Not your private t\^ete-\`a-t\^ete: leveraging the power of higher-order networks to study animal communication
Iacopo Iacopini, Jennifer R Foote, Nina H Fefferman, Elizabeth P, Derryberry, Matthew J Silk

TL;DR
This paper demonstrates how higher-order network models, like hypergraphs, can provide new insights into animal vocal communication by capturing complex multi-individual interactions often overlooked by traditional dyadic approaches.
Contribution
The study introduces higher-order network models to animal communication research, showing their advantages over conventional dyadic networks through three illustrative case studies.
Findings
Higher-order networks alter predictions of group departure outcomes.
They reveal different song synchronization patterns.
They inform models of cultural evolution in vocal communication.
Abstract
Animal communication is frequently studied with conventional network representations that link pairs of individuals who interact, for example, through vocalisation. However, acoustic signals often have multiple simultaneous receivers, or receivers integrate information from multiple signallers, meaning these interactions are not dyadic. Additionally, non-dyadic social structures often shape an individual's behavioural response to vocal communication. Recently, major advances have been made in the study of these non-dyadic, higher-order networks (e.g., hypergraphs and simplicial complexes). Here, we show how these approaches can provide new insights into vocal communication through three case studies that illustrate how higher-order network models can: a) alter predictions made about the outcome of vocally-coordinated group departures; b) generate different patterns of song…
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Taxonomy
TopicsAnimal Vocal Communication and Behavior · Animal Behavior and Reproduction · Insect and Arachnid Ecology and Behavior
