Abstract
We propose a method for examining and measuring the complexity of animal social networks that are characterized using association indices. The method focusses on the diversity of types of dyadic relationship within the social network. Binomial mixture models cluster dyadic relationships into relationship types, and variation in the preponderance and strength of these relationship types can be used to estimate association complexity using Shannon’s information index. We use simulated data to test the method and find that models chosen using integrated complete likelihood give estimates of complexity that closely reflect the true complexity of social systems, but these estimates can be downwardly biased by low-intensity sampling and upwardly biased by extreme overdispersion within components. We also illustrate the use of the method on two real datasets. The method could be extended for use on interaction rate data using Poisson mixture models or on multidimensional relationship data using multivariate mixture models.Significance statementAnimals from many species interact socially with multiple individuals, and these interactions form a social network. Pairs of individuals have social relationships that differ in their strength and type. This social complexity has long interested behavioural biologists, particularly in the context of social cognition. Measuring social complexity, however, presents challenges. We propose a new method for measuring the complexity of animal social networks. Our approach is based on quantifying variation in the strengths of social connections (measured using association indices) which we use to classify different types of pairwise relationships. We, then, use the number, strength and prevalence of these different types of relationships to measure association complexity. Our approach can be used to compare association complexity between populations and/or species. We provide code that researchers can use with their own datasets.
Highlights
Social complexity is a much used concept in behavioural ecology (Kappeler 2019, Topical collection on Social complexity)
The correlation between the estimates of S via Integrated Completed Likelihood (ICL) and true complexities across all parameters was 0.9, while Akaike Information Criterion (AIC) and Bayesian Information Criteria (BIC) had overall correlations of 0.79 and 0.78, respectively. This high correlation for ICL across sampling efforts, network sizes, and social structures indicates that estimates of S based on models chosen via ICL are highly comparable between networks
At low sampling efforts (D < 40), ICL does give estimates of S less correlated with true complexities than AIC or BIC, but it rapidly tends towards a perfect correlation with increased sampling effort
Summary
Social complexity is a much used concept in behavioural ecology (Kappeler 2019, Topical collection on Social complexity). Measures of social complexity have been sought and used for a variety of reasons, perhaps most notably to test the social intelligence hypothesis for the evolution of cognition (Kwak et al 2018; Kappeler 2019, Topical collection on Social complexity) and the social complexity hypothesis for the evolution of communication (Freeberg et al 2012). In studies of non-human societies, the term social complexity has primarily been used in two broad ways. Social complexity is used to describe the number of different types (roles) of individuals that make up a social group (e.g., Blumenstein and Armitage 1998; Groenewoud et al 2016).
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