Details

Autor(en) / Beteiligte

• Mankin, R. W
• Smith, M. T
• Tropp, J. M
• Atkinson, E. B
• Jong, D. Y

Titel

Detection of Anoplophora glabripennis (Coleoptera: Cerambycidae) Larvae in Different Host Trees and Tissues by Automated Analyses of Sound-Impulse Frequency and Temporal Patterns

Ist Teil von

• Journal of economic entomology, 2008-06, Vol.101 (3), p.838-849

Ort / Verlag

Lanham, MD: Entomological Society of America

Erscheinungsjahr

2008

Quelle

MEDLINE

Beschreibungen/Notizen

• Anoplophora glabripennis (Motschulsky) (Coleoptera: Cerambycidae), an invasive pest quarantined in the United States, is difficult to detect because the larvae feed unseen inside trees. Acoustic technology has potential for reducing costs and hazards of tree inspection, but development of practical methods for acoustic detection requires the solution of technical problems involving transmission of resonant frequencies in wood and high background noise levels in the urban environments where most infestations have occurred. A study was conducted to characterize sounds from larvae of different ages in cambium, sapwood, and heartwood of bolts from three host tree species. Larval sounds in all of the tested trees and tissues consisted primarily of trains of brief, 3–10-ms impulses. There were no major differences in the spectral or temporal pattern characteristics of signals produced by larvae of different ages in each tissue, but larval sounds in sapwood often had fewer spectral peaks than sounds in cambium and heartwood. A large fraction, but not all background sounds could be discriminated from larval sounds by automated spectral analyses. In 3-min recordings from infested bolts, trains containing impulses in patterns called bursts occurred frequently, featuring 7–49 impulses separated by small intervals. Bursts were rarely detected in uninfested bolts. The occurrence of bursts was found to predict infestations more accurately than previously used automated spectral analyses alone. Bursts and other features of sounds that are identifiable by automated techniques may ultimately lead to improved pest detection applications and new insight into pest behavior.