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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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 (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的影响有差别，ONI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，ONI对围网渔业的影响速度较快，不存在滞后。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, and 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.
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Key words:
- Thunnus albacares /
- Dynamic ARIMA /
- CPUE /
- Oceanic Niño index /
- Tropical Western Central Pacific Ocean
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图 1 一阶差分后的自相关函数 (ACF) 和偏相关函数 (PACF) 图
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。
Figure 1. ACF and PACF diagrams after first difference
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.
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
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。
Figure 2. ACF and PACF diagrams of general ARIMA model residual sequence
+ 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.
图 3 动态 ARIMA模型拟合残差检验 (ACF和Q-Q图)
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。
Figure 3. Residual test of dynamic ARIMA model (ACF and Q-Q diagrams)
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.
图 4 常规ARIMA模型和动态ARIMA模型的拟合优度比较
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。
Figure 4. Goodness of fit comparison between general ARIMA model and dynamic ARIMA model
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.
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 from 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.
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 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 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 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 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 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. 
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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 modelAIC AICc BIC 延绳钓赤道以北
North of equator for
longline(0,1,0) 81.6 81.74 83 (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 (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 boldfaced numbers indicate that the model is the optimal model.
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