Determining the Number of Instars in Simulium quinquestriatum (Diptera: Simuliidae) Using k-Means Clustering via the Canberra Distance

Abstract

Simulium quinquestriatum Shiraki (Diptera: Simuliidae), a human-biting fly that is distributed widely across Asia, is a vector for multiple pathogens. However, the larval development of this species is poorly understood. In this study, we determined the number of instars in this pest using three batches of field-collected larvae from Guiyang, Guizhou, China. The postgenal length, head capsule width, mandibular phragma length, and body length of 773 individuals were measured, and k-means clustering was used for instar grouping. Four distance measures-Manhattan, Euclidean, Chebyshev, and Canberra-were determined. The reported instar numbers, ranging from 4 to 11, were set as initial cluster centers for k-means clustering. The Canberra distance yielded reliable instar grouping, which was consistent with the first instar, as characterized by egg bursters and prepupae with dark histoblasts. Females and males of the last cluster of larvae were identified using Feulgen-stained gonads. Morphometric differences between the two sexes were not significant. Validation was performed using the Brooks-Dyar and Crosby rules, revealing that the larval stage of S. quinquestriatum is composed of eight instars.

Instar Determination of Blattella asahinai (Blattodea: Ectobiidae) From Digital Measurements of the Pronotum Using Gaussian Mixture Modeling and the Number of Cercal Annuli

“…Sardesai (1969) correlated fecal pellet size to instar in Lepidoptera, for example. Other clustering methods, such as k -means clustering, have proved advantageous in instar determination (Yang et al 2018) and further research can elucidate additional applications for clustering in modeling growth.…”

Determining the number of instars in potato tuber moth Phthorimaea operculella (Zeller) using density‐based DBSCAN clustering

“…Overlapping size‐frequency distributions occurred between head capsule length and mandible width of second to third larval instars; however, these instars were distinguished by the three clustering algorithms, indicating that these algorithms can be used to determine instars of insects. The Gaussian mixture model (centroid‐based clustering) and the k ‐means clustering method (distribution‐based clustering) have been successfully used to determine instars of insects in previous studies (Wu et al, ; Yang et al, ). However, the DBSCAN clustering algorithm has yet to be used to determine instars of insects.…”

“…The Gaussian mixture model (centroid-based clustering) and the kmeans clustering method (distribution-based clustering) have been successfully used to determine instars of insects in previous studies (Wu et al, 2013;Yang et al, 2018). However, the DBSCAN clustering algorithm has yet to be used to determine instars of insects.…”

“…In this study, four initial cluster centres were selected based on the results of the DBSCAN clustering and Gaussian mixture models. Detailed information regarding these procedures has been described in previous studies (Lucas, ; Yang et al, ).…”

“…However, occasionally, as was the case for Simulium quinquestriatum , the overlapping size‐frequency distributions prevent instar grouping and analyses (Baba, ). To account for these problems, some cluster analyses such as Gaussian mixture models (GMM; Wu, Appel, & Hu, ), the linear discriminant method (LDA; Hunt & Chapman, ), the K‐means clustering algorithm (Yang et al, ), univariate kernel density estimation (Chen & Seybold, ), the adaptive kernel smoothing method (Cen et al, ) and the adaptive bivariate kernel smoothing method (Cen et al, ) have been proposed.…”