秋季黄海中南部鱼类群落对饵料生物的摄食量

研究鱼类与饵料生物之间食物定量关系进而为多鱼种资源评估提供依据,2000~2002年秋季(10~11月)在黄海中南部海域进行了定点底拖网调查,应用Eggers模型,计算了带鱼(Trichiurus lepturus)、小黄鱼(Pseudosciaena polyactis)、黄(Lophius litulon)、细纹狮子鱼(Liparis tanakae)等23种鱼类在秋季对饵料生物的摄食量。结果表明:黄海中南部23种鱼类在秋季对饵料生物的总摄食量约为309万t,其中,鱼(Engraulis japonicus)的摄食量最高(在250万t以上),占总摄食量的80.9%。中上层和底层鱼类对饵料生物的摄食量分别为262万t和47万t左右,占总摄食量的84.7%和15.3%,鱼和细纹狮子鱼分别是中上层和底层鱼类中最主要的捕食者。磷虾类是中上层鱼类最主要的食物来源,其次是桡足类、端足类和毛颚类;虾类和鱼类是底层鱼类最主要的食物来源,其次是磷虾类。太平洋磷虾(Euphausia pacifica)、中华哲水蚤(Calanus sinicus)、细长脚虫戎(Themisto gracilipes)、脊腹褐虾(Crangon affinis)和鱼同时是黄海中南部被摄食量最高的5种饵料生物,它们被摄食的生物量之和约为233万t,占总摄食量的75.5%。     

Frequency-selective attenuation of sound propagation and reverberation in shallow water

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Habitat characteristics and spatial arrangement affecting the diversity of fish and decapod assemblages of seagrass (Zostera marina) beds around the coast of Jersey (English Channel)

Recent research and management plans for seagrass habitats have called for landscape level approaches. The present study examines the spatial utilisation of subtidal seagrass beds by fish and decapods around the coast of Jersey (49°N 02° W). A hierarchical scale of landscape configuration and the plant characteristics of eight seagrass beds were measured and the contributions of these variables as predictors of the properties of the fish and decapod assemblages were evaluated using multiple linear regression models. The results indicated that total diversity had a negative relationship with transect heterogeneity and total species number had a weak negative association with increasing fragmentation. Both total diversity and total species number showed a positive relationship with depth. In fact, in all models of species number and densities, values were higher in deeper seagrass beds. Total decapod density increased with aggregation of seagrass patches within a landscape. In addition to landscape configuration, smaller-scale structural changes in both canopy height and epiphyte load appeared to influence densities of decapod crustaceans. At night, fewer patterns could be explained by the independent variables in the model.