首页期刊介绍征稿简则下载专区期刊征订电子期刊联系我们帮助English
 
基于生活史特征的数据有限条件下渔业资源评估方法比较
Html全文阅读】 【文章下载
下载次数:123次
作者:史登福1 2  张魁1  陈作志1 
单位:1. 中国水产科学研究院南海水产研究所, 农业农村部外海渔业开发重点实验室, 广东 广州 510300;
2. 上海海洋大学海洋科学学院, 上海 201306
关键词:渔业资源 评估模型 数据缺乏 生活史特征 北大西洋大青鲨渔业 
分类号:S931
出版年·卷·期(页码):2020·27·1(12-23)
摘要:
渔业资源评估是开展渔业资源管理,维系渔业可持续发展的基础工作。传统的渔业资源评估方法需要统计产量、资源丰度指数甚至年龄结构等大量数据,由于调查经费和数据的缺乏,全球仅1%的鱼种进行过系统性的资源评估。近年来,在数据有限(data-limited)条件下如何开展资源评估已日益成为学术界的关注热点。本文将基于生活史特征的评估方法分为仅需要生活史参数,需要产量数据和生活史参数,需要产量数据、生活史参数及体长或年龄数据等3大类,分别从方法、数据要求、输出结果及局限性进行了系统回顾分析,提供了关于生活史特征参数的常见估算方法,并就其中两种模型对北大西洋大青鲨(Prionace glauca)的可持续渔获量进行了初步评估与比较。最后,对数据缺乏模型的使用及模型在中国近海渔业资源评估中的运用提出了建议。
Fishery stock assessment is a basic component of modern management, required to maintain sustainable fishery development. Traditional methods require a large amount of statistical data assessing yield, abundance index, and age structure. Due to limited funding and data for such surveys, only 1% of fish stocks have systematic assessments conducted. Therefore, it is difficult to assess maximum sustainable yield (MSY) or determine allowable catch for most fishery resources using traditional methods. In recent years, stock assessment using limited available data has become a focus of increasing academic research. A good assessment model based on incomplete data would allow managers to assess the risk of overexploitation, current population biomass, sustainable yield, optimal fishing mortality, and population status relative to reference points such as current total catch limits. These parameters can then be used to determine appropriate fishing limits for the target population. Such models use different assumptions and have different limitations. Therefore, it is necessary to select an appropriate model that will minimize error in the results when evaluating target resources. Where more than one model is available, they can be compared to assess which obtains the best results. In assessment of fishery resources using data-poor methods, more and more attention is being paid to characteristic life history parameters such as intrinsic growth rate, natural mortality coefficient, and so on. Under conditions that combine the yield of the evaluated population with the corresponding life cycle parameters, a more reliable MSY value or sustainable yield can be obtained. In this paper, assessment models based on life-history characteristics are divided into three categories:(1) models that only use life history parameters; (2) models that incorporate catch data and life-history parameters; (3) models that incorporate catch data, life-history parameters, and lifespan or age data. The introductions, data requirements, output results, and limitations of each model is reviewed and systematically analyzed. In addition, several common life-history parameter estimation methods are provided. A simple preliminary assessment of sustainable catch was conducted for North Atlantic blue shark (Prionace glauca) using Catch-MSY and DCAC models, and results were compared. Results calculated using the DCAC model are similar to those obtained with the Catch-MSY model. Maximum sustainable yield of blue shark was about 3.0×104 tons. This paper also provides suggestions on use of data-limited models and applications to assessment of offshore fishery resources in China. The current survey of Chinese offshore fishery resources started late, and recorded data are relatively few, therefore most fisheries had difficulty estimating by traditional methods. The natural mortality coefficient of fish off China's coasts is generally >0.2; thus, assessment errors using DCAC and DB-SRA models will be large. The catch-MSY model can fit the present resource situation well, and is often used to evaluate fishery resources off China's coasts. In view of the low reliability of the upper limit estimate used by the Catch-MSY model to estimate environmental capacity, the SS model can be compared with it in future resource assessments to provide more theoretical support for protection and scientific assessment of offshore fishery resources.
该文献标准引用格式:
SHI Dengfu, ZHANG Kui, CHEN Zuozhi.Comparison of assessment methods utilizing life-history characteristics in data-limited fisheries[J].Journal of Fishery Sciences of China,2020,27(1):12-23.[史登福, 张魁, 陈作志.基于生活史特征的数据有限条件下渔业资源评估方法比较[J].中国水产科学,2020,27(1):12-23.]
参考文献:
[1] Maunder M N, Punt A E. A review of integrated analysis in fisheries stock assessment[J]. Fisheries Research, 2013, 142:61-74.
[2] Guan W J, Tian S Q, Zhu J F, et al. A review of fisheries stock assessment models[J]. Journal of Fishery Sciences of China, 2013, 20(5):1112-1120.[官文江, 田思泉, 朱江峰, 等. 渔业资源评估模型的研究现状与展望[J]. 中国水产科学, 2013, 20(5):1112-1120.]
[3] Costello C, Ovando D, Hilborn R, et al. Status and solutions for the world's unassessed fisheries[J]. Science, 2012, 338(6106):517-520.
[4] 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.
[5] Cadrin S X, Dickey-Collas M. Stock assessment methods for sustainable fisheries[J]. ICES Journal of Marine Science, 2015, 72(1):1-6.
[6] Geng Z, Zhu J F, Xia M, et al. Research progress in fishery stock assessment using data-poor/limited methods[J]. Transactions of Oceanology and Limnology, 2018(5):130-137.[耿喆, 朱江峰, 夏萌, 等. 数据缺乏条件下的渔业资源评估方法研究进展[J]. 海洋湖沼通报, 2018(5):130-137.]
[7] McCully Phillips S R, Scott F, Ellis J R. Having confidence in productivity susceptibility analyses:A method for underpinning scientific advice on skate stocks?[J]. Fisheries Research, 2015, 171:87-100.
[8] Punt A E, Smith D C, Smith A D M. Among-stock comparisons for improving stock assessments of data-poor stocks:The "Robin Hood" approach[J]. ICES Journal of Marine Science, 2011, 68(5):972-981.
[9] 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.
[10] 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.
[11] Martell S, Froese R. A simple method for estimating MSY from catch and resilience[J]. Fish and Fisheries, 2013, 14(4):504-514.
[12] 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.
[13] Ralston S, Punt A E, Hamel O S, et al. A meta-analytic approach to quantifying scientific uncertainty in stock assessments[J]. Fisheries Bulletin, 2011, 109:217-231.
[14] Hordyk A R, Loneragan N R, Prince J D. An evaluation of an iterative harvest strategy for data-poor fisheries using the length-based spawning potential ratio assessment methodology[J]. Fisheries Research, 2015, 171:20-32.
[15] Cope J M, Punt A E. Length-based reference points for data-limited situations:Applications and restrictions[J]. Marine and Coastal Fisheries, 2009, 1(1):169-186.
[16] McCully Phillips S R, Scott F, Ellis J R. Having confidence in productivity susceptibility analyses:A method for underpinning scientific advice on skate stocks?[J]. Fisheries Research, 2015, 171:87-100.
[17] Ormseth O A, Spencer P D. An assessment of vulnerability in Alaska groundfish[J]. Fisheries Research, 2011, 112(3):127-133.
[18] Pikitch E K. Use of a mixed-species yield-per-recruit model to explore the consequences of various management policies for the Oregon flatfish fishery[J]. Canadian Journal of Fisheries and Aquatic Sciences, 1987, 44(S2):s349-s359.
[19] Myers R A, Mertz G, Barrowman N J. Spatial scales of variability in cod recruitment in the North Atlantic[J]. Canadian Journal of Fisheries and Aquatic Sciences, 1995, 52(9):1849-1862.
[20] Gulland J A. The fish resources of the oceans[R]. FAO Fisheries Technical Paper, 1970(97):1-425.
[21] Zhang K, Liao B C, Xu Y W, et al. Assessment for allowable catch of fishery resources in the South China Sea based on statistical data[J]. Haiyang Xuebao, 2017, 39(8):25-33.[张魁, 廖宝超, 许友伟, 等. 基于渔业统计数据的南海区渔业资源可捕量评估[J]. 海洋学报, 2017, 39(8):25-33.]
[22] Restrepo V R, Thompson G G, Mace P M. Technical guidance on the use of precautionary approaches to implementing National Standard 1 of the Magnuson-Stevens Fishery Conservation and Management Act[R]. NOAA Technical Memorandum NMFS-F/SPO, 1998:23-24.
[23] Newman D, Carruthers T, MacCall A, et al. Improving the science and management of data-limited fisheries:An evaluation of current methods and recommended approaches[R]. New York:NRDC Report, 2014, 1-36.
[24] 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.
[25] Cortés E, Brooks E N. Application of data-poor stock assessment methods to Atlantic sharks[C]//Proceedings of the 144th Annual Meeting of the American Fisheries Society, 2014.
[26] Wiedenmann J, Free C M, Jensen O P. Evaluating the performance of data-limited methods for setting catch targets through application to data-rich stocks:A case study using Northeast US fish stocks[J]. Fisheries Research, 2019, 209:129-142.
[27] 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.
[28] Deriso R B. Harvesting strategies and parameter estimation for an age-structured model[J]. Canadian Journal of Fisheries and Aquatic Sciences, 1980, 37(2):268-282.
[29] Pella J J, Tomlinson P K. A generalized stock production model[J]. Inter-American Tropical Tuna Commission Bulletin, 1969, 13(3):416-497.
[30] Fletcher R I. On the restructuring of the Pella-Tomlinson system[J]. Fishery Bulletin, 1978, 76(3):515-521.
[31] McAllister M K, Babcock E A, Pikitch E K, et al. Application of a non-equilibrium generalized production model to South and North Atlantic swordfish:Combining Bayesian and demographic methods for parameter estimation[R]. Colllective Volume of Scientifics Papers. ICCAT, 2000, 51:1523-1550.
[32] Wetzel C R, Punt A E. Model performance for the determination of appropriate harvest levels in the case of data-poor stocks[J]. Fisheries Research, 2011, 110(2):342-355.
[33] Wetzel C R, Punt A E. Evaluating the performance of data-moderate and catch-only assessment methods for US west coast groundfish[J]. Fisheries Research, 2015, 171:170-187.
[34] Sweka J A, Neuenhoff R, Withers J, et al. Application of a depletion-based stock reduction analysis (DB-SRA) to Lake Sturgeon in Lake Erie[J]. Journal of Great Lakes Research, 2018, 44(2):311-318.
[35] Zhang K, Zhang J, Xu Y W, et al. Application of a catch-based method for stock assessment of three important fisheries in the East China Sea[J]. Acta Oceanologica Sinica, 2018, 37(2):102-109.
[36] Froese R, Pauly D. Estimation of life history key facts. In:FishBase 2000:Concepts, Design and Data Sources. (eds Froese R and Pauly D)[R]. Philippines:ICLARM, 2000.
[37] Schaefer M B. Some aspects of the dynamics of populations important to the management of the commercial Marine fisheries[J]. Bulletin of Mathematical Biology, 1991, 53(1-2):253-279.
[38] Haddon M. Modelling and Quantitative Methods in Fisheries, Second Edition[M]. London:CRC Press, 2011:121-128.
[39] 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.
[40] Geng Z, Zhu J F, Wang Y, et al. Stock assessment for Indian Ocean blue marlin (Makaira nigricans) using Catch-MSY model[J]. Haiyang Xuebao, 2019, 41(8):26-35.[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.]
[41] Lee H H, Maunder M N, Piner K R, et al. Estimating natural mortality within a fisheries stock assessment model:An evaluation using simulation analysis based on twelve stock assessments[J]. Fisheries Research, 2011, 109(1):89-94.
[42] Geng P, Zhang K, Xu S N, et al. Assessment of natural mortality coefficients in fish stocks:a review[J]. Journal of Fishery Sciences of China, 2018, 25(3):694-704.[耿平, 张魁, 徐姗楠, 等. 鱼类自然死亡系数评估研究进展[J]. 中国水产科学, 2018, 25(3):694-704.]
[43] Hoening J M. Empircal use of longevity data to estimate mortality rates[J]. Fishery Bulletin, 1983, 82:898-903.
[44] Pauly D. On the interrelationships between natural mortality, growth parameters, and mean environmental temperature in 175 fish stocks[J]. ICES Journal of Marine Science, 1980, 39(2):175-192.
[45] Jensen A L. Beverton and Holt life history invariants result from optimal trade-off of reproduction and survival[J]. Canadian Journal of Fisheries and Aquatic Sciences, 1996, 53(4):820-822.
[46] Li Z, Liu Q. A study of ELEFAN and SLCA for estimating growth parameters[J]. Transactions of Oceanology and Limnology, 2007(3):81-87.[李壮, 刘群. 应用渔业体长分析方法ELEFAN和SLCA估算鱼类生长参数的研究[J]. 海洋湖沼通报, 2007(3):81-87.]
[47] Zhan B Y. Fish Stock Assessment[M]. Beijing:China Agriculture Press, 1995:109-113.[詹秉义. 渔业资源评估[M]. 北京:中国农业出版社, 1995:109-113.]
[48] Matías Braccini J, 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.
[49] Campana S E, Joyce W, Manning M J. Bycatch and discard mortality in commercially caught blue sharks (Prionace glauca) assessed using archival satellite pop-up tags[J]. Marine Ecology Progress, 2009, 387(12):241-253.
[50] Zhang K, Liu Q, Liao B C, et al. Comparative effects of distorted fishery data on assessment results of two non-equilibriun surplus production models[J]. Journal of Fisheries of China, 2018, 42(9):1378-1389.[张魁, 刘群, 廖宝超, 等. 渔业数据失真对两种非平衡剩余产量模型评估结果的影响比较[J]. 水产学报, 2018, 42(9):1378-1389.]
服务与反馈:
Html全文阅读】【文章下载】【发表评论】【查看评论】【加入收藏
提示:查看此文需要支付$0.00
关于我们  |  联系我们  |  期刊介绍  |   在线留言
Copyright  ©  2009 中国水产科学杂志
京ICP备09074735号-7
京公网安备1101060260001号