Standardizing CPUE of Ommastrephes bartramii for Chinese squid-jigging fishery in Northwest Pacific Ocean

Siquan Tian; Xinjun Chen; Yong Chen; Liuxiong Xu; Xiaojie Dai

doi:10.1007/s00343-009-9199-7

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Standardizing CPUE of Ommastrephes bartramii for Chinese squid-jigging fishery in Northwest Pacific Ocean

Updated：2023-05-16

- Standardizing CPUE of Ommastrephes bartramii for Chinese squid-jigging fishery in Northwest Pacific Ocean
- Journal of Oceanology and Limnology Vol. 27, Issue 4, Pages: 729-739(2009)
- Affiliations：
  
  1. College of Marine Sciences Shanghai Ocean University
  2. Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources Ministry of Education
  3. Key Laboratory of Shanghai Education Commission for Oceanic Fisheries Resources Exploitation
  4. School of Marine Sciences University of Maine
- Author bio：
- Funds：
- DOI：10.1007/s00343-009-9199-7
  CLC：
- Online First：04 December 2009，
  
  Published：2009
- Accepted：
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Tian, S., Chen, X., Chen, Y. et al. Standardizing CPUE of Ommastrephes bartramii for Chinese squid-jigging fishery in Northwest Pacific Ocean., Chin. J. Ocean. Limnol. 27, 729–739 (2009). https://doi.org/10.1007/s00343-009-9199-7
DOI：

Tian, S., Chen, X., Chen, Y. et al. Standardizing CPUE of Ommastrephes bartramii for Chinese squid-jigging fishery in Northwest Pacific Ocean., Chin. J. Ocean. Limnol. 27, 729–739 (2009). https://doi.org/10.1007/s00343-009-9199-7 DOI：

摘要

Abstract

Generalized linear models (GLM) and generalized additive models (GAM) were used to standardize catch per unit fishing effort (CPUE) of Ommastrephes bartramii for Chinese squid-jigging fishery in Northwest Pacific Ocean. Three groups of variables were considered in the standardization: spatial variables (longitude and latitude)

temporal variables (year and month) and environmental variables

including sea surface temperature (SST)

sea surface salinity (SSS) and sea level height (SLH). CPUE was treated as the dependent variable and its error distribution was assumed to be log-normal in each model. The model selections of GLM and GAM were based on the finite sample-corrected Akaike information criterion (AICC) and pseudo-coefficient (Pcf) combined P-value

respectively. Both GAM and GLM analysis showed that the month was the most important variable affecting CPUE and could explain 21.3% of variability in CPUE while other variables only explained 8.66%. The interaction of spatial and temporal variables weakly influenced the CPUE. Moreover

spatio-temporal factors may be more important in influencing the CPUE of this squid than environmental variables. The standardized and nominal CPUEs were similar and had the same trends in spatio-temporal distribution

but the standardized CPUE values tended to be smaller than the nominal CPUE. The CPUE tended to have much higher monthly variation than annual variations and their values increased with month. The CPUE became higher with increasing latitude-high CPUE usually occurred in 145°E–148°E and 149°E–162°E. The CPUE was higher when SST was 14–21°C and the SLH from −22 cm to −18 cm. In this study

GAM tended to be more suitable than GLM in analysis of CPUE.

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