Detecting social transmission in networks

William Hoppitt, Neeltje J. Boogert, Kevin N. Laland

Research output: Contribution to journalArticlepeer-review

112 Citations (Scopus)

Abstract

In recent years researchers have drawn attention to a need for new methods with which to identify the spread of behavioural innovations through social transmission in animal populations. Network-based analyses seek to recognize diffusions mediated by social learning by detecting a correspondence between patterns of association and the flow of information through groups. Here we introduce a new order of acquisition diffusion analysis (OADA) and develop established time of acquisition diffusion analysis (TADA) methods further. Through simulation we compare the merits of these and other approaches, demonstrating that OADA and TADA have greater power and lower Type I error rates than available alternatives, and specifying when each approach should be deployed. We illustrate the new methods by applying them to reanalyse an established dataset corresponding to the diffusion of foraging innovations in starlings, where OADA and TADA detect social transmission that hitherto had been missed. The methods are potentially widely applicable by researchers wishing to detect social learning in natural and captive populations of animals, and to facilitate this we provide code to implement OADA and TADA in the statistical package R.
Original languageEnglish
Pages (from-to)544-555
Number of pages12
JournalJournal of Theoretical Biology
Volume263
Issue number4
DOIs
Publication statusPublished - 21 Apr 2010

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