Computer Simulation of Fish Behavior in Relation to Fishing Gear-I

Matuda, Kô; Sannomiya, Nobuo

doi:10.2331/suisan.46.689

Cited by 20 publications

(11 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To determine the behavioral decision of a fish, we first defined a total force vector (F i ) on any individual i during time t as a sum of social and random forces (Inagaki et al 1976, Matuda & Sannomiya 1980, Aoki 1982, Niwa 1994, Sannomiya & Duostari 1996:…”

Section: Methodsmentioning

confidence: 99%

Individual behavior and emergent properties of fish schools: a comparison of observation and theory

Viscido¹,

Parrish²,

Grünbaum³

2004

Mar. Ecol. Prog. Ser.

181

148

View full text Add to dashboard Cite

Polarity, group velocity, and inter-individual spacing are characteristics of fish schools that strongly affect individual school members. However, these characteristics are group-level 'emergent properties': collective outcomes of behavioral interactions among members, not under direct control of any single member. The relationships between members' behaviors and the emergent group properties they produce are complex and poorly understood. In this study, we quantified 3D trajectories of all individual fish within 4-and 8-fish populations of Danio aequipinnatus, using stereo videography and a computerized tracking algorithm. We compared group polarity, group speed, and mean nearest-neighbor distances of schools within these populations to a simulation model that explored how fish responded to attraction/repulsion, alignment and random forces. Real fish exhibited a high degree of temporal variability in both polarity and group speed. Polarity and speed of simulated schools depended very strongly on the strength of the alignment force. Time-averaged polarity of real fish schools was most similar to simulated schools when alignment force was 1 to 5% of the attraction/repulsion force. For both real and simulated fish, a clear relationship existed between group speed and polarity: polarized groups were faster than non-polarized groups. We propose a multi-dimensional state space where several emergent property statistics are represented along the axes, and suggest certain 'preferred' ranges of state space within which animal groups tend to localize, and in which they can sustain distinct types of regular architecture.

show abstract

Section: Methodsmentioning

confidence: 99%

Individual behavior and emergent properties of fish schools: a comparison of observation and theory

Viscido¹,

Parrish²,

Grünbaum³

2004

Mar. Ecol. Prog. Ser.

181

148

View full text Add to dashboard Cite

show abstract

“…This size of school requires a Lagrangian model. Many individual-based Lagrangian models for schooling motion are based on attraction, repulsion, and parallel-orientation behaviors of individuals (Inagaki et al, 1976;Aoki, 1982;Matsuda & Sannomiya, 1985;Warburton & Lazarus, 1991;Huth & Wissel, 1992;Niwa, 1994Niwa, , 1996Gueron et al, 1996;GruK mbaum, 1998). We based our model on Aoki's (1982) model and the Huth & Wissel model because of their simplicity and because they simulated well the observed size, polarity, and #uctuations in the distance between individuals of natural "sh schools (Aoki, 1982;Huth & Wissel, 1992).…”

Section: Introductionmentioning

confidence: 99%

Order and Flexibility in the Motion of Fish Schools

Inada

Kawachi

2002

Journal of Theoretical Biology

132

117

View full text Add to dashboard Cite

“…When S F is −1, there is a low density of neighbouring fish within visual range S V , and fish may move towards the nearest neighbour. However, when S F is 1, there is too great a density of fish and neighbouring fish are too near and so fish could be repulsed (Matuda and Sannomiya, 1980). Where there is a greater density, fish will tend to move apart and sometimes rise up.…”

Section: Index Of Fish Densitymentioning

confidence: 97%

“…In relation to fishing gear, a deterministic mathematical model of fish behaviour (Matuda and Sannomiya, 1980) was constructed with a physical state equation of fishes as physical particles and using the concept of attractive and repulsive forces. This model was applied to a trap net (Matuda and Sannomiya, 1985) and to a trawl model (Satou et al, 1996).…”

Section: Introductionmentioning

confidence: 99%