Spatio‐Temporal Variation in Aerial Arthropod Abundance Revealed by Weather Radars

Mungee, M., Lukach, M., Shortall, ChrisORCID logo, Bell, James, Duncan, E. J., Addison, F., Brown, L. E., Kunin, W., Hassall, C. and Neely III, R. R. (2025) Spatio‐Temporal Variation in Aerial Arthropod Abundance Revealed by Weather Radars. Global Change Biology, 31 (10). e70425. 10.1111/gcb.70425
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Arthropod declines have been reported widely; however, a lack of comprehensive data has hindered our ability to assess their large-scale generality and drivers. Here, we used a novel and freely available dataset – atmospheric scans from a network of meteorological radars – to quantify aerial abundance of both diurnal and nocturnal arthropods across the United Kingdom, spanning different geographic regions and land cover types. Based on observations between 2014 and 2021, and across more than 35,000 km², we estimate numbers of arthropods flying over the UK at heights between 500 and 700 meters above ground at 1.12 (± 0.01) x 1013 49 individuals during the diurnal (0800–1400 UTC) and 5.02 (± 0.01) x 1012 during the nocturnal (including dusk, 1800–2200 UTC) period, showing significant spatial heterogeneity. Although spatial patterns differed, both diurnal and nocturnal arthropods increased in the south and declined mainly in the far north; on average, only nocturnal arthropods showed an overall decline. Aerial abundance of both diurnal and nocturnal arthropods showed positive relationships with woodland, grassland, and urban landcover, and negative relationships with artificial light intensity and arable landcover. Our study highlights the importance of spatial variation in temporal biodiversity trends and illustrates the need for comparative studies between nocturnal and diurnal arthropods. Notably, by extracting vertical profiles of radar reflectivity and polarization signatures, we demonstrate how weather radar datasets can be used to quantify aerial arthropod abundance, detect diurnal and seasonal activity patterns, and examine their environmental drivers across large spatial and temporal scales


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