Back

Visual threats reduce blood-feeding and trigger escape responses in Aedes aegypti mosquitoes.

Wynne, N. E.; Chandrasegaran, K.; Fryzlewicz, L.; Vinauger, C.

2022-01-10 animal behavior and cognition
10.1101/2022.01.08.475512 bioRxiv
Show abstract

The diurnal mosquitoes Aedes aegypti are vectors of several arboviruses, including dengue, yellow fever, and Zika viruses. To find a host to feed on, they rely on the sophisticated integration of olfactory, visual, thermal, and gustatory cues reluctantly emitted by the hosts. If detected by their target, this latter may display defensive behaviors that mosquitoes need to be able to detect and escape. In humans, a typical response is a swat of the hand, which generates both mechanical and visual perturbations aimed at a mosquito. While the neuro-sensory mechanisms underlying the approach to the host have been the focus of numerous studies, the cues used by mosquitoes to detect and identify a potential threat remain largely understudied. In particular, the role of vision in mediating mosquitoes ability to escape defensive hosts has yet to be analyzed. Here, we used programmable visual displays to generate expanding objects sharing characteristics with the visual component of an approaching hand and quantified the behavioral response of female mosquitoes. Results show that Ae. aegypti is capable of using visual information to decide whether to feed on an artificial host mimic. Stimulations delivered in a LED flight arena further reveal that landed females Ae. aegypti display a stereotypical escape strategy by taking off at an angle that is a function of the distance and direction of stimulus introduction. Altogether, this study demonstrates mosquitoes can use isolated visual cues to detect and avoid a potential threat. Summary StatementIn isolation, visual stimuli programmed to mimic a human swat prevent mosquitoes from blood-feeding by triggering take-offs and escape responses.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.