Application of a Frequency Distribution Method for Determining Instars of the Beet Armyworm (Lepidoptera: Noctuidae) from Widths of Cast Head Capsules

• Published in: Journal of economic entomology Vol. 106; no. 2; pp. 800 - 806
• Main Authors: Chen, Y, Seybold, S J
• Format: Journal Article
• Language: English
• Published: 01-04-2013
• Subjects: Lepidoptera; Noctuidae; Spodoptera exigua
• ISSN: 0022-0493
• DOI: 10.1603/EC12258

Instar determination of field-collected insect larvae has generally been based on the analysis of head capsule width frequency distributions or bivariate plotting, but few studies have tested the validity of such methods. We used head capsules from exuviae of known instars of the beet armyworm, Spodoptera exigua (Huebner) (Lepidoptera: Noctuidae), to determine the larval instars with the frequency distribution method and Dyar's rule. Head capsule widths of S. exigua ranged from 0.313 to 1.446 mm. The number of instars from the analytical method matched that from the observed data. Based on misclassification rules derived from nonlinear least square fitting of the head capsule width data, the theoretical misclassification rates ranged from 0.62 to 1.92%. Comparing the theoretical distribution to the observed data, the observational misclassification probabilities ranged from 1.18 to 3.03%. There were also 10 head capsules, eight third instars and two fourth instars, not classified into any of the known instars based on the theoretical distributions. Dyar's growth ratios of successive instars ranged from 1.41 to 1.65, and those based on the observed data and theoretical distribution were similar. Both approaches yielded a linear relationship between the natural logarithm of the mean head capsule width and the instar number, which indicates full representation of the larval instars. The results demonstrated that the frequency distribution-based method was robust, although we recommend caution when using such methods to classify head capsules into specific instar classes. Application of computer algorithms should also be accompanied by visual inspection to determine instars from the frequency distribution.