Morphological analysis of five populations of soft shell turtle Trionyx sinensis
Xiaoyou Hong
Abstract
Xiaoyou Hong
Abstract
In this paper,16 morphometric parameters were measured in five populations of soft shell turtle Trionyx sinensis,including Dongting(DT),Yellow River(HH),Huangsha(HS),Japan(RB) and Luka(LK,the hybrid of DT♀ and HH♂),and analyzed using multiple regression.Multivariate analysis revealed that the DT,HH,and LK populations were grouped in one cluster,while the HS and RB population were grouped in another cluster.According to principal component analysis,the extracted two components with contribution of 51.93% and 11.02% interpreted 62.95% of the total variation,and the HS and RB populations were separated from the other three populations.In discriminant analysis,9 out of 16 parameters were selected to establish discriminant formulae for the five populations,with the discriminant accuracy of 61.7%-89.5%.The discriminant analysis also revealed two major clusters in the five populations of soft shell turtle.
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In this paper,16 morphometric parameters were measured in five populations of soft shell turtle Trionyx sinensis,including Dongting(DT),Yellow River(HH),Huangsha(HS),Japan(RB) and Luka(LK,the hybrid of DT♀ and HH♂),and analyzed using multiple regression.Multivariate analysis revealed that the DT,HH,and LK populations were grouped in one cluster,while the HS and RB population were grouped in another cluster.According to principal component analysis,the extracted two components with contribution of 51.93% and 11.02% interpreted 62.95% of the total variation,and the HS and RB populations were separated from the other three populations.In discriminant analysis,9 out of 16 parameters were selected to establish discriminant formulae for the five populations,with the discriminant accuracy of 61.7%-89.5%.The discriminant analysis also revealed two major clusters in the five populations of soft shell turtle.
Key concepts: Principal component analysis, Turtle (robot), Linear discriminant analysis, Population, Biology, Multivariate statistics, Cluster (spacecraft), Discriminant