Morphological Variations Analysis of Different Populations of Paphia euglypta Along South China Sea
Wu Jixing
Abstract
Wu Jixing
Abstract
Three mulitivariation analysis methods (cluster analysis,principal component analysis and discriminate analysis) were used to compare the morphological characters of four populations of Paphia euglypta in Yangjiang,Zhanjiang,Haikou and Beihai. The results of clusters analysis and principal component analysis indicated that the morphological characters were similar among the populations in Yangjiang,Zhanjiang and Haikou,but quite different from that of Beihai. In the principal component analysis,two principal components were constructed,the contributory ratio of the first principal component was 32.840%,that of the second principal component was 26.106%,and the cumulative contributory ratio was 58.945%. The results of discriminant analysis revealed that there were significant differences among the four populations (P0.01). The identification accuracy was 46.7%~100% (P1) and 40.5%~96.7% (P2). The total discriminant accuracy was 64.7%. The Mantel test results showed that Euclidean distance was positively correlated with geographical distance (r=0.0974,P=0.5770).
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Three mulitivariation analysis methods (cluster analysis,principal component analysis and discriminate analysis) were used to compare the morphological characters of four populations of Paphia euglypta in Yangjiang,Zhanjiang,Haikou and Beihai. The results of clusters analysis and principal component analysis indicated that the morphological characters were similar among the populations in Yangjiang,Zhanjiang and Haikou,but quite different from that of Beihai. In the principal component analysis,two principal components were constructed,the contributory ratio of the first principal component was 32.840%,that of the second principal component was 26.106%,and the cumulative contributory ratio was 58.945%. The results of discriminant analysis revealed that there were significant differences among the four populations (P0.01). The identification accuracy was 46.7%~100% (P1) and 40.5%~96.7% (P2). The total discriminant accuracy was 64.7%. The Mantel test results showed that Euclidean distance was positively correlated with geographical distance (r=0.0974,P=0.5770).
Key concepts: Principal component analysis, Linear discriminant analysis, Euclidean distance, Biology, Statistics, Mathematics, Geometry