浅水湖泊鱼类群落结构评估: 复合网目刺网与网簖的比较

ASSESSING FISH ASSEMBLAGES IN A SHALLOW YANGTZE RIVER LAKE USING MULTI-MESH GILLNETS AND DENSE-MESH WEIRS

  • 摘要: 准确评估鱼类群落结构是渔业管理和鱼类资源养护的必要前提, 而不同渔具采样结果往往会产生较大差异。研究同时采用欧洲标准采样网具-复合网目刺网(Multi-mesh gillnet)和中国传统网具-网簖对长江中游典型浅水湖泊扁担塘的鱼类群落结构进行了评估。2种网具共采集到27种鱼类, 并发现黄尾鲴(Xenocypris daviai)和湖鲚(Coilia nasus taihuensis)2种新记录种, 扁担塘的鱼类群落结构较1999年和2003年均发生了较明显的改变。2种渔具捕获的鱼类组成、相对丰度和生物量以及鱼类体长分布频率均存在显著性差异。基于鱼类的相对生物量和相对丰度的NMDS排序表明2种网具捕获到的鱼类群落结构也存在显著差异。另外还比较了复合网目刺网与其他定量采样网具间的差异, 作者认为复合网目刺网比较适合长江中下游浅水湖泊鱼类群落研究的定量取样, 但仅凭单一网具评估鱼类群落结构具有局限性。

     

    Abstract: To assess possible bias of different fishing methods is essential to appropriate fisheries management. In the current study, fish assemblage structure of a shallow Yangtze River lake was assessed by combining one international standard sampling gear (multi-mesh gillnet), and one traditional Chinese gear (the dense-mesh weir). Using Lake Biandantang as a case study, a total of 27 fish species were collected from the two gears combined, including 2 new species that had not been recorded previously in this lake. Results suggested that fish assemblages had changed greatly compared to a previous study conducted in the 1990s. Specifically, differences were found in species composition, abundance, biomass, and length distributions collected from the two gears. Difference in total length (TL) distributions of fishes caught was the most conspicuous change. Fishes collected from the weir ranged from 40—70 mmTL, whereas fishes collected from gillnets ranged from 90—140 mmTL. Multivariate ordinations based on relative abundance and biomass data also indicated fish assemblage structural differences between the two gears. The comparative results showed that the multi-mesh gillnet was effective at assessing fish assemblages in shallow lakes, such as those found along the middle and lower reaches of the Yangtze River. Additionally, assessments using only one gear could have some limitations with respect to interpreting fish assemblage changes over time.

     

/

返回文章
返回