Constructing, conducting and interpreting animal social network analysis

By Damien Farine, Hal Whitehead. Journal of Animal Ecology, 2015

831 citations recorded by the source corpus.

Summary Animal social networks are descriptions of social structure which, aside from their intrinsic interest for understanding sociality, can have significant bearing across many fields of biology. Network analysis provides a flexible toolbox for testing a broad range of hypotheses, and for describing the social system of species or populations in a quantitative and comparable manner. However, it requires careful consideration of underlying assumptions, in particular differentiating real from observed networks and controlling for inherent biases that are common in social data. We provide a practical guide for using this framework to analyse animal social systems and test hypotheses. First, we discuss key considerations when defining nodes and edges, and when designing methods for collecting data. We discuss different approaches for inferring social networks from these data and displaying them. We then provide an overview of methods for quantifying properties of nodes and networks, as well as for testing hypotheses concerning network structure and network processes. Finally, we provide information about assessing the power and accuracy of an observed network. Alongside this manuscript, we provide appendices containing background information on common programming routines and worked examples of how to perform network analysis using the r programming language. We conclude by discussing some of the major current challenges in social network analysis and interesting future directions. In particular, we highlight the under‐exploited potential of experimental manipulations on social networks to address research questions.

Subfields: Ecology, Evolution, Behavior and Systematics, Social Psychology, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/5a45e47a6ee2f80223ae1d50ad1c418d2769564a

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Citations in the corpus

Environment modulates population social structure: Experimental evidence from replicated social networks of wild lizards (2016, methodology): “Hence, because each instance of the randomized data contained the same number of individuals and associations as the observed data, our analysis accounted for the potential for significant effects resulting from our sampling regime (see Box 5 in Farine and Whitehead (2015) for an example).”
The role of habitat configuration in shaping social structure: a gap in studies of animal social complexity (2019, background): “A pre-requisite for individuals to interact with others is that they need to be in relatively close physical proximity (Farine 2015), and for most animals, the social environment is first and foremost determined by their movement decisions (Emlen and Oring 1977; Farine et al. 2016; Bonnell et al.…”
How demographic processes shape animal social networks (2019, background): “Edges can be defined using various criteria, such as directly observed interactions or inferred associations using comembership in spatiotemporal groups (Croft et al. 2008; Farine and Whitehead 2015).”
Trends and perspectives on the use of animal social network analysis in behavioural ecology: a bibliometric approach (2019, background): “…recommend that, where possible, future studies validate biologging devices by observing individuals with biologging devices to develop an understanding of what individuals are doing when they are in close proximity to one another (Carter, Lee, & Marshall, 2015; Castles et al., 2014; Farine, 2015).”
A guide to null models for animal social network analysis (2017, methodology): “One such method is null models, which are used to generate the patterns expected from the data in the absence of the process of interest (Croft et al. 2011; Farine & Whitehead 2015).”
Nine quick tips for analyzing network data (2019, background): “Consider the case of contact networks inferred from movement data [24]: One can either simulate trajectories keeping some properties of the original data and then build a contact network or directly simulate a "realistic" contact network.”