中文摘要：

根据2013年至2015年春、秋两季在海州湾近岸的张网调查数据,应用多元统计分析方法对海州湾张网渔获物种类组成的时空变化进行研究,并分析了渔获物种类组成与主要环境、捕捞因子的关系。结果表明：共调查到103种渔获物,后续分析采用相对丰富度大于1%的共23个物种;相似性百分比分析表明,六丝钝尾虾虎鱼（Amblychaeturichthys hexanema）和小黄鱼（Larimichthys polyactis）是第一、第二聚类组渔获物种类组成相似的典型种,其贡献率分别为26.68%和41.74%,同时这2个物种也是组间渔获物种类组成差异的分歧种;非度量多维标度分析和单因素相似性分析表明,海州湾张网渔获物种类组成站位和月份间差异性显著（P〈0.05）,而年份间无显著性差异（P〉0.05）;冗余分析表明,影响渔获物种类组成的主要环境因子为海表温度,其次为离岸距离、叶绿素a、海表盐度、海面风速及深度,底质类型影响不显著,最主要捕捞因子为有效网次,其次为日均渔获量,6个环境因子和2个捕捞因子共同解释了54.11%的渔获物种类组成的差异。本研究旨在通过分析海州湾近岸张网渔获物的种类组成及其与环境、捕捞因子的关系,为海州湾张网渔业以及小型渔业的研究提供科学依据。

英文摘要：

The Administrative Departoaent of Fisheries and Researchers has focused almost exclusively on the in- dustrialized fishing sector. Nevertheless, small-scale fisheries account for more than 90% of the world＇s capture fisheries and fish workers. Thus, complete understanding of the dynamics of small-scale fisheries and exploring the factors that drive fishery activity are urgently needed. Many multivariate methods have been applied to small-scale fishery data in order to distinguish the factors that influence catch composition. This study used mul- tivariate methods to evaluate the spatial variations of catch composition and analyzed the relationships between environmental/fishing factors and catch composition. The survey data from 2013 to 2015 were obtained from 3 stations in Haizhou Bay where stow nets are used. In all, 103 species were captured. We selected 23 species whose weight was more than 1~/0 of all catch species, to analyze the factors that influenced the catch composition. The 23 catch species accounted for 85.79% of the total weight. Cluster analysis （CA） was used to assess the catch species composition, and the CA dendrogram showed two groups, which were significant at the 95% confidence interval （P〈0.05）. Approximately 15 catch species were included in the first cluster and 8 in the second cluster. According to the results of similarity percentage analysis, Amblychaeturichthys hexanema, Syngnathus acus, and Alpheus heterocarpus were the main contributors to the similarity between the samples of the first cluster, and their simi- larity contribution rates were 26.68%, 16.86%, and 11.01%, respectively. The second cluster was represented by Larimichthys polyactis, Portunus trituberculatus, and Odontamblyopus rubicundus, and their similarity contribu- tion rates were 41.74%, 18.50%, and 17.13%, respectively. Further, the two clusters were distinguished from each other by A. hexanema and L. polyactis, and their variance contributions were 11.87% and 10.28%, respectively. The non-metric multidim