The how and why of active sensing in animal collectives

• Applicant: Dr. Thejasvi Ravindra Beleyur
• Subject Area: Sensory and Behavioural Biology
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 502056951

How animals engage in collective behaviour has been dominantly investigated in passive sensing, visual animals like humans, birds and fish. Such behaviours can be explained by a set of simple behavioural heuristics like ‘follow your neighbour’ applied across each individual in the group. Visually driven groups can reach massive sizes because given sufficient light, each group member can detect its neighbours without problem. In strong contrast to passive sensing animal groups are active sensing groups such as echolocating bats and electrolocating fish. Active sensing animals emit probes of energy into the environment and detect objects by how these probes are altered. Echolocating bats emit extremely loud ultrasonic calls and listen for the faint returning echoes to detect objects around them. When in groups, echolocating bats suffer from ‘jamming’, as each bat’s calls prevents the detection of its neighbour’s returning echoes. Despite this mutual jamming, multiple species of bats display impressive feats of collective behaviour such as mating swarms outside and inside caves, and evening emergences in million-strong groups. The collective behaviour of active sensing animals is very understudied. To my knowledge, experimental studies have been restricted to tractable small groups, or uni/bi-modal data sets (audio/video/on-body tags). Methodologically, the study of large groups of echolocating bats has been hindered by the lack of analytical methods to track individuals in audio with overlapping calls. Theoretically, studies have focussed on the sensory basis of echolocation in groups – with little attention to how collective movement arises and is maintained despite the limited sensory information each individual can access. This proposal is split into three parts to overcome current limitations in active sensing research: 1) computational, 2) methods development and 3) experimental characterisation. In part one I will build dynamic models of active sensing groups to explore the interplay between echolocation strategy and collective movement, along with its evolutionary origins through simulations. In part two, I will develop analysis workflows to handle complex multi-sensor data consisting of LiDAR, thermal and audio data. In part three I will analyse the Ushichka dataset – a multi-sensor, multi-channel dataset with LiDAR, thermal and audio data of echolocating bat groups in the field. I will use the previously developed analytical workflows to characterise echolocation and collective movement of echolocating bats. This proposal will provide a multi-faceted perspective on 1) how active sensing groups are able to sustain collective movement, 2) methodologically stimulate the analysis of complex multi-sensor data by developing open-source software tools to do so and 3) provide the first holistic characterisations of active sensing collectives in their natural environments.

• DFG Programme: WBP Position