Antoine Cribellier

Mating in Anopheles mosquitoes depends on swarming, in which males aggregate at dusk above visual ground markers and females enter to copulate. Ground markers are known to anchor swarm location, yet whether marker geometry influences swarm spatial structure and individual flight dynamics remains unquantified. We combined semi-field experiments with 3D infrared video-tracking to test how ground marker shape affects swarm organization and flight behavior in Anopheles coluzzii . Five marker shapes of 1 m² each were tested in semi-field (Bama, Burkina Faso), yielding 7,060 swarming trajectories across 20 recordings. Swarms centroid position remained stable across marker shapes ( p > 0.3), confirming that swarm localization is anchored to marker presence rather than to its specific shape. Global kinematics showed overlapping distributions among marker shapes (q = 0.75–0.92). In contrast, marker geometry significantly influenced swarm horizontal dispersion (q < 0.001) and inter-saccade displacement along both horizontal axes (q < 0.05 and q < 0.001), with marker width along the N-S axis predicting swarm dispersion along the same axis ( p < 0.01, effect size = 0.226). This study shows that visual ground cues shape swarm organization at two levels: marker presence sets swarm location, while marker width controls individual turning distances.

Quantitative measurements of insect flight behaviour are essential for understanding the biomechanics, control, and ecology of flight, yet obtaining such measurements under free-flight conditions remains challenging. Small body size, rapid wing motion, visual symmetry, and frequent occlusions complicate three-dimensional pose estimation, often requiring restrictive experimental setups or substantial manual annotation. We present FLiTrak3D, an open-source Python package for estimating insect flight kinematics from multi-view videography. It combines machine-learning-based markerless tracking with biomechanical modelling to reconstruct and parametrise insect body and wing motion. The workflow integrates image preprocessing, including dynamic image cropping and enhancement, two-dimensional bodypart localisation, three-dimensional reconstruction, and optimisation-based skeletal fitting. A key innovation is the use of spatio-temporal encoding across synchronised camera views and adjacent frames to improve neural-network awareness of spatial and temporal context during bodypart localisation. Multi-view image stitching allows the network to use cross-view spatial relationships, reducing left-right bodypart misidentifications, while temporal encoding stacks consecutive greyscale frames into RGB images provide short-term motion information. A species-specific skeleton is then fitted to reconstructed keypoints to enforce kinematic constraints, and estimate body and wing orientations. We demonstrate the approach using free-flying Aedes aegypti mosquitoes, achieving bodypart localisation errors close to human labelling, and realistic flight kinematics.

Swarming is a widespread collective behavior in animals, often thought to emerge from complex interactions among individuals. Here, we show that mating swarms of malaria mosquitoes can emerge from simple behavioral rules with minimal interaction between individuals. We analyzed two published experimental datasets with three-dimensional flight tracks of Anopheles coluzzii mosquitoes swarming in the lab under simulated sunset conditions and above a visual marker. We found that individuals alternate between straight flight and rapid turning maneuvers known as saccades. These saccades are triggered at the edge of the swarm, and their directions are biased along the sunset direction. This behavior was found consistent between both laboratory datasets, including swarming of solitary individuals; this indicates that inter‑individual interactions may play a limited role within the small-to-medium sized swarms that we studied. We developed a simple agent-based model incorporating three behavioral rules: attraction to the swarm center, alignment normal to the sunset horizon, both driven by environmental cues, and repulsion through short-range collision avoidance. This parsimonious model reproduced key features of the observed laboratory mosquito swarms, including looping flight paths, central density peaks, and directional alignment. Even in the absence of direct inter‑individual interactions, the model generated realistic emergent swarm dynamics, suggesting that social coordination is not required for swarm emergence. Although our lab-based findings remain to be confirmed across more naturalistic conditions, they suggest that the most parsimonious hypothesis behind male mosquito swarming is that it is primarily guided by environmental perception rather than social interaction. This minimal framework offers a new perspective on insect swarming and may apply broadly to the many other insect species that form mating swarms. Understanding the behavioral rules of mosquito swarming could inform strategies for vector control and improve the effectiveness of interventions targeting mosquito reproduction.

Background: Malaria vectors reproduce through in-flight copulation within swarms, which remains poorly understood. Gaining insights into swarming and mating behavior is essential for optimizing novel vector control strategies including sterile insect technique, genetically modified mosquitoes, and behavior based intervention. This study investigates the factors influencing swarm dynamics and mating efficiency in Anopheles coluzzii . Methods: We surveyed swarms across 40 residential compounds in Burkina Faso, georeferencing swarming sites and recording swarming times and height. In a subset of three compounds selected for detailed characterization, we also measured inter-swarm distances, counted swarm size from photography, and mating pairs through direct observation during swarming. Furthermore, we collected 30% of male mosquitoes from swarms to measure wing length and perform PCR analyses. We monitored environmental variables including temperature, humidity, wind speed, and luminosity. Finally, we performed spatial and statistical analysis using ArcGIS and R to determine how swarm and mating dynamics are correlated and how they depend on biological and environmental conditions. Results: We identified 169 Anopheles coluzzii swarms and found strong evidence of spatial clustering (General G: P < 0.001; Moran’s I = 0.2, P < 0.001), with localized hotspots. Swarming occurred between 18:05 h and 18:45 h, extending into darkness at ~19:15 h. Swarms had an average height of 2.87 m (range: 1.0–3.2 m) and consisted of 83–2783 mosquitoes. Swarm size strongly predicted pairing success in Anopheles coluzzii ( t = 9.16, P < 0.001) with larger swarms producing more pairs. However, individual pairing efficiency decreased with swarm size ( t = −3.515, P < 0.001). Male size positively influenced individual pairing efficiency ( t = 3.25, P = 0.002) but did not affect swarm size or total pairing frequency. Inter-swarm distances varied nonrandomly, suggesting interactions between neighboring and/or swarm markers. Conclusions: This study shows that An. coluzzii swarming is shaped by both biological and environmental factors. While larger males achieved higher individual mating efficiency, swarm size was the strongest predictor of mating success. Larger swarms yielded more mating pairs overall, although efficiency declined with increasing density. In addition, swarms formed in clustered nonrandom patterns within compounds. These results highlight the interplay between male traits and environment in shaping swarming dynamics. Graphical

Background: Swarming is a key reproductive behaviour in Anophelesmosquitoes, yet several aspects of its spatiotemporal dynamics remain poorly understood. In particular, the period after sunset has received little attention due to limitations in field visibility and tracking resolution. Additionally, the role of females in swarm formation, and their positional fidelity to swarm-inducing markers, has been largely overlooked. Current studies mostly focus on male swarming behaviour and often ignore female contributions to swarm structure and maintenance. Addressing these gaps is essential to understand sex-specific behavioural patterns that shape malaria transmission dynamics. Methods: This study elucidates the swarming activity of virgin female and male Anopheles coluzzii under controlled laboratory conditions using infrared light-based three-dimensional tracking during and after sunset. Results: Our recordings reveal that both sexes engage in swarming, but females initiate swarming 14 s prior to males. During the peak swarming activity at sunset, females flew 31% faster than males (mean speeds of 0.67 m/s and 0.51 m/s, respectively), and females showed a broader dispersion in spatial positioning. Finally, females also maintain structured swarming activity for a longer period, up to at least one-hour after sunset. Despite these differences, both sexes formed aggregations directly above the visual swarm-inducing ground marker, suggesting shared spatial orientation mechanisms. Conclusions: These findings provide novel insights into the temporal persistence and structural features of female mosquito swarming, challenging traditional assumptions of male-dominated swarm behaviour and offering implications for future studies on vector ecology and control.

Swarming is a widespread collective behavior in animals, often thought to emerge from complex interactions among individuals. Here, we show that mating swarms of malaria mosquitoes can emerge from simple behavioral rules with minimal interaction between individuals. We analyzed two published experimental datasets with three-dimensional flight tracks of Anopheles coluzzii mosquitoes swarming above a visual marker under simulated sunset conditions. We found that individuals alternate between straight flight and rapid turning maneuvers known as saccades. These saccades are triggered at the edge of the swarm, and their directions are biased along the sunset direction. This behavior was found consistent between both datasets and across swarm sizes, including solitary individuals, indicating that inter-individual interactions play a limited role within the studied size range. We developed a simple agent-based model incorporating three behavioral rules: attraction to the swarm center, alignment normal to the sunset horizon, both driven by environmental cues, and repulsion through short-range collision avoidance. The model reproduced key features of natural mosquito swarms, including looping flight paths, central density peaks, and directional alignment. Even in the absence of direct inter-individual interactions, the model generated realistic emergent swarm dynamics, demonstrating that social coordination is not required for swarm emergence. Our findings suggest that mosquito swarming is primarily guided by environmental perception rather than social interaction. This minimal framework offers a new perspective on insect swarming and may apply broadly to the many other insect species that form mating swarms. Understanding these rules could inform strategies for vector control and improve the effectiveness of interventions targeting mosquito reproduction. Author Summary: Mosquito swarms are a familiar sight in many parts of the world, but the rules that govern their movement have remained mysterious. In our study, we investigated how male malaria mosquitoes behave in mating swarms. Using high-speed videography and computer simulations, we discovered that these swarms do not rely on complex social interactions. Instead, each mosquito follows a few simple rules based on visual cues from the environment, like the position of the sunset and a ground marker. These rules are enough to produce the looping flight patterns and dense swarms observed in nature. Surprisingly, even a single mosquito can perform this behavior in isolation. This minimal approach challenges the idea that swarming requires strong social coordination. It also opens new possibilities for controlling malaria mosquito populations by targeting the environmental factors that influence mating behavior. Since many insects rely on swarming to reproduce, our findings may help explain similar behaviors across species and inform broader strategies for insect population management.

Malaria mosquitoes mate in swarms. Here, they must rely on multiple sensory cues in shaping their individual responses, such as during mate recognition, swarm maintenance, and collision avoidance. While male mosquitoes are known to use faint female flight tones for recognizing their mates, the role of other sensory modalities remains less explored. By combining free-flight and tethered flight simulator experiments with Anopheles coluzzii, we demonstrate that swarming mosquitoes integrate visual and acoustic information to track conspecifics and avoid collisions. In tethered experiments, acoustic stimuli gated male steering responses to visual objects simulating nearby female mosquitoes, whereas visual cues alone triggered changes in wingbeat amplitude and frequency. Free-flight experiments show that mosquitoes modulate their flight responses to nearby conspecifics similarly to tethered animals, allowing for collision avoidance within swarms. These findings suggest that combined visual and acoustic information contributes to conspecific recognition within swarms, and, for males, permits female tracking while avoiding collisions. Teaser Malaria mosquitoes can use sight and sound to navigate swarms, dodging collisions while seeking out mates.

To detect and escape from a looming threat, night-flying insects must rely on other senses than vision alone. Nocturnal mosquitoes have been described to escape looming objects in the dark, but how they achieve this is still unknown. Here, we show how night-active female malaria mosquitoes escape from a rapidly looming object that simulates the defensive action of a blood-host. By combining videography-based automatic tracking with numerical simulations of the attacker-induced airflow, we first show that night-flying mosquitoes use airflow-sensing to detect the danger and trigger their escape. Secondly, by combining these data with mechanistic movement modelling, we unravelled how mosquitoes control their escape manoeuvres: they actively steer away from the danger, and passively travel with the bow-wave produced by the attacker. Our results demonstrate that night-flying mosquitoes escaping from a looming object use the object-induced airflow both to detect the danger, and as fluid medium to move with for avoiding collision. This shows that the escape strategy of flying insects is more complex than previous visually-induces escape flight studies suggest. As mosquitoes are average-sized insects, a combined airflow-induced and visual-induced escape strategy is expected to be common amongst millions of flying insect species. Also, our research helps explain the high escape performance of mosquitoes from counterflow-based odour-baited mosquito traps. It can therefore provide new insights for the development of novel trapping techniques for integrative vector management.