Abstract
Scientists have long been interested in studying social structures within groups of gregarious animals. However, obtaining evidence about interactions between members of a group is difficult. Recent technologies, such as Global Positioning System technology, have made it possible to obtain a vast wealth of animal movement data, but inferring the underlying (latent) social structure of the group from such data remains an important open problem. While intuitively appealing measures of social interaction exist in the literature, they typically lack formal statistical grounding. In this article, we provide a statistical approach to the problem of inferring the social structure of a group from the movement patterns of its members. By constructing an appropriate null model, we are able to construct a significance test to detect meaningful affiliations between members of the group. We demonstrate our method on large-scale real-world data sets of positional data of flocks of Merino sheep, Ovis aries.
Highlights
In recent years, a wide variety of techniques have been developed to obtain animal positional data on a large-scale, and in a relatively cheap manner
A wide variety of techniques have been developed to obtain animal positional data on a large-scale, and in a relatively cheap manner. This includes video-tracking techniques [1, 2], radio-frequency identification tags [3, 4], such as passive integrated transponder tags [5], and Global Positioning System (GPS) technology [6,7,8]. The availability of such movement data has led to a surge of research into the use of movement patterns in analysing the social structure of gregarious animals
In this paper we have introduced a novel significance test for inferring the underlying affiliation network of a group from the movement patterns of its members
Summary
A wide variety of techniques have been developed to obtain animal positional data on a large-scale, and in a relatively cheap manner This includes video-tracking techniques [1, 2], radio-frequency identification tags [3, 4], such as passive integrated transponder tags [5], and Global Positioning System (GPS) technology [6,7,8]. The availability of such movement data has led to a surge of research into the use of movement patterns in analysing the social structure of gregarious animals. The behavioural patterns of gregarious animals are complex and intricate, and the scope for further analyses, as well as the development of new analytical tools, is vast
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