Abstract
Collective animal behaviour is the study of how interactions between individuals produce group level patterns, and why these interactions have evolved. This study has proved itself uniquely interdisciplinary, involving physicists, mathematicians, engineers as well as biologists. Almost all experimental work in this area is related directly or indirectly to mathematical models, with regular movement back and forth between models, experimental data and statistical fitting. In this paper, we describe how the modelling cycle works in the study of collective animal behaviour. We classify studies as addressing questions at different levels or linking different levels, i.e. as local, local to global, global to local or global. We also describe three distinct approaches—theory-driven, data-driven and model selection—to these questions. We show, with reference to our own research on species across different taxa, how we move between these different levels of description and how these various approaches can be applied to link levels together.
Highlights
All biological systems have complex organization at different levels: molecules interact to form subcellular organules, cells organize together to produce tissues and so on
We provide a basic classification of the different levels at which collective animal behaviour is studied
There is a particular methodology to approaching collective animal behaviour that emerges from the above examples
Summary
All biological systems have complex organization at different levels: molecules interact to form subcellular organules, cells organize together to produce tissues and so on. The research goal is to find mutual relations between these two levels of description This point is especially relevant when looking at collective motion or spatial structures created by animal groups. Because collective animal behaviour occurs on temporal and spatial scales that we can relate to, means that it has become possible to automate tracking of interactions This gives collective animal behaviour research a head start on many other areas of complex systems research, but it means that we need to develop a clear framework within which to analyse, quantify and make use of these data. To what extent can we go from observing global patterns, to using simple models to describe plausible local interactions between individuals, to empirically quantify these interactions, and predict and check consistency of the empirical rules with the global patterns?
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