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Determining association networks in social animals: choosing spatial–temporal criteria and sampling rates

Hamed Haddadi, Andrew King Orcid Logo, 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 Orcid Logo

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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...

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Published in: Behavioral Ecology and Sociobiology
ISSN: 0340-5443 1432-0762
Published: 2011
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URI: https://cronfa.swan.ac.uk/Record/cronfa13509
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last_indexed 2018-02-09T04:44:17Z
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spelling 2013-09-17T15:15:17.0588875 v2 13509 2012-12-05 Determining association networks in social animals: choosing spatial–temporal criteria and sampling rates cc115b4bc4672840f960acc1cb078642 0000-0002-6870-9767 Andrew King Andrew King true false 2012-12-05 SBI 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. Journal Article Behavioral Ecology and Sociobiology 65 8 1659 1668 0340-5443 1432-0762 31 12 2011 2011-12-31 10.1007/s00265-011-1193-3 COLLEGE NANME Biosciences COLLEGE CODE SBI Swansea University 2013-09-17T15:15:17.0588875 2012-12-05T10:26:56.9634930 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Biosciences Hamed Haddadi 1 Andrew King 0000-0002-6870-9767 2 Alison P Wills 3 Damien Fay 4 John Lowe 5 A. Jennifer Morton 6 Stephen Hailes 7 Alan M Wilson 8
title Determining association networks in social animals: choosing spatial–temporal criteria and sampling rates
spellingShingle Determining association networks in social animals: choosing spatial–temporal criteria and sampling rates
Andrew King
title_short Determining association networks in social animals: choosing spatial–temporal criteria and sampling rates
title_full Determining association networks in social animals: choosing spatial–temporal criteria and sampling rates
title_fullStr Determining association networks in social animals: choosing spatial–temporal criteria and sampling rates
title_full_unstemmed Determining association networks in social animals: choosing spatial–temporal criteria and sampling rates
title_sort Determining association networks in social animals: choosing spatial–temporal criteria and sampling rates
author_id_str_mv cc115b4bc4672840f960acc1cb078642
author_id_fullname_str_mv cc115b4bc4672840f960acc1cb078642_***_Andrew King
author Andrew King
author2 Hamed Haddadi
Andrew King
Alison P Wills
Damien Fay
John Lowe
A. Jennifer Morton
Stephen Hailes
Alan M Wilson
format Journal article
container_title Behavioral Ecology and Sociobiology
container_volume 65
container_issue 8
container_start_page 1659
publishDate 2011
institution Swansea University
issn 0340-5443
1432-0762
doi_str_mv 10.1007/s00265-011-1193-3
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Biosciences, Geography and Physics - Biosciences{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Biosciences, Geography and Physics - Biosciences
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description 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.
published_date 2011-12-31T03:15:28Z
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