Aerial displays of starlings (Sturnus vulgaris ) at their communal roosts are complex: thousands of individuals form multiple flocks which are continually changing shape and density, while splitting and merging. To understand these complex displays both empirical data and models are needed. Whereas detailed empirical data were recently collected through video recordings and position measurements by stereo photography of flocks of thousands of starlings, there are as yet no models that generate these complex patterns. Numerous computer models in biology, however, suggest that patterns of single groups of moving animals may emerge by self-organisation from movement and local coordination (through attraction, alignment and avoidance of collision). In this paper, we investigated whether this approach can be extended to generate patterns resembling these aerial displays of starlings. We show in a model that to generate many of the patterns measured empirically in real starlings we have to extend the usual rules of local coordination with specifics of starling behaviour, mainly 1) their aerial locomotion, 2) a low and constant number of interaction-partners and 3) preferential movement above a 'roosting area'. Our model can be used as a tool for the study of these displays, because it provides new integrative hypotheses about the mechanisms underlying these displays and of swarming patterns in biological systems in general.

Models of swarming (based on avoidance, alignment and attraction) produce patterns of behaviour also seen in schools of fish. However, the significance of such similarities has been questioned, because some model assumptions are unrealistic [e.g. speed in most models is constant with random error, the perception is global and the size of the schools that have been studied is small (up to 128 individuals)]. This criticism also applies to our former model, in which we demonstrated the emergence of two patterns of spatial organization, i.e. oblong school form and high frontal density, which are supposed to function as protection against predators. In our new model we respond to this criticism by making the following improvements: individuals have a preferred ‘cruise speed’ from which they can deviate in order to avoid others or to catch up with them. Their range of perception is inversely related to density, with which we take into account that high density limits the perception of others that are further away. Swarm sizes range from 10 to 2000 individuals. The model is three‐dimensional. Further, we show that the two spatial patterns (oblong shape and high frontal density) emerge by self‐organization as a side‐effect of coordination at two speeds (of two or four body lengths per second) for schools of sizes above 20. Our analysis of the model leads to the development of a new set of hypotheses. If empirical data confirm these hypotheses, then in a school of real fish these patterns may arise as a side‐effect of their coordination in the same way as in the model.

Models of self-organization have proved useful in revealing what processes may underlie characteristics of swarms. In this study, we review model-based explanations for aspects of the shape and internal structure of groups of fish and of birds travelling undisturbed (without predator threat). Our models attribute specific collective traits to locomotory properties. Fish slow down to avoid collisions and swim at a constant depth, whereas birds fly at low variability of speed and lose altitude during turning. In both the models of fish and birds, the 'bearing angle' to the nearest neighbour emerges as a side-effect of the 'blind angle' behind individuals and when group size becomes larger, temporary subgroups may increase the complexity of group shape and internal structure. We discuss evidence for model-based predictions and provide a list of new predictions to be tested empirically.

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