摘要
以江苏省长江水系3个不同湖泊(太湖、石臼湖和固城湖)产中华绒螯蟹(Eriocheir sinensis)群体为研究对象,分别对其进行形态学和元素"指纹"特征的比较研究。基于16个组形态数据的框架测量及相应的逐步判别分析结果表明,虽然太湖、石臼湖和固城湖产中华绒螯蟹3群体之间在形态学上存在一定的差异,且判别准确率分别为70%、90%和80%,但其差异程度尚未达到能够有效区分3个群体的水平。统一使用中华绒螯蟹第三步足作为标准试样,应用电感耦合等离子质谱仪测定了其中21种元素的含量和分布特征,其中Cr、Co、As、Se、Cd、Pb、Tl、Ag、Mo、Ni未检出。与形态判别结果不同的是,3湖泊产中华绒螯蟹第三步足元素含量和分布的总体差异极为显著,太湖、石臼湖和固城湖产中华绒螯蟹的判别准确率分别高达100%、80%和100%。这表明本研究所尝试的中华绒螯蟹第三步足元素"指纹"分析判别方法具有有效区别不同产地中华绒螯蟹的潜力。
Morphological characteristics and elemental fingerprints were compared in Chinese mitten crabs(Eri-ocheir sinensis) from three lakes(Taihu Lake,Shijiu Lake and Gucheng Lake) in Jiangsu Province.Truss network analysis using 16 length measurements suggested that crabs from the three lakes could not be reliably character-ized based on morphological characteristics.The accuracy rates yielded by discriminant analysis were 70% for crabs from Taihu Lake,90% for those from Shijiu Lake,and 80% for those from Gucheng Lake.An Agilent 7500ce ICP-MS was used to measure the concentrations of 21 chemical elements(Na,K,Ca,Fe,Mg,Sr,Ag,Al,As,Ba,Cd,Co,Cr,Cu,Mn,Mo,Ni,Pb,Se,Tl and Zn) in the third pereiopod of the crabs from the three lakes.The concentrations of Cr,Co,As,Se,Cd,Pb,Tl,Ag,Mo,and Ni were below the detection limit.However,in contrast to the morphological analysis,the origin of the crabs from the three lakes could be consistently identified from their elemental fingerprints.The corresponding accuracy rates yielded by discriminant analysis were 100% for crabs from Taihu Lake,80% for those from Shijiu Lake,and 100% for those from Gucheng Lake.We conclude that the approach of using environmentally determined elemental fingerprints in the third pereiopod of the crabs is a potentially valid method for identifying the geographical origin of Chinese mitten crabs.
出处
《中国水产科学》
CAS
CSCD
北大核心
2012年第1期84-93,共10页
Journal of Fishery Sciences of China
基金
农业部公益性行业科研专项(nyhyzx07-045)
中央级公益性科研院所基本科研业务费专项资金项目(2011JBFA14)
关键词
中华绒螯蟹
形态
元素“指纹”
原产地
判别分析
Eriocheir sinensis
morphological characters
elemental fingerprint
geographical origin
discriminant analysis