[3G4-OS-15b] 移動系列のデータマイニングと機械学習(2/2)

[3G4-OS-15b-02] A basic study on the mechanism of group behavior of wild bats using movement pattern measurement and granger causality during nesting

〇Kazusa Ushio¹, Emyo Fujioka¹, Keisuke Fuji², Hitoshi Habe³, Hiroaki Kawashima⁴, Shizuko Hiryu¹ (1. Univ. of Doshisha, 2. Univ. of Nagoya, 3. Univ. of Kinki, 4. Univ. of Hyogo)

Keywords:wild bat, machine learning

Bats recognize their surrounding environment by processing the echoes of ultrasonic waves emitted by themselves. Many species of bats live in groups, and many individuals emerge together from roosts. In this study, we used high-sensitivity video cameras to measure the flight trajectories of bats emerging from the cave in three dimensions, and investigated their flight trails. As a result, we found there were three behavioral patterns during emerging: exiting the cave, returning to the cave, and some other action. In addition, we applied the Granger causality method (Fujii et al., NeurlPS'21) to analyze the swarm behavior mechanism of emerging bats. The results showed that forward individuals flew in such a way that they were "repulsed" from or "approached" the other individuals. This suggests that bats, which use sound to understand their environment, are also influenced by backward individuals, which cannot be captured visually, suggesting that bats have a unique swarming mechanism that differs from model animals for group behavior, mainly visual animals.