海洋学研究 ›› 2018, Vol. 36 ›› Issue (4): 60-67.DOI: 10.3969/j.issn.1001-909X.2018.04.008

• 研究论文 • 上一篇    下一篇

基于不同环境因子的中西太平洋鲣鱼资源丰度灰色预测模型构建

方舟1,2,3,4, 陈洋洋1, 陈新军*1,2,3,4, 郭立新1   

  1. 1.上海海洋大学 海洋科学学院,上海 201306;
    2.大洋渔业资源可持续开发教育部重点实验室,上海201306;
    3.国家远洋渔业工程技术研究中心,上海 201306;
    4.农业部大洋渔业开发重点实验室,上海201306
  • 收稿日期:2018-08-07 修回日期:2018-09-18 出版日期:2018-12-15 发布日期:2022-11-18
  • 通讯作者: * 陈新军(1967-),男,教授,主要从事海洋渔业资源研究。E-mail:xjchen@shou.edu.cn
  • 作者简介:方舟(1988-),男,浙江杭州市人,讲师,主要从事渔业资源方面的研究。E-mail: zfang@shou.edu.cn
  • 基金资助:
    海洋公益性行业科研专项项目资助(20155014);上海市科技创新计划资助(15DZ1202200)

The grey predict model construction of abundance forecasting for skipjack tuna (Katsuwonus pelamis) in the Western and Central Pacific Ocean based on different environmental factors

FANG Zhou1,2,3,4, CHEN Yang-yang1, CHEN Xin-jun*1,2,3,4, GUO Li-xin1   

  1. 1. College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China;
    2. The Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai 201306, China;
    3. National Engineering Research Center for Oceanic Fisheries, Shanghai 201306, China;
    4. Key Laboratory of Oceanic Fisheries Exploration, Ministry of Agriculture, Shanghai 201306, China
  • Received:2018-08-07 Revised:2018-09-18 Online:2018-12-15 Published:2022-11-18

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摘要： 鲣Katsuwonus pelamis广泛分布于各大洋热带和亚热带海域,其中以中西太平洋资源量最为丰富。综合评价环境因子对鲣鱼资源量的影响,构建科学的资源预报模型可为我国可持续合理开发该鱼种提供参考。本研究利用1998—2013年中西太平洋渔获量数据,以单位捕捞努力量渔获量(CPUE)为资源相对丰度指标,利用灰色关联方法分析鲣鱼资源相对丰度与环境因子之间的关联度,选取合适的环境因子,并基于不同环境因子构建不同的灰色预测模型对鲣鱼资源相对丰度进行预测,比较选择最优模型。结果表明, 中西太平洋鲣鱼的产量逐年递增,而CPUE在年间有着较大的波动。灰色关联分析认为,海表面温度与CPUE的平均关联度最大,其次为Nino3.4区海表温度距平值,其他的环境因子与CPUE的关联度较小。基于多环境因子的预测模型中,包含所有因子(海表面温度、海表面高度、叶绿素质量浓度a和Nino3.4区海表温度距平值)的模型M1有着最佳的拟合效果,实际值与预测值的相对误差为6.475 2,相关系数为0.687 4;而基于单一环境因子的预测模型中,去除11月SST数据的模型S2有着最佳的拟合效果,实际值与预测值的相对误差为7.419 2,相关系数为0.791 0。相比多环境因子的预测模型,单一环境因子预测模型有着较高的稳定性,实际值与预测值直接相关性也较高,可以作为中西太平洋鲣鱼资源相对丰度预报的最优模型。

关键词: 鲣鱼, 环境因子, 灰色系统, 资源相对丰度, 预测模型

Abstract: Skipjack tuna (Katsuwonus pelamis) is widely distributed in the tropical and subtropical water of the worlds ocean, and has high abundance in the Western and Central Pacific Ocean. Evaluating the relationship between its abundance and environment factors using forecasting model is pivotal for sustainable exploration of this species. According to the catch data of skipjack tuna in the Western and Central Pacific during 1998-2013, we used catch per unit effort (CPUE) as an indicator of abundance and analyzed the grey correlation between each environmental factor and CPUE. The optimal model was constructed by choosing suitable environmental factor and comparing the prediction of multiple grey forecasting models with different environmental factors. The results showed that the catch of skipjack tuna gradually increased year after year, whereas CPUE fluctuate dramatically within years. The model M1 including four environmental factors, sea surface temperature, sea surface height, chlorophyll-a, and sea surface temperature anomaly in Nino3.4, was the best model among models with multiple environmental factors, the models mean relative error was 6.475 2 and correlation was 0.687 4 between fitting abundance sequence and predict abundance. The model S2 eliminating SST in November and containing that of May and June, was the best model among the models with single environmental factor, the models mean relative error was 7.419 2 and correlation was 0.791 0 between fitting abundance sequence and predict abundance. The model based on single environmental factor showed a stable and high correlation between actual and predict value, comparing with models based on multiple environmental factors. Therefore, the former forecasting model should be used as suitable model for the prediction of skipjack tuna abundance in the Western and Central Pacific Ocean.

Key words: skipjack tuna, environmental factor, grey system, relative abundance, forecasting model

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