Group size, density and social dynamics in farm animals

Farm animals are social species with a strong tendency to form groups. Living in groups has associated costs and benefits. The costs refer mostly to competition for food, or access to other valuable resources that may lower the individuals’ fitness, while the benefits includes (but are not limited t...

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Bibliographic Details
Published in:Applied animal behaviour science 2007-03, Vol.103 (3), p.185-204
Main Authors: Estevez, Inma, Andersen, Inger-Lise, Nævdal, Eric
Format: Article
Language:English
Subjects:
Aggression
Density
Farm animals
Group size
Social behaviour
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Summary:Farm animals are social species with a strong tendency to form groups. Living in groups has associated costs and benefits. The costs refer mostly to competition for food, or access to other valuable resources that may lower the individuals’ fitness, while the benefits includes (but are not limited to) lower predation risk and increased available time to forage. Variation in the size of a group in natural populations is self-regulated through cost–benefits balance and can be considered a byproduct of the environmental conditions, as animals will join or leave the group depending on the overall benefits. This possibility does not exist in the farm environment, as animals will have no opportunities to leave a “costly” group setting, creating a situation of increased aggressive interactions that may favor despotic behaviour, with negative consequences for some individuals in the group. However, recent studies suggest that social behaviour of farm animals is not restricted to hierarchy formation and it is much more plastic and dynamic than previously thought. This behavioural plasticity allows animals to change strategies and adapt more easily to varying environmental (social and physical) conditions within a confined group. In this paper, we discuss three recently developed theoretical models that show how changes in social strategies are related to variations in group size. We argue that these models are built on different assumptions and explain how this leads to different predictions.
ISSN:0168-1591
1872-9045
DOI:10.1016/j.applanim.2006.05.025