The impact of spatial autocorrelation on CPUE standardization between two different fisheries

Luoliang XU; Xinjun CHEN; Wenjiang GUAN; Siquan TIAN; Yong CHEN

doi:10.1007/s00343-018-6294-7

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The impact of spatial autocorrelation on CPUE standardization between two different fisheries

Aquaculture and Fisheries | Updated：2023-05-19

- The impact of spatial autocorrelation on CPUE standardization between two different fisheries
- Chinese Journal of Oceanology and Limnology Vol. 36, Issue 3, Pages: 973-980(2018)
- Affiliations：
  
  1.College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
  2.Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources(Shanghai Ocean University), Ministry of Education, Shanghai 201306, China
  3.National Engineering Research Center for oceanic Fisheries, Shanghai Ocean University, Shanghai 201306, China
  4.School of Marine Sciences, University of Maine, Orono, ME 04469, USA
- Author bio：
  
  CHEN Xinjun, xjchen@shou.edu.cn
- Funds：
- DOI：10.1007/s00343-018-6294-7
  CLC：
- Received：12 November 2016，
  
  Accepted：19 January 2017，
  
  Online First：13 March 2017，
  
  Published：2018-05
- Accepted：
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Luoliang XU, Xinjun CHEN, Wenjiang GUAN, et al. The impact of spatial autocorrelation on CPUE standardization between two different fisheries[J]. Chinese Journal of Oceanology and Limnology, 2018, 36(3): 973-980.
DOI：

Luoliang XU, Xinjun CHEN, Wenjiang GUAN, et al. The impact of spatial autocorrelation on CPUE standardization between two different fisheries[J]. Chinese Journal of Oceanology and Limnology, 2018, 36(3): 973-980. DOI： 10.1007/s00343-018-6294-7.

摘要

Abstract

Catch per unit of effort (CPUE) data can display spatial autocorrelation. However

most of the CPUE standardization methods developed so far assumes independency of observations for the dependent variable

which is often invalid. In this study

we collected data of two fisheries

squid jigging fishery and mackerel trawl fishery. We used standard generalized linear model (GLM) and spatial GLMs to compare the impact of spatial autocorrelation on CPUE standardization for different fisheries. We found that spatialGLMs perform better than standard-GLM for both fisheries. The overestimation of precision of CPUE estimates was observed in both fisheries. Moran's Ⅰ was used to quantify the level of autocorrelation for the two fisheries. The results show that autocorrelation in mackerel trawl fishery was much stronger than that in squid jigging fishery. According to the results of this paper

we highly recommend to account for spatial autocorrelation when using GLM to standardize CPUE data derived from commercial fisheries.

关键词

Keywords

references

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