For decades, researchers have speculated how echolocating bats deal with masking by conspecific calls when flying in aggregations. To date, only a few attempts have been made to mathematically quantify the probability of jamming, or its effects. We developed a comprehensive sensorimotor predator-prey simulation, modeling numerous bats foraging in proximity. We used this model to examine the effectiveness of a spectral Jamming Avoidance Response (JAR) as a solution for the masking problem. We found that foraging performance deteriorates when bats forage near conspecifics, however, applying a JAR does not improve insect sensing or capture. Because bats constantly adjust their echolocation to the performed task (even when flying alone), further shifting the signals' frequencies does not mitigate jamming. Our simulations explain how bats can hunt successfully in a group despite competition and despite potential masking. This research demonstrates the advantages of a modeling approach when examining a complex biological system.

Echolocating bats rely on active acoustic sensing to perceive their environment. When multiple bats fly together, echolocating simultaneously, the calls emitted by nearby conspecifics could interfere with and mask the echoes necessary for orientation. Nowhere is this impairment of sensing more dramatic than when thousands of bats emerge from a cave at the same time. Here, we tracked the movement of tens of greater mouse-tailed bats flying within a group of thousands. By mounting miniature microphones onboard some of the bats, we monitored the acoustic scene from the point of view of an individual bat within the echolocating collective. We found that bats experienced a very high level of conspecific acoustic masking when emerging from their cave, which dropped within seconds as the bats spread out in space. A comprehensive sensorimotor model, based on the unique data that we collected, revealed how bats content with this severe echo masking almost without collisions. Our results demonstrate that even under severe masking, bats are hardly impaired sensorially, and we suggest how they are able to maneuver smoothly and avoid collisions, even at high densities, without applying a jamming avoidance response.

One of the most difficult sensorimotor behaviors exhibited by flying animals is the ability to track another flying animal based on its sound emissions. From insects to mammals, animals display this ability in order to localize and track conspecifics, mate or prey. The pursuing individual must overcome multiple non-trivial challenges including the detection of the sounds emitted by the target, matching the input received by its (mostly) two sensors, localizing the direction of the sound target in real time and then pursuing it. All this has to be done rapidly as the target is constantly moving. In this project, we set to mimic this ability using a physical bio-mimetic autonomous drone. We equipped a miniature commercial drone with our in-house 2D sound localization electronic circuit which uses two microphones (mimicking biological ears) to localize sound signals in real-time and steer the drone in the horizontal plane accordingly. We focus on bat signals because bats are known to eavesdrop on conspecifics and follow them, but our approach could be generalized to other biological signals and other man-made signals. Using two different experiments, we show that our fully autonomous aviator can track the position of a moving sound emitting target and pursue it in real-time. Building an actual robotic-agent, forced us to deal with real-life difficulties which also challenge animals. We thus discuss the similarities and differences between our and the biological approach.

For decades, researchers have speculated how echolocating bats deal with acoustic interference created by conspecifics when flying in aggregations. It is thus surprising that there has been no attempt to quantify what are the chances of being jammed, or how such jamming would affect a bat’s hunting. To test this, we developed a computer model, simulating numerous bats foraging in proximity. We used a comprehensive sensorimotor model of a hunting bat, taking into consideration the physics of sound propagation and bats’ hearing physiology. We analyzed the instantaneous acoustic signals received by each bat, and were able to tease apart the effects of acoustic interference and of direct resource competition. Specifically, we examined the effectiveness of the spectral Jamming Avoidance Response - a shift in signal frequencies - which has been suggested as a solution for the jamming problem. As expected, we found that hunting performance deteriorates when bats forage near conspecific. However, applying a Jamming Avoidance Response did not improve hunting, and our simulations clearly demonstrate the reason: bats have adequate natural signal variability due to their constant adjustment of echolocation signals to the task. The probability to be jammed is thus small and further shifting the frequencies does not mitigate spectral jamming. Our simulations reveal both negative and positive insight: they show how bats can hunt successfully in a group despite potential sensory interference and they suggest that a Jamming Avoidance Response is not useful.

Bats face a complex navigation challenge when emerging from densely populated roosts, where vast numbers take off at once in dark, confined spaces. Each bat must avoid collisions with walls and conspecifics while locating the exit, all amidst overlapping acoustic signals. This crowded environment creates the risk of acoustic jamming, in which the calls of neighboring bats interfere with echo detection, potentially obscuring vital information. Despite these challenges, bats navigate these conditions with remarkable success. Although bats have access to multiple sensory cues, here we focused on whether echolocation alone could provide sufficient information for orientation under such high-interference conditions. To explore whether and how they manage this challenge, we developed a sensorimotor model that mimics the bats’ echolocation behavior under high-density conditions. Our model suggests that the problem of acoustic jamming may be less severe than previously assumed. Frequent calls with short inter-pulse intervals (IPI) increase the sensory input flow, allowing integration of echoic information across multiple calls. When combined with simple movement-guidance strategies—such as following walls and avoiding nearby obstacles—this accumulated information enables effective navigation in dense acoustic environments. Together, these findings demonstrate a plausible mechanism by which bats may overcome acoustic interference and underscore the role of signal redundancy in supporting robust echolocation-based navigation. Beyond advancing our understanding of bat behavior, they also offer valuable insights for swarm robotics and collective movement in complex environments.

Bats face a complex navigation challenge when emerging from densely populated roosts, where vast numbers take off at once in dark, confined spaces. Each bat must avoid collisions with walls and conspecifics while locating the exit, all amidst overlapping acoustic signals. This crowded environment creates the risk of acoustic jamming, in which the calls of neighboring bats interfere with echo detection, potentially obscuring vital information. Despite these challenges, bats navigate these conditions with remarkable success. Although bats have access to multiple sensory cues, here, we focused on whether echolocation alone could provide sufficient information for orientation under such high-interference conditions. To explore whether and how they manage this challenge, we developed a sensorimotor model that mimics the bats’ echolocation behavior under high-density conditions. Our model suggests that the problem of acoustic jamming may be less severe than previously assumed. Frequent calls with short inter-pulse intervals (IPI) increase the sensory input flow, allowing integration of echoic information across multiple calls. When combined with simple movement-guidance strategies—such as following walls and avoiding nearby obstacles—this accumulated information enables effective navigation in dense acoustic environments. Together, these findings demonstrate a plausible mechanism by which bats may overcome acoustic interference and underscore the role of signal redundancy in supporting robust echolocation-based navigation. Beyond advancing our understanding of bat behavior, they also offer valuable insights for swarm robotics and collective movement in complex environments.