基于GAM模型分析夏秋季南奥克尼群岛南极磷虾渔场时空分布及与环境因子之间的关系

doi:10.3724/SP.J.1084.2012.00266

极地研究 ›› 2012, Vol. 24 ›› Issue (3): 266-273.DOI: 10.3724/SP.J.1084.2012.00266

基于GAM模型分析夏秋季南奥克尼群岛南极磷虾渔场时空分布及与环境因子之间的关系

朱国平^{1, 2, 3, 4} 朱小艳² 徐怡瑛² 夏辉² 李莹春² 徐鹏翔² 许柳雄^{1, 2, 3, 4}

¹国家远洋渔业工程技术研究中心，上海 210306；

²上海海洋大学海洋科学学院，上海 201306；

³大洋渔业资源可持续开发省部共建教育部重点实验室，上海海洋大学，上海 201306；
⁴农业部大洋渔业资源环境科学观测实验站，上海海洋大学，上海 201306

收稿日期:2012-02-28 修回日期:2012-03-22 出版日期:2012-09-30 发布日期:2012-09-30
通讯作者: 许柳雄
基金资助:
国家“863”计划项目;公益性行业（农业）科研专项;农业部南极海洋生物资源开发与利用项目;上海市重点学科建设项目

THE SPATIOTEMPORAL DISTRIBUTION OF FISHING GROUNDS FOR ANTARCTIC KRILL(EUPHAUSIA SUPERBA) AROUND THE SOUTH ORKNEY ISLANDS IN AUSTRAL SUMMER-AUTUMN AND ITS RELATION TO ENVIRONMENTAL FACTORS BASED ON A GENERALIZED ADDITIVE MODEL

Zhu Guoping^{1, 2, 3, 4}, Zhu Xiaoyan², Xu Yiying², Xia Hui², Li Yingchun², Xu Pengxiang², Xu Liuxiong^{1, 2, 3, 4}

¹ National Engineering Research Center for Oceanic Fisheries, Shanghai 201306, China;

² College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China;

³The Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai Ocean University, Shanghai 201306, China;

⁴ Scientific Observing and Experimental Station of Oceanic Fishery Resources, Ministry of Agriculture, Shanghai Ocean University, Shanghai 201306, China

Received:2012-02-28 Revised:2012-03-22 Online:2012-09-30 Published:2012-09-30

摘要/Abstract

摘要： 基于中国南极磷虾渔业科学观察员收集的数据，利用广义可加模型（GAM），对2009—2010渔季和2010—2011渔季夏秋季南奥克尼群岛周边水域南极磷虾渔场时空分布及其与表温和海况之间的关系进行了研究。结果表明，GAM模型对单位捕捞努力量渔获量（CPUE）总偏差解释率为41.78%，其中贡献最大的为月份，贡献率为15.53%；奥克尼群岛水域CPUE分布模式均有明显的月份差异。1月份平均CPUE值最低，而4月份平均CPUE值最高，各月份平均CPUE存在显著性差异。渔场主要集中在60°12′—60°30′S，45°30′—47°30′W。南极磷虾作业渔场的适宜海水表温范围为0.1—1.8℃，最适表温范围为0.5—1.5℃。各海况等级间及不同渔船的平均CPUE均存在显著性差异。逐步GAM模型分析结果表明，影响CPUE的因子按重要性从大到小依次为月份、渔船、经度、海表温度、纬度和海况。

关键词: 南极磷虾, 南奥克尼群岛, GAM, 渔场

Abstract: The present study analyze the spatial-temporal distribution of Antarctic krill (Euphausia superba) fishery in the South Orkney Islands in the austral summer-autumn during 2009-2010 season and 2010-2011 season using the generalized additive model (GAM) based on the data collected from the scientific observers of Chinese Antarctic krill fishery, the relationship between the spatial-temporal distribution of Antarctic krill fishery and sea surface temperature (SST) and sea state are also analyzed. The results show that the rate of deviance explained about CPUE (catch per unit effort) is 41.78 %, the factor that provides the largest contribution is month and the contribution rate is 15.53 %. Significant difference can be found for the distribution pattern on CPUE in different months. The average CPUE value attain the lowest in January and reach the highest in April; significant difference can also be occurred in average CPUE value among months. The main fishing ground is concentrated in 60°12′S to 60°30′S, 45°30′W to 47°30′W. The suitable SST range is 0.1 to 1.8 ℃ and the most suitable range of SST is 0.5 to 1.5 ℃. The average CPUE values have remarkably difference among sea states and vessels. The results of stepwise GAM show that the relative importance of the six variables affecting CPUE of Antarctic krill fishery in the South Orkney Islands can be described as the following order: month, vessel, longitude, SST, latitude, and sea state.

Key words: Antarctic krill, South Orkney Islands, generalized Additive Model, fishing ground

朱国平朱小艳徐怡瑛夏辉李莹春徐鹏翔许柳雄. 基于GAM模型分析夏秋季南奥克尼群岛南极磷虾渔场时空分布及与环境因子之间的关系[J]. 极地研究, 2012, 24(3): 266-273.

Zhu Guoping，Zhu Xiaoyan，Xu Yiying，Xia Hui，Li Yingchun，Xu Pengxiang，Xu Liuxiong. THE SPATIOTEMPORAL DISTRIBUTION OF FISHING GROUNDS FOR ANTARCTIC KRILL(EUPHAUSIA SUPERBA) AROUND THE SOUTH ORKNEY ISLANDS IN AUSTRAL SUMMER-AUTUMN AND ITS RELATION TO ENVIRONMENTAL FACTORS BASED ON A GENERALIZED ADDITIVE MODEL[J]. ADVANCES IN POLAR SCIENCE, 2012, 24(3): 266-273.

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基于GAM模型分析夏秋季南奥克尼群岛南极磷虾渔场时空分布及与环境因子之间的关系

THE SPATIOTEMPORAL DISTRIBUTION OF FISHING GROUNDS FOR ANTARCTIC KRILL(EUPHAUSIA SUPERBA) AROUND THE SOUTH ORKNEY ISLANDS IN AUSTRAL SUMMER-AUTUMN AND ITS RELATION TO ENVIRONMENTAL FACTORS BASED ON A GENERALIZED ADDITIVE MODEL

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