2004Europe PMC (PubMed Central)Requires access

Morphological variations and discriminant analysis of different geographical populations of Tegillarca granosa

Yongpu Zhang, Zhi‐Hua Lin, Ying Xuepin

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Abstract

Based on nine morphological characters of populations of Tegillarca granosa from Guangxi, Zhejiang, Shandong,of China and Lishui, Korea, multivariate morphometrics was used to investigate their morphological variations among the four different geographical populations. ANOVA indicated that four populations showed significant morphological variations respectively (P 0.01). In the result of principal component analysis, two principal components (PC) were constructed by factor loading, in which the first principal component (PC1) was affected by shell width, shell height and shell moisture weight, and the PC2 was affected by hinge tooth, and the contributory ratios of two PC were 41.02% and 20.41%, respectively. The results of principal component analysis and cluster analysis revealed that the morphological chTegillraca granosa from Zhejiang population and Shandong population were similar. The discriminant functions of four populations were established, and the discriminant accuracy was 90.80%, 80.89%, 71.76%, and 97.46% respectively, so the average discriminant accuracy was 86.23%.

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What this paper is about

Based on nine morphological characters of populations of Tegillarca granosa from Guangxi, Zhejiang, Shandong,of China and Lishui, Korea, multivariate morphometrics was used to investigate their morphological variations among the four different geographical populations. ANOVA indicated that four populations showed significant morphological variations respectively (P 0.01). In the result of principal component analysis, two principal components (PC) were constructed by factor loading, in which the first principal component (PC1) was affected by shell width, shell height and shell moisture weight, and the PC2 was affected by hinge tooth, and the contributory ratios of two PC were 41.02% and 20.41%, respectively. The results of principal component analysis and cluster analysis revealed that the morphological chTegillraca granosa from Zhejiang population and Shandong population were similar. The discriminant functions of four populations were established, and the discriminant accuracy was 90.80%, 80.89%, 71.76%, and 97.46% respectively, so the average discriminant accuracy was 86.23%.

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Available abstract

Based on nine morphological characters of populations of Tegillarca granosa from Guangxi, Zhejiang, Shandong,of China and Lishui, Korea, multivariate morphometrics was used to investigate their morphological variations among the four different geographical populations. ANOVA indicated that four populations showed significant morphological variations respectively (P 0.01). In the result of principal component analysis, two principal components (PC) were constructed by factor loading, in which the first principal component (PC1) was affected by shell width, shell height and shell moisture weight, and the PC2 was affected by hinge tooth, and the contributory ratios of two PC were 41.02% and 20.41%, respectively. The results of principal component analysis and cluster analysis revealed that the morphological chTegillraca granosa from Zhejiang population and Shandong population were similar. The discriminant functions of four populations were established, and the discriminant accuracy was 90.80%, 80.89%, 71.76%, and 97.46% respectively, so the average discriminant accuracy was 86.23%.

Key concepts: Principal component analysis, Biology, Morphometrics, Linear discriminant analysis, Multivariate statistics, Population, Veterinary medicine, Morphological analysis

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