Journal article 1282 views
Determining association networks in social animals: choosing spatial–temporal criteria and sampling rates
,
Alison P Wills,
Damien Fay,
John Lowe,
A. Jennifer Morton,
Stephen Hailes,
Alan M Wilson
Behavioral Ecology and Sociobiology, Volume: 65, Issue: 8, Pages: 1659 - 1668
Swansea University Author:
Andrew King
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DOI (Published version): 10.1007/s00265-011-1193-3
Abstract
Social Network Analysis has become an important methodological tool for advancing our understanding of human and animal group behaviour. However, researchers tend to rely on arbitrary distance and time measures when defining ‘contacts’ or ‘associations’ between individuals based on preliminary obser...
| Published in: | Behavioral Ecology and Sociobiology |
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| ISSN: | 0340-5443 1432-0762 |
| Published: |
2011
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa13509 |
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| Abstract: |
Social Network Analysis has become an important methodological tool for advancing our understanding of human and animal group behaviour. However, researchers tend to rely on arbitrary distance and time measures when defining ‘contacts’ or ‘associations’ between individuals based on preliminary observation. Otherwise, criteria are chosen on the basis of the communication range ofsensor devices (e.g. bluetooth communication ranges) or the sampling frequencies of collection devices (e.g. Global Positioning System devices). Thus, researchers lack an established protocol for determining both relevant association distances and minimum sampling rates required to accurately represent the network structure under investigation. In this paper, we demonstrate how researchers can use experimental and statistical methods to establish spatial and temporal association patterns and thus correctly characterise social networks in both time and space. To do this, we first perform a mixing experiment with Merino sheep (Ovisaries) and use a community detection algorithm that allows us to identify the spatial and temporal distance at which we can best identify clusters of previously familiar sheep. This turns out to be within 2–3 m of each other for at least 3 min. We then calculate the network graph entropy rate—a measure of ease of spreading of information (e.g. a disease) in a network—to determine the minimum sampling rate required to capture the variability observed in our sheep networks during distinct activity phases. Our resultsindicate the need for sampling intervals of less than a minute apart. The tools that we employ are versatile and could be applied to a wide range of species and social network datasets, thus allowing an increase in both the accuracy and efficiency of data collection when exploring spatial association patterns in gregarious species. |
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| College: |
Faculty of Science and Engineering |
| Issue: |
8 |
| Start Page: |
1659 |
| End Page: |
1668 |