykiss, poisson, truite arc en ciel.

English descriptors

mix :
- rainbow, CAPTURABILITE, CAPTURE-MARQUAGE-RECAPTURE, MODELE HIERARCHIQUE, SELECTIVITE DES CAPTURES, STATISTIQUE BAYESIENNE, fish, lake and pond, rainbow trout, trout .

Abstract

Fisheries research often involves a repetitive sampling protocol for multiple ecological units (our example is gillnetting of lakes). Estimates of abundance based on catch per unit effort can be erroneous due to lake effects on sampling efficiency. Conversely dividing data into individual lakes may lead to poor inference due to sparse data. Hierarchical Bayesian analysis compromises between these two extreme methods by estimating parameters for an individual lake, but borrowing information from other lakes. We estimated size-selective gillnet efficiency with mark-recapture data across a series of lakes subject to a constant netting effort. Hierarchical Bayesian analysis was able to prevent unrealistic selectivity functions that arose from individual lake analysis. Furthermore, the hierarchical approach was able to derive accurate parameter estimates with very few mark-recaptures in sub-sampled data trials. This paper demonstrates the hierarchical methodology for the estimation of fishery selectivity parameters. The results could be used to derive informative priors for future research which uses the proposed gillnet protocol.

Url:

https://hal.archives-ouvertes.fr/hal-01453589

DOI: 10.1016/j.fishres.2006.09.009

Links to Exploration step

Hal:hal-01453589

Le document en format XML

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<front><div type="abstract" xml:lang="en">Fisheries research often involves a repetitive sampling protocol for multiple ecological units (our example is gillnetting of lakes). Estimates of abundance based on catch per unit effort can be erroneous due to lake effects on sampling efficiency. Conversely dividing data into individual lakes may lead to poor inference due to sparse data. Hierarchical Bayesian analysis compromises between these two extreme methods by estimating parameters for an individual lake, but borrowing information from other lakes. We estimated size-selective gillnet efficiency with mark-recapture data across a series of lakes subject to a constant netting effort. Hierarchical Bayesian analysis was able to prevent unrealistic selectivity functions that arose from individual lake analysis. Furthermore, the hierarchical approach was able to derive accurate parameter estimates with very few mark-recaptures in sub-sampled data trials. This paper demonstrates the hierarchical methodology for the estimation of fishery selectivity parameters. The results could be used to derive informative priors for future research which uses the proposed gillnet protocol. </div>
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                <term xml:lang="fr">truite arc en ciel</term>
                <term xml:lang="fr">canada</term>
                <term xml:lang="fr">amérique du nord</term>
                <term xml:lang="fr">lac et étang</term>
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            <abstract xml:lang="en">Fisheries research often involves a repetitive sampling protocol for multiple ecological units (our example is gillnetting of lakes). Estimates of abundance based on catch per unit effort can be erroneous due to lake effects on sampling efficiency. Conversely dividing data into individual lakes may lead to poor inference due to sparse data. Hierarchical Bayesian analysis compromises between these two extreme methods by estimating parameters for an individual lake, but borrowing information from other lakes. We estimated size-selective gillnet efficiency with mark-recapture data across a series of lakes subject to a constant netting effort. Hierarchical Bayesian analysis was able to prevent unrealistic selectivity functions that arose from individual lake analysis. Furthermore, the hierarchical approach was able to derive accurate parameter estimates with very few mark-recaptures in sub-sampled data trials. This paper demonstrates the hierarchical methodology for the estimation of fishery selectivity parameters. The results could be used to derive informative priors for future research which uses the proposed gillnet protocol. </abstract>
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Estimation of gillnet efficiency and selectivity across multiple sampling units: A hierarchical Bayesian analysis using mark-recapture data

Estimation of gillnet efficiency and selectivity across multiple sampling units: A hierarchical Bayesian analysis using mark-recapture data

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