News Release

Lidar technology demonstrates how light levels determine mosquito 'rush hour'

Lidar reveals activity anomaly of malaria vectors during pan-African eclipse

Peer-Reviewed Publication

American Association for the Advancement of Science (AAAS)

The first study to remotely track wild mosquito populations using laser radar (lidar) technology found that mosquitoes in a southeastern Tanzanian village are most active during morning and evening "rush hour" periods, suggesting these may be the most effective times to target the insects with sprays designed to prevent the spread of malaria. Mikkel Brydegaard and colleagues also observed heightened mosquito activity during the 2016 pan-African solar eclipse, finding males to be 87 times more active and females 7.4 times more active than usual during daytime hours. These findings suggest that light levels affect the prevalence of mosquitoes in flight, creating opportunities for the development of light-based measures to modify mosquito behavior and prevent the spread of malaria. Although pesticides, vaccination programs, and bed-netting campaigns have made progress in the effort to reduce malaria deaths, this mosquito-borne disease remains a major challenge on the African continent. Since both the insects and the malaria-causing parasite they carry can evolve rapidly, resistance to existing measures can quickly develop, resulting in a need for new measures that target all mosquito life stages. To investigate whether lidar may help scientists detect and measure mosquitoes in the wild, Brydegaard et al. set up a 596-meter lidar transect hovering three to five meters above ground, detecting 312,191 insects over a five-day observation period. The researchers estimated changes in relative mosquito abundance throughout each day, by tracking a range of frequencies between 85 and 850 hertz to identify the telltale signatures of insect wingbeats. Lidar enabled the team to distinguish mosquitoes from moths, flies, midges, and bees based on their high wingbeat frequency, and even to differentiate between different mosquito species and sexes, leading them to characterize this technology as a groundbreaking new tool for estimating malaria infection risks over large distances.


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