Generalized linear Bayesian models for standardizing CPUE: an application to a squid-jigging fishery in the northwest Pacific Ocean

Jie Cao; Xinjun Chen; Yong Chen; Bilin Liu; Jin Ma; Siliang Li

doi:10.3989/scimar.2011.75n4679

Authors

Jie Cao College of Marine Sciences, Shanghai Ocean University
Xinjun Chen College of Marine Sciences, Shanghai Ocean University - The Key Laboratory of Shanghai Education Commission for Oceanic Fisheries Resources Exploitation - The Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education
Yong Chen School of Marine Sciences, University of Maine - The Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education
Bilin Liu College of Marine Sciences, Shanghai Ocean University - The Key Laboratory of Shanghai Education Commission for Oceanic Fisheries Resources Exploitation - The Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education
Jin Ma College of Marine Sciences, Shanghai Ocean University
Siliang Li College of Marine Sciences, Shanghai Ocean University

DOI:

https://doi.org/10.3989/scimar.2011.75n4679

Keywords:

generalized linear Bayesian models, CPUE standardization, Ommastrephes bartramii, Chinese squid-jigging fishery, northwest Pacific Ocean

Abstract

Generalized linear Bayesian (GLBM) non-hierarchical and hierarchical models were developed for standardization of catch per unit effort (CPUE). The GLBM containing the covariates of month, latitude, sea surface temperature (SST), sea surface salinity (SSS) and sea level height (SLH) had the best fit for the Chinese squid-jigging fishery of Ommastrephes bartramii in the northwest Pacific Ocean based on deviance information criteria. This best-fitting model tends to be more ecologically sound than other CPUE standardization models, such as generalized linear models and generalized additive models. GLBM was also used to deal with the problems of estimating stock abundance index (i.e. standardized CPUE) resulting from increased spatial heterogeneity of spatial dynamics of fishing efforts in the squid fishery by predicting the standardized CPUE for unfished areas. The standardized CPUE based on data including predicted CPUE of unfished areas was lower than the derived CPUE based on data with observed CPUE alone, in particular during the fishing peak of August to October. This study indicates that it is more appropriate to use the standardized CPUE derived from data including both predicted CPUE of unfished areas and observed CPUE of fished area as a stock abundance index. We suggest that the proposed method be used in CPUE standardization to account for impacts of large spatial heterogeneity of fishing efforts in fisheries.

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