The identification of blow flies (Diptera: Calliphoridae) in real time using wingbeat frequency

J. Pinto; C. O'Brien; P.A. Magni; I.R. Dadour

Wingbeat frequency and harmonics are being used to successfully identify agricultural pests and disease vectors in the field. This method is a viable alternative for identifying the forensically important blow fly (Diptera: Calliphoridae). The use of morphometric features and DNA to identify blow flies is difficult, time consuming and expensive, and are complicated by changes in species distribution and the appearance of invasive and hybrid species. Blow flies are the first to colonise decomposing remains and are ubiquitous. They provide valuable evidence when calculating time since death, as the development of the blow fly is highly predictable when using temperature and experimental reference data for the correctly identified species. However, species misidentification results in significant errors in estimating the time since death, as different blow fly species often have different developmental times and life history traits. Wingbeat frequency is measured using an optical sensor to record light fluctuations produced by the wings of an insect partially blocking the light when it flies between a laser beam and a phototransistor array. These fluctuations are then analysed by a classification model built and trained using a machine learning algorithm to identify each specimen by species and sex. This method enables the unbiased identification of an adult blow fly species with a reportable potential rate of error. The adult blow fly is the most mobile stage of the life cycle, determining the species’ presence in a decomposing community, and the speed at which it arrives. However, its role has been largely overlooked with much of the research being focused on the larvae. Flying adult flies are also difficult to study due to their small size and speed. The use of wingbeat frequency data to track and identify blowflies in real time will enable the research of arrival patterns of adult blow flies and the factors that influence detection and acceptance of a site for oviposition, by using a staged crime scene involving decomposing remains. Preliminary testing of common species belonging to the Lucilia and Calliphora genera has so far produced noisy, yet promising classification data. It is believed that by adjusting the placement of the sensor, a smoother signal will be produced, and the identification of each species will be possible. Flying insects have evolved to identify and communicate with each other by using their specific wingbeat frequency, and this offers a unique and promising identification tool to the already busy but often challenging field.

The identification of blow flies (Diptera: Calliphoridae) in real time using wingbeat frequency

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