WANG Xiao, LIU Wenjun, ZHANG Jian. Effect of Oceanic Niño index on interannual CPUE of yellowfin tuna (Thunnus albacares) in Western and Central Pacific Ocean based on ARIMA model[J]. South China Fisheries Science, 2023, 19(4): 10-20. DOI: 10.12131/20230007
Citation: WANG Xiao, LIU Wenjun, ZHANG Jian. Effect of Oceanic Niño index on interannual CPUE of yellowfin tuna (Thunnus albacares) in Western and Central Pacific Ocean based on ARIMA model[J]. South China Fisheries Science, 2023, 19(4): 10-20. DOI: 10.12131/20230007

Effect of Oceanic Niño index on interannual CPUE of yellowfin tuna (Thunnus albacares) in Western and Central Pacific Ocean based on ARIMA model

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  • Received Date: January 14, 2023
  • Revised Date: February 18, 2023
  • Accepted Date: March 01, 2023
  • Available Online: March 07, 2023
  • As a highly migratory pelagic fish, yellowfin tuna (Thunnus albacares) has high ecological and economic value. The Western and Central Pacific Ocean (WCPO) is the sea area with the highest tuna production of all oceans. In order to understand and predict the response of yellowfin tuna to climate change at different life stages in WCPO, we used the catch data of yellowfin tuna in purse seining and pelagic longlining and Oceanic Niño index (ONI) data from 1990 to 2020 in the WCPO to validate the applicability of general ARIMA (Autoregressive integrated moving average) model and dynamic ARIMA model, so as to explore the influence of the ONI on the interannual CPUE (Catch per unit effort) of yellowfin tuna. The results show that: 1) General ARIMA models could be used for long-term fitting of annual CPUE of yellowfin tuna in the WCPO, taking full account of the variability characteristics of annual CPUE of yellowfin tuna. 2) Compared with the general ARIMA model, the dynamic ARIMA model provided a better fit and a higher correlation between the fitted and true values, as well as smaller mean absolute and root mean square errors. 3) The influence of the ONI on the annual CPUE of yellowfin tuna differed between the northern and southern equatorial regions of the WCPO, with the ONI being a more critical factor and a better model fit relatively north of the equator. 4) The ONI had different impacts on the annual CPUE of yellowfin tuna in different fisheries in the WCPO, with a 1–2 years' lag in the ONI for the yellowfin tuna longline fishery in the WCPO, and a faster impact on the purse seine fishery during strong El Niño and strong La Niña events, without a lag.
  • [1]
    WU Y L, LAN K W, TIAN Y. Determining the effect of multiscale climate indices on the global yellowfin tuna (Thunnus albacares) population using a time series analysis[J]. Deep Sea Res II, 2020, 175: 104808-104819. doi: 10.1016/j.dsr2.2020.104808
    [2]
    MCCLATCHIE S. Sardine biomass is poorly correlated with the Pacific decadal oscillation off California[J]. Geophysical Res Lett, 2012, 39(13): 1-6.
    [3]
    BRUCE F P, MONICA P R. Climate change impacts on fisheries and aquaculture: a global analysis[M]. New York: John Wiley & Sons, 2017: 523-568.
    [4]
    王雅萌, 陈芃, 陈新军. 气候变化下西北太平洋大海洋生态系海表面温度特征分析[J]. 上海海洋大学学报, 2021, 30(5): 875-883. doi: 10.12024/jsou.20200603074
    [5]
    郭爱, 陈新军, 范江涛. 中西太平洋鲣鱼时空分布及其与ENSO关系探讨[J]. 水产科学, 2010, 29(10): 591-595. doi: 10.3969/j.issn.1003-1111.2010.10.006
    [6]
    周为峰, 陈亮亮, 崔雪森, 等. 异常气候下温跃层及时空因子中西太平洋黄鳍金枪鱼渔场分布的影响[J]. 中国农业科技导报, 2021, 23(10): 192-201.
    [7]
    SYAMSUDDIN M L, SAITOH SI, HIRAWAKE T, et al. Effects of El Niño-Southern Oscillation events on catches of bigeye tuna (Thunnus obesus) in the Eastern Indian Ocean off Java[J]. Fish Bull, 2013, 111(2): 175-188. doi: 10.7755/FB.111.2.5
    [8]
    ERAUSKIN-EXTRAMIANA M, ARRIZABALAGA H, HOBDAY A J, et al. Large-scale distribution of tuna species in a warming Ocean[J]. Global Change Biol, 2019, 25(6): 2043-2060. doi: 10.1111/gcb.14630
    [9]
    Food and Agriculture Organization of the United Nations. FAO STAT statistics database[R]. Roma: FAO, 2018: 284-290.
    [10]
    Western and Central Pacific Fisheries Commission. Tuna fishery yearbook[R]. The Federated States of Micronesia: WCPFC, 2019: 139-144.
    [11]
    OLSON R J, YOUNG J W, MENARD F, et al. Bioenergetics, trophicecology, and niche separation of tunas[J]. Adv Mar Biol, 2016, 74: 199-344.
    [12]
    杨胜龙, 靳少非, 吴祖立, 等. 太平洋金枪鱼渔场关键次表层环境变量的季节变化[J]. 海洋科学, 2015, 39(5): 36-46.
    [13]
    朱国平, 许柳雄. 东太平洋金枪鱼延绳钓大眼金枪鱼渔场与表层温度之间的关系[J]. 海洋环境科学, 2007(4): 333-336. doi: 10.3969/j.issn.1007-6336.2007.04.009
    [14]
    曹晓怡, 周为峰, 樊伟, 等. 大眼金枪鱼渔场与环境关系的研究进展[J]. 海洋渔业, 2008, 30(2): 176-182. doi: 10.3969/j.issn.1004-2490.2008.02.012
    [15]
    杨胜龙, 张胜茂, 蒋兴伟, 等. 热带大西洋大眼金枪鱼和黄鳍金枪鱼渔场温跃层的时空变化特征[J]. 应用海洋学学报, 2013, 32(3): 349-357. doi: 10.3969/J.ISSN.2095-4972.2013.03.007
    [16]
    李鹏, 许柳雄, 周成, 等. 中西太平洋金枪鱼围网自由鱼群渔场重心变动及其与南方涛动指数的关系[J]. 南方水产科学, 2020, 16(2): 20-27.
    [17]
    宋利明, 李玉伟, 高攀峰. 帕劳群岛附近海域延绳钓渔场大眼金枪鱼 (Thunnus obesus)的环境偏好[J]. 海洋与湖沼, 2009, 40(6): 768-776. doi: 10.3321/j.issn:0029-814X.2009.06.015
    [18]
    SOYKAN C U, EGUCHI T, KOHIN S, et al. Prediction of fishing effort distributions using boosted regression trees[J]. Ecol Appl, 2014, 24(1): 71-83. doi: 10.1890/12-0826.1
    [19]
    周永东, 徐汉祥, 潘国良, 等. 东海区鲳鱼、小黄鱼资源量及其持续渔获量的估算[J]. 浙江海洋学院学报(自然科学版), 2013, 32(1): 1-5.
    [20]
    宋大德, 汪金涛, 陈新军, 等. 时间序列分析模型在黄海南部小黄鱼资源量预测中的应用[J]. 海洋学报, 2020, 42(12): 26-33.
    [21]
    MONLLOR-HURTADO A, PENNINO M G, SANCHEZ-LIZASO J L. Shift in tuna catches due to ocean warming[J]. PLoS One, 2017, 12(6): 178-196.
    [22]
    ROBERT H S, DAVID S S. 时间序列分析及其应用[M]. 李红成, 译. 北京: 机械工业出版社, 2020: 202-230.
    [23]
    李颖若, 韩婷婷, 汪君霞, 等. ARIMA 时间序列分析模型在臭氧浓度中长期预报中的应用[J]. 环境科学, 2021, 42(7): 3119-3126.
    [24]
    乔宇, 闫振飞, 冯承莲, 等. 几种典型模型在物种敏感度分布中的应用和差异分析[J]. 环境工程, 2021, 39(10): 85-95. doi: 10.13205/j.hjgc.202110012
    [25]
    FOSTER R G, ROENNEBERG T. Human responses to the geophysical daily, annual and lunar cycles[J]. Curr Biol, 2008, 18(17): R784-R794. doi: 10.1016/j.cub.2008.07.003
    [26]
    袁红春, 高子玥, 张天蛟. 基于改进的XGboost模型预测南太平洋长鳍金枪鱼资源丰度[J]. 海洋湖沼通报, 2022, 44(2): 112-120.
    [27]
    杨旭, 黄进, 秦泽宇, 等. 基于多尺度特征融合的人群计数算法[J]. 计算机系统应用, 2022, 33(1): 226-235.
    [28]
    章贤成, 汪金涛, 陈新军. 基于BP神经网络的阿根廷滑柔鱼资源CPUE标准化研究[J]. 渔业科学进展, 2022, 43(2): 11-20.
    [29]
    杨彩莉, 杨晓明, 朱江峰. 不同类型厄尔尼诺事件中环境因子对中西太平洋金枪鱼围网鲣分布响应[J]. 南方水产科学, 2021, 17(3): 8-18. doi: 10.12131/20210014
    [30]
    王少琴, 许柳雄, 朱国平, 等. 中西太平洋金枪鱼围网的黄鳍金枪鱼CPUE时空分布及其与环境因子的关系[J]. 大连海洋大学学报, 2014, 29(3): 15-18.
    [31]
    WHITNEY J E, AL-CHOKHACHY R, BUNNELL D B, et al. Physiological basis of climate change impacts on North American inland fishes[J]. Fisheries, 2016, 41(7): 332-345. doi: 10.1080/03632415.2016.1186656
    [32]
    LEHODEY P, ALHEIT J, BARANGE M, et al. Climate variability, fish, and fisheries[J]. J Climate, 2006, 19(20): 5009-5030. doi: 10.1175/JCLI3898.1
    [33]
    BRANDER K M. Global fish production and climate change[J]. Proc Nat Acad Sci USA, 2007, 104(50): 19709-19714. doi: 10.1073/pnas.0702059104
    [34]
    LEHODEY P, SENINA I, CALMETTES B, et al. Modelling the impact of climate change on Pacific skipjack tuna population and fisheries[J]. Climatic Change, 2012, 119(1): 95-109.
    [35]
    石肖飞, 王啸, 王佚兮, 等. 热带中西太平洋海域黄鳍金枪鱼的摄食生物学特性[J]. 南方水产科学, 2022, 18(1): 43-51. doi: 10.12131/20210140
    [36]
    TREMBLAY-BOYER L, MCKECHNIE S, PILLING G, et al. Stock assessment of yellowfin tuna in the Western and Central Pacific Ocean, WCPFC-SC13-2017/SA-WP-06[R]. Rarotonga, Cook Islands: WCPFC, 2017: 20-32.
    [37]
    代丹娜, 刘洪生, 戴小杰, 等. ENSO现象与东太平洋黄鳍金枪鱼围网CPUE时空分布的关系[J]. 上海海洋大学学报, 2011, 20(4): 571-578.
    [38]
    SUMAILA U R, CHEUNG W W, LAM V W, et al. Climate change impacts on the biophysics and economics of world fisheries[J]. Nat Climate Change, 2011, 1(9): 449-456. doi: 10.1038/nclimate1301
    [39]
    SUN C, WANG W R, YEH S. Reproductive ecology of yellowfin tuna in the Central and Western Pacific Ocean, WCPFC-SC1 BI WP-1[R]. Noumea New Caledonia: WCPFC, 2005: 15-26.
    [40]
    MUHLING B A, LAMKIN J T, ALEMANY F, et al. Reproduction and larval biology in tunas, and the importance of restricted area spawning grounds[J]. Fish Pacific, 2017, 27(4): 697-732.
    [41]
    蔡格菁, 傅海彬, 蒋仁斌, 等. 基于ARIMA模型的渔业经济预测及其优化[J]. 计算机与现代化, 2019(4): 88-91.
    [42]
    李勇, 冯家成, 李娜, 等. 太湖水体 Chl-a 预测模型 ARIMA 在引排水方案优化中的应用[J]. 环境工程, 2022, 40(10): 72-79.
    [43]
    FREE C M, THORSON J T, PINSKY M L, et al. Impacts of histori-cal warming on marine fisheries production[J]. Science, 2019, 363(6430): 979-983. doi: 10.1126/science.aau1758
    [44]
    PLAGANYI E. Climate change impacts on fisheries[J]. Science, 2019, 363(6430): 930-931. doi: 10.1126/science.aaw5824
    [45]
    HAZEN E L, JORGENSEN S, RYKACZEWSKI R R, et al. Predicted habitat shifts of Pacific top predators in a changing climate[J]. Nat Climate Change, 2013, 3(3): 234-238. doi: 10.1038/nclimate1686
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