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A general production model with dependence between data from multiple surveys

A general production model with dependence between data from multiple surveys A fishery‐independent survey for stock assessments is made sometimes more than once per year to detect a difference in relative sizes of fish populations (e.g., catch‐per‐unit‐effort [CPUE]) in response to a seasonal change in fish spatial distributions. Many managers tended to treat such data independently instead of systematically synthesizing them. A primary objective of this study was to synthesize all survey data via a simple hierarchical structure. I used the general (Pella‐Tomlinson) surplus production model for the illustration, because the purpose of this study was not a stock assessment, and the model was simpler than an age‐structured model. The surplus production model has about an eight decade history (since Graham's paper in 1935) and has been prominent in fish population dynamics. The logistic (Graham‐Schaefer) version was useful in the sense of simplifying the dynamics of a fish population in relation to its intrinsic growth, natural mortality, recruitment, density‐dependence, and fishery catch, but it was criticized because of its unrealistic limitations. Subsequently, the general version was suggested to accommodate flexibility and be realistic. In this study, I inferred parameters in the general surplus production model, simultaneously synthesizing all available data even from different temporal ranges. I used Georges Bank yellowtail flounder (Limanda ferruginea) data for demonstration. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Applied Ichthyology Wiley

A general production model with dependence between data from multiple surveys

Journal of Applied Ichthyology , Volume 34 (3) – Jan 1, 2018

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References (23)

Publisher
Wiley
Copyright
Copyright © 2018 Blackwell Verlag GmbH
ISSN
0175-8659
eISSN
1439-0426
DOI
10.1111/jai.13622
Publisher site
See Article on Publisher Site

Abstract

A fishery‐independent survey for stock assessments is made sometimes more than once per year to detect a difference in relative sizes of fish populations (e.g., catch‐per‐unit‐effort [CPUE]) in response to a seasonal change in fish spatial distributions. Many managers tended to treat such data independently instead of systematically synthesizing them. A primary objective of this study was to synthesize all survey data via a simple hierarchical structure. I used the general (Pella‐Tomlinson) surplus production model for the illustration, because the purpose of this study was not a stock assessment, and the model was simpler than an age‐structured model. The surplus production model has about an eight decade history (since Graham's paper in 1935) and has been prominent in fish population dynamics. The logistic (Graham‐Schaefer) version was useful in the sense of simplifying the dynamics of a fish population in relation to its intrinsic growth, natural mortality, recruitment, density‐dependence, and fishery catch, but it was criticized because of its unrealistic limitations. Subsequently, the general version was suggested to accommodate flexibility and be realistic. In this study, I inferred parameters in the general surplus production model, simultaneously synthesizing all available data even from different temporal ranges. I used Georges Bank yellowtail flounder (Limanda ferruginea) data for demonstration.

Journal

Journal of Applied IchthyologyWiley

Published: Jan 1, 2018

Keywords: ; ; ; ;

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