Detecting social transmission in networks

William Hoppitt (1) , Neeltje J. Boogert (2) , Kevin N. Laland (1)

1 University of St Andrews [Scotland]
2 Department of Biology [Montréal]

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 () and develop established () methods further. Through simulation we compare the merits of these and other approaches, demonstrating that and 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 and 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 and in the statistical package .

Mots clés: Social learning; Network based diffusion analysis; Culture; Traditions; Order of acquisition

William Hoppitt, Neeltje J. Boogert, Kevin N. Laland. Detecting social transmission in networks. Journal of Theoretical Biology, 2010, 263 (4), pp.544. ⟨10.1016/j.jtbi.2010.01.004⟩. ⟨hal-00578723⟩