袁兴伟, 严利平, 刘尊雷, 程家骅. 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. DOI: 10.3969/j.issn.2095-0780.2014.06.003
引用本文: 袁兴伟, 严利平, 刘尊雷, 程家骅. 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. DOI: 10.3969/j.issn.2095-0780.2014.06.003
YUAN Xingwei, YAN Liping, LIU Zunlei, CHENG Jiahua. 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. DOI: 10.3969/j.issn.2095-0780.2014.06.003
Citation: YUAN Xingwei, YAN Liping, LIU Zunlei, CHENG Jiahua. 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. DOI: 10.3969/j.issn.2095-0780.2014.06.003

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

A performance comparison of stock density estimation of Larimichthys polyactis in the East China Sea using different models based on bottom trawl survey

  • 摘要: 单位捕捞努力渔获量(catch per unit effort,CPUE)是衡量资源丰度的相对指标,其均值的估算是渔业资源评估与管理中一项极为重要的基础性工作。文章以2004年~2006年东海区底拖网大面定点调查渔获的小黄鱼(Larimichthys polyactis)为例,利用算术平均值法、重抽样估值法、对数正态分布法、-分布的最小方差无偏估计法和Finney-Sichel估值法分别对小黄鱼资源密度数据进行标准化处理,并评估不同方法的相对优劣性和稳健性。结果表明,基于对数正态分布的3种方法的均值估值较大,尤以-分布法最大,而算术平均值法和重抽样估值法的估计较小,且均值较接近;两两配对样本t检验结果显示,-分布法的估值显著高于其他方法的结果(P0.05),而其余4种估值结果并无显著性差异(P0.05);-分布法和重抽样估值法的变异系数较接近,均小于其他结果;结合小黄鱼空间分布的特征及大面积调查自身特点,-分布法被认为是5种估值方法中适用性和稳健性最好的方法。

     

    Abstract: Estimation of average catch per unit effort (CPUE) which is a relative index to measure fish abundance is important foundational work for stock assessment and fishery management. Based on the data of Larimichthys polyactis collected from the bottom trawl survey conducted in the East China Sea from 2004 to 2006, we calculated the average stock density of L.polyactis by arithmetic mean estimator (AM), resampling mean estimator (RM), lognormal distribution mean estimator (LM),-distribution mean estimator (DM) and Finney-Sichel mean estimator (FM), and evaluated the superiority (or inferiority) and robustness of different estimators. Results indicate that the mean estimators based on lognormal distribution were bigger than the last two estimators, and the-distribution mean estimator was the biggest one, while the last two estimators were much closer. The paired-sample t test reveals that the-distribution mean estimator was bigger than the other four estimators (P0.05); meanwhile, there was no difference among theother four estimators (P0.05). The coefficient variation (CV) of the-distribution mean estimator and the resampling mean estimator were less than the other three estimators. It is concluded that-distribution mean estimator was the best estimation method with good applicability and robustness.

     

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