ALT: Graceful Deer Hopping at Sunset

Publication date: October 2026 Source: Animal Behaviour, Volume 240 Author(s): J. Godard, M.C. Tello-Ramos, T.A. Hurly, A.B. Barron, S.D. Healy

The interdisciplinary usage of Social Network Analysis (SNA) means that, when researchers calculate global network metrics, the research interest can range from the ultimate ecological and evolutionary pressures that select for specific group-level structures or the proximate mechanisms by which those structures emerge. Despite major methodological advances, social structures measured via SNA metrics are rarely connected to the underlying forces shaped by latent individual dispositions, ecological constraints, and their interplay with group-level affordances. We build a model to simulate fission-fusion spatial association data from a specified set of parameters in order to address the "black box" of animal SNA. We broke down animal social systems into three building blocks: individual dispositions, external factors, and the social structure that emerges. Each of these building blocks was represented by one or more model parameters, which together describe a set of underlying processes that lead to different measurable features of animal social structure, which we quantified with commonly used global network metrics within an SNA framework (e.g., clustering coefficient, density, modularity). In doing so, our model directly links global network metrics to the underlying behavioural processes that generate them and demonstrates how global network metrics can be influenced by changes to their underlying behavioural processes. Holding all other parameters constant, we find that subtle changes to 1) demography (i.e., group size) and ecology (i.e., size and variability of foraging parties), 2) variation of individual behaviour and related observation bias, 3) imposed group sub-structures, and 4) observation effort, affect all network metrics calculated non-linearly and in some cases non-monotonically. Our findings indicate that failure to account for the underlying behavioural processes can bias inference drawn from SNA. This model and conceptual framework provide a novel tool with which to begin opening the black box of SNA and to systematically address the evolution of diverse group-level social structures.

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