2008Unpublished venueRequires access

Analysis of effects of metric traits on body weight of turbot Scophthalmus maximus

Sheng Luan

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Abstract

The effects of metric traits on body weight of turbot were analyzed, using correlation analysis and path analysis. Data for this study were collected from 114 fry 90 days after hatch in Haiyang city, Shandong Province. The total length (X1), body length (X2), head length (X3), snout length (X4), body depth (X5), caudal peduncle depth (X6), caudal peduncle length (X7)and body weight (Y) were measured. Total length was eliminated from the variable data set because it was co-linear with body length. Seven metric traits were used as independent variables, and body weight was used as a dependent variable for path analysis. Path coefficients (Pi), determination coefficients (di) and correlation index (R2) were calculated in path analysis. The results showed that all seven correlation coefficients between each metric traits and the weight achieved were all very significantly different (P0.01). The path coefficients (Pi) of the body length (X2), body depth (X5) and caudal peduncle depth (X6) to the body weight all reached a level of significance. These attributes were very indicative of determining the body weight. Judging from the result of high correlation index (R2=0.895), the main variables (X2, X5, X6) were selected. The multiple regression equation of body length (X2), body depth (X5) and caudal peduncle depth (X6) to the body weight was obtained. This paper provides a theoretical support for genetic breeding of turbot.

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What this paper is about

The effects of metric traits on body weight of turbot were analyzed, using correlation analysis and path analysis. Data for this study were collected from 114 fry 90 days after hatch in Haiyang city, Shandong Province. The total length (X1), body length (X2), head length (X3), snout length (X4), body depth (X5), caudal peduncle depth (X6), caudal peduncle length (X7)and body weight (Y) were measured. Total length was eliminated from the variable data set because it was co-linear with body length. Seven metric traits were used as independent variables, and body weight was used as a dependent variable for path analysis. Path coefficients (Pi), determination coefficients (di) and correlation index (R2) were calculated in path analysis. The results showed that all seven correlation coefficients between each metric traits and the weight achieved were all very significantly different (P0.01). The path coefficients (Pi) of the body length (X2), body depth (X5) and caudal peduncle depth (X6) to the body weight all reached a level of significance. These attributes were very indicative of determining the body weight. Judging from the result of high correlation index (R2=0.895), the main variables (X2, X5, X6) were selected. The multiple regression equation of body length (X2), body depth (X5) and caudal peduncle depth (X6) to the body weight was obtained. This paper provides a theoretical support for genetic breeding of turbot.

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Available abstract

The effects of metric traits on body weight of turbot were analyzed, using correlation analysis and path analysis. Data for this study were collected from 114 fry 90 days after hatch in Haiyang city, Shandong Province. The total length (X1), body length (X2), head length (X3), snout length (X4), body depth (X5), caudal peduncle depth (X6), caudal peduncle length (X7)and body weight (Y) were measured. Total length was eliminated from the variable data set because it was co-linear with body length. Seven metric traits were used as independent variables, and body weight was used as a dependent variable for path analysis. Path coefficients (Pi), determination coefficients (di) and correlation index (R2) were calculated in path analysis. The results showed that all seven correlation coefficients between each metric traits and the weight achieved were all very significantly different (P0.01). The path coefficients (Pi) of the body length (X2), body depth (X5) and caudal peduncle depth (X6) to the body weight all reached a level of significance. These attributes were very indicative of determining the body weight. Judging from the result of high correlation index (R2=0.895), the main variables (X2, X5, X6) were selected. The multiple regression equation of body length (X2), body depth (X5) and caudal peduncle depth (X6) to the body weight was obtained. This paper provides a theoretical support for genetic breeding of turbot.

Key concepts: Peduncle (anatomy), Snout, Path analysis (statistics), Path coefficient, Body weight, Mathematics, Correlation, Turbot

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