庙岛群岛南部海域大型底栖动物群落结构及其与环境因子的关系

为了解庙岛群岛南部海域大型底栖动物群落结构特点及其与环境因子的关系,于2012年11月和2013年2月、5月、8月(分别代表秋季、冬季、春季、夏季)进行了4个航次的采样,分析了大型底栖动物群落结构及其与水深、水温、透明度和MD(沉积物中值粒径)、w(OM)(OM为有机质)、w(Chla)(Chla为叶绿素a)、w(Pha)(Pha为脱镁叶绿酸)等环境因子间的关系. 结果表明:调查共发现大型底栖动物164种,其中环节动物多毛类82种,节肢动物甲壳类39种,软体动物29种,棘皮动物9种,其他类群动物5种；季节上表现为夏季>冬季>春季>秋季. IRI(相对重要性指数)计算结果表明,不同季节的优势种存在差异,但主要优势种大多为环节动物多毛类；其中,秋季最主要的优势种为彩虹明樱蛤(Moerella iridescens),其IRI为4 280；冬季和春季均为梳鳃虫(Terebellides stroemii),IRI分别为1 092和435；夏季为刚鳃虫(Chaetozone setosa),IRI为542. Spearman相关分析表明,底栖动物丰度和沉积物含水率呈显著正相关(P<0.05)；生物量与σi(沉积物分选系数)、w(OM)呈显著负相关,而与水深呈极显著正相关. MD、透明度、w(Pha)组合可以很好地解释研究海域不同底栖动物群落结构的差异. 

Macrobenthic Community Structure and Its Relationship with Environmental Factors in the Southern Waters of the Miaodao Archipelago

Abstract: To study the characteristics of the macrobenthic community and its relationship with environmental factors in the southern waters of the Miaodao Archipelago, macrobenthos samples as well as environmental factors (sediment grain size characteristics, organic matter, chlorophyll a, phaeophorbide (Pha) concentrations, water depth, temperature, transparency, etc.) were collected in November 2012 and February, May and August 2013. A total of 164 macrobenthic species were identified, including 82 species of Polychaeta, 39 species of Crustacea, 29 species of Mollusca, 9 species of Echinodermata and 5 of other taxa. Species number was the highest in summer, intermediate in winter and spring, and lowest in autumn. The index of relative importance (IRI) showed that the dominant species varied in different seasons, and the most dominant species was Polychaeta. Moerella iridescens (Bivalvia) was the dominant species in autumn (IRI = 4280), whereas Terebellides stroemii (Polychaeta) was dominant both in winter (IRI = 1092) and in spring (IRI = 435), and Chaetozone setosa (Polychaeta) was dominant in summer (IRI = 542). Based on Spearman correlation analysis, the abundance of macrobenthos was significantly positively correlated with sediment water content. The biomass was significantly negatively correlated with the sediment sorting coefficient and organic matter content, but positively correlated with water depth. Results of BIOENV analysis showed that the combination of sediment median size, water transparency and sediment phaeophorbide concentration well explained the differences of community structure among sampling stations.