基于空间相关性的西北太平洋柔鱼CPUE标准化研究

CPUE标准化方法通常都假设名义CPUE之间是相互独立且没有相关性,然而鱼类集群分布通常存在着空间相关性,为此本研究以西北太平洋柔鱼的CPUE标准化为例,采用1999-2012年6-11月中国鱿钓生产数据以及对应的海表面温度和叶绿素浓度的环境数据,将空间相关性加入广义线性模型(general linear model,GLM)中.在空间GLM模型中运用4个距离模型(指数模型、球面模型、线性模型和高斯模型),进行标准GLM模型和4种空间GLM模型的CPUE标准化结果比较.结果发现,4种空间GLM模型均比标准GLM模型的最小信息准则(akaike information criterion,AIC)更小,标准化结果更准确.同时,在4个距离模型中,指数模型的AIC值最小,其CPUE标准化结果最佳.研究表明,在CPUE标准化中,鉴于鱼类集群与分布特性,应该充分考虑空间相关性这一因素.

A study of incorporating spatial autocorrelation into CPUE standardization with an application to Ommastrephes bartramii in the northwest Pacific Ocean

Catch per unit effort(CPUE)of a fishery is often used as an abundance index which is usually assumed to be proportional to the stock abundance. Observed fisheries CPUE data are, however, influenced by many factors, in addition to fish population abundance, including spatial-temporal factors such as area and season and environmental factors such as sea surface temperature(SST)and sea surface salinity. The impacts of these factors on CPUE may shift the assumed proportionality between observed CPUE and stock abundance. Thus, CPUE standardization is needed to remove the impacts of factors other than population abundance. Many statistical models have been developed for CPUE standardization such as General Linear Model(GLM)and General Additive Model(GAM). Generally, statistical methods always assume the independence of the observed CPUEs. However, this assumption is invalid for a fish school and distribution because of their spatial autocorrelation. Therefore, in this study, we take a CPUE standardization of red flying squid(Ommastrephes bartramii)as an example. Based on the fishing data in jigging fishery by Chinese fishing fleet and the corresponding SST data and the Chlorophylla data in the Northwest Pacific Ocean from June to November from 1999 to 2012, the spatial autocorrelation is incorporated into the standard general linear model(GLM). Four distance models(Gaussian, exponential, linear and spherical)are examined for spatial autocorrelation using the CPUE standardization of red flying squid. It is found that the four spatial-GLMs always produce the better goodness-of-fit to the data than that for the standard GLM. And the exponential model generates the best goodness-of-fit to the data in the four distance models. Therefore, it is suggested that spatial autocorrelation into CPUE standardization should be considered when the nominal CPUEs are strongly spatially autocorrelated.