标题：Describing phenotypic diversity in an outbreak population of Rice Black Bugs from Balangao, Diplahan, Zamboanga Sibugay, Philippines, using principal component analysis and K-means clustering of morphological attributes
期刊名称：Advances in Environmental Sciences - International Journal of the Bioflux Society
印刷版ISSN：2066-7620
电子版ISSN：2065-7647
出版年度：2013
卷号：5
期号：01
页码：15-22.
出版社：Bioflux
摘要：Rice black bugs (RBB) are believed to be a serious pest of rice infesting all growth stages of the plant. Different management approaches have already been applied to control and regulate populations of this pest. However, control efforts have been muddled by lack of understanding of the taxonomy of this insect resulting from immense intra- and inter-population diversity in phenotypic traits. Here, a total of thirty traits were scored from an outbreak population consisting of one hundred and twenty female RBB from Buug, Zamboanga Sibugay and analyzed using principal component analysis. Plots of the two principal components summarizing 68.8% of the total variation and subsequent K-means clustering showed that this population of RBB belong to at least four groups distributed as follows: group 1 – 49 individuals; group 2 – 46; group 3 - 14 and group 4 – 11. These individuals are polymorphic for eleven traits only, specifically on the relative lengths of the Tylus and the Jugum, presence of Cicatrices humps, number of antennal segments, shape and reach of the Scutellum, shape of the junction of vein R+M in the outer wing, number of closed marginal cells, number of longitudinal veins below discal cell, and Proboscis reach. The importance of these traits to intra-population divergence and life history of the RBB has yet to be determined. Thus, further studies should be conducted to determine the genetic and functional bases of the observed diversity. This information is necessary for the proper formulation of management strategies for the control and regulation of populations of this insect.
关键词：Rice black bugs; RBB; phenotypic diversity; K-means clustering; principal component analysis;Scotinophara molavica.
