Transfer Entropy Analysis of Interactions between Bats Using Position and Echolocation Data

Abstract

Many animal species, including many species of bats, exhibit collective behavior where groups of individuals coordinate their motion. Bats are unique among these animals in that they use the active sensing mechanism of echolocation as their primary means of navigation. Due to their use of echolocation in large groups, bats run the risk of signal interference from sonar jamming. However, several species of bats have developed strategies to prevent interference, which may lead to different behavior when flying with conspecifics than when flying alone. This study seeks to explore the role of this acoustic sensing on the behavior of bat pairs flying together. Field data from a maternity colony of gray bats (Myotis grisescens) were collected using an array of cameras and microphones. These data were analyzed using the information theoretic measure of transfer entropy in order to quantify the interaction between pairs of bats and to determine the effect echolocation calls have on this interaction. This study expands on previous work that only computed information theoretic measures on the 3D position of bats without echolocation calls or that looked at the echolocation calls without using information theoretic analyses. Results show that there is evidence of information transfer between bats flying in pairs when time series for the speed of the bats and their turning behavior are used in the analysis. Unidirectional information transfer was found in some subsets of the data which could be evidence of a leader–follower interaction.

A physics perspective on collective animal behavior

“…Finally, an emerging line of inquiry into the properties of animal groups that has not yet gained much traction in the physics community but that may prove to be very valuable is the use of techniques from information theory to try to make inferences about the flow of information in the group [107]. The most common quantity that has been used for this purpose is the transfer entropy, which provides an estimate of how much information flows from one individual to another and has been used to study leader-follower relationships in bats [108,109]. Accurate estimates of the transfer entropy require long time series and large data ensembles, and so it has not yet become a widely used tool.…”

Validating a data-driven framework for vehicular traffic modeling

“…Specifically, transfer entropy (TE) and conditional transfer entropy (CTE) can quantify coupling between time-series variables, and therefore identify candidate variables for a model. These metrics have found successful applications in the study of various complex systems including human brain activity [24][25][26], animal collective behavior [27][28][29][30][31], climate modeling [32], policy-making [33][34][35][36][37] and financial markets [38,39]. However, its application within vehicular traffic systems has been relatively limited [40][41][42][43].…”

Time-separated Mutual Information Reveals Key Characteristics of Asymmetric Leader-Follower Interactions in Golden Shiners

“…Because of the higher-dimensional joint probabilities involved, accurately estimating the transfer entropy is a nontrivial task. The k-nearest neighbors estimator for mutual information first proposed by Kraskov, Stögbauer, and Grassberger [19] (henceforth abbreviated as the KSG estimator) has become the standard in many information theory studies [20][21][22]; but, although the algorithm can be straightforwardly generalized to estimate transfer entropy, it has been shown that this generalization reduces the accuracy of the method [23]. Alternative estimation schemes do exist, such as the use of kernel density estimators, but comparative studies have found them to perform, at best, only marginally better than the KSG method [24,25].…”