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

许骆良; 陈新军; 官文江; 田思泉; 陈勇

doi:10.1007/s00343-018-6294-7

Your Location：

Home >

Browse articles >

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
- The impact of spatial autocorrelation on CPUE standardization between two different fisheries
- 海洋湖沼学报（英文） 2018年36卷第3期页码：973-980
- 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：
  
  the National High Technology Research and Development Program of China (863 Program)(2012AA092303);the Public Science and Technology Research Funds Projects of Ocean(20155014);the Shanghai Leading Academic Discipline Project;the Funding Program for Outstanding Dissertation in Shanghai Ocean University;Y. Chen was supported by SHOU International Center for Marine Studies and Shanghai 1000 Talent Program
- DOI：10.1007/s00343-018-6294-7
  中图分类号：
- 收稿：2016-11-12，
  
  录用：2017-1-19，
  
  网络首发：2017-03-13，
  
  纸质出版：2018-05
- Accepted：
Scan QR Code
许骆良, 陈新军, 官文江, 等. The impact of spatial autocorrelation on CPUE standardization between two different fisheries[J]. 海洋湖沼学报（英文）, 2018,36(3):973-980.

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.
许骆良, 陈新军, 官文江, 等. The impact of spatial autocorrelation on CPUE standardization between two different fisheries[J]. 海洋湖沼学报（英文）, 2018,36(3):973-980. DOI： 10.1007/s00343-018-6294-7.

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

Bhattarai K R, Vetaas O R, Grytnes J A. 2004. Fern species richness along a central Himalayan elevational gradient, Nepal. Journal of Biogeography, 31(3):389-400.

Campbell R A. 2004. CPUE standardisation and the construction of indices of stock abundance in a spatially varying fishery using general linear models. Fisheries Research, 70(2-3):209-227.

Campbell R A. 2015. Constructing stock abundance indices from catch and effort data:some nuts and bolts. Fisheries Research, 161:109-130.

Carl G, Kühn I. 2007. Analyzing spatial autocorrelation in species distributions using Gaussian and logit models.Ecological Modelling, 207(2-4):159-170.

Cressie N A. 1993. Statistics for Spatial Data. Wiley, New York. p.1-202.

Dormann C F. 2007. Effects of incorporating spatial autocorrelation into the analysis of species distribution data. Global Ecology and Biogeography, 16(2):129-138.

Dunn P K. 2009. Improving comparisons between models for CPUE. Fisheries Research, 97(1-2):148-149.

Guan W J, Tian S Q, Wang X F, Zhu J F, Chen X J. 2014. A review of methods and model selection for standardizing CPUE. Journal of Fishery Sciences of China, 21(4):852-862. (in Chinese with English abstract)

Guisan A, Thuiller W. 2005. Predicting species distribution:offering more than simple habitat models. Ecology Letters, 8(9):993-1 009.

Haining R. 2003. Spatial Data Analysis:Theory and Practice.Cambridge University Press, Cambridge. p.50-290.

Harley S J, Myers R A, Dunn A. 2001. Is catch-per-unit-effort proportional to abundance? Canadian Journal of Fisheries and Aquatic Sciences, 58(9):1 760-1 772.

Hawkins B A, Porter E E. 2003. Does herbivore diversity depend on plant diversity? The case of California butterflies. The American Naturalist, 161(1):40-49.

Hinton M G, Maunder M N. 2004. Methods for standardizing CPUE and how to select among them. Col. Vol. Sci. Pap.ICCAT, 56(1):169-177.

Jiao Y, O'reilly R, Smith E, Orth D. 2016. Integrating spatial synchrony/asynchrony of population distribution into stock assessment models:a spatial hierarchical Bayesian statistical catch-at-age approach. ICES Journal of Marine Science:Journal du Conseil, 73(7):1 725-1 738.

Kleisner K M, Walter J F, Diamond S L, Die D J. 2010.Modeling the spatial autocorrelation of pelagic fish abundance. Marine Ecology Progress Series, 411:203-213.

Li G, Cao J, Zou X R, Chen X J, Runnebaum J. 2016. Modeling habitat suitability index for Chilean jack mackerel( Trachurus murphyi ) in the South East Pacific. Fisheries Research, 178:47-60.

Li Z G, Ye Z J, Wan R, Zhang C. 2015. Model selection between traditional and popular methods for standardizing catch rates of target species:a case study of Japanese Spanish mackerel in the gillnet fishery. Fisheries Research, 161:312-319.

Liebhold A, Koenig W D, Bjørnstad O N. 2004. Spatial synchrony in population dynamics. Annual Review of Ecology, Evolution, and Systematics, 35(1):467-490.

Maunder M N, Sibert J R, Fonteneau A, Hampton J, Kleiber P, Harley S J. 2006. Interpreting catch per unit effort data to assess the status of individual stocks and communities.ICES Journal of Marine Science, 63(8):1 373-1 385.

Nelder J A, Wedderburn R W M. 1972. Generalized linear models. Journal of the Royal Statistical Society. Series A, 135(3):370-384.

Nishida T, Chen D G. 2004. Incorporating spatial autocorrelation into the general linear model with an application to the yellowfin tuna ( Thunnus albacares )longline CPUE data. Fisheries Research, 70(2-3):265-274.

Seibel B A. 2013. The jumbo squid, Dosidicus gigas (Ommastrephidae), living in oxygen minimum zones Ⅱ:Blood-oxygen binding. Deep Sea Research Part Ⅱ:Topical Studies in Oceanography, 95:139-144.

Stewart J S, Field J C, Markaida U, Gilly W F. 2013. Behavioral ecology of jumbo squid ( Dosidicus gigas ) in relation to oxygen minimum zones. Deep Sea Research Part Ⅱ:Topical Studies in Oceanography, 95:197-208.

Thorson J T, Shelton A O, Ward E J, Skaug H J. 2015.Geostatistical delta-generalized linear mixed models improve precision for estimated abundance indices for West Coast groundfishes. ICES Journal of Marine Science, 72(5):1 297-1 310.

Tian S Q, Chen X J, Chen Y, Xu L X, Dai X J. 2009.Standardizing CPUE of Ommastrephes bartramii for Chinese squid-jigging fishery in Northwest Pacific Ocean.Chinese Journal of Oceanology and Limnology, 27(4):729-739.

Tian S Q, Han C, Chen Y, Chen X J. 2013. Evaluating the impact of spatio-temporal scale on CPUE standardization.Chinese Journal of Oceanology and Limnology, 31(5):935-948.

Walsh W A, Brodziak J. 2015. Billfish CPUE standardization in the Hawaii longline fishery:model selection and multimodel inference. Fisheries Research, 166:151-162.

Yu H, Jiao Y, Winter A. 2011. Catch-rate standardization for yellow perch in Lake Erie:a comparison of the spatial generalized linear model and the generalized additive model. Transactions of the American Fisheries Society, 140(4):905-918.

Yu W, Yi Q, Chen X J, Chen Y. 2016. Modelling the effects of climate variability on habitat suitability of jumbo flying squid, Dosidicus gigas , in the Southeast Pacific Ocean off Peru. ICES Journal of Marine Science, 73(2):239-249.

Zhang T L, Lin G. 2008. Identification of local clusters for count data:a model-based Moran's Ⅰ test. Journal of Applied Statistics, 35(3):293-306.

浏览量

Downloads

CSCD

文章被引用时，请邮件提醒。

Submit

工具集

关联资源

Detection of spatial hot spots and variation for the neon flying squid Ommastrephes bartramii resources in the northwest Pacific Ocean

双转子轴间滑油颗粒运动轨迹参数化模型

V形前缘对激波入射边界层流动影响的数值模拟与分析

压电阻抗法表征固体推进剂老化的研究

基于模态频率的V型缺口梁裂纹扩展速率模型