ZHANG Jiarong, YANG Xiaoming, DAI Xiaojie, ZOU Lijin. Relationship between catch rate of longline albacore (Thunnus alalunga) and environmental factors in South Pacific[J]. South China Fisheries Science, 2020, 16(1): 69-77. DOI: 10.12131/20190178
Citation: ZHANG Jiarong, YANG Xiaoming, DAI Xiaojie, ZOU Lijin. Relationship between catch rate of longline albacore (Thunnus alalunga) and environmental factors in South Pacific[J]. South China Fisheries Science, 2020, 16(1): 69-77. DOI: 10.12131/20190178

Relationship between catch rate of longline albacore (Thunnus alalunga) and environmental factors in South Pacific

More Information
  • Received Date: September 08, 2019
  • Revised Date: October 24, 2019
  • Available Online: December 02, 2019
  • Based on the data of albacore logbook (Thunnus alalunga) collected by mainland China from 2015–2017 in the South Pacific and the marine environmental data in the same period, we analyzed the relationship between catch rate and environmental factors, so as to examine the effects of environmental factors at different depths on T. alalunga by establishing a GAM (Generalized additive model). In addition, we had obtained the correlation coefficient of each environmental factor (those with large correlation were grouped and modeled) by correlation analysis. The results show that: 1) Sea surface temperature and sea temperature at depth of 120 m, sea surface temperature and sea surface height, sea temperature at depth of 120 m and sea surface height, sea temperature and sea salinity at depth of 300 m were highly correlated factors. However, sea surface salinity, chlorophyll a concentration and northward sea surface wind had no significant correlation with the other environmental factors. 2) The explained cumulative deviance was 30%–40%; the environmental factors sorted by importance are as follows: sea temperature at depth of 120 m, sea surface temperature, sea temperature at depth of 300 m, sea salinity at depth of 120 m, sea surface height, sea salinity at depth of 300 m, sea surface salinity, mixed layer depth, northward sea surface wind, eastward sea surface wind and chlorophyll a concentration. 3) The sea temperature at depth of 120 m was negatively correlated with CPUE (Catch per unit effort) at 15–30 ℃. The trend of sea surface temperature was similar to the sea temperature at depth of 120 m, with a positive correlation at 25–28 ℃. The sea temperature at depth of 300 m and CPUE showed a significant positive relationship at 10–18 ℃.
  • [1]
    TREMBLAY B L, HAMPTON J, MCKECHNIE S, et al. Stock assessment of South Pacific albacore tuna (WCPFC-SC14-2018/SA-WP-05)[R]. Busan, Republic of Korea: The Pacific Community (SPC), 2018: 13.
    [2]
    BRIAND K, MOLONY B, LEHODEY P. A study on the variability of albacore (Thunnus alalunga) longline catch rates in the southwest Pacific Ocean[J]. Fish Oceanogra, 2011, 20(6): 517-529. doi: 10.1111/j.1365-2419.2011.00599.x
    [3]
    宋利明, 谢凯, 赵海龙, 等. 库克群岛海域海洋环境因子对长鳍金枪鱼渔获率的影响[J]. 海洋通报, 2017(1): 96-106. doi: 10.11840/j.issn.1001-6392.2017.01.013
    [4]
    NIKOLIC N, MORANDEAU G, HOARAU L, et al. Review of albacore tuna, Thunnus alalunga, biology, fisheries and management[J]. Rev Fish Biol Fish, 2017, 27: 775-810. doi: 10.1007/s11160-016-9453-y
    [5]
    SAGARMINAGA Y, ARRIZABALAGA H. Spatio-temporal distribution of albacore (Thunnus alalunga) catches in the northeastern Atlantic: relationship with the thermal environment[J]. Fish Oceanogr, 2010, 19(2): 121-134. doi: 10.1111/j.1365-2419.2010.00532.x
    [6]
    DOMOKOS R, SEKI M P, POLOVINA J J, et al. Oceanographic investigation of the American Samoa albacore (Thunnus alalunga) habitat and longline fishing grounds[J]. Fish Oceanogr, 2007, 16(6): 555-572. doi: 10.1111/j.1365-2419.2007.00451.x
    [7]
    毛江美, 陈新军, 余景. 基于神经网络的南太平洋长鳍金枪鱼渔场预报[J]. 海洋学报, 2016, 38(10): 34-43.
    [8]
    闫敏, 张衡, 伍玉梅, 等. 基于GAM模型研究时空及环境因子对南太平洋长鳍金枪鱼渔场的影响[J]. 大连海洋大学学报, 2015, 30(6): 681-685.
    [9]
    GONI N, DIDOUAN C, ARRIZABALAGA H, et al. Effect of oceanographic parameters on daily albacore catches in the Northeast Atlantic[J]. Deep-Sea Res II, 2015, 113: 73-80.
    [10]
    杨胜龙, 张忭忭, 唐宝军, 等. 基于GAM模型分析水温垂直结构对热带大西洋大眼金枪鱼渔获率的影响[J]. 中国水产科学, 2017, 24(4): 875-883.
    [11]
    王茹琳, 李庆, 封传红, 等. 基于MaxEnt的西藏飞蝗在中国的适生区预测[J]. 生态学报, 2017, 37(24): 8556-8566.
    [12]
    周兆丁, 吕锟, 沈瑾, 等. 统计软件SPSS相关分析及应用[J]. 电脑知识与技术, 2019, 15(20): 301-302.
    [13]
    徐国强, 朱文斌, 张洪亮, 等. 基于GAM模型分析印度洋大眼金枪鱼和黄鳍金枪鱼渔场分布与不同环境因子关系[J]. 海洋学报, 2018, 40(12): 70-82.
    [14]
    VENABLES W N, DICHMONT C M. GLMs, GAMs and GLMMs: an overview of theory for applications in fisheries research[J]. Fish Res, 2004, 70(2/3): 319-337.
    [15]
    王军, 傅伯杰, 邱扬, 等. 黄土丘陵小流域土壤水分的时空变异特征——半变异函数[J]. 地理学报, 2000, 55(4): 428-438. doi: 10.3321/j.issn:0375-5444.2000.04.005
    [16]
    朱耿平, 刘强, 高玉葆. 提高生态位模型转移能力来模拟入侵物种的潜在分布[J]. 生物多样性, 2014, 22(2): 223-230.
    [17]
    YANG X Q, KUSHWAHA S, SARAN S, et al. Maxent modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda L. in Lesser Himalayan foothills[J]. Ecol Eng, 2013, 51: 83-87. doi: 10.1016/j.ecoleng.2012.12.004
    [18]
    CHEN I C, LEE P F, TZENG W N. Distribution of albacore (Thunnus alalunga) in the Indian Ocean and its relation to environmental factors[J]. Fish Oceanogr, 2005, 14(1): 71-80. doi: 10.1111/j.1365-2419.2004.00322.x
    [19]
    LAN K W, KAWAMURA H, LEE M A, et al. Relationship between albacore (Thunnus alalunga) fishing grounds in the Indian Ocean and the thermal environment revealed by cloud-free microwave sea surface temperature[J]. Fish Res, 2012, 113(1): 1-7. doi: 10.1016/j.fishres.2011.08.017
    [20]
    储宇航, 戴小杰, 田思泉, 等. 南太平洋延绳钓长鳍金枪鱼生物学组成及其与栖息环境关系[J]. 海洋渔业, 2016, 38(2): 130-139. doi: 10.3969/j.issn.1004-2490.2016.02.003
    [21]
    樊伟, 张晶, 周为峰. 南太平洋长鳍金枪鱼延绳钓渔场与海水表层温度的关系分析[J]. 大连水产学院学报, 2007, 22(5): 366-371.
    [22]
    郭刚刚, 张胜茂, 樊伟, 等. 南太平洋长鳍金枪鱼垂直活动水层空间分析[J]. 南方水产科学, 2016, 12(5): 123-130. doi: 10.3969/j.issn.2095-0780.2016.05.016
    [23]
    HOYLE S, LANGLEY A, HAMPTON J. Stock assessment of albacore tuna in the south Pacific Ocean (WCPFC-SC4-2008/SA-WP-8)[R]. Port Moresby, Papua New Guinea: The Pacific Community (SPC), 2008: 7.
    [24]
    WILLIAMS A J, ALLAIN V, NICOL S J, et al. Vertical behavior and diet of albacore tuna (Thunnus alalunga) vary with latitude in the South Pacific Ocean[J]. Deep-Sea Res II, 2015, 113: 154-169.
    [25]
    范永超, 戴小杰, 朱江峰, 等. 南太平洋长鳍金枪鱼延绳钓渔业CPUE标准化[J]. 海洋湖沼通报, 2017(1): 122-132.
    [26]
    蒋汉凌.南太平洋长鳍金枪鱼渔场与环境因素关系的研究[D]. 上海: 上海海洋大学, 2014: 29.
    [27]
    宋婷婷, 樊伟, 伍玉梅. 卫星遥感海面高度数据在渔场分析中的应用综述[J]. 海洋通报, 2013, 32(4): 474-480. doi: 10.11840/j.issn.1001-6392.2013.04.017
    [28]
    杨晓明, 王学昉, 田思泉, 等. 赤道太平洋中部围网自由群的空间点模式的影响因子[J]. 水产学报, 2018, 42(8): 1220-1228.
    [29]
    SAGARMINAGA Y, ARRIZABALAGA H. Relationship of Northeast Atlantic albacore juveniles with surface thermal and chlorophyll-a fronts[J]. Deep-Sea Res II, 2014, 107: 54-63.
    [30]
    赖诗涵.中西太平洋鲔延绳钓黄鳍鲔潜在栖地分布与预测模式建置之研究[D]. 基隆: 国立台湾海洋大学, 2018: 18.
    [31]
    BAKUN A, BLACK B A, BOGRAD S J, et al. Anticipated effects of climate change on coastal upwelling ecosystems[J]. Curr Clim Change Rep, 2015, 1(2): 85-93. doi: 10.1007/s40641-015-0008-4
    [32]
    陈芃, 陈新军, 雷林. 秘鲁上升流对秘鲁鳀渔场的影响[J]. 水产学报, 2018, 36(9): 1367-1377.
  • Related Articles

    [1]LI Lu, LIN Dongming. Study on prey species biodiversity in Southeast Pacific based on Dosidicus gigas stomach analysis[J]. South China Fisheries Science. DOI: 10.12131/20240293
    [2]LI Dongxu, ZOU Xiaorong, ZHOU Shuting. Spatio-temporal distribution of Thunnus albacares CPUE and its relationship with environmental factors in central Pacific Ocean[J]. South China Fisheries Science, 2024, 20(4): 68-76. DOI: 10.12131/20240047
    [3]FEI Jiaojiao, LI Cheng, ZHANG Jian, TENG Yuxiu, WU Yuntao, SHI Jiangao. Effects of seamount characteristics in Central and Western Pacific Ocean on CPUEs of yellowfin tuna (Thunnus albacares) in longline and purse seine fisheries[J]. South China Fisheries Science, 2024, 20(2): 1-10. DOI: 10.12131/20230200
    [4]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
    [5]WANG Zhihua, YANG Xiaoming, TIAN Siquan. Spatial pattern characteristics of albacore tuna resources at different spatial scales in South Pacific[J]. South China Fisheries Science, 2023, 19(2): 31-41. DOI: 10.12131/20220046
    [6]BAI Siqi, ZOU Xiaorong, ZHANG Peng, DING Peng. Study on spatial heterogeneity effect of environmental factors on distribution of Chilean jack mackerel in Southeast Pacific Ocean[J]. South China Fisheries Science, 2021, 17(1): 17-24. DOI: 10.12131/20200172
    [7]HOU Juan, ZHOU Weifeng, FAN Wei, ZHANG Heng. Research on fishing grounds forecasting models of albacore tuna based on ensemble learning in South Pacific[J]. South China Fisheries Science, 2020, 16(5): 42-50. DOI: 10.12131/20200022
    [8]GUO Ganggang, ZHANG Shengmao, FAN Wei, CHEN Xinjun, YANG Shenglong. Spatial analysis of vertical active layer of albacore tuna (Thunnus alalunga) in the South Pacific Ocean[J]. South China Fisheries Science, 2016, 12(5): 123-130. DOI: 10.3969/j.issn.2095-0780.2016.05.016
    [9]ZHAI Tianchen, DAI Xiaojie, ZHU Jiangfeng, CHEN Liwen. Gonad maturity stage of female bigeye tuna (Thunnus obesus) in the southern Pacific Ocean[J]. South China Fisheries Science, 2016, 12(1): 102-110. DOI: 10.3969/j.issn.2095-0780.2016.01.014
    [10]CHEN Chunguang, ZHANG Min, ZOU Xiaorong, LU Qiwei, XU Xiao, LIANG Yanwei. Investigation on monthly variation in central fishing ground for Chilean jack mackerel (Trachurus murphyi) in the Southeast Pacific Ocean[J]. South China Fisheries Science, 2014, 10(5): 60-67. DOI: 10.3969/j.issn.2095-0780.2014.05.009
  • Cited by

    Periodical cited type(13)

    1. 赵诣,袁红春. 基于多通道单回归的太平洋长鳍金枪鱼渔场预测模型与可解释性研究. 水生生物学报. 2025(03): 15-27 .
    2. 姚紫荆,杨晓明,吴峰,田思泉. 基于参数最优地理探测器的南太平洋长鳍金枪鱼渔业资源分布驱动力研究. 海洋渔业. 2025(02): 153-162 .
    3. 杜艳玲,马玉玲,汪金涛,陈珂,林泓羽,陈刚. 基于ConvLSTM-CNN预测太平洋长鳍金枪鱼时空分布趋势. 海洋通报. 2024(02): 174-187 .
    4. 李东旭,邹晓荣,周淑婷. 中太平洋黄鳍金枪鱼CPUE时空分布及其与环境因子的关系. 南方水产科学. 2024(04): 68-76 . 本站查看
    5. 丁鹏,邹晓荣,许回,丁淑仪,白思琦,张子辉. 基于BP神经网络的长鳍金枪鱼渔获量与气候因子关系研究. 海洋学报. 2024(09): 88-95 .
    6. 何露雪,付东洋,李忠炉,王焕,孙琰,刘贝,余果. 南海西北部蓝圆鲹时空分布及其与环境因子的关系. 渔业科学进展. 2023(01): 24-34 .
    7. 王志华,杨晓明,田思泉. 南太平洋长鳍金枪鱼资源不同尺度的空间格局特征. 南方水产科学. 2023(02): 31-41 . 本站查看
    8. 韩霈武,王岩,方舟,陈新军. 北太平洋柔鱼不同群体耳石日增量对海洋环境的响应研究. 海洋学报. 2022(01): 101-112 .
    9. 宋利明,任士雨,洪依然,张天蛟,隋恒寿,李彬,张敏. 大西洋热带海域长鳍金枪鱼渔场预报模型的比较. 海洋与湖沼. 2022(02): 496-504 .
    10. 方伟,周胜杰,赵旺,杨蕊,胡静,于刚,马振华. 黄鳍金枪鱼5月龄幼鱼形态性状对体质量的相关性及通径分析. 南方水产科学. 2021(01): 52-58 . 本站查看
    11. 周胜杰,杨蕊,于刚,吴洽儿,马振华. 青干金枪鱼和小头鲔循环水养殖生长研究. 水产科学. 2021(03): 339-346 .
    12. 宋利明,许回. 金枪鱼延绳钓渔获性能研究进展. 中国水产科学. 2021(07): 925-937 .
    13. 谢笑艳,汪金涛,陈新军,陈丕茂. 南印度洋长鳍金枪鱼渔获率与水深温度关系研究. 南方水产科学. 2021(05): 86-92 . 本站查看

    Other cited types(15)

Catalog

    Recommendations
    Interannual variation of fish communities and their environmental factors in pearl river estuary from 2018 to 2023
    MA Jingjing et al., SOUTH CHINA FISHERIES SCIENCE, 2024
    Research progress and prospects on benefit assessment of marine ranching
    YUAN Huarong et al., SOUTH CHINA FISHERIES SCIENCE, 2024
    Assessment of fishery resources in southern sea area of yintan marine ranching, guangxi province
    NIU Lulian et al., SOUTH CHINA FISHERIES SCIENCE, 2024
    Characteristics of fish community structure and its relationship with environmental factors in marine ranching zone in southern area of yintan in guangxi
    YU Jie et al., SOUTH CHINA FISHERIES SCIENCE, 2024
    Effects of oceanic mesoscale eddies on longline catches ofthunnus alalungain the south pacific ocean
    ZHENG Chunwen et al., JOURNAL OF SHANGHAI OCEAN UNIVERSITY, 2025
    Inertia force coefficient and resistance coefficient of monofilament and rope in tuna longline gear#br##br#
    SONG Liming et al., FISHERY MODERNIZATION, 2024
    Analyzing the spatio-temporal correlation between tide and shipping behavior at estuarine port for energy-saving purposes
    Shu, Yaqing et al., APPLIED ENERGY, 2024
    A fishery predator-prey model with anti-predator behavior and complex dynamics induced by weighted fishing strategies
    Tian, Yuan et al., MATHEMATICAL BIOSCIENCES AND ENGINEERING, 2023
    Dependence of daily precipitation and wind speed over coastal areas: evidence from china's coastline
    HYDROLOGY RESEARCH, 2023
    Long term capturability of atmospheric water on a global scale
    WATER RESOURCES RESEARCH
    Powered by
    Article views (4677) PDF downloads (49) Cited by(28)
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return