摘要
当鱼类一个世代的资源量和渔获量数据已知,POPE(1972)提出的股分析(cohortanalysis,CA)模型可以用来求算鱼类的自然死亡系数(M)。在以往的计算过程中来自模型和数据的误差往往被忽略。文章讨论了用股分析模型求算M的方法,并运用广义线性模型(generalizedlinearmodel,GzLM)探讨了3种不同误差结构(正态,对数正态和伽马)对求算结果的影响。蒙特卡罗(MonteCarlo)模拟分析显示,当数据的噪音(即变异系数coefficientofvariation,CV)小于大约10%时可以得到M较好的估计值。不同的误差结构会影响M的估算,其中对数正态分布的GzLM误差得到了最好的结果。构造了长寿命小自然死亡系数和短寿命大自然死亡系数的2个鱼类种群,模拟结果表明这种方法更适用于寿命短而自然死亡系数大的种群。同样假设以上3种误差结构,将该方法应用到黄海鳀鱼(Engraulisjaponicus)渔业数据上。与其它2种误差结构相比,对数正态的GzLM误差结构同样得到了良好的结果。由于低龄鱼具有较为准确的观测数据,其M的估计值好于高龄鱼。
Pope＇s （1972） cohort analysis model can be used to estimate fish natural mortality coefficient （M） when series abundance and catch data are available. Errors in both the model and data are usually neglected in usual calculations, regardless of whether it is realistic. This paper discusses the M estimation using Pope＇s cohort analysis model, and a generalized linear model （GzLM） is used to explore the effect on the estimated results of three error structures （normal, lognormal and gamma） . Monte Carlo simulation analyses show that when white noises （coefficient of variation, CV） in the data are less than about 10%, the estimated values of M are mostly reliable. The estimation quality of M using Pope＇s model can be influenced by the assumption about the error structure in the estimation, and that lognormal distribution is appropriate for the Pope＇s model. Two species of long-lived with low M and short-lived with high M were generated, and the simulation analysis indicates that the method performs better for short-lived species with high M. We then applied this method to the data of the Yellow Sea anchovy （Engraulis japonicus） under the three error structures. The results obtained from lognormal GzLM distribution are more viable than other distributions, and the estimated values of M are viable for young ages, for their more accurate observed data, than that of older ages.
出处
《南方水产》
2006年第3期7-15,共9页
South China Fisheries Science
基金
国家自然科学基金项目(30271025)
关键词
资源量
渔获量
自然死亡系数
股分析模型
广义线性模型
鳀
abundance data
catch data
natural mortality coefficient
cohort analysis model
generalized linear model
anchovy
Engraulis japonicus
