Incorporating spatial autocorrelation into CPUE standardization with an application to the <i>Illex argentinus</i>

Li Na; Chen Xinjun; Wang Ran

doi:10.3969/j.issn.0253-4193.2018.02.006

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Li Na, Chen Xinjun, Wang Ran. Incorporating spatial autocorrelation into CPUE standardization with an application to the Illex argentinus[J]. Haiyang Xuebao, 2018, 40(2): 61-68. doi: 10.3969/j.issn.0253-4193.2018.02.006

Citation:

Li Na, Chen Xinjun, Wang Ran. Incorporating spatial autocorrelation into CPUE standardization with an application to the Illex argentinus[J]. Haiyang Xuebao, 2018, 40(2): 61-68. doi: 10.3969/j.issn.0253-4193.2018.02.006

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Incorporating spatial autocorrelation into CPUE standardization with an application to the Illex argentinus

doi: 10.3969/j.issn.0253-4193.2018.02.006

1.
College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
2.
College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China;National Engineering Research Center for Oceanic Fisheries, Shanghai 201306, China;Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai 201306, China;Scientific Observing and Experimental Station of Oceanic Fishery Resources, Ministry of Agriculture, Shanghai 201306;Key Laboratory of Oceanic Fisheries Exploration, Ministry of Agriculture, Shanghai 201306, China

Received Date: 2017-05-29

Abstract

Abstract

Observational catch per unit effort (CPUE) data are not independent to each other but have spatial autocorrelation. So far, most of the existing CPUE standardization methods assume that the independency of CPUE in spatial level. In this study, Illex argentinus were selected as case study to explore CPUE standardization based on the fishing data of Chinese jigging fishery and the corresponding data of Sea surface temperature and the Chlorophyll a in the Southeast Atlantic Ocean from January to May from 2000 to 2014. To compare the effect of spatial autocorrelation on CPUE standardization, generalized linear model (GLM) was chosen as basic model, and the spatial autocorrelation was incorporated into the standard GLM. According to Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), spatial-GLM performed better than standard-GLM, and the exponential model generated the best goodness-of-fit to the data among four distance models. Also, the precision of standard GLM were more overestimated than that of spatial autocorrelated GLM. It is recommended for us to take spatial autocorrelation into consideration when GLM is used to standardize CPUE data derived from commercial fisheries based on the present study.
- spatial autocorrelation,
- CPUE standardization,
- generalized linear model,
- Illex argentinus

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References(41)

References

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