Abstract

Social animals exhibit collective behavior whereby they negotiate to reach an agreement, such as the coordination of group motion. Bats are unique among most social animals, since they use active sensory echolocation by emitting ultrasonic waves and sensing echoes to navigate. Bats’ use of active sensing may result in acoustic interference from peers, driving different behavior when they fly together rather than alone. The present study explores quantitative methods that can be used to understand whether bats flying in pairs move independently of each other or interact. The study used field data from bats in flight and is based on the assumption that interactions between two bats are evidenced in their flight patterns. To quantify pairwise interaction, we defined the strength of coupling using model-free methods from dynamical systems and information theory. We used a control condition to eliminate similarities in flight path due to environmental geometry. Our research question is whether these data-driven methods identify directed coupling between bats from their flight paths and, if so, whether the results are consistent between methods. Results demonstrate evidence of information exchange between flying bat pairs, and, in particular, we find significant evidence of rear-to-front coupling in bats’ turning behavior when they fly in the absence of obstacles.

Highlights

  • Animal groups, such as bird flocks, fish schools, and insect swarms, frequently exhibit coordination—often referred to as collective behavior—that results from social interactions among individuals [1]

  • Interaction among individuals in a group is a strong determinant of collective behavior, whereby information propagates across the group and enables high-level coordination

  • Trajectory curvature was used as the 1D time series for this analysis since we considered steering, or change in direction, to measure interaction

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Summary

Introduction

Animal groups, such as bird flocks, fish schools, and insect swarms, frequently exhibit coordination—often referred to as collective behavior—that results from social interactions among individuals [1]. A fitting example of active-sensing animals is bats that echolocate with ultrasound These animals can vary the intensity, direction, and timing of their signal and have a high level of control that enables the extraction of targeted information from their environment [4]. To directly observe the information that a bat may gather from the sensing signals of its peers, acoustic data need to be captured from the bat’s location via an onboard microphone, which is a difficult problem due to the small size and low weight of these animals In spite of these challenges, it is important to understand how bats interact via active sensing, since it may have advantages over passive sensing for groups of individuals.

Methods to Detect Directed Coupling
Transfer Entropy
Convergent Cross Map
Differences between TE and CCM
Experimental Setup and Data Collection
Data Analysis
Results
Discussion
Full Text
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