English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Animal communication is frequently studied with conventional networkrepresentations that link pairs of individuals who interact, for example,through vocalisation. However, acoustic signals often have multiplesimultaneous receivers, or receivers integrate information from multiplesignallers, meaning these interactions are not dyadic. Additionally, non-dyadicsocial structures often shape an individual's behavioural response to vocalcommunication. Recently, major advances have been made in the study of thesenon-dyadic, higher-order networks (e.g., hypergraphs and simplicial complexes).Here, we show how these approaches can provide new insights into vocalcommunication through three case studies that illustrate how higher-ordernetwork models can: a) alter predictions made about the outcome ofvocally-coordinated group departures; b) generate different patterns of songsynchronisation than models that only include dyadic interactions; and c)inform models of cultural evolution of vocal communication. Together, ourexamples highlight the potential power of higher-order networks to study animalvocal communication. We then build on our case studies to identify keychallenges in applying higher-order network approaches in this context andoutline important research questions these techniques could help answer.

Not your private t\^ete-\`a-t\^ete: leveraging the power of higher-order networks to study animal communication