Modelling density-dependent fish shoal distributions in the laboratory and field

Authored by Iain D Couzin, E Hensor, R James, J Krause

Date Published: 2005

DOI: 10.1111/j.0030-1299.2005.13513.x

Sponsors: Biotechnology and Biological Sciences Research Council (BBSRC) United States National Science Foundation (NSF) Pew Program in Biocomplexity

Platforms: No platforms listed

Model Documentation: Other Narrative Mathematical description

Model Code URLs: Model code not found

Abstract

Density-dependent variables have long been established as an important area of ecological research, but the effects of the local density of conspecifics on grouping behaviour are less well-studied. We compared the influence of the density of conspecifics on the shoal size distribution of killifish, Fundulus diaphanus, in the laboratory and the field. In both environments we observed an increase in shoal size and shoal number with the density of individuals present. The increase in shoal size was markedly steeper in the field than in the laboratory, but direct comparison of the two was complicated by the fact that the absolute numbers of fish present at the field site were considerably higher than those used in the laboratory trials. We developed an individual-based model that was first used as a null model of shoal formation (defined by proximity to others) in fish with no shoaling tendency over the same range of densities used in the laboratory. Group size increased much more rapidly with increasing density in the laboratory than predicted by the null model. When we incorporated shoaling behaviour into our model, the laboratory results could be reproduced with high accuracy. However, when extrapolated to match conditions in the field, the model predicted smaller, more numerous shoals than were actually observed. We suggest this is due to heterogeneity of the field environment because fish were found to be highly aggregated in certain areas of our field site. The predictive power of laboratory studies for the field is discussed with regards to using individual-based modelling as a tool for deriving such predictions.

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Simulation behavior Schools Mechanism patterns scale Organization Individuals Aggregations