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Couzin, I.D.; Krause, J.; James, R.; Ruxton, G.D.; Franks, N.R. |
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Title |
Collective Memory and Spatial Sorting in Animal Groups |
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Journal Article |
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Year |
2002 |
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Journal of Theoretical Biology |
Abbreviated Journal |
J. Theor. Biol. |
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218 |
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1 |
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1-11 |
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Abstract |
We present a self-organizing model of group formation in three-dimensional space, and use it to investigate the spatial dynamics of animal groups such as fish schools and bird flocks. We reveal the existence of major group-level behavioural transitions related to minor changes in individual-level interactions. Further, we present the first evidence for collective memory in such animal groups (where the previous history of group structure influences the collective behaviour exhibited as individual interactions change) during the transition of a group from one type of collective behaviour to another. The model is then used to show how differences among individuals influence group structure, and how individuals employing simple, local rules of thumb, can accurately change their spatial position within a group (e.g. to move to the centre, the front, or the periphery) in the absence of information on their current position within the group as a whole. These results are considered in the context of the evolution and ecological importance of animal groups. |
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0022-5193 |
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Equine Behaviour @ team @ |
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5310 |
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Author |
Franks, D.; James, R.; Noble, J.; Ruxton, G. |
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Title |
A foundation for developing a methodology for social network sampling |
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Journal Article |
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Year |
2009 |
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Behavioral Ecology and Sociobiology |
Abbreviated Journal |
Behav. Ecol. Sociobiol. |
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63 |
Issue |
7 |
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1079-1088-1088 |
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Biomedical and Life Sciences |
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Researchers are increasingly turning to network theory to understand the social nature of animal populations. We present a computational framework that is the first step in a series of works that will allow us to develop a quantitative methodology of social network sampling to aid ecologists in their social network data collection. To develop our methodology, we need to be able to generate networks from which to sample. Ideally, we need to perform a systematic study of sampling protocols on different known network structures, as network structure might affect the robustness of any particular sampling methodology. Thus, we present a computational tool for generating network structures that have user-defined distributions for network properties and for key measures of interest to ecologists. The user defines the values of these measures and the tool will generate appropriate network randomizations with those properties. This tool will be used as a framework for developing a sampling methodology, although we do not present a full methodology here. We describe the method used by the tool, demonstrate its effectiveness, and discuss how the tool can now be utilized. We provide a proof-of-concept example (using the assortativity measure) of how such networks can be used, along with a simulated egocentric sampling regime, to test the level of equivalence of the sampled network to the actual network. |
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Springer Berlin / Heidelberg |
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0340-5443 |
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Equine Behaviour @ team @ |
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5194 |
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