基于空间自相关的阿根廷滑柔鱼CPUE标准化研究

李娜; 陈新军; 王冉

doi:10.3969/j.issn.0253-4193.2018.02.006

基于空间自相关的阿根廷滑柔鱼CPUE标准化研究

doi: 10.3969/j.issn.0253-4193.2018.02.006

1.
上海海洋大学海洋科学学院, 上海 201306
2.
上海海洋大学海洋科学学院, 上海 201306;国家远洋渔业工程技术研究中心, 上海 201306;大洋渔业资源可持续开发教育部重点实验室, 上海 201306;农业部大洋渔业资源环境科学观测实验站, 上海 201306;农业部大洋渔业开发重点实验室, 上海 201306

基金项目: 海洋局公益性行业专项（20155014）；上海市科技创新行动计划（14DZ1205000）。

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出版历程
- 收稿日期: 2017-05-29

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

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

摘要

摘要: CPUE的观测往往不是独立的，而是存在空间相关性的。但是，大多数的CPUE标准化方法通常都假设名义CPUE在空间上是相互独立的。为此，本研究以西南大西洋阿根廷滑柔鱼为例，采用2000-2014年1-5月中国大陆鱿钓生产统计数据以及对应的海表温度和叶绿素浓度数据，选择广义线性模型（general linear model，GLM）为基础模型，将空间自相关加入到GLM中，比较标准GLM和4种加入空间自相关的空间GLM的CPUE标准化。根据最小信息准则（Akaike Information Criterion，AIC）及贝叶斯信息准则（Bayesian Information Criterion，BIC），空间自相关的GLM的CPUE标准化结果优于标准GLM，其中指数模型的CPUE标准化结果最佳。同时，标准GLM与空间自相关的GLM相比，存在精确度过高估计的问题。因此，在CPUE标准化中，应充分考虑空间自相关这一因素。
- 空间自相关 /
- CPUE标准化 /
- 广义线性模型 /
- 阿根廷滑柔鱼
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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