基于Bootstrap的ELEFAN方法在评估方氏云鳚群体生长参数中的应用
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摘要
渔业数据有限性是小型渔业资源评估所面临的常见问题。电子体长频率分析(electroniclengthfrequency analysis,ELEFAN)常用于年龄数据难以获取或缺失的渔业,但该方法的可靠性尚待检验。本研究根据2013―2018年春、秋季共11个航次的海州湾底拖网调查数据,分别使用传统的ELEFAN与结合Bootstrap的ELEFAN方法,比较了2013―2015年与2016―2018年两个时间段内海州湾方氏云鳚(Pholis fangi)群体von-Bertalanffy生长方程中参数之间的变化。结果显示,在海州湾海域,方氏云鳚的生长参数具有显著变化, 2013―2018年,群体的极限体长变小,生长速率加快,说明海州湾方氏云鳚群体近年来呈现小型化的趋势。相比传统的ELEFAN方法,结合Bootstrap的ELEFAN方法能够给出较为稳健的参数估计,受采样随机性的影响较小,可以较好地应用于数据缺乏的小型渔业中。本研究加深了对方氏云鳚种群动态的认识,并推动了基于体长频率的生长参数估算方法在数据有限资源评估中的应用。
Fish stock assessment usually requires a wide range of supporting data, including an abundance index,production, age structure. However, some data are hardly available in many fisheries because of limited research funding and social attention. Therefore, many fisheries, particularly small-scale fisheries, often do not have sufficient data to support fish stock assessment and are considered data-limited or data-poor. An increasing amount of literature has been focused on the development of data-poor stock assessment methods in recent decades, among which electronic length frequency analysis(ELEFAN) is a prevalent method that uses length frequency distribution data to assess the status of fisheries. One crucial application of the ELEFAN is the estimation of growth parameters in the Von Bertalanffy growth function(VBGF). However, the method is based on certain optimization algorithms and cannot provide information on its precision or confidence intervals for growth parameters, which implies that the reliability of ELEFAN needs to be tested in future studies. This study used a bootstrap approach to evaluate the uncertainty of the ELEFAN method based on the survey data of Pholis fangi in Haizhou Bay. This species is one of the dominant species in Haizhou Bay and plays an important role in the food web and ecosystem of the Yellow Sea. Although the declines in fishery resources have drawn increasing attention in many regions of the world, relevant studies have commonly focused on large-scale fisheries, whereas small-scale fisheries, such as that of P. fangi, has been largely overlooked. Therefore, the biological characteristics of this species and their temporal changes is not well understood. This study was focused on the temporal changes in VBGF growth parameters of P. fangi in Haizhou Bay at different survey periods. We conducted annual bottom trawl surveys in Haizhou Bay in the spring and autumn from 2013 to 2018, and used the ELEFAN method to estimate the VBGF growth parameters infinite length(L?) and growth parameter(K) of P. fangi. In addition, the bootstrapped ELEFAN was used to evaluate the variation in the growth parameters, and the difference was compared between2013?2015 and 2016?2018. We analyzed the robustness of ELEFAN with respect to three aspects:(1) the effect of bin size of body length on parameter estimation,(2) the selection of different optimization algorithms(Simulated Annealing, SA; Genetic Algorithm, GA; Response Surface Analysis, RSA), and(3) the confidence intervals of parameter estimation through the bootstrap approach. The results showed that the VBGF growth parameters of P.fangi in Haizhou Bay changed significantly during 2013-2018, and the decreased infinite length(L?) and increased growth parameter(K) indicated that there was a significant trend of miniaturization. The bin size of body length significantly affected the goodness of model fit and improper bin size settings might lead to unreasonable parameter estimations. Bootstrapped ELEFAN provided robust parameter estimations compared to the conventional ELEFAN approach, and the bootstrapped results were less affected by the randomness of sample data. The Genetic Algorithm could benefit from parallel computing in the TropfishR package, which significantly sped up computation. This study improved the understanding of population dynamics of P. fangi. In particular, the changes of growth characteristics of this species may have a substantial impact on the Haizhou Bay ecosystem. We demonstrated that bootstrapped ELEFAN performed well and could be applied to the prevalent data-poor and small-scale fisheries.
Fish stock assessment usually requires a wide range of supporting data, including an abundance index,production, age structure. However, some data are hardly available in many fisheries because of limited research funding and social attention. Therefore, many fisheries, particularly small-scale fisheries, often do not have sufficient data to support fish stock assessment and are considered data-limited or data-poor. An increasing amount of literature has been focused on the development of data-poor stock assessment methods in recent decades, among which electronic length frequency analysis(ELEFAN) is a prevalent method that uses length frequency distribution data to assess the status of fisheries. One crucial application of the ELEFAN is the estimation of growth parameters in the Von Bertalanffy growth function(VBGF). However, the method is based on certain optimization algorithms and cannot provide information on its precision or confidence intervals for growth parameters, which implies that the reliability of ELEFAN needs to be tested in future studies. This study used a bootstrap approach to evaluate the uncertainty of the ELEFAN method based on the survey data of Pholis fangi in Haizhou Bay. This species is one of the dominant species in Haizhou Bay and plays an important role in the food web and ecosystem of the Yellow Sea. Although the declines in fishery resources have drawn increasing attention in many regions of the world, relevant studies have commonly focused on large-scale fisheries, whereas small-scale fisheries, such as that of P. fangi, has been largely overlooked. Therefore, the biological characteristics of this species and their temporal changes is not well understood. This study was focused on the temporal changes in VBGF growth parameters of P. fangi in Haizhou Bay at different survey periods. We conducted annual bottom trawl surveys in Haizhou Bay in the spring and autumn from 2013 to 2018, and used the ELEFAN method to estimate the VBGF growth parameters infinite length(L?) and growth parameter(K) of P. fangi. In addition, the bootstrapped ELEFAN was used to evaluate the variation in the growth parameters, and the difference was compared between2013?2015 and 2016?2018. We analyzed the robustness of ELEFAN with respect to three aspects:(1) the effect of bin size of body length on parameter estimation,(2) the selection of different optimization algorithms(Simulated Annealing, SA; Genetic Algorithm, GA; Response Surface Analysis, RSA), and(3) the confidence intervals of parameter estimation through the bootstrap approach. The results showed that the VBGF growth parameters of P.fangi in Haizhou Bay changed significantly during 2013-2018, and the decreased infinite length(L?) and increased growth parameter(K) indicated that there was a significant trend of miniaturization. The bin size of body length significantly affected the goodness of model fit and improper bin size settings might lead to unreasonable parameter estimations. Bootstrapped ELEFAN provided robust parameter estimations compared to the conventional ELEFAN approach, and the bootstrapped results were less affected by the randomness of sample data. The Genetic Algorithm could benefit from parallel computing in the TropfishR package, which significantly sped up computation. This study improved the understanding of population dynamics of P. fangi. In particular, the changes of growth characteristics of this species may have a substantial impact on the Haizhou Bay ecosystem. We demonstrated that bootstrapped ELEFAN performed well and could be applied to the prevalent data-poor and small-scale fisheries.
引文
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[7]Li L,Song N,Gao T X.Analysis of Pholis fangi by control region sequence[C]//Society of Ichthyology of Chinese Society of Oceanology and Limnology,Society of Ichthyology of China Zoology Society.Summary Collection of Symposium Papers.2012.[李琳,宋娜,高天翔.方氏云鳚群体控制区序列分析[C]//中国海洋湖沼学会鱼类学分会,中国动物学会鱼类学分会.2012年学术研讨会论文摘要汇编.2012.]
[8]Li M,Li Z G,Xu B D,et al.Effects of spatiotemporal and environmental factors on the distribution and abundance of Pholis fangi in Haizhou Bay using a generalized additive model[J].Journal of Fishery Sciences of China,2015,22(4):812-819.[李敏,李增光,徐宾铎,等.时空和环境因子对海州湾方氏云鳚资源丰度分布的影响[J].中国水产科学,2015,22(4):812-819.]
[9]Pauly D,Christensen V,Froese R,et al.Fishing down aquatic food webs[J].American Scientist,2000,88(1):46-51.
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[11]Ye J Q,Xu Z L,Chen J J,et al.Resources status analysis of large yellow croaker in Guanjingyang using von Bertalanffy growth equation and fishing mortality parameters[J].Journal of Fisheries of China,2012,36(2):238-246.[叶金清,徐兆礼,陈佳杰,等.基于生长和死亡参数变化的官井洋大黄鱼资源现状分析[J].水产学报,2012,36(2):238-246.]
[12]Guo X P,Jin X S,Dai F Q.Growth variation of small yellow croaker(Pseudosciaena polyactis Bleeker)in Bohai sea[J].Journal of Fishery Sciences of China,2006,13(2):243-249.[郭旭鹏,金显仕,戴芳群.渤海小黄鱼生长特征的变化[J].中国水产科学,2006,13(2):243-249.]
[13]Zhu J C,Zhao X Y,Li F G.Growth characters of the anchovy stock in the Yellow Sea with its annual and seasonal variations[J].Marine Fisheries Research,2007,28(3):64-72.[朱建成,赵宪勇,李富国.黄海鳀鱼的生长特征及其年际与季节变化[J].渔业科学进展,2007,28(3):64-72.]
[14]Pilling G M,Apostolaki P,Failler P,et al.Assessment and management of data-poor fisheries[M]//Advances in Fisheries.Blackwell Publishing,2008,12:37-42.
[15]Costello C,Ovando D,Hilborn R,et al.Status and solutions for the world’s unassessed fisheries[J].Science,2012,338(6106):517-520.
[16]Bentley N.Data and time poverty in fisheries estimation:potential approaches and solutions[J].ICES Journal of Marine Science,2015,72(1):323-328.
[17]Xu B D,Zhang C L,Xue Y,et al.Optimization of sampling effort for a fishery-independent survey with multiple goals[J].Environmental Monitoring and Assessment,2015,187:252.
[18]General Administration of Quality Supervision,Inspection and Quarantine.Specifications for oceanographic survey(Part 6):Marine biological survey(GB/T 12763.6-2007)[S].Beijing:Standards Press of China,2007.[国家质量监督检验检疫总局.GB/T 12763.6-2007海洋调查规范,第6部分:海洋生物调查[S].北京:中国标准出版社,2007.]
[19]Bertalanffy L V.A quantitative theory of organic growth(inquiries on growth laws.II)[J].Human Biology,1938,10(2):181-213.
[20]Pauly D,David N.ELEFAN I,a BASIC program for the objective extraction of growth parameters from length-frequency data[J].Meeresforschung,1981,28:205-211.
[21]Bellido J M,Pierce G J,Romero J L,et al.Use of frequency analysis methods to estimate growth of anchovy(Engraulis encrasicolus L.1758)in the Gulf of Cádiz(SW Spain)[J].Fisheries Research,2000,48(2):107-115.
[22]Chen Z Z,Qiu Y S.Estimation of growth and mortality parameters of Parargyrops edita Tanaka in Beibu Bat[J].Journal of Fisheries of China,2003,27(3):251-257.[陈作志,邱永松.北部湾二长棘鲷生长和死亡参数估计[J].水产学报,2003,27(3):251-257.]
[23]Chen G B,Qiu Y S.Growth,Mortality and Rational Utilization of Horse Mackerel in Nothern South China Sea[J].Journal of Zhanjiang Ocean University,2004,24(1):35-40.[陈国宝,邱永松.南海北部竹筴鱼的生长、死亡及合理利用[J].广东海洋大学学报,2004,24(1):35-40.]
[24]Jaiswar A K,Chakraborty S K,Raja R,et al.Population dynamics of lizard fish Saurida tumbil(Teleostomi/Synodontidae)from Mumbai,west coast of India[J].Indian Journal of Geo-Marine Sciences,2003,32(2):147-150.
[25]Taylor M H,Mildenberger T K.Extending electronic length frequency analysis in R[J].Fisheries Management and Ecology,2017,24(4):330-338.
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