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Analysing animal social network dynamics: the potential of stochastic actor‐oriented models
Author(s) -
Fisher David N.,
Ilany Amiyaal,
Silk Matthew J.,
Tregenza Tom
Publication year - 2017
Publication title -
journal of animal ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.134
H-Index - 157
eISSN - 1365-2656
pISSN - 0021-8790
DOI - 10.1111/1365-2656.12630
Subject(s) - computer science , data science , covariate , strengths and weaknesses , principal (computer security) , social network (sociolinguistics) , biological network , dynamic network analysis , class (philosophy) , range (aeronautics) , meaning (existential) , ecology , artificial intelligence , machine learning , biology , psychology , computer security , social psychology , computer network , materials science , world wide web , computational biology , composite material , psychotherapist , social media
SummaryAnimals 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 ( SAOM s) are a principal example. SAOM s 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 SAOM s to animal networks, outlining findings and assessing the strengths and weaknesses of SAOM s when applied to animal rather than human networks. We go on to highlight the types of ecological and evolutionary processes that SAOM s can be used to study.SAOM s 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 SAOM s will not be useable in all study systems. It remains unclear how robust SAOM s are to missing data and uncertainty around social relationships. Ultimately, we encourage the careful application of SAOM s 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.