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An Adaptive Kernel Smoothing Method for Classifying Austrosimulium tillyardianum (Diptera: Simuliidae) Larval Instars

文献类型： 外文期刊

作者： Cen, Guanjun 1 ; Yu, Yonghao 1 ; Zeng, Xianru 1 ; Long, Xiuzhen 1 ; Wei, Dewei 1 ; Gao, Xuyuan 1 ; Zeng, Tao 1 ;

作者机构： 1.Guangxi Acad Agr Sci, Guangxi Key Lab Biol Crop Dis & Insect Pests, Inst Plant Protect, Nanning 530007, Peoples R China

关键词： Austrosimulium tillyardianum;instar determination method;adaptive kernel smoothing estimation;bandwidth selection

期刊名称：JOURNAL OF INSECT SCIENCE （ 影响因子：1.857； 五年影响因子：1.904 ）

ISSN： 1536-2442

年卷期： 2015 年 15 卷

页码：

收录情况： SCI

摘要： In insects, the frequency distribution of the measurements of sclerotized body parts is generally used to classify larval instars and is characterized by a multimodal overlap between instar stages. Nonparametric methods with fixed bandwidths, such as histograms, have significant limitations when used to fit this type of distribution, making it difficult to identify divisions between instars. Fixed bandwidths have also been chosen somewhat subjectively in the past, which is another problem. In this study, we describe an adaptive kernel smoothing method to differentiate instars based on discontinuities in the growth rates of sclerotized insect body parts. From Brooks' rule, we derived a new standard for assessing the quality of instar classification and a bandwidth selector that more accurately reflects the distributed character of specific variables. We used this method to classify the larvae of Austrosimulium tillyardianum (Diptera: Simuliidae) based on five different measurements. Based on head capsule width and head capsule length, the larvae were separated into nine instars. Based on head capsule postoccipital width and mandible length, the larvae were separated into 8 instars and 10 instars, respectively. No reasonable solution was found for antennal segment 3 length. Separation of the larvae into nine instars using head capsule width or head capsule length was most robust and agreed with Crosby's growth rule. By strengthening the distributed character of the separation variable through the use of variable bandwidths, the adaptive kernel smoothing method could identify divisions between instars more effectively and accurately than previous methods.

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