DOI: 10.5061/dryad.nvx0k6dvz
title: Mosquitoes escape looming threats by actively steering into the bow-wave induced by the attacker

description: To detect and escape a threat, night flying insects must rely on other
 senses than vision alone. Here we study how anthropophilic malaria
 mosquitoes can escape a swatting hand in the dark using high-speed
 videography and numerical simulations. We show that these night flying
 mosquitoes escape looming objects by using the object-induced airflow in
 two ways. They first actively steer into the bow-wave  produced
 by the attacker, and then passively travel with this bow-wave away from
 the attacker; these two aspects explain two-thirds and one-third of their
 escape accelerations, respectively. Thus, flying mosquitoes being attacked
 in the dark rely both on airflow-sensing to trigger their escape, and on
 attacker-induced airflow to maximize their escape performance. Similar
 escape strategies are probably common among small lightweight insects.
description: ## Data Files on Dryad **Data_S1_-_Experiment 1** Database of the
 experiment #1. Flight tracks of all Anopheles coluzzii mosquitoes attacked
 by the (real and virtual) mechanical swatter, and flying in various light
 conditions. A Matlab (all_data.mat) file containing three-dimensional
 tracks of all flying mosquitoes, described as the time t (s) and the
 spatial {x, y, z} (m) coordinates of the mosquito at each video frame. The
 coordinates are in meters, and in the world reference frame as defined in
 figure 1, with z oriented vertically up, and the origin of the coordinate
 frame at the center of the flight arena. The trajectories were determined
 as described in the materials and methods. Metadata for each track
 includes age, swatter type (hollow or solid) and mode (on or off), light
 condition, as well as temperature and humidity over time in the arena
 during each experiment. In addition, the file contains the kinematic of
 the mechanical swatter, as well as the geometry of the flight arena and
 the swatter. **Data_S2_-_Experiment2** Database of the experiment #2.
 Flight kinematics of escaping Anopheles coluzzii mosquitoes attacked by
 the mechanical swatter (opaque or transparent disk) in twilight light
 condition. A Matlab (all_data.mat) file containing three-dimensional
 tracks of all escaping mosquitoes as well as all body and wing kinematics
 parameters (positions and angles) over time. These kinematics parameters
 were determined using the custom-made three-dimensional tracker (Code S3).
 Metadata for each track includes age, swatter type (opaque or
 transparent), as well as temperature and humidity over time in the arena
 during each experiment. In addition, the file contains the kinematic of
 the mechanical swatter, as well as the geometry of the flight arena and
 the swatter. **Data S3 - CFD** Computation fluid dynamic (CFD) results.
 Means (mean folder) and standard deviation (std folder) of the airflow
 velocities induced by the swatter movement can be found in .csv files (one
 for each time step (1 ms) and in polar coordinates (r,theta,y)). The
 piston kinematic and the probe positions can also be found in separated
 .csv files (piston_kinematic.corrected.csv, probe_pos.csv,
 prob_pos_cart.csv, prove_pos_polar.csv). ## Code Files on Zenodo Code S1.
 (separate file) Python code used to track mosquitoes body and wings
 kinematics. Recordings of mosquitoes were preprocessed (cropped and
 stitched) to be analyzed with Deeplabcut. Then a three-dimensional
 mosquito skeleton was fitted to the two-dimensional tracking results from
 Deeplabcut to estimate the body and wing kinematics. Scripts to generate
 stroboscopic images and example videos are also included. Code S2.
 (separate file) Analysis codes. Contains all original Matlab codes that
 were written to perform the analysis of the article and to generate the
 panels of Fig. 1-4. Also contains JAGS modelling codes used for doing the
 Bayesian estimations in Fig. 1, 3-4. Instructions to run the full analysis
 can be found in the readme.md. \- Move the Experiment1 and Experiment2
 datasets in \_Data \- Run the following scripts, figures will be saved in
 the \_Figures folder. Experiment #1: \- Run main_analysis_experiment1.m
 One subset of the full dataset will be analysed (lines 116): subset_name =
 '_Subset-disk_types-transparent'; % The dataset analysed in the
 article (both solid and perforated transparent disk with a clear mesh +
 control with swatter turned off) \- Run get_groups_estimayed_means to
 estimate the means of the colision probabilities for the various light
 conditions and disk types (Fig. 1E) Experiment #2: \- Run
 main_analysis_experiment2.m