• DOI:10.2307/1543482
• Corpus ID: 377484

Self-Organized Fish Schools: An Examination of Emergent Properties

@article{Parrish2002SelfOrganizedFS,
  title={Self-Organized Fish Schools: An Examination of Emergent Properties},
  author={Julia K. Parrish and Steven V. Viscido and Daniel Gr{\"u}nbaum},
  journal={The Biological Bulletin},
  year={2002},
  volume={202},
  pages={296 - 305}
}

• J. Parrish, S. Viscido, D. Grünbaum
• Published 1 June 2002
• Psychology
• The Biological Bulletin

Heterogeneous, “aggregated” patterns in the spatial distributions of individuals are almost universal across living organisms, from bacteria to higher vertebrates. Whereas specific features of aggregations are often visually striking to human eyes, a heuristic analysis based on human vision is usually not sufficient to answer fundamental questions about how and why organisms aggregate. What are the individual-level behavioral traits that give rise to these features? When qualitatively similar spatial patterns arise from purely physical mechanisms, are these patterns in organisms biologically significant, or are they simply epiphenomena that are likely characteristics of any set of interacting autonomous individuals? If specific features of spatial aggregations do confer advantages or disadvantages in the fitness of group members, how has evolution operated to shape individual behavior in balancing costs and benefits at the individual and group levels? Mathematical models of social behaviors such as schooling in fishes provide a promising avenue to address some of these questions. However, the literature on schooling models has lacked a common framework to objectively and quantitatively characterize relationships between individual-level behaviors and group-level patterns. In this paper, we briefly survey similarities and differences in behavioral algorithms and aggregation statistics among existing schooling models. We present preliminary results of our efforts to develop a modeling framework that synthesizes much of this previous work, and to identify relationships between behavioral parameters and group-level statistics.