Behavioral Ecology and Sociobiology

, Volume 65, Issue 8, pp 1659–1668 | Cite as

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

  • Hamed Haddadi
  • Andrew J. KingEmail author
  • Alison P. Wills
  • Damien Fay
  • John Lowe
  • A. Jennifer Morton
  • Stephen Hailes
  • Alan M. Wilson


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 of sensor 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 (Ovis aries) 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 results indicate 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.


Spatial–temporal associations Social networks Sampling rate Social behaviour Sheep Flocking 



Thanks to the Structure and Motion Laboratory at the Royal Veterinary College, Kyle Roskilly for help with the GPS measurement equipment and post-processing tools and Skye Rudiger and all of the staff at South Australian Research Development Institute (SARDI) for the support of our fieldwork. Thanks also to Declan McKeever for the discussions on transmission distances of infectious diseases and to Julian Drewe and two anonymous reviewers for their constructive feedback on an earlier version of this manuscript. This work was funded by CHDI Inc.


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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Hamed Haddadi
    • 1
  • Andrew J. King
    • 1
    Email author
  • Alison P. Wills
    • 1
  • Damien Fay
    • 2
  • John Lowe
    • 1
  • A. Jennifer Morton
    • 3
  • Stephen Hailes
    • 4
  • Alan M. Wilson
    • 1
  1. 1.Structure and Motion Laboratory, The Royal Veterinary CollegeUniversity of LondonHatfieldUK
  2. 2.Computer LaboratoryUniversity of CambridgeCambridgeUK
  3. 3.Department of PharmacologyUniversity of CambridgeCambridgeUK
  4. 4.Department of Computer ScienceUniversity College of LondonLondonUK

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