Suitability analysis of wind data for habitat forecasting of the Pacific saury fishery in northwestern Pacific Ocean

LIU Yu; XU Ying; ZHENG Quan-an

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LIU Yu，XU Ying，ZHENG Quan-an. Suitability analysis of wind data for habitat forecasting of the Pacific saury fishery in northwestern Pacific Ocean[J]. Haiyang Xuebao，2024, 46(x)：1–9

Citation:

LIU Yu，XU Ying，ZHENG Quan-an. Suitability analysis of wind data for habitat forecasting of the Pacific saury fishery in northwestern Pacific Ocean[J]. Haiyang Xuebao，2024, 46(x)：1–9

Citation:

LIU Yu，XU Ying，ZHENG Quan-an. Suitability analysis of wind data for habitat forecasting of the Pacific saury fishery in northwestern Pacific Ocean[J]. Haiyang Xuebao，2024, 46(x)：1–9

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Suitability analysis of wind data for habitat forecasting of the Pacific saury fishery in northwestern Pacific Ocean

LIU Yu^{1, 2
,},
XU Ying^{3, 4
,
,},
ZHENG Quan-an⁵

1.
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China
2.
College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
3.
National Satellite Ocean Application Service, Beijing 100081, China
4.
Key Laboratory of Space Ocean Remote Sensing and Application, Ministry of Natural Resources, Beijing 100081, China
5.
College of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang 524088, China

Available Online: 2024-08-19

Abstract

Abstract

To analysis the suitability of using wind field data for forecasting Pacific saury habitat in the northwest Pacific, this paper use the generalized additive model to fit the habitat suitability index (HSI) for Pacific saury in summer and autumn, based on the Chinese fishery data, environmental data and four types of wind field data included the China-France oceanography satellite (CFOSAT) during June to November in 2019−2020.Result indicates that, (1) Weighted analysis shows distinct seasonal variation of environmental variables on catch per unit effort, with chlorophyll concentration and sea surface temperature having the highest weights in summer and autumn, respectively, while wind speed had the lowest weight and directly proportional to the weight. (2) The average accuracy of the four data in summer and autumn is 68.37% and 76.65%, respectively, and CFOSAT reaching the highest accuracy of 80.94% in autumn. (3) The high-HSI areas are consistent with the fishing grounds of Pacific saury, while the high-value regions of scatter meter in autumn show clear and focus. The advantages of using wind speed on the forecast model in autumn, which can explain the influence of transient variation factors on the migration and aggregation of Pacific saury.
- pacific saury fishing ground,
- GAM,
- Habitat forecast,
- CFOSAT,
- Northwestern Pacific Ocean

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References(21)

References

[1]	Zavolokin A. Priority species[EB/OL]. [2018-06-09]. https://www.npfc.int/priority-species. (查阅网上资料,请核对网址与文献是否相符 Zavolokin A. Priority species[EB/OL]. [2018-06-09]. https://www.npfc.int/priority-species. (查阅网上资料,请核对网址与文献是否相符)
[2]	花传祥, 朱清澄, 许巍, 等. 北太平洋秋刀鱼生活史和资源渔场研究进展[J]. 中国水产科学, 2019, 26(4): 811−821. Hua Chuanxiang, Zhu Qingcheng, Xu Wei, et al. Review of the life history, resources and fishing grounds of the Pacific saury in the North Pacific Ocean[J]. Journal of Fishery Sciences of China, 2019, 26(4): 811−821.
[3]	孟令文, 朱清澄, 花传祥, 等. 栖息地指数模型在北太公海秋刀鱼渔情预报中的应用[J]. 海洋湖沼通报, 2018(6): 142−149. Meng Lingwen, Zhu Qingcheng, Hua Chuanxiang, et al. Study on fishery forecast of Cololabis saira in the Northen Pacific based on habitat model[J]. Transactions of Oceanology and Limnology, 2018(6): 142−149.
[4]	刘瑜, 花传祥. 基于GAM和权重分析的西北太平洋秋刀鱼渔情预报研究[J]. 中国水产科学, 2021, 28(7): 888−895. Liu Yu, Hua Chuanxiang. Forecasting Pacific saury (Cololabis saira) fisheries based on GAM and weighted analysis in the northwest Pacific[J]. Journal of Fishery Sciences of China, 2021, 28(7): 888−895.
[5]	Syah A F, Saitoh S I, Alabia I D, et al. Predicting potential fishing zones for Pacific saury (Cololabis saira) with maximum entropy models and remotely sensed data[J]. Fishery Bulletin, 2016, 114(3): 330−342. doi: 10.7755/FB.114.3.6
[6]	Miyamoto H, Suyama S, Vijai D, et al. Predicting the timing of Pacific saury (Cololabis saira) immigration to Japanese fishing grounds: a new approach based on natural tags in otolith annual rings[J]. Fisheries Research, 2019, 209: 167−177. doi: 10.1016/j.fishres.2018.09.016
[7]	朱文涛, 陈新军, 汪金涛, 等. 基于灰色系统的西北太平洋秋刀鱼资源丰度预测[J]. 广东海洋大学学报, 2018, 38(6): 13−17. Zhu Wentao, Chen Xinjun, Wang Jintao, et al. Predicting the abundance of Pacific saury based on grey system in the northwest Pacific[J]. Journal of Guangdong Ocean University, 2018, 38(6): 13−17.
[8]	吴祖立, 唐峰华, 樊伟, 等. 西北太平洋柔鱼渔场台风时空变化及其对柔鱼产量的影响分析[J]. 海洋环境科学, 2018, 37(6): 907−913. Wu Zuli, Tang Fenghua, Fan Wei, et al. Analysis of temporal and spatial variation of typhoons in the squid fishing ground in the northwest Pacific Ocean and its influence on the catch of Ommastrephes Bartramii[J]. Marine Environmental Science, 2018, 37(6): 907−913.
[9]	Sun Junchuan, Wei Zexun, Xu Tengfei, et al. Development of a fine-resolution atmosphere-wave-ocean coupled forecasting model for the South China Sea and its adjacent seas[J]. Acta Oceanologica Sinica, 2019, 38(4): 154−166. doi: 10.1007/s13131-019-1419-1
[10]	刘建强, 蒋兴伟, 郎姝燕, 等. 中法海洋卫星及其典型应用[J]. 卫星应用, 2021(9): 41−48. (查阅网上资料, 未找到对应的英文翻译, 请补充
[11]	Hua Chuanxiang, Li Fei, Zhu Qingcheng, et al. Habitat suitability of Pacific saury (Cololabis saira) based on a yield-density model and weighted analysis[J]. Fisheries Research, 2020, 221: 105408. doi: 10.1016/j.fishres.2019.105408
[12]	Elith J, Leathwick J R, Hastie T. A working guide to boosted regression trees[J]. Journal of Animal Ecology, 2008, 77(4): 802−813. doi: 10.1111/j.1365-2656.2008.01390.x
[13]	Howell E A, Kobayashi D R. El Niño effects in the Palmyra Atoll region: oceanographic changes and bigeye tuna (Thunnus obesus) catch rate variability[J]. Fisheries Oceanography, 2006, 15(6): 477−489. doi: 10.1111/j.1365-2419.2005.00397.x
[14]	范秀梅, 唐峰华, 崔雪森, 等. 基于栖息地指数的西北太平洋日本鲭渔情预报模型构建[J]. 海洋学报, 2020, 42(12): 34−43. Fan Xiumei, Tang Fenghua, Cui Xuesen, et al. Habitat suitability index for chub mackerel (Scomber japonicus) in the Northwest Pacific Ocean[J]. Haiyang Xuebao, 2020, 42(12): 34−43.
[15]	龚彩霞, 陈新军, 高峰, 等. 栖息地适宜性指数在渔业科学中的应用进展[J]. 上海海洋大学学报, 2011, 20(2): 260−269. Gong Caixia, Chen Xinjun, Gao Feng, et al. Review on habitat suitability index in fishery science[J]. Journal of Shanghai Ocean University, 2011, 20(2): 260−269.
[16]	王海平, 董林. 2019年西北太平洋和南海台风活动概述[J]. 海洋气象学报, 2020, 40(2): 1−9. Wang Haiping, Dong Lin. Overview of typhoon activities over western North Pacific and the South China Sea in 2019[J]. Journal of Marine Meteorology, 2020, 40(2): 1−9.
[17]	Hua Chuanxiang, Zhu Qingcheng, Shi Yongchuang, et al. Comparative analysis of CPUE standardization of Chinese Pacific saury (Cololabis saira) fishery based on GLM and GAM[J]. Acta Oceanologica Sinica, 2019, 38(10): 100−110. doi: 10.1007/s13131-019-1486-3
[18]	Kurita Y. Energetics of reproduction and spawning migration for Pacific saury (Cololabis saira)[J]. Fish Physiology and Biochemistry, 2003, 28(1/4): 271−272.
[19]	王同宇, 张书文, 蒋晨, 等. 西北太平洋台风对冷涡及叶绿素浓度的影响[J]. 广东海洋大学学报, 2019, 39(5): 85−95. Wang Tongyu, Zhang Shuwen, Jiang Chen, et al. Research on cold core eddy change and Phytoplankton bloom induced by Typhoons: case studies in Northwest Pacific Ocean[J]. Journal of Guangdong Ocean University, 2019, 39(5): 85−95.
[20]	曹睿星, 官文江, 高峰, 等. 基于最大熵和栖息地指数模型预测东、黄海日本鲭渔场分布[J]. 海洋学报, 2023, 45(9): 72−81. Cao Ruixing, Guan Wenjiang, Gao Feng, et al. Prediction of chub mackerel fishing ground distribution in the East China Sea and Yellow Sea based on maximum entropy model and habitat suitability index model[J]. Haiyang Xuebao, 2023, 45(9): 72−81.
[21]	王雅萌, 汪金涛, 陈新军, 等. 基于BP神经网络的西北太平洋柔鱼资源丰度时空变化研究[J]. 海洋学报, 2021, 43(6): 81−89. Wang Yameng, Wang Jintao, Chen Xinjun, et al. Spatio-temporal dynamic of abundance index of Neon flying squid in relation to environmental variables in the Northwest Pacific Ocean using BP neural network[J]. Haiyang Xuebao, 2021, 43(6): 81−89.