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 Zhou, CHEN Yang-yang, CHEN Xin-jun, GUO Li-xin

Journal of Marine Sciences ›› 2018, Vol. 36 ›› Issue (4) : 60-67.

PDF(1278 KB)
PDF(1278 KB)
Journal of Marine Sciences ›› 2018, Vol. 36 ›› Issue (4) : 60-67. DOI: 10.3969/j.issn.1001-909X.2018.04.008

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
Author information +
History +

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

Cite this article

Download Citations
FANG Zhou, CHEN Yang-yang, CHEN Xin-jun, GUO Li-xin. 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[J]. Journal of Marine Sciences. 2018, 36(4): 60-67 https://doi.org/10.3969/j.issn.1001-909X.2018.04.008

References

[1] COLLETTE B B, NAUEN C E. FAO species catalogue: Vol. 2 Scombrids of the world—an annotated and illustrated catalogue of tunas, mackerels, bonitos and related species known to date[R]. Rome: FAO Fisheries Synopsis, 1983.
[2] JIN Shao-fei, FAN Wei. Review and perspectives on skipjack tuna fishery under global climate change[J]. Fishery Information and Strategy, 2014, 29(4): 272-279.
靳少非,樊伟. 鲣鱼资源开发利用研究现状及未来气候变化背景下研究展望[J].渔业信息与战略, 2014, 29(4): 272-279.
[3] TANG Hao, XU Liu-xiong, CHEN Xin-jun, et al. Effects of spatiotemporal and environmental factors on the fishing ground of skipjack tuna (Katsuwonus pelamis) in the Western and Central Pacific Ocean based on generalized additive model[J]. Marine Environmental Science, 2013, 32(4): 518-522.
唐浩,许柳雄,陈新军, 等.基于 GAM 模型研究时空及环境因子对中西太平洋鲣鱼渔场的影响[J]. 海洋环境科学,2013,32(4): 518-522.
[4] YANG Sheng-long, ZHOU Su-fang, ZHOU Wei-feng, et al. The relationship between skipjack tuna (Katsuwonus pelamis) catch and water temperature and surface salinity in the west-central Pacific Ocean based on Argo data[J]. Journal of Dalian Fisheries University, 2010, 25(1): 34-40.
杨胜龙,周甦芳,周为峰, 等.基于Argo数据的中西太平洋鲣渔获量与水温、表层盐度关系的初步研究[J]. 大连水产学院学报, 2010, 25(1): 34-40.
[5] ZHOU Su-fang. Impacts of the El Niño Southern Oscillation on skipjack tuna purse-seine fishing grounds in the Western and Central Pacific Ocean[J]. Journal of Fishery Sciences of China, 2005,12(6): 739-744.
周甦芳.厄尔尼诺—南方涛动现象对中西太平洋鲣鱼围网渔场的影响[J]. 中国水产科学,2005,12(6): 739-744.
[6] LEHODEY P, BERTIGNAC M, HAMPTON J, et al. EL Niño Southern Oscillation and tuna in the Western Pacific[J]. Nature, 1997, 389(6 652): 715-718.
[7] GUO Ai, CHEN Xin-jun. The relationship between ENSO and tuna purse-seine resource abundance and fishing grounds distribution in the Western and Centra1 Pacific Ocean[J].Marine Fisheries, 2005, 27(4): 338-342.
郭爱,陈新军. ENSO与中西太平洋金枪鱼围网资源丰度及其渔场变动关系[J]. 海洋渔业,2005, 27(4):338-342.
[8] CHEN Yang-yang, CHEN Xin-jun. Influence of El Niño/La Niña on the abundance index of skipjack in the Western and Central Pacific Ocean[J]. Journal of Shanghai Ocean University, 2017, 26(1):113-120.
陈洋洋,陈新军. 厄尔尼诺/拉尼娜现象对中西太平洋鲣鱼资源丰度的影响[J]. 上海海洋大学学报,2017, 26(1):113-120.
[9] CHEN Xin-jun, GAO Feng, GUAN Wen-jiang, et al. Review of fishery forecasting technology and its models[J]. Journal of Fisheries of China, 2013,37(8): 1 270-1 280.
陈新军,高峰,官文江,等. 渔情预报技术及模型研究进展[J]. 水产学报, 2013, 37(8):1 270-1 280.
[10] DENG Ju-long. Basic method of gray system[M]. Wuhan: Huazhong University of Science and Technology Press, 1987: 20-60.
邓聚龙. 灰色系统基本方法[M]. 武汉:华中理工大学出版社,1987: 20-60.
[11] LIU Si-feng, YANG Ying-jie, WU LI-feng, et al. Theory and application of grey system[M]. the seventh edition. Beijing: Science Press, 2014: 12-205.
刘思峰, 杨英杰, 吴利峰,等. 灰色系统理论及其应用[M].第七版. 北京: 科学出版社, 2014: 12-205.
[12] CHEN Xin-jun, TIAN Si-quan, YE Xu-chang. Study on population structure of flying squid in Northwestern Pacific based on gray system theory[J]. Journal of Shanghai Fisheries University, 2002, 11(4): 335-341.
陈新军,田思泉,叶旭昌. 西北太平洋柔鱼种群的灰色聚类[J]. 上海水产大学学报,2002,11(4):335-341.
[13] GAO Xue, CHEN Xin-jun, YU Wei. Forecasting model of abundance index of winter-spring cohort of Neon flying squid(Ommastrephes bartramii)in the Northwest Pacific based on grey system theory[J]. Acta Oceanologica Sinica, 2017, 39(6):55-61.
高雪, 陈新军, 余为. 基于灰色系统的西北太平洋柔鱼冬春生群资源丰度预测模型[J]. 海洋学报, 2017, 39(6):55-61.
[14] WANG Jin-tao, CHEN Xin-jun. Changes and prediction of the fishing ground gravity of skipjack (Katsuwonus pelamis) in Western-Central Pacific[J].Periodical of Ocean University of China, 2013,43(8): 44-48.
汪金涛, 陈新军. 中西太平洋鲣鱼渔场的重心变化及其预测模型建立[J]. 中国海洋大学学报, 2013, 43(8): 44-48.
[15] CHEN Xin-jun. Grey system theory in fisheries science[M]. Beijing: China Agriculture Press, 2003:94-105.
陈新军. 灰色系统理论在渔业科学中的应用[M]. 北京:中国农业出版社,2003:94-105.
[16] LONGHURST A, SATHYENDRANATH S, PLATT T, et al. An estimate of global primary production in the ocean from satellite radiometer data[J]. Journal of Plankton Research, 1995, 17(6): 1 245-1 271.
[17] GUO Ai, CHEN Xin-jun. Studies on the habitat suitability index based on the vertical structure of water temperature for skipjack Katsuwonus pelamis purse-seine fishery in the West-central Pacific Ocean[J]. Marine Fisheries, 2009, 31(1): 1-9.
郭爱,陈新军. 利用水温垂直结构研究中西太平洋鲣鱼栖息地指数[J]. 海洋渔业,2009, 31(1) : 1-9.
[18] YE Tai-hao, FENG Bo, YAN Yun-rong, et al. The relationship between skipjack (Katsuwonus pelamis) catch and vertical water temperature and salinity in the West-central Pacific Ocean[J]. Transactions of Oceanology and Limnology, 2012(1): 49-55.
叶泰豪, 冯波, 颜云榕, 等. 中西太平洋鲣渔场与温盐垂直结构关系的研究[J]. 海洋湖沼通报, 2012(1): 49-55.
[19] YU Wei, CHEN Xin-jun. Analysis of environmental conditions and their influence on the abundance of neon flying squid in the Northwest Pacific Ocean[J]. Acta Ecologica Sinica, 2015, 35(15): 5 032-5 039.
余为, 陈新军. 西北太平洋柔鱼栖息地环境因子分析及其对资源丰度的影响[J]. 生态学报, 2015, 35(15):5 032-5 039.
[20] XIE Bing, WANG Jin-tao, CHEN Xin-jun, et al. Forecasting model of abundance index for Cololabis saira in the Northwest Pacific Ocean[J]. Journal of Guangdong Ocean University, 2015, 35(6):58-63.
谢斌, 汪金涛, 陈新军,等. 西北太平洋秋刀鱼资源丰度预报模型构建比较[J]. 广东海洋大学学报, 2015, 35(6):58-63.
[21] LI Zeng-guang, WAN Rong, YE Zhen-jiang, et al. Use of random forests and support vector machines to improve annual egg production estimation[J]. Fisheries Science, 2017, 83(1): 1-11.
PDF(1278 KB)

Accesses

Citation

Detail

Sections
Recommended

/