JOURNAL ARTICLE
Antennal Sensilla in Longhorn Beetles (Coleoptera: Cerambycidae).
Published In: Annals of the Entomological Society of America, 2023, v. 116, n. 2. P. 83 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Haddad, Stephanie; Clarke, Dave J; Jeong, Soo-Hyun; Mitchell, Robert F; McKenna, Duane D 3 of 3
Abstract
This article focuses on the morphology, classification, and diversity of antennal sensilla—microscopic sensory organs—in longhorn beetles (family Cerambycidae), emphasizing the need for standardized terminology to facilitate comparative studies. Longhorn beetles possess prominent antennae critical for chemosensation, which mediates host plant and mate recognition through various sensilla types, including Böhm bristles, sensilla chaetica, chemosensory hairs, sensilla basiconica, dome-shaped organs, sensilla coeloconica, and sensilla auricillica. The review synthesizes data from 30 publications covering 29 species across five cerambycid subfamilies, highlighting inconsistencies and gaps in sensilla identification and function, particularly due to limited transmission electron microscopy (TEM) and electrophysiological studies. The authors provide scanning electron microscopy (SEM) images of ten additional species to exemplify sensilla types and propose a preliminary standardized classification framework to support future research on the sensory biology, chemical ecology, and phylogenetics of longhorn beetles.
Additional Information
- Source:Annals of the Entomological Society of America. 2023/03, Vol. 116, Issue 2, p83
- Document Type:Article
- Subject Area:Anatomy and Physiology
- Publication Date:2023
- ISSN:0013-8746
- DOI:10.1093/aesa/saac026
- Accession Number:162394349
- Copyright Statement:Copyright of Annals of the Entomological Society of America is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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