基于ARIMA的海洋尼诺指数对中西太平洋黄鳍金枪鱼年际CPUE的影响

王啸; 刘文俊; 张健

doi:10.12131/20230007

基于ARIMA的海洋尼诺指数对中西太平洋黄鳍金枪鱼年际CPUE的影响

doi: 10.12131/20230007

王啸^1,,
刘文俊²,
张健^{2, 3, ,}

1.
上海开创远洋渔业有限公司，上海 200082
2.
上海海洋大学海洋科学学院，上海 201306
3.
国家远洋渔业工程技术研究中心/大洋渔业资源可持续开发教育部重点实验室，上海 201306

基金项目: 农业农村部全球重要鱼种资源动态监测评估项目；国家重点研发计划项目 (2020YFD0900803)

详细信息

作者简介:
王啸：王　啸 (1995—)，男，硕士，研究方向为远洋渔业资源。E-mail: 457506879@qq.com

通讯作者:
张　健 (1979—)，男，副教授，博士，研究方向为生态型渔具渔法。E-mail: j-zhang@shou.edu.cn

中图分类号: S 931.3
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- 被引次数: 0
出版历程
- 收稿日期: 2023-01-15
- 修回日期: 2023-02-19
- 录用日期: 2023-03-02
- 网络出版日期: 2023-03-09
- 刊出日期: 2023-08-05

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

WANG Xiao^1
,,
LIU Wenjun²,
ZHANG Jian^{2, 3
, ,}

1.
Shanghai Kai Chuang Deep Sea Fisheries Co., Ltd., Shanghai 200082, China
2.
College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
3.
National Engineering Research Centre for Oceanic Fisheries/Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai 201306, China

摘要

摘要: 黄鳍金枪鱼 (Thunnus albacares) 为高度洄游的大洋性鱼类，有较高的生态和经济价值，中西太平洋 (Western and Central Pacific Ocean, WCPO) 是全球金枪鱼捕捞产量最高的海区。为了解和预测中西太平洋黄鳍金枪鱼不同渔业对气候变化的反应，根据1990—2020年世界各国在中西太平洋的围网和延绳钓作业以及海洋尼诺指数 (Oceanic Niño index, ONI) 数据，分析了常规自回归积分滑动平均模型 (Autoregressive Integrated Moving Average Model, ARIMA) 和加入ONI标准差为协变量的动态ARIMA模型在渔业资源量研究中的适用性，以及ONI对中西太平洋黄鳍金枪鱼年际单位捕捞努力量渔获量 (Catch per unit effort, CPUE) 的影响。结果表明：1) 常规ARIMA模型能够充分考虑中西太平洋黄鳍金枪鱼年CPUE的变化特征，可用于黄鳍金枪鱼年CPUE的长期拟合；2) 相比常规ARIMA模型，动态ARIMA模型的拟合度更好，拟合值和真实值的相关性更高，同时平均绝对误差、均方根误差更小；3) ONI对中西太平洋赤道南北海域黄鳍金枪鱼的年CPUE影响不同，相对而言，在赤道以北，ONI的影响因素更关键，模型的拟合度更高；4) ONI对中西太平洋不同渔业的黄鳍金枪鱼的年CPUE影响有差别，对中西太平洋黄鳍金枪鱼延绳钓渔业存在滞后1~2年的影响，而在强厄尔尼诺和强拉尼娜现象时，对围网渔业的影响速度较快，不存在滞后。
- 黄鳍金枪鱼 /
- 动态ARIMA /
- CPUE /
- 海洋尼诺指数 /
- 中西太平洋
Abstract: 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.
- Thunnus albacares /
- Dynamic ARIMA /
- CPUE /
- Oceanic Niño index /
- Western and Central Pacific Ocean

HTML全文

图 1 一阶差分后的自相关函数 (ACF) 和偏相关函数 (PACF) 图

注：a—b. 延绳钓赤道以北；c—d. 延绳钓赤道以南；e—f. 围网赤道以北；g—h. 围网赤道以南；图2—图4同此。

Figure 1. ACF and PACF diagrams after first difference

Note: a–b. North of the equator for longline; c–d. South of the equator for longline; e–f. North of the equator for purse seine; g–h. South of the equator for purse seine. The same case in Fig. 2–Fig. 4.

下载: 全尺寸图片幻灯片

图 2 常规ARIMA模型残差序列自相关函数 (ACF) 和偏相关函数 (PACF) 图

Figure 2. ACF and PACF diagrams of general ARIMA model residual sequence

下载: 全尺寸图片幻灯片

图 3 动态 ARIMA模型拟合残差检验 (ACF 和 Q-Q 图)

Figure 3. Residual test of dynamic ARIMA model (ACF and Q-Q diagrams)

下载: 全尺寸图片幻灯片

图 4 常规 ARIMA 模型和动态 ARIMA 模型的拟合优度比较

Figure 4. Comparison of goodness of fit between general ARIMA model and dynamic ARIMA model

下载: 全尺寸图片幻灯片

图 5 1990—2020 年中西太平洋不同渔业黄鳍金枪鱼年 CPUE 模型拟合值与实际值关系

a. 延绳钓赤道以北；b. 延绳钓赤道以南；c. 围网赤道以北；d. 围网赤道以南。

Figure 5. Relationship between fitted and actual values of annual CPUE model for yellowfin tuna from different fisheries in Western and Central Pacific during 1990−2020

a. North of the equator for longline; b. South of the equator for longline; c. North of the equator for purse seine; d. South of the equator for purse seine.

下载: 全尺寸图片幻灯片

表 1 不同区域渔业的水平时间序列和一阶拆分序列的单位根检验表

Table 1. Unit root test of horizontal sequence and difference sequence for various regional fisheries

时间序列　　　　 Time series	数据处理 Data processing	单位根检验 Unit root test	显著性水平 Significance level
时间序列　　　　 Time series	数据处理 Data processing	单位根检验 Unit root test	1%	5%	10%
延绳钓赤道以北 North of equator for longline	水平序列	−1.296	−2.62	−1.95	−1.61
延绳钓赤道以北 North of equator for longline	一阶差分序列	−4.922	−4.15	−3.50	3.18
延绳钓赤道以南 South of equator for longline	水平序列	−0.586	−2.62	−1.95	−1.61
延绳钓赤道以南 South of equator for longline	一阶差分序列	−5.571	−4.15	−3.50	3.18
围网赤道以北 North of equator for purse seine	水平序列	−0.746	−2.62	−1.95	−1.61
围网赤道以北 North of equator for purse seine	一阶差分序列	−4.818	−4.15	−3.50	3.18
围网赤道以南 South of equator for purse seine	水平序列	−0.808	−2.62	−1.95	−1.61
围网赤道以南 South of equator for purse seine	一阶差分序列	−4.818	−4.15	−3.50	3.18
注：粗体字表示该序列为平稳时间序列。 Note: The boldface numbers indicate that the series is a stationary time series.

下载: 导出CSV

表 2 ARIMA 时间序列全部可能模型以及参数评价

Table 2. All possible models and evaluation of parameters of ARIMA time series

时间序列 Time series	一般 ARIMA 模型 General ARIMA model	模型赤池信息量准则 AIC	小样本校正的 AIC AICc	贝叶斯信息准则 BIC
延绳钓赤道以北 North of equator for longline	(0,1,0)	81.60	81.74	83.00
	(0,1,1)	83.06	83.51	85.87
	(1,1,0)	83.29	83.73	86.09
	(1,1,1)	83.69	84.62	87.9
延绳钓赤道以南 South of equator for longline	(0,1,1)	98.58	99.03	101.39
	(0,1,4)	97.14	99.64	104.15
	(1,1,1)	100.57	101.49	104.77
	(1,1,4)	98.51	102.16	106.92
围网赤道以北 North of equator for purse seine	(0,1,0)	116.51	116.65	117.91
	(0,1,1)	110.06	110.51	112.86
	(1,1,0)	109.95	110.39	112.75
	(1,1,1)	111.62	112.54	115.82
围网赤道以南 South of equator for purse seine	(2,1,0)	103.42	104.35	107.63
	(0,1,2)	102.8	103.72	107.00
	(2,1,2)	103.93	106.43	110.94
	(0,1,0)	110.24	110.39	111.64
注：粗体字表示该模型为最优模型。 Note: The boldface numbers indicate the optimal model.

下载: 导出CSV

参考文献(45)

[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