Analysing animal social network dynamics: the potential of stochastic actor‐oriented models. (1st February 2017)

• Record Type: Journal Article
• Authors: Fisher, David N.; Ilany, Amiyaal; Silk, Matthew J.; Tregenza, Tom
• Editors: Sheldon, Ben
• Is Part Of: Journal of animal ecology. Volume 86:Number 2(2017:Mar.)
• Page Start: 202
• Page End: 212
• Publication Date: 2017-02-01
• Subjects: animal communities -- dynamics -- individual‐based models -- network‐based diffusion analysis -- social networks -- transmission; Animal ecology -- Periodicals; 591.7
• DOI: 10.1111/1365-2656.12630
• Languages: English
• ISSNs: 0021-8790

Abstract

Summary: Animals are embedded in dynamically changing networks of relationships with conspecifics. These dynamic networks are fundamental aspects of their environment, creating selection on behaviours and other traits. However, most social network‐based approaches in ecology are constrained to considering networks as static, despite several calls for such analyses to become more dynamic. 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. 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. 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. 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. Abstract : As a highly social species, we are fascinated by our changing social relationships and those of animals. The dynamic analysis of social networks is regularly called for but infrequently delivered. The authors review a dynamic network analysis technique that can be used to relate social relationships to ecological and evolutionary processes.