Understanding animal social structure: exponential random graph models in animal behaviour research
© 2017 The Authors. Published by Elsevier Ltd on behalf of The Association for the Study of Animal Behaviour. Open Access funded by Natural Environment Research Council. Under a Creative Commons license: https://creativecommons.org/licenses/by/4.0/
Reason for embargo
The social environment is a pervasive influence on the ecological and evolutionary dynamics of animal populations. Recently, social network analysis has provided an increasingly powerful and diverse toolset to enable animal behaviour researchers to quantify the social environment of animals and the impact that it has on ecological and evolutionary processes. However, there is considerable scope for improving these methods further. We outline an approach specifically designed to model the formation of network links, exponential random graph models (ERGMs), which have great potential for modelling animal social structure. ERGMs are generative models that treat network topology as a response variable. This makes them ideal for answering questions related directly to how and why social associations or interactions occur, from the modelling of population level transmission, through within-group behavioural dynamics to social evolutionary processes. We discuss how ERGMs have been used to study animal behaviour previously, and how recent developments in the ERGM framework can increase the scope of their use further. We also highlight the strengths and weaknesses of this approach relative to more conventional methods, and provide some guidance on the situations and research areas in which they can be used appropriately. ERGMs have the potential to be an important part of an animal behaviour researcher's toolkit and fully integrating them into the field should enhance our ability to understand what shapes animal social interactions, and identify the underlying processes that lead to the social structure of animal populations.
M.J.S. is funded by a NERC grant NE/M004546/1. D.N.F. is funded by the Natural Sciences and Engineering Research Council of Canada.
This is the author accepted manuscript. The final version is available from Elsevier Masson via the DOI in this record.
Vol. 132, pp. 137 - 146