In order to study morphological diversity of codling moth, Cydia pomonella （L.） using thin-plate spline analysis, nine geographical populations from four north western provinces of Iran namely East Azarbayjan, West ...In order to study morphological diversity of codling moth, Cydia pomonella （L.） using thin-plate spline analysis, nine geographical populations from four north western provinces of Iran namely East Azarbayjan, West Azarbayjan, Ardebil and Zandjan were collected during 2003 and 2004. 575 and 564 images were prepared from fore and hind wings, respectively. Then 15 and 11 landmarks were determined from fore and hind wings, respectively. With transforming of landmark＇s two dimensional coordinate data into partial warp scores, 26 and 18 scores were generated for fore and hind wings, respectively. Cluster analysis based on wing shape variables using Ward＇s algorithm assigned nine geographical populations into two groups. The pattern of grouping based on fore and hind wings was different in both sexes. Principal component analysis revealed discrimination between geographic populations and confirmed the result of cluster analysis. Among environmental parameters, wind speed showed the highest correlation with wing shape variables. Non significant correlation was observed between geographic and morphological distance matrices as revealed by Mantel test.展开更多
文摘In order to study morphological diversity of codling moth, Cydia pomonella （L.） using thin-plate spline analysis, nine geographical populations from four north western provinces of Iran namely East Azarbayjan, West Azarbayjan, Ardebil and Zandjan were collected during 2003 and 2004. 575 and 564 images were prepared from fore and hind wings, respectively. Then 15 and 11 landmarks were determined from fore and hind wings, respectively. With transforming of landmark＇s two dimensional coordinate data into partial warp scores, 26 and 18 scores were generated for fore and hind wings, respectively. Cluster analysis based on wing shape variables using Ward＇s algorithm assigned nine geographical populations into two groups. The pattern of grouping based on fore and hind wings was different in both sexes. Principal component analysis revealed discrimination between geographic populations and confirmed the result of cluster analysis. Among environmental parameters, wind speed showed the highest correlation with wing shape variables. Non significant correlation was observed between geographic and morphological distance matrices as revealed by Mantel test.
