Listening to Slugs: Acceptability and Consumption of Molluscicide Pellets by the Grey Field Slug, Deroceras reticulatum.

Gastropod damage to crop plants has a significant economic impact on agricultural and horticultural industries worldwide, with the Grey Field Slug (Deroceras reticulatum (Müller)) considered the main mollusc pest in the United Kingdom and in many other temperate areas. The prevailing form of crop protection is pellets containing the active ingredient, metaldehyde. Metaldehyde can cause paralysis and death in the mollusc, depending on the amount ingested. The paralysing effects may result in reduced pellet consumption. A greater understanding of metaldehyde consumption may reveal an area that can be manipulated using novel molluscicide formulations. Novel pellet types included commercial metaldehyde pellets coated so that metaldehyde is released more slowly. In both laboratory and arena trials, an audio sensor was used to record individual slugs feeding on a variety of pellet types, including commercially available toxic pellets (metaldehyde and ferric phosphate) and novel metaldehyde formulations. The sensor was used to record the length of each bite and the total number of bites. There was no significant difference in the length of bites between pellet types in laboratory trials. Novel pellets were not consumed more than commercial pellet types. Commercial pellet types did not differ in consumption.

Standardisation of bioacoustic terminology for insects.

After reviewing the published literature on sound production in insects, a standardised terminology and controlled vocabularies have been created. This combined terminology has potential for use in automated identification systems, evolutionary studies, and other use cases where the synthesis of bioacoustic traits from the literature is required. An example implementation has been developed for the BioAcoustica platform. It is hoped that future development of controlled vocabularies will become a community effort.

Active sound production of scarab beetle larvae opens up new possibilities for species-specific pest monitoring in soils.

Root-feeding Scarabaeidae larvae can pose a serious threat to agricultural and forest ecosystems, but many details of larval ecology are still unknown. We developed an acoustic data analysis method based on active sound production by larvae (i.e. stridulations) for gaining new insights into larval ecology. In a laboratory study, third instar larvae of the Common Cockchafer (Melolontha melolontha) (n = 38) and the Forest Cockchafer (M. hippocastani) (n = 15) kept in soil-filled containers were acoustically monitored for 5 min each, resulting in the first known stridulation recordings for each species. Subsequent continuous monitoring of three M. hippocastani larvae over several hours showed that a single larva could stridulate more than 70 times per hour, and stridulation rates increased drastically with increasing larval abundance. The new fractal dimension-based data analysis method automatically detected audio sections with stridulations and provided a semi-quantitative estimate of stridulation activity. It is the first data analysis method specifically targeting Scarabaeidae larvae stridulations in soils, enabling for the first time non-invasive species-specific pest monitoring.

Automated bioacoustic identification of species.

Research into the automated identification of animals by bioacoustics is becoming more widespread mainly due to difficulties in carrying out manual surveys. This paper describes automated recognition of insects (Orthoptera) using time domain signal coding and artificial neural networks. Results of field recordings made in the UK in 2002 are presented which show that it is possible to accurately recognize 4 British Orthoptera species in natural conditions under high levels of interference. Work is under way to increase the number of species recognized.

Automated bioacoustic identification of species

Pesquisas sobre a identificação automatizada de animais através da bioacústica estão se ampliando, principalmente em vista das dificuldades para realizar levantamentos diretos. Este artigo descreve o reconhecimento automático de insetos Orthoptera utilizando a codificação de sinal no domínio temporal e redes neurais artificiais. Resultados de registros sonoros feitos no campo no Reino Unido em 2002 são apresentados, mostrando ser possível reconhecer corretamente 4 espécies britânicas de Orthoptera em condiçäes naturais com altos níveis de interferências. Estão em andamento trabalhos para aumentar o número de espécies identificadas.