南海北部近海竹荚鱼资源密度的概率分布型特征
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摘要
竹荚鱼(Trachurusjaponicus)是中国南海北部近海主要渔获物之一,其空间分布具有非均匀性。根据2014—2017年南海北部近海两个周期(2014—2015年为第1周期, 2016—2017年为第2周期)的底拖网调查数据,以单位捕捞努力量(catchperuniteffort,CPUE)表征资源密度,采用柯尔莫可洛夫-斯米洛夫检验(Kolmogorov-Smirnov,K-S test)探索了11种概率分布特征。结果表明,南海北部陆架区竹荚鱼不具明显的概率分布特征,而北部湾海域竹荚鱼资源密度服从对数正态分布型且第二周期较第一周期更为显著。同时,采用基于对数正态理论模型法(对数正态模型与Delta模型)与调查设计法(均值法)对该海域进行资源密度估值比较,发现基于对数正态理论模型更切合竹荚鱼资源密度结构分布特征,而在对数正态理论模型中Delta模型法更适用于该物种资源密度估值。
The jack mackerel(Trachurus japonicus) is one of the main catches in the northern South China Sea, and its spatial distribution is non-uniform. According to the two cycles of bottom trawl survey data in the northern South China Sea from 2014 to 2017(2014–2015 is the first cycle, 2016–2017 is the second cycle), resource density is characterized by the catch per unit effort(CPUE). The Kolmogorov-Smirnov test(K-S test) was used to explore 11 probability distribution features. The results showed that the continental shelf area of the northern South China Sea had no obvious probability distribution, while the jack mackerel resource density in the Beibu Gulf obeys the lognormal distribution pattern, and the second cycle is more significant than the first cycle. At the same time, using a lognormal theory model(lognormal model and delta model) and an investigative design method(arithmetic method) to compare the estimation of the resource density within a certain area, we found that the lognormal model is more fit to describe the distribution characteristics of the density structure of jack mackerel, and the delta model is more suitable to estimate the jack mackerel resource density.
The jack mackerel(Trachurus japonicus) is one of the main catches in the northern South China Sea, and its spatial distribution is non-uniform. According to the two cycles of bottom trawl survey data in the northern South China Sea from 2014 to 2017(2014–2015 is the first cycle, 2016–2017 is the second cycle), resource density is characterized by the catch per unit effort(CPUE). The Kolmogorov-Smirnov test(K-S test) was used to explore 11 probability distribution features. The results showed that the continental shelf area of the northern South China Sea had no obvious probability distribution, while the jack mackerel resource density in the Beibu Gulf obeys the lognormal distribution pattern, and the second cycle is more significant than the first cycle. At the same time, using a lognormal theory model(lognormal model and delta model) and an investigative design method(arithmetic method) to compare the estimation of the resource density within a certain area, we found that the lognormal model is more fit to describe the distribution characteristics of the density structure of jack mackerel, and the delta model is more suitable to estimate the jack mackerel resource density.
引文
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[23]Chen Z X,Li H Q.The pattern of probability distribution on the density of demersal fishes stock in the northern continental shelf of the South China Sea[J].Journal of Fisheries of China,1987,11(2):159-166.[陈正兴,李辉权.南海北部大陆架底层鱼类资源密度概率分布型的研究[J].水产学报,1987,11(2):159-166.]
[24]Pennington M.Estimating the mean and variance from highly skewed marine data[J].Fishery Bulletin,1996,94:498-505.
[25]Smith S J.Use of statistical models for the estimation of abundance from groundfish trawl survey data[J].Canadian Journal of Fisheries and Aquatic Sciences,1990,47(5):894-903.
[26]Mc Conaughey R A,Conquest L L.Trawl survey estimation using a comparative approach based on lognormal theory[J].Fishery Bulletin,1993,91(1):107-118.
[27]Syrjala S E.Critique on the use of the delta distribution for the analysis of trawl survey data[J].ICES Journal of Marine Science,2000,57(4):831-842.
[28]Huang L M,Li J,Zhang Y Z,et al.Current fishery resource assessment in the Minjiang River Estuary and its neighboring waters[J].Journal of Tropical Oceanography,2010,29(5):142-148.[黄良敏,李军,张雅芝,等.闽江口及附近海域渔业资源现存量评析[J].热带海洋学报,2010,29(5):142-148.]
[2]Yang L,Zhang X F,Tan Y G,et al.Analysis on the catch composition of light-purse seine in the northern South China Sea[J].South China Fisheries Science,2009,5(6):65-70.[杨吝,张旭丰,谭永光,等.南海北部灯光围网渔获组成分析[J].南方水产科学,2009,5(6):65-70.]
[3]Wang X F,Feng B,Lu H S.Catch composition and species diversity of the bottom trawl fishery in the northern South China Sea in 2008[J].Fishery Modernization,2010,37(1):47-51.[王学锋,冯波,卢伙胜.2008年南海北部拖网渔获组成及其多样性分析[J].渔业现代化,2010,37(1):47-51.]
[4]Zhao X W,Cui L F,Li S M,et al.China Fishery Statistical Yearbook[M].Beijing:China Agriculture Press,2016:45-48.[赵兴武,崔利锋,李书民,等.中国渔业统计年鉴[M].北京:中国农业出版社,2016:45-48.]
[5]Brodie W B,Wells R.Northwest Atlantic fisheries organization scientific research document[C].Dartmouth:NAFO Index of Meeting Document,1985:85-106.
[6]Smith S J.A comparison of estimators of location for skewed populations with application to ground fish trawl surveys[J].Canadian Special Publication of Fisheries and Aquatic Sciences,1981,58:154-163.
[7]Yuan X W,Jiang Y Z,Yan L P.Comparison on difference of the stock density of Psenopsis anomala in the East China Sea by means of different estimating methods[J].Marine Fisheries,2009,31(1):10-15.[袁兴伟,姜亚洲,严利平.东海区刺鲳资源密度不同估算方法的差异比较[J].海洋渔业,2009,31(1):10-15.]
[8]Yuan X W,Jiang Y Z,Cheng J H.Estimating the average stock density with dominating large catches based onΔ-distribution model[J].Progress in Fishery Sciences,2011,32(1):1-7.[袁兴伟,姜亚洲,程家骅.利用Δ-分布模型法评估调查数据带有极端值的渔业生物的平均资源密度[J].渔业科学进展,2011,32(1):1-7.]
[9]Li F,Li X S,Zhao X Y.Bottom trawl survey data analysis based on Delta-distribution model and its application in the estimation of small yellow croak and silver pomfret in the Yellow Sea[J].Journal of Fisheries of China,2008,32(1):145-151.[李凡,李显森,赵宪勇.底拖网调查数据的Delta-模型分析及其在黄海小黄鱼和银鲳资源评估中的应用[J].水产学报,2008,32(1):145-151.]
[10]Pennington M.Some Statistical Techniques for Estimating Abundance Indices from Trawl Surveys[M].Woods Hole:Northwest Atlantic Fisheries Organization,1985.
[11]Chen D L,Meng T X,Zhou B B.Studies on the spatial distribution pattern of an important food organism-Pacific Euphausiid(Euphasia Picifica)Hansen,in the Yellow Sea by means of spatial probability distribution pattern of biological population[J].Transactions of Oceanology and Limnology,1981(1):3-12.[陈敦隆,孟田湘,周彬彬.应用生物种群空间概率分布型研究黄海重要饵料生物--太平洋磷虾Euphasia Picifica Hansen的空间分布型[J].海洋湖沼通报,1981(1):3-12.]
[12]Nishida T,Chen D G.Incorporating spatial autocorrelation into the general linear model with an application to the yellowfin tuna(Thunnus albacares)longline CPUE data[J].Fisheries Research,2004,70(2-3):265-274.
[13]Jia X P,Li C H,Lin Z L,et al.Fishery Ecological Environment and Fishery Resources in Beibu Gulf[M].Beijing:Science Press,2003.[贾晓平,李纯厚,林昭进,等.北部湾渔业生态环境与渔业资源[M].北京:科学出版社,2003.]
[14]Yuan X W,Yan L P,Liu Z L,et al.A performance comparison of stock density estimation of Larimichthys polyactis in the East China Sea using different models based on bottom trawl survey[J].South China Fisheries Science,2014,10(6):20-26.[袁兴伟,严利平,刘尊雷,等.基于底拖网调查的东海区小黄鱼资源密度不同估算方法差异比较[J].南方水产科学,2014,10(6):20-26.]
[15]Chen X J.Fishery Resources and Fisheries[M].Beijing:China Ocean Press,2014.[陈新军.渔业资源与渔场学[M].北京:海洋出版社,2014.]
[16]Sun D R.A study on fishery resources and sustainable fishery development in the Beibu Bay[D].Qingdao:Ocean University of China,2008.[孙典荣.北部湾渔业资源与渔业可持续发展研究[D].青岛:中国海洋大学,2008.]
[17]Shu Y Q,Wang Q,Zu T T.Progress on shelf and slope circulation in the northern South China Sea[J].Science China Earth Sciences,2018,48(3):276-287.[舒业强,王强,俎婷婷.南海北部陆架陆坡流系研究进展[J].中国科学:地球科学,2018,48(3):276-287.]
[18]Jiang R,Wang Y S.A preliminary ecological modeling study on response of the East Guangdong coastal ecosystem to summer coastal upwelling[J].Ecological Science,2017,36(1):25-34.[江睿,王友绍.粤东沿岸生态系统对夏季沿岸上升流响应的生态模型初探[J].生态科学,2017,36(1):25-34.]
[19]Lin Z T.The study on distribution of the primary productivity north and west of the South China Sea[D].Zhanjiang:Guangdong Ocean University,2017.[林智涛.南海北部与西部初级生产力影响因子探究[D].湛江:广东海洋大学,2017.]
[20]Brus D J,de Gruijter J J.Design-based versus model-based estimates of spatial means:Theory and application in environmental soil science[J].Environmetrics,1993,4(2):123-152.
[21]Thompson M.Theory of Sample Surveys[M].London:CRCPress,1997.
[22]Su F Z,Zhou C H,Shao Q Q,et al.GIS spatio-temporal analysis of fishery resources in East China Sea[J].High Technology Letters,2001,11(5):60-63.[苏奋振,周成虎,邵全琴,等.东海区鱼类资源变化GIS时空分析[J].高技术通讯,2001,11(5):60-63.]
[23]Chen Z X,Li H Q.The pattern of probability distribution on the density of demersal fishes stock in the northern continental shelf of the South China Sea[J].Journal of Fisheries of China,1987,11(2):159-166.[陈正兴,李辉权.南海北部大陆架底层鱼类资源密度概率分布型的研究[J].水产学报,1987,11(2):159-166.]
[24]Pennington M.Estimating the mean and variance from highly skewed marine data[J].Fishery Bulletin,1996,94:498-505.
[25]Smith S J.Use of statistical models for the estimation of abundance from groundfish trawl survey data[J].Canadian Journal of Fisheries and Aquatic Sciences,1990,47(5):894-903.
[26]Mc Conaughey R A,Conquest L L.Trawl survey estimation using a comparative approach based on lognormal theory[J].Fishery Bulletin,1993,91(1):107-118.
[27]Syrjala S E.Critique on the use of the delta distribution for the analysis of trawl survey data[J].ICES Journal of Marine Science,2000,57(4):831-842.
[28]Huang L M,Li J,Zhang Y Z,et al.Current fishery resource assessment in the Minjiang River Estuary and its neighboring waters[J].Journal of Tropical Oceanography,2010,29(5):142-148.[黄良敏,李军,张雅芝,等.闽江口及附近海域渔业资源现存量评析[J].热带海洋学报,2010,29(5):142-148.]