Modeling of Autonomous Decentralized Mechanism in Fish Behavior

Sannomiya, Nobuo; Shimada, Akira; Nakamine, Hiroshi

doi:10.9746/sicetr1965.29.211

Cited by 16 publications

(4 citation statements)

References 2 publications

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“…This result is contrary to Nakamine and Sannomiya [18]. They supposed the existence of a repulsive force which acts on fish to keep their distance from the wall.…”

Section: Discussioncontrasting

confidence: 77%

Analysis of juvenile tuna movements as correlated random walk

et al. 2011

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We explore how a stochastic model provides the most promising avenue towards predicting fish movement. To construct a stochastic model describing fish movement, trajectories of ten juveniles in a water tank were analyzed from a stochastic point of view. The heading angle was defined as a random variable. Our analysis found that the most probable forward heading angle was between 0°and 22.5°(probability *78%), followed by angles between 22.5°and 45°(probability *10%). We also found that the choice of future heading angle depends on the current heading angle. Therefore, we treated heading angle state as a first-order Markov process and constructed a correlated random walk model describing juvenile movement in a water tank. Our stochastic model simulated a trajectory similar to observed trajectories. We used the model as a tool for estimating the probability distribution of potential fish path outcomes. We derived the distribution of potential outcomes from a large number of simulations (N = 1000) and investigated these trajectories. We collected a set of juvenile trajectories that collided with the tank and estimated the probability of juvenile collisions with the tank.

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“…This result is contrary to Nakamine and Sannomiya [18]. They supposed the existence of a repulsive force which acts on fish to keep their distance from the wall.…”

Section: Discussioncontrasting

confidence: 77%

Analysis of juvenile tuna movements as correlated random walk

et al. 2011

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“…We used the parameters estimated for bitterling using the time-series data obtained from experiments performed in previous studies (Sannomiya et al, 1993;Nakamine and Sannomiya, 1995). Five bitterling were placed in a 150 !…”

Section: Model Parametersmentioning

confidence: 99%

“…Sannomiya et al (1990Sannomiya et al ( , 1993 and Sannomiya (1995, 1998) developed an individual-based model in which individuals' behaviour is decided by certain external forces that have a defined function. Since there are no stochastic processes in this model, the effect of each external force on the behaviour of a school of fish can be quantitatively evaluated in more detail.…”

Section: Introductionmentioning

confidence: 99%

Mathematical model of fish schooling behaviour in a set-net

Takagi

Moritomi

Iwata

et al. 2004

ICES Journal of Marine Science

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N. 2004. Mathematical model of fish schooling behaviour in a set-net. e ICES Journal of Marine Science, 61: 1214e1223.We investigated the validity of a mathematical model to describe fish schooling behaviour towards a simple set-net model. We apply a model considered to be ''an autonomous decentralized system'' and based on Newton's equation of motion. It includes the parameter M, which indicates ''the quantity of information exchange'' (i.e. the number of neighbours that affect an individual's behaviour) and strongly affects fish school size and schooling behaviour in an enclosed space. To evaluate the model, simulations of fish schooling behaviour in a set-net model consisting of a leading fence and a box-shaped trap similar to a primitive type of set-net were compared with experimentally observed behaviour of bitterling and mackerel, with a focus on M. A small M induces improper behaviour because there is low cooperation among fish in a school. On the other hand, if M is too large, improper simulation results of individuals in deadlock states in the trap are obtained as a result of excessive information exchange among the fish. The results suggest that the mathematical model can describe the behaviour in a set-net model adequately when M is greater than 2 and less than 10.

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“…In an earlier paper (Sannomiya et al 1993), a mathematical model was proposed for studying the behaviour of a small school. In that model, we were concerned with the motion of each individual in the school.…”

Section: Introductionmentioning

confidence: 99%

Simulation and analysis of the behaviour of a fish school with many individuals by using an aggregated model

Tian

Sannomiya

1997

International Journal of Systems Science

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To overcome the difficulty in obtaining observational data offish behaviour, an aggregated model is presentedfor studying the behaviour ofa fish school with many individuals. In this model, the motion ofafish school is described by the motions of the centre of gravity of the school and four representative individuals which are located at the boundary of the school. In this study, the behaviour of a fish school with 20 individuals swimming in a water tank is investigated by using this model. A simulation is carried out for the same boundary condition as the water tank experiment. Consequently, the difference ofthe behaviour between the centre ofgravity and the four individuals as well as the relationship between the school speed and school size is analysed by comparing the simulation result with the observation data.

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Modeling of Autonomous Decentralized Mechanism in Fish Behavior

Cited by 16 publications

References 2 publications

Analysis of juvenile tuna movements as correlated random walk

Analysis of juvenile tuna movements as correlated random walk

Mathematical model of fish schooling behaviour in a set-net

Simulation and analysis of the behaviour of a fish school with many individuals by using an aggregated model

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