Lag effect of climate change on CPUE of Thunnus albacares and Katsuwonus pelamis in the western and central Pacific Ocean purse seine fishery: An LSTM-Based study
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摘要: 黄鳍金枪鱼（Thunnus albacares）和鲣（Katsuwonus pelamis）是大洋性高度洄游物种，也是全球大洋性渔业的主要捕捞对象，其种群分布和资源密度容易受气候变化所引起的海洋环境变化影响，且存在响应滞后。为了探索气候变化对中西太平洋海域（WCPO）低龄黄鳍金枪鱼和鲣群体影响及滞后效应，本研究基于长短期记忆神经网络(LSTM)分析了海洋尼诺指数（ONI）对1982年至2021年间WCPO围网黄鳍金枪鱼和鲣单位捕捞努力量渔获量（CPUE）的影响，利用不同时间步长模拟不同滞后期（0～12个月）下CPUE对ONI响应。结果表明：LSTM适用于对黄鳍金枪鱼和鲣等大洋性种群资源密度与ONI等环境因素间滞后效应的分析；WCPO赤道南北不同海域围网黄鳍金枪鱼和鲣CPUE对ONI的响应存在滞后，且不同海域的最佳滞后期均为12个月；最佳滞后期与渔获群体年龄相当，表明WCPO黄鳍金枪鱼和鲣的繁殖能力或幼鱼存活率易受到气候变化及其引起的海洋环境变动影响，表现出时长为捕捞年龄的滞后时间。研究方法与结果为后续开展WCPO关键物种群体分布研究提供了资源变动机制上的新思路。Abstract: Yellowfin tuna (Thunnus albacares) and skipjack tuna (Katsuwonus pelamis) are pelagic and highly migratory species, serving as primary targets in global pelagic fisheries. Their population distribution and abundance are susceptible to the impacts of climate-induced changes in the marine environment, exhibiting a response lag. In order to explore the influence of climate change on the juvenile populations of yellowfin tuna and skipjack tuna in the western and central Pacific Ocean (WCPO) and the associated lag effects, this study, based on Long Short-Term Memory (LSTM) neural networks, analyzed the impact of the Oceanic Niño index (ONI) on the Catch per Unit Effort (CPUE) of yellowfin tuna and skipjack tuna in the WCPO purse seine fishery from 1982 to 2021. Different time step lengths were employed to simulate the lag effects (0−12 months) of CPUE response to ONI. The results indicate LSTM is a suitable tool for analyzing the lag effects of relationship between the abundance of pelagic species, such as yellowfin tuna and skipjack tuna, and environmental factors like ONI. In the WCPO regions north and south of the equator, there exists a time lag in the response of juvenile yellowfin tuna and skipjack tuna CPUE to ONI, with the optimal lag period being 12 months for each region. The correspondence of the optimal lag period with the age of the harvested population (nearly 1 year) suggests that the reproductive capacity or survival rate of juvenile yellowfin tuna and skipjack tuna is influenced by climate change and the resulting changes in the marine environment. The research methodology and results provide new insights for subsequent studies in analyzing the stock dynamics and distribution of key species in the WCPO.
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Key words:
- Thunnus albacares /
- Katsuwonus pelamis /
- Oceanic Niño index /
- lag effect /
- LSTM /
- western and central Pacific Ocean
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图 2 中西太平洋围网黄鳍金枪鱼、鲣CPUE及ONI时序
a. 黄鳍金枪鱼赤道以北CPUE，b. 黄鳍金枪鱼赤道以南CPUE，c. 鲣赤道以北CPUE，d. 鲣赤道以南CPUE，e. ONI
Fig. 2 The time series of CPUEs of Thunnus albacares and Katsuwonus pelamis in the purse seine fishery of the western and central Pacific Ocean, and the ONI
a. CPUE of Thunnus albacares in the region north of the equator, b. CPUE of Thunnus albacares in the region south of the equator, c. CPUE of Katsuwonus pelamis in the region north of the equator, d. CPUE of Katsuwonus pelamis in the region south of the equator, e. ONI
图 3 滞后时间12个月下LSTM模型的迭代次数和损失值
a. 黄鳍金枪鱼赤道以北，b. 黄鳍金枪鱼赤道以南，c. 鲣赤道以北，d. 鲣赤道以南
Fig. 3 The iteration count and loss values of the LSTM model with a lag time of 12 months
a. Thunnus albacares in the region north of the equator, b. Thunnus albacares in the region south of the equator, c. Katsuwonus pelamis in the region north of the equator, d. Katsuwonus pelamis in the region south of the equator
图 4 滞后时间12个月下CPUE预测值与实际值的比较
a. 黄鳍金枪鱼赤道以北，b. 黄鳍金枪鱼赤道以南，c. 鲣赤道以北，d. 鲣赤道以南
Fig. 4 Comparison of CPUEs with a lag time of 12 months between predicted and actual values
a. Thunnus albacares in the region north of the equator, b. Thunnus albacares in the region south of the equator, c. Katsuwonus pelamis in the region north of the equator, d. Katsuwonus pelamis in the region south of the equator
表 1 WCPO赤道南北海域黄鳍金枪鱼和鲣CPUE及ONI的分布总况
Tab. 1 Overview of CPUEs of Thunnus albacares and Katsuwonus pelamis in WCPO south and north of the equator, along with the ONI
项目 海域 Min P25 P50 P75 Max 平均 黄鳍金枪鱼CPUE 赤道以北 0.779 2.750 4.823 6.914 18.208 5.118 赤道以南 0 6.129 7.489 9.321 16.460 7.735 鲣CPUE 赤道以北 3.217 9.362 13.380 17.077 32.286 13.491 赤道以南 6.858 14.803 18.758 22.559 35.583 18.777 海洋尼诺指数 \ −1.870 −0.603 −0.050 0.510 2.710 0.016 
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表 2 不同滞后时间下模型预测误差的统计结果
Tab. 2 Statistical results of model prediction errors under different lag time
滞后时间/月 黄鳍金枪鱼 鲣 赤道以北 赤道以南 赤道以北 赤道以南 MAPE MAE RMSE MAPE MAE RMSE MAPE MAE RMSE MAPE MAE RMSE 0 9.90828 0.14917 0.15540 1.79009 0.10379 0.12012 3.28223 0.19211 0.23032 0.75190 0.14909 0.17050 1 2.97018 0.04652 0.06101 2.58407 0.15056 0.16129 2.56779 0.15519 0.18690 0.58860 0.13732 0.17983 2 3.52192 0.06414 0.06885 2.78081 0.14993 0.17108 2.32455 0.14219 0.16376 0.60977 0.12596 0.14845 3 9.52831 0.14418 0.14977 1.37389 0.09502 0.12549 3.84945 0.22736 0.26697 0.79580 0.17736 0.18951 4 7.90549 0.10914 0.12458 1.35588 0.08262 0.09750 5.67477 0.38144 0.39281 0.95240 0.21992 0.25508 5 4.71259 0.06653 0.07531 1.53034 0.08917 0.10222 5.84598 0.36893 0.39745 0.51162 0.10161 0.11268 6 4.69040 0.06422 0.07334 1.39996 0.07823 0.11137 5.46671 0.34900 0.37072 1.28165 0.28839 0.31291 7 6.63161 0.12643 0.13064 1.24703 0.07832 0.10089 5.51127 0.34509 0.37413 0.66225 0.14054 0.15561 8 5.21586 0.08945 0.09143 2.90610 0.16607 0.17832 7.08355 0.45903 0.48260 1.01102 0.21614 0.21883 9 6.85734 0.09821 0.10819 2.59891 0.14291 0.15865 3.05708 0.18717 0.21077 0.64069 0.14251 0.15294 10 9.78094 0.15027 0.15517 1.72018 0.11157 0.13687 4.22249 0.26537 0.28988 0.57465 0.12541 0.15013 11 5.93768 0.08297 0.09254 0.90011 0.06141 0.07853 2.54923 0.16213 0.19257 0.43067 0.09660 0.10529 12 2.53704 0.04456 0.05823 0.85169 0.05599 0.06346 1.01160 0.08631 0.12771 0.35689 0.07898 0.09953 注：加粗字体表示对应最优参数。
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