Accidental introduction and/or spread of invasive non-native species (INNS) can result from a range of activities including agriculture, transport, trade and recreation. Researchers represent an important group of stakeholders who undertake activities in the field that could potentially facilitate the spread of INNS. Biosecurity is key to preventing the introduction and spread of INNS. Risk perceptions are a fundamental component in determining behaviour, so understanding how researchers perceive the risks associated with their activities can help us understand some of the drivers of biosecurity behaviour in the field. The aim of this study was to investigate researchers' perceptions of risk in relation to their field activities and whether risk perceptions influenced behaviour. We gathered quantitative data on perceptions of risk and biosecurity practices using an online questionnaire. Only 35% of all respondents considered their field activities to pose some risk in terms of spreading INNS. Higher risk perception was found in those who undertook high risk activities or where INNS were known/expected to be present. However, whilst respondents with experience of INNS were more likely to report consciously employing biosecurity in the field, this did not translate into better actual biosecurity practices. Awareness of biosecurity campaigns did in fact increase perception of risk, perceived and actual biosecurity behaviour. However, there remains a disconnect between reported and actual biosecurity practices, including a lack of understanding about what constitutes good biosecurity practice. These findings should be used to improve targeted awareness raising campaigns and help create directed training on biosecurity practices.

Contemporary analyses of insect population trends are based, for the most part, on a large body of heterogeneous and short-term datasets of diurnal species that are representative of limited spatial domains. This makes monitoring changes in insect biomass and biodiversity difficult. What is needed is a method for monitoring that provides a consistent, high-resolution picture of insect populations through time over large areas during day and night. Here, we explore the use of X-band weather surveillance radar (WSR) for the study of local insect populations using a high-quality, multi-week time series of nocturnal moth light trapping data. Specifically, we test the hypotheses that (i) unsupervised data-driven classification algorithms can differentiate meteorological and biological phenomena, (ii) the diversity of the classes of bioscatterers are quantitatively related to the diversity of insects as measured on the ground and (iii) insect abundance measured at ground level can be predicted quantitatively based on dual-polarization Doppler WSR variables. Adapting the quasi-vertical profile analysis method and data clustering techniques developed for the analysis of hydrometeors, we demonstrate that our bioscatterer classification algorithm successfully differentiates bioscatterers from hydrometeors over a large spatial scale and at high temporal resolutions. Furthermore, our results also show a clear relationship between biological and meteorological scatterers and a link between the abundance and diversity of radar-based bioscatterer clusters and that of nocturnal aerial insects. Thus, we demonstrate the potential utility of this approach for landscape scale monitoring of biodiversity.

Electromagnetic modelling may be used as a tool for understanding the radar cross section (RCS) of volant animals. Here, we examine this emerging method in detail and delve deeper into the specifics of the modelling process for a single noctuid moth, with the hope of illuminating the importance of different aspects of the process by varying the morphometric and compositional properties of the model. This was accomplished by creating a high-fidelity three-dimensional insect model by micro-CT scanning a gold-palladium-coated insect. Electromagnetic simulations of the insect model were conducted by applying different morphological and compositional configurations using the WiPL-D Pro 3D Electromagnetic Solver. The simulation results show that high-resolution modelling of insects has advantages compared to the simple ellipsoidal models used in previous studies. We find that the inclusion of wings and separating the composition of the body, wings, and legs and antennae have an impact on the resulting RCS of the specimen. Such modifications to the RCS are missed when a prolate spheroid model is used and should not be ignored in future studies. Finally, this methodology has been shown to be useful in exploring the changes in the RCS that result from variations in specimen size. As such, utilising this methodology further for more species will improve the ability to quantitatively interpret aeroecological observations of weather surveillance radars and special-purpose entomological radars.