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Obstacle avoiding patterns and cohesiveness of fish school.

Linh Thi Hoai Nguyen¹, Việt Tôn Tạ², Atsushi Yagi³

¹Department of Information and Physical Sciences, Graduate School of Information Science and Technology, Osaka University, Suita, Osaka 565-0871, Japan.

Journal of Theoretical Biology

|July 25, 2016

Summary

This study models fish school obstacle avoidance using stochastic differential equations (SDEs), identifying four distinct patterns. These patterns reveal insights into fish schooling cohesiveness, offering a new measurement method.

Keywords:

34K50 34K60 Cohesiveness Fish schooling Obstacle avoiding Particle systems Pattern formation

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Area of Science:

Mathematical Biology
Animal Behavior
Complex Systems

Background:

Fish schooling exhibits complex collective behaviors.
Understanding obstacle avoidance is crucial for fish survival and group dynamics.
Quantifying fish school cohesiveness remains a challenge.

Purpose of the Study:

To model fish school obstacle avoidance using stochastic differential equations (SDEs).
To identify and characterize distinct obstacle avoiding patterns in fish schools.
To define and measure fish school cohesiveness based on these patterns.

Main Methods:

Development of a stochastic differential equation (SDE) model for fish school dynamics.
Analysis of simulation data to identify emergent obstacle avoiding patterns.
Formulation of a quantitative definition for fish school cohesiveness.

Main Results:

Identification of four primary obstacle avoiding patterns: Rebound, Pullback, Pass and Reunion, and Separation.
Demonstration that these patterns are sensitive to changes in model parameters.
Evidence linking observed patterns to quantifiable measures of school cohesiveness.

Conclusions:

The proposed SDE model effectively captures fish school obstacle avoidance.
Distinct patterns emerge during obstacle negotiation, reflecting underlying group behavior.
Fish school cohesiveness can be scientifically defined and measured through observable avoidance patterns.