The development of an unsupervised hierarchical clustering analysis of dual‐polarization weather surveillance radar observations to assess nocturnal insect abundance and diversity

Abstract

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.

Monitoring aerial insect biodiversity: a radar perspective

“…A comparison with ground sampling confirmed that the identified target classes were ecologically meaningful; i.e. the number of target classes observed on radar correlated well with macro-moth diversity on the ground [ 73 ]. While the taxonomic resolution is still coarse, differentiating broad groups of aerial taxa may also be useful in itself, e.g.…”

“…While the taxonomic resolution is still coarse, differentiating broad groups of aerial taxa may also be useful in itself, e.g. for interactions between insects and insectivores [ 73 ].…”

“…Australia, India, Europe. Dual-pol data products improve estimates of shape, size and variety of animals aloft, and have been a key innovation for ecological applications [ 34 ], particularly by improving target differentiation [ 72 , 73 ]. Other rapidly moving fields such as machine learning or convolutional neural networks will probably expand and improve the (automated) identification of insect and other aerial organisms from radar signals [ 94 , 95 ].…”

Temperature, not net primary productivity, drives continental-scale variation in insect flight activity

“…Radars have a long history of use in monitoring animals in the air [19,20], have been extensively validated [21,22], and weather radar has become a standard tool to quantify bird migration [23,24]. Novel methods are rapidly being developed to adapt weather radar for quantitative entomological applications [25,26], with recent studies using weather radar to quantify mayfly emergence [27], monitor pest exodus flights [28], and differentiate classes of insects [29]. Weather radar only observes insects in flight in the free airspace (i.e.…”

Derivation of aerial insect concentration with a 94 GHz FMCW cloud radar

“…Scanning radars further have the potential to cover spatially large regions. During recent years, the deployment of specialized radar-based detection methods for aerial fauna has become more prevalent, especially for movements of up to several hundred meters up in the air (e.g., Hu et al 2016, Lukach et al 2022, Wainwright et al 2023, Haest et al 2024b, Bauer et al 2024). To study larger flying animals, like birds and bats, radar instruments with wavelengths of a few up to several centimeters, like X-band (λ = 2.5-3.75 cm; e.g.…”