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 |
[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
|