Dynamics of mosquito swarms over a moving marker

TL;DR

This study models mosquito swarming behavior around a visual marker using a feedback loop with delayed second order dynamics, revealing potential social interaction indicators and aiding future swarm analysis.

Contribution

It introduces a novel system identification approach to characterize mosquito swarm dynamics and links internal delay to social interactions within swarms.

Findings

Swarm-marker relationship modeled by delayed second order dynamics

Internal delay correlates with number of mosquitoes in the swarm

Model fitting enables comparison of different species' swarming behavior

Abstract

Insect swarms are a model system for understanding collective behavior where the collective motion appears in disorder. To initiate and maintain a swarm in place, flying insects often use a visual external cue called a marker. In mosquitoes, understanding the swarming behavior and its relation to the marker has an additional medical relevance since swarming often precedes mating in the wild, thus constituting an important stage to intercept for controlling mosquito population. In this paper, we conduct preliminary experiments to characterize the visual coupling between a swarm of mosquitoes and a marker. A laboratory microcosm with artificial lighting was built to stimulate consistent swarming in the malarial mosquito Anopheles stephensi. The experimental setup was used to film a mosquito swarm with a stereo camera system as a marker was moved back-and-forth with different frequencies. System identification analysis of the frequency response shows that the relationship between the swarm and the marker can be described by delayed second order dynamics in a feedback loop. Further, the length of the internal time delay appears to correlate with the number of mosquitoes swarming on the marker indicating that such a delay may be able capture social interactions within swarming systems. For insect swarms, model fitting of trajectory data provides a way to numerically compare swarming behaviors of different species with respect to marker characteristics. These preliminary results motivate investigating linear dynamic system in feedback as a framework for modeling insect swarms and set the stage for future studies.