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Article

Characterizing Pairwise U-Turn Behavior in Fish: A Data-Driven Analysis

1
School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
2
College of Information and Control Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
*
Author to whom correspondence should be addressed.
Entropy 2023, 25(12), 1639; https://doi.org/10.3390/e25121639
Submission received: 24 October 2023 / Revised: 4 December 2023 / Accepted: 7 December 2023 / Published: 9 December 2023

Abstract

We applied the time-series clustering method to analyze the trajectory data of rummy-nose tetra (Hemigrammus rhodostomus), with a particular focus on their spontaneous paired turning behavior. Firstly, an automated U-turn maneuver identification method was proposed to extract turning behaviors from the open trajectory data of two fish swimming in an annular tank. We revealed two distinct ways of pairwise U-turn swimming, named dominated turn and non-dominated turn. Upon comparison, the dominated turn is smoother and more efficient, with a fixed leader–follower relationship, i.e., the leader dominates the turning process. Because these two distinct ways corresponded to different patterns of turning feature parameters over time, we incorporated the Toeplitz inverse covariance-based clustering (TICC) method to gain deeper insights into this process. Pairwise turning behavior was decomposed into some elemental state compositions. Specifically, we found that the main influencing factor for a spontaneous U-turn is collision avoidance with the wall. In dominated turn, when inter-individual distances were appropriate, fish adjusted their positions and movement directions to achieve turning. Conversely, in closely spaced non-dominated turn, various factors such as changes in distance, velocity, and movement direction resulted in more complex behaviors. The purpose of our study is to integrate common location-based analysis methods with time-series clustering methods to analyze biological behavioral data. The study provides valuable insights into the U-turn behavior, motion characteristics, and decision factors of rummy-nose tetra during pairwise swimming. Additionally, the study extends the analysis of fish interaction features through the application of time-series clustering methods, offering a fresh perspective for the analysis of biological collective data.
Keywords: rummy-nose tetra; fish interaction; time-series clustering; data analysis rummy-nose tetra; fish interaction; time-series clustering; data analysis

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MDPI and ACS Style

Tao, Y.; Zhou, Y.; Zheng, Z.; Lei, X.; Peng, X. Characterizing Pairwise U-Turn Behavior in Fish: A Data-Driven Analysis. Entropy 2023, 25, 1639. https://doi.org/10.3390/e25121639

AMA Style

Tao Y, Zhou Y, Zheng Z, Lei X, Peng X. Characterizing Pairwise U-Turn Behavior in Fish: A Data-Driven Analysis. Entropy. 2023; 25(12):1639. https://doi.org/10.3390/e25121639

Chicago/Turabian Style

Tao, Yuan, Yuchen Zhou, Zhicheng Zheng, Xiaokang Lei, and Xingguang Peng. 2023. "Characterizing Pairwise U-Turn Behavior in Fish: A Data-Driven Analysis" Entropy 25, no. 12: 1639. https://doi.org/10.3390/e25121639

APA Style

Tao, Y., Zhou, Y., Zheng, Z., Lei, X., & Peng, X. (2023). Characterizing Pairwise U-Turn Behavior in Fish: A Data-Driven Analysis. Entropy, 25(12), 1639. https://doi.org/10.3390/e25121639

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