Outputs

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 in-sect model by micro-CT scanning a gold-palladium coated insect. Electromagnetic simulations of the insect model were conducted by applying different morphological and compositional configuration. The simulations 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 has 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 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.

Correct, timely and meaningful interpretation of polarimetric weather radar observations requires an accurate understanding of hydrometeors and their associated microphysical processes along with well-developed techniques that automatize their recognition in both the spatial and temporal dimensions of the data. This study presents a novel technique for identifying different types of hydrometeors from quasi-vertical profiles (QVPs). In this new technique, the hydrometeor types are identified as clusters belonging to a hierarchical structure. The number of different hydrometeor types in the data is not predefined, and the method obtains the optimal number of clusters through a recursive process. The optimal clustering is then used to label the original data. Initial results using observations from the National Centre for Atmospheric Science (NCAS) X-band dualpolarization Doppler weather radar (NXPol) show that the technique provides stable and consistent results. Comparison with available airborne in situ measurements also indicates the value of this novel method for providing a physical delineation of radar observations. Although this demonstration uses NXPol data, the technique is generally applicable to similar multivariate data from other radar observations.