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应用Catch-MSY模型评估印度洋蓝枪鱼资源

耿喆 朱江峰 王扬 戴小杰

耿喆,朱江峰,王扬,等. 应用Catch-MSY模型评估印度洋蓝枪鱼资源[J]. 海洋学报,2019,41(8):26–35,doi:10.3969/j.issn.0253−4193.2019.08.003
引用本文: 耿喆,朱江峰,王扬,等. 应用Catch-MSY模型评估印度洋蓝枪鱼资源[J]. 海洋学报,2019,41(8):26–35,doi:10.3969/j.issn.0253− 4193.2019.08.003
Geng Zhe,Zhu Jiangfeng,Wang Yang, et al. Stock assessment for Indian Ocean blue marlin ( Makaira nigricans) using Catch-MSY model[J]. Haiyang Xuebao,2019, 41(8):26–35,doi:10.3969/j.issn.0253−4193.2019.08.003
Citation: Geng Zhe,Zhu Jiangfeng,Wang Yang, et al. Stock assessment for Indian Ocean blue marlin ( Makaira nigricans ) using Catch-MSY model[J]. Haiyang Xuebao,2019, 41(8):26–35,doi:10.3969/j.issn.0253− 4193.2019.08.003

应用Catch-MSY模型评估印度洋蓝枪鱼资源

doi: 10.3969/j.issn.0253-4193.2019.08.003
基金项目: 国家自然科学基金(41676120)。
详细信息
    作者简介:

    耿喆(1993—),男,安徽省马鞍山市人,研究方向为渔业资源评估。E-mail:gengzhe1993@sohu.com

    通讯作者:

    朱江峰,男,教授,主要从事渔业资源评估研究和生态学。E-mail: jfzhu@shou.edu.cn

  • 中图分类号: S 931.1

Stock assessment for Indian Ocean blue marlin (Makaira nigricans) using Catch-MSY model

  • 摘要: Catch-MSY模型可仅依靠渔获量数据进行渔业资源评估,在数据缺乏状况下能暂时替代标准资源评估模型。本研究以印度洋蓝枪鱼(Makaira nigricans)为例,根据有、无信息的内禀增长率r和环境容纳量K的先验分布,设立15组情景进行模型灵敏度分析、资源评估和预测。结果表明,参数rK呈强烈的负相关,而最大可持续产量(Maximum Sustainable Yield,MSY)与参数r呈正相关;数据时间序列长度对评估结果影响有限,而模型对起止年渔获量较为敏感。资源状况评估表明,印度洋蓝枪鱼资源生物量状况良好,即B2015/BMSY大于1;而开发状况除其中两种情景外,均为过度捕捞,即F2015/FMSY大于1。资源预测表明,为使未来10年内B/BMSY>1的概率超过50%,需将渔获量缩减至当前渔获量的90%(13.86 kt);考虑到该模型在数据缺乏状况下会更加保守,若将当前渔获量的100%~110%(15.40~16.94 kt)设为管理目标,则未来5年内B/BMSY >1的概率超过50%。
  • 图  1  印度洋蓝枪鱼历史渔获量(1950-2015年)

    Fig.  1  Trend of catch of blue marlin in the Indian Ocean (1950 to 2015)

    图  2  不同情景的参数先验与后验分布

    Fig.  2  Prior and posterior distribution of the key parameters based on different scenarios

    图  3  不同情景下最大可持续产量估算结果

    Fig.  3  Estimated maximum sustainable yield based on different scenarios

    图  4  不同时间序列下的资源评估结果

    a. 相对生物量变动;b. 相对捕捞死亡系数变动

    Fig.  4  Stock assessment result based on different time series

    a. Time trends of relative biomass; b. time trends of relative fishing mortality

    图  5  印度洋蓝枪鱼资源状况(Kobe图)

    a. S1A情景;b. S1B情景;c. S2A情景;d. S3A情景;黑色点为蒙特卡洛模拟结果,灰色点为几何平均值

    Fig.  5  Kobe plots of stock status of the Indian Ocean blue marlin

    a. S1A scenario; b. S1B scenario; c. S2A scenario; d. S3A scenario; black dots based on Monte Carlo simulations; grey dots based on geometric means

    表  1  参数r的经验分布

    Tab.  1  Empirical distribution for parameter r

    恢复力 r分布范围
    0.600~1.500
    适中 0.200~0.800
    0.050~0.500
    很弱 0.015~0.100
    下载: 导出CSV

    表  2  评估起止年资源比率

    Tab.  2  Default values for initial and final stock status

    起止年渔获量/CMAX B/K
    起始年 <0.5 0.50~0.90
    ≥0.5 0.30~0.60
    终止年 >0.5 0.30~0.70
    ≤0.5 0.01~0.40
    下载: 导出CSV

    表  3  印度洋蓝枪鱼Catch-MSY模型情景设置

    Tab.  3  Scenarios of the Indian Ocean blue marlin Catch-MSY model

    情景 自然死亡系数 r K 渔获量时间序列
    S1A U[0,1] ln(K)~U[ln(2×CMAX),ln(50×CMAX)] 1950–2015年
    S1B U[0,1] lognormal(10×CMAX,0.4) 1950–2015年
    S2A LM(M,0.1) 2ωM ln(K)~U[ln(2×CMAX),ln(50×CMAX)] 1950–2015年
    S2B LM(M,0.1) 2ωM lognormal(10×CMAX,0.4) 1950–2015年
    S3A LM(1.5M,0.1) 2ωM ln(K)~U[ln(2×CMAX),ln(50×CMAX)] 1950–2015年
    S3B LM(1.5M,0.1) 2ωM lognormal(10×CMAX,0.4) 1950–2015年
    S4A LM(0.5M,0.1) 2ωM ln(K)~U[ln(2×CMAX),ln(50×CMAX)] 1950–2015年
    S4B LM(0.5M,0.1) 2ωM lognormal(10×CMAX,0.4) 1950–2015年
    S5A LM(2M,0.1) 2ωM ln(K)~U[ln(2×CMAX),ln(50×CMAX)] 1950–2015年
    S5B LM(2M,0.1) 2ωM lognormal(10×CMAX,0.4) 1950–2015年
    S6_1960 U[0,1] ln(K)~U[ln(2×CMAX), ln(50×CMAX)] 1960–2015年
    S6_1970 U[0,1] ln(K)~U[ln(2×CMAX), ln(50×CMAX)] 1970–2015年
    S6_1980 U[0,1] ln(K)~U[ln(2×CMAX), ln(50×CMAX)] 1980–2015年
    S6_1990 U[0,1] ln(K)~U[ln(2×CMAX), ln(50×CMAX)] 1990–2015年
    S6_2000 U[0,1] ln(K)~U[ln(2×CMAX), ln(50×CMAX)] 2000–2016年
      注:0.5M、1.5M、2M表示自然死亡系数均值(0.22 a–1)乘以0.5、1.5、2倍;U[0,1] 表示服从0到1的均匀分布;LM(M,0.1)表示服从均值为M,标准差为0.1的对数正态分布。
    下载: 导出CSV

    表  4  印度洋蓝枪鱼管理参考点及部分参数

    Tab.  4  Management related parameters and reference points of the Indian Ocean blue marlin

    参数和参考点 S1A S1B S2A S2B S3A S3B
    r/a-1 0.32 0.32 0.38 0.38 0.57 0.56
    K/kt 146.00 151.73 130.41 135.49 95.92 102.34
    MSY/kt 12.07 12.14 12.48 12.82 13.70 14.31
    BMSY/kt 73.00 75.87 65.21 67.75 47.96 51.17
    B2015/BMSY 1.16 1.16 1.15 1.20 1.20 1.26
    FMSY/a-1 0.18 0.17 0.21 0.21 0.34 0.33
    F2015/FMSY 1.11 1.11 1.08 1.01 0.93 0.84
    下载: 导出CSV

    表  5  Kobe II管理策略矩阵决策表

    Tab.  5  Decision table of Kobe II strategy matrix

    参考点 60% 70% 80% 90% 100% 110% 120% 130% 140%
    9 240 t 10 780 t 12 320 t 13 860 t 15 400 t 16 940 t 18 480 t 20 020 t 21 560 t
    B2020>BMSY 84.99 81.42 76.79 70.69 62.85 53.46 43.70 33.83 24.08
    F2020<FMSY 82.29 72.59 59.75 43.76 26.71 10.84 0.00 0.00 0.00
    B2025>BMSY 86.13 79.97 70.99 58.83 43.48 27.22 11.38 3.36 1.89
    F2025<FMSY 83.36 72.99 58.20 39.52 19.53 2.95 0.00 0.00 0.00
    B2030>BMSY 86.33 78.71 67.13 51.80 33.20 14.45 0.98 0.25 0.05
    F2030<FMSY 83.90 72.97 57.05 36.96 16.31 1.58 0.00 0.00 0.00
    B2035>BMSY 86.32 77.66 64.48 47.27 26.88 7.81 0.03 0.00 0.00
    F2035<FMSY 84.14 72.87 56.14 35.41 14.75 1.21 0.00 0.00 0.00
      注:描述Byear>BMSYFyear<FMSY,绿色代表两个指标均大于50%,黄色代表仅有一个大于50%,红色代表均小于50%。
    下载: 导出CSV
  • [1] Squire J L. Migration patterns of Istiophoridae in the Pacific Ocean as determined by cooperative tagging programs[C]//Proceedings of the International Billfish Symposium. Hawaii: NOAA, 1974, 2: 226–237.
    [2] Block B A, Booth D T, Carey F G. Depth and temperature of the blue marlin, Makaira nigricans, observed by acoustic telemetry[J]. Marine Biology, 1992, 114(2): 175−183. doi: 10.1007/BF00349517
    [3] 戴小杰, 许柳雄. 世界金枪鱼渔业渔获物物种原色图鉴[M]. 北京: 海洋出版社, 2007: 176-177.

    Dai Xiaojie, Xu Liuxiong. Illustrations of Catch Species for Global Tuna Fishery[M]. Beijing: Ocean Press, 2007: 176–177.
    [4] Shimose T, Fujita M, Yokawa K, et al. Reproductive biology of blue marlin Makaira nigricans around Yonaguni Island, southwestern Japan[J]. Fisheries Science, 2009, 75(1): 109−119. doi: 10.1007/s12562-008-0006-8
    [5] Sun C L, Chang Y J, Tszeng C C, et al. Reproductive biology of blue marlin (Makaira nigricans) in the western Pacific Ocean[J]. Fishery Bulletin, 2009, 107(4): 420−432.
    [6] 耿喆, 朱江峰, 夏萌, 等. 运用数据缺乏方法估算印度洋大青鲨可持续渔获量[J]. 中国水产科学, 2017, 24(5): 1099−1106.

    Geng Zhe, Zhu Jiangfeng, Xia Meng, et al. Estimate of sustainable yield of blue shark (Prionace glauca) in the Indian Ocean using data-poor approach[J]. Journal of Fishery Sciences of China, 2017, 24(5): 1099−1106.
    [7] Wang S P, Huang B Q. Stock assessment of blue marlin (Makaira nigricans) in the Indian Ocean using Stock Synthesis: IOTC–2016–WPB14-25_Rev1[R]. Victoria: Indian Ocean Tuna Commission, 2016.
    [8] ISC. Stock assessment update for Blue Marlin (Makaira nigricans) in the Pacific Ocean through 2014[R]. Sapporo: International Scientific Committee for Tuna and Tuna-Like Species in the North Pacific Ocean, 2016.
    [9] Restrepo V R, Thompson G G, Mace P M, et al. Technical guidance on the use of precautionary approaches to implementing National Standard 1 of the Magnuson-Stevens Fishery Conservation and Management Act[J]. NOAA Technical Memorandum, 1998, 31: 54.
    [10] Carruthers T R, Punt A E, Walters C J, et al. Evaluating methods for setting catch limits in data-limited fisheries[J]. Fisheries Research, 2014, 153: 48−68. doi: 10.1016/j.fishres.2013.12.014
    [11] Martell S, Froese R. A simple method for estimating MSY from catch and resilience[J]. Fish and Fisheries, 2013, 14(4): 504−514. doi: 10.1111/j.1467-2979.2012.00485.x
    [12] Kimura D K, Tagart J V. Stock reduction analysis, another solution to the catch equations[J]. Canadian Journal of Fisheries and Aquatic Sciences, 1982, 39(11): 1467−1472. doi: 10.1139/f82-198
    [13] Kimura D K, Balsiger J W, Ito D H. Generalized stock reduction analysis[J]. Canadian Journal of Fisheries and Aquatic Sciences, 1984, 41(9): 1325−1333. doi: 10.1139/f84-162
    [14] Froese R, Demirel N, Coro G, et al. Estimating fisheries reference points from catch and resilience[J]. Fish and Fisheries, 2017, 18(3): 506−526. doi: 10.1111/faf.12190
    [15] Andrade H A. Stock reduction analysis of striped marlin (Tetrapturus audax) caught in the Indian Ocean: IOTC-2017-WPB15-34[R]. San Sebastian: Indian Ocean Tuna Commission, 2017.
    [16] Andrade H A. Stock reduction analysis of blue shark (Prionace glauca) caught in the Indian Ocean: IOTC-2017-WPEB13-30[R]. San Sebastian: Indian Ocean Tuna Commission, 2017.
    [17] Newman D, Berkson J, Suatoni L. Current methods for setting catch limits for data-limited fish stocks in the United States[J]. Fisheries Research, 2015, 164: 86−93. doi: 10.1016/j.fishres.2014.10.018
    [18] Rosenberg A A, Fogarty M J, Cooper A B, et al. Developing new approaches to global stock status assessment and fishery production potential of the seas[R]. FAO Fisheries and Aquaculture Circular No. 1086, Rome: FAO, 2014: 1–175.
    [19] Schaefer M B. Some aspects of the dynamics of populations important to the management of the commercial marine fisheries[J]. Inter-American Tropical Tuna Commission Bulletin, 1954, 1(2): 23−56.
    [20] Froese R, Palomares M L D, Pauly D. Estimation of life history key facts[M]//Froese R, Pauly D. FishBase 2000: Concepts, Design and Data Sources. Philippines: ICLARM, 2000.
    [21] Froese R, Demirel N, Sampang A. An overall indicator for the good environmental status of marine waters based on commercially exploited species[J]. Marine Policy, 2015, 51: 230−237. doi: 10.1016/j.marpol.2014.07.012
    [22] IOTC. Status of the Indian Ocean blue marlin (BUM: Makaira nigricans) resource: IOTC–2016–SC19–ES13[R]. Victoria: Indian Ocean Tuna Commission, 2017.
    [23] Zhou S, Sharma R. Stock assessment of two neritic tuna species in Indian Ocean, kawakawa and Longtail tuna using catch-based stock reduction methods. IOTC–2013– WPNT03-25[R]. Victoria: Indian Ocean Tuna Commission, 2013.
    [24] Zhou Shijie, Yin Shaowu, Thorson J T, et al. Linking fishing mortality reference points to life history traits: an empirical study[J]. Canadian Journal of Fisheries and Aquatic Sciences, 2012, 69(8): 1292−1301. doi: 10.1139/f2012-060
    [25] McAllister M K, Duplisea D E. Production Model Fitting and Projection for Atlantic Redfish (Sebastes Fasciatus and Sebastes Mentella) to Assess Recovery Potential and Allowable Harm[M]. Quebec: Fisheries and Oceans Canada, 2011.
    [26] McAllister M K. A generalized Bayesian surplus production stock assessment software (BSP2)[J]. ICCAT's Collective Volume of Scientific Papers, 2014, 70(4): 1725−1757.
    [27] R Core Team. The R project for statistical computing[EB/OL]. [2015–06–25/2017–03–01].Vienna, Austria: R Foundation for Statistical Computing. http://www.r-project.org/
    [28] Kell L T, Mosqueira S I, De Bruyn P, et al. A Kobe Strategy Maatrix based upon probabilistic reference points: an example using a biomass dynamic assessment model[J]. ICCAT's Collective Volume of Scientific Papers, 2012, 68(3): 1030−1043.
    [29] Guan Wenjiang, Tang Lin, Zhu Jiangfeng, et al. Application of a Bayesian method to data-poor stock assessment by using Indian Ocean albacore (Thunnus alalunga) stock assessment as an example[J]. Acta Oceanologica Sinica, 2016, 35(2): 117−125. doi: 10.1007/s13131-016-0814-0
    [30] Kokkalis A, Eikeset A M, Thygesen U H, et al. Estimating uncertainty of data limited stock assessments[J]. ICES Journal of Marine Science, 2017, 74(1): 69−77. doi: 10.1093/icesjms/fsw145
    [31] Arnold L M, Heppell S S. Testing the robustness of data-poor assessment methods to uncertainty in catch and biology: a retrospective approach[J]. ICES Journal of Marine Science, 2015, 72(1): 243−250. doi: 10.1093/icesjms/fsu077
    [32] MacCall A D. Depletion-corrected average catch: a simple formula for estimating sustainable yields in data-poor situations[J]. ICES Journal of Marine Science, 2009, 66(10): 2267−2271. doi: 10.1093/icesjms/fsp209
    [33] Dick E J, MacCall A D. Depletion-based stock reduction analysis: a catch-based method for determining sustainable yields for data-poor fish stocks[J]. Fisheries Research, 2011, 110(2): 331−341. doi: 10.1016/j.fishres.2011.05.007
    [34] Cope J M. Implementing a statistical catch-at-age model (Stock Synthesis) as a tool for deriving overfishing limits in data-limited situations[J]. Fisheries Research, 2013, 142: 3−14. doi: 10.1016/j.fishres.2012.03.006
    [35] Methot R D, Wetzel C R. Stock synthesis: a biological and statistical framework for fish stock assessment and fishery management[J]. Fisheries Research, 2013, 142: 86−99. doi: 10.1016/j.fishres.2012.10.012
    [36] Costello C, Ovando D, Hilborn R, et al. Status and solutions for the world’s unassessed fisheries[J]. Science, 2012, 338(6106): 517−520. doi: 10.1126/science.1223389
    [37] Braccini J M, Gillanders B M, Walker T I. Hierarchical approach to the assessment of fishing effects on non-target chondrichthyans: case study of Squalus megalops in southeastern Australia[J]. Canadian Journal of Fisheries and Aquatic Sciences, 2006, 63(11): 2456−2466. doi: 10.1139/f06-141
    [38] Tribuzio C A, Kruse G H. Demographic and risk analyses of spiny dogfish (Squalus suckleyi) in the Gulf of Alaska using age- and stage-based population models[J]. Marine and Freshwater Research, 2011, 62(12): 1395−1406. doi: 10.1071/MF11062
    [39] Butterworth D S, Punt A E. Experiences in the evaluation and implementation of management procedures[J]. ICES Journal of Marine Science, 1999, 56(6): 985−998. doi: 10.1006/jmsc.1999.0532
    [40] Deroba J J, Butterworth D S, Methot R D, et al. Simulation testing the robustness of stock assessment models to error: some results from the ICES strategic initiative on stock assessment methods[J]. ICES Journal of Marine Science, 2015, 72(1): 19−30. doi: 10.1093/icesjms/fst237
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出版历程
  • 收稿日期:  2018-05-25
  • 修回日期:  2018-08-28
  • 网络出版日期:  2021-04-21
  • 刊出日期:  2019-08-25

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