Using multilayer network analysis to explore the temporal dynamics of collective behavior. Issue 1 (2nd September 2020)

• Record Type: Journal Article
• Authors: Fisher, David N; Pinter-Wollman, Noa
• Editors: Zhi-Yun, Jia
• Is Part Of: Current zoology. Volume 67:Issue 1(2021)
• Page Start: 71
• Page End: 80
• Publication Date: 2020-09-02
• Subjects: collective behavior -- dynamic network -- multilayer network -- multiplex -- social stability -- Stegodyphus; Zoology -- Periodicals; Zoology -- China -- Periodicals; 590.5
• DOI: 10.1093/cz/zoaa050
• Languages: English
• ISSNs: 1674-5507

Abstract

Abstract: Social organisms often show collective behaviors such as group foraging or movement. Collective behaviors can emerge from interactions between group members and may depend on the behavior of key individuals. When social interactions change over time, collective behaviors may change because these behaviors emerge from interactions among individuals. Despite the importance of, and growing interest in, the temporal dynamics of social interactions, it is not clear how to quantify changes in interactions over time or measure their stability. Furthermore, the temporal scale at which we should observe changes in social networks to detect biologically meaningful changes is not always apparent. Here we use multilayer network analysis to quantify temporal dynamics of social networks of the social spider Stegodyphus dumicola and determine how these dynamics relate to individual and group behaviors. We found that social interactions changed over time at a constant rate. Variation in both network structure and the identity of a keystone individual was not related to the mean or variance of the collective prey attack speed. Individuals that maintained a large and stable number of connections, despite changes in network structure, were the boldest individuals in the group. Therefore, social interactions and boldness are linked across time, but group collective behavior is not influenced by the stability of the social network. Our work demonstrates that dynamic social networks can be modeled in a multilayer framework. This approach may reveal biologically important temporal changes to social structure in other systems.