我国东、黄海鲐鱼灯光围网渔业CPUE标准化研究

日本鲐是我国近海重要的中上层鱼类资源之一,评估其资源量需要对单位捕捞努力量渔获量（CPUE）进行标准化。影响CPUE标准化的因素很多，包括季节、区域和海洋环境等。本文利用广义线型模型（GLM）和广义加性模型（GAM），结合时空、捕捞船、表温等因子，对1998-2006年东、黄海大型灯光围网渔业鲐鱼CPUE进行标准化，并评价各因子对CPUE的影响。首先应用GLM模型评价时间、空间、环境以及捕捞渔船参数对CPUE的影响，并确定显著性变量。其次，将显著性变量逐一加入GAM模型，根据Akaike信息法则（AIC），选择最优的GAM模型。最后，利用最优的GAM模型对CPUE标准化，并定量分析时间、空间、环境以及捕捞渔船参数对鲐鱼CPUE的影响。GLM模型结果表明：8个变量对CPUE有重要影响，依次为年、船队、船队与年的交互效应、月、船队与月份的交换效应、经度、纬度和海表温。根据AIC，包含上述8个显著性变量的GAM模型为最优模型，对CPUE偏差的解释为27.78%。GAM模型结果表明：高CPUE分别出现在夏季海表温为28～31 ℃的东海中部和冬季海表温为12～16 ℃的黄海；1998-2006年，标准化后的CPUE呈逐年下降趋势，与持续增长的捕捞努力量有关。

CPUE standardization of chub mackerel (Scomber japonicus) for Chinese large lighting-purse seine fishery in the East China Sea and Yellow Sea

Chub mackerel (Scomber japonicus) is one of the important pelagic fishery resources in the China s coastal waters. It is needed to standardize the catch per unit effort (CPUE) in the stock assessment. Many factors including seasonal, regional and marine environmental conditions affect the CPUE. In this paper, generalized linear model (GLM) and generalized additive model (GAM), by which temporal, spatial, environmental, and fisheries vessels variables were chosen for analysis, were used to standardize CPUE of chub mackerel for Chinese large lighting-purse seine fishery in the East China Sea and Yellow Sea from 1998 to 2006, and evaluate impacts of environmental variables on CPUE. Firstly, GLM was applied to evaluate impacts of temporal, spatial, environmental, fisheries operational variables on the CPUE, and the significant factors. Then the significant variables were used in the GAM one by one to select an optimal GAM by using the Akaike Information Criterion (AIC). The derived GAM was used to quantify the effects of temporal, spatial, environmental, and fisheries operational variables on the Chub mackerel catch rates and to derive standardized CPUE. The GLM analysis revealed the importance of eight variables ranked by decreasing magnitude:Year, Fleet, Fleet × Year, Month, Fleet × Month, Longitude, Latitude and Sea surface temperature. The final GAM including eight significant variables derived from GLM analysis was the optimal model based on AIC and explained 27. 78% of the variance in nominal CPUE. GAM analysis indicated that high CPUEs were found in the central East China Sea at sea surface temperatures ranging from 28 to 31℃ in summer and in the Yellow Sea at sea surface temperatures from 12 to 16℃ in winter. The standardized CPUE tended to decrease from 1998 to 2006, which might result from increased fishing efforts.