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Damien R. Farine

Researcher at University of Zurich

Publications -  170
Citations -  8197

Damien R. Farine is an academic researcher from University of Zurich. The author has contributed to research in topics: Population & Social network. The author has an hindex of 40, co-authored 149 publications receiving 6092 citations. Previous affiliations of Damien R. Farine include University of California & Edward Grey Institute of Field Ornithology.

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Constructing, conducting and interpreting animal social network analysis

TL;DR: The under‐exploited potential of experimental manipulations on social networks to address research questions is highlighted, and an overview of methods for quantifying properties of nodes and networks, as well as for testing hypotheses concerning network structure and network processes are provided.
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Experimentally induced innovations lead to persistent culture via conformity in wild birds

TL;DR: In providing the first experimental demonstration of conformity in a wild non-primate, and of cultural norms in foraging techniques in any wild animal, the results suggest a much broader taxonomic occurrence of such an apparently complex cultural behaviour.
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Shared decision-making drives collective movement in wild baboons

TL;DR: Tracking wild baboons with a high-resolution global positioning system and analyzing their movements relative to one another reveals that a process of shared decision-making governs baboon movement, suggesting that democratic collective action emerging from simple rules is widespread, even in complex, socially stratified societies.
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Individual personalities predict social behaviour in wild networks of great tits (Parus major)

TL;DR: The results provide strong evidence that songbirds follow alternative social strategies related to personality, which has implications not only for the causes of social network structure but also for the strength and direction of selection on personality in natural populations.
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A guide to null models for animal social network analysis.

TL;DR: It is shown that permutations of the raw observational (or ‘pre‐network’) data consistently account for underlying structure in the generated social network, and thus can reduce both type I and type II error rates.