Analysing animal social network dynamics: the potential of stochastic actor-oriented models

David N. Fisher* (Corresponding Author), Amiyaal Ilany, Matthew J. Silk, Tom Tregenza

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

34 Citations (Scopus)
8 Downloads (Pure)


1. Animals are embedded in dynamically changing networks of relationships with con-specifics. These dynamic networks are fundamental aspects of their environment, creating selection on behaviours and other traits. However, most social network-based approaches inecology are constrained to considering networks as static, despite several calls for such analyses to become more dynamic.
2. There are a number of statistical analyses developed in the social sciences that are increasingly being applied to animal networks, of which stochastic actor-oriented models (SAOMs)are a principal example. SAOMs are a class of individual-based models designed to model transitions in networks between discrete time points, as influenced by network structure and covariates. It is not clear, however, how useful such techniques are to ecologists, and whether they are suited to animal social networks.
3. We review the recent applications of SAOMs to animal networks, outlining findings and assessing the strengths and weaknesses of SAOMs when applied to animal rather than human networks. We go on to highlight the types of ecological and evolutionary processes that SAOMs can be used to study.
4. SAOMs can include effects and covariates for individuals, dyads and populations, which can be constant or variable. This allows for the examination of a wide range of questions of interest to ecologists. However, high-resolution data are required, meaning SAOMs will not be useable in all study systems. It remains unclear how robust SAOMs are to missing data and uncertainty around social relationships.
5. Ultimately, we encourage the careful application of SAOMs in appropriate systems, with dynamic network analyses likely to prove highly informative. Researchers can then extend the basic method to tackle a range of existing questions in ecology and explore novel lines of questioning
Original languageEnglish
Pages (from-to)202-212
Number of pages11
JournalJournal of Animal Ecology
Issue number2
Early online date1 Feb 2017
Publication statusPublished - Mar 2017

Bibliographical note

We thank Bea Downing, Lucy Steward, Mike Kings and Jonathan Jarrett for useful discussions surrounding network analysis and for testing the R code. We also thank Tom Snijders for providing useful comments that improved the manuscript. Alecia Carter, Damien Farine and an anonymous reviewer provided extensive reviews of the manuscript that substantially improved it. Finally, we thank Rolando Rodrıguez-Munoz and Luke Meadows who helped collect the data for the Supporting Information.
We have no competing interests.
Funding for this research was provided by NERC
(studentship no.: NE/H02249X/1; grant no.: NE/H02364X/1


  • animal communities
  • dynamics
  • individual-based models
  • network-based diffusion analysis
  • social networks
  • transmission


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