期刊
JOURNAL OF ANIMAL ECOLOGY
卷 90, 期 1, 页码 8-26出版社
WILEY
DOI: 10.1111/1365-2656.13307
关键词
culture; disease transmission; network-based diffusion analysis; social learning; social network analysis; social transmission
资金
- European Horizon 2020 Research and Innovation Programme under ERC Starting Grant [BeeDanceGap 638873]
Although demonstrating social learning in freely interacting animals has been challenging, network-based diffusion analysis (NBDA) provides a method for detecting social learning; This paper provides a comprehensive guide for using NBDA, including its mathematical framework, types of questions it can address, social network selection, NBDA variant selection, interpretation of model output, and practical recommendations for model selection.
Although social learning capabilities are taxonomically widespread, demonstrating that freely interacting animals (whether wild or captive) rely on social learning has proved remarkably challenging. Network-based diffusion analysis (NBDA) offers a means for detecting social learning using observational data on freely interacting groups. Its core assumption is that if a target behaviour is socially transmitted, then its spread should follow the connections in a social network that reflects social learning opportunities. Here, we provide a comprehensive guide for using NBDA. We first introduce its underlying mathematical framework and present the types of questions that NBDA can address. We then guide researchers through the process of selecting an appropriate social network for their research question; determining which NBDA variant should be used; and incorporating other variables that may impact asocial and social learning. Finally, we discuss how to interpret an NBDA model's output and provide practical recommendations for model selection. Throughout, we highlight extensions to the basic NBDA framework, including incorporation of dynamic networks to capture changes in social relationships during a diffusion and using a multi-network NBDA to estimate information flow across multiple types of social relationship. Alongside this information, we provide worked examples and tutorials demonstrating how to perform analyses using the newly developednbdapackage written in the R programming language.
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