Network-based diffusion analysis reveals context-specific dominance of dance communication in foraging honeybees

Abstract

The honeybee (Apis mellifera) dance communication system is a marvel of collective behaviour, but the added value it brings to colony foraging efficiency is poorly understood. In temperate environments, preventing communication of foraging locations rarely decreases colony food intake, potentially because simultaneous transmission of olfactory information also plays a major role in foraging. Here, we employ social network analyses that quantify information flow across multiple temporally varying networks (each representing a different interaction type) to evaluate the relative contributions of dance communication and hivebased olfactory information transfer to honeybee recruitment events. We show that virtually all successful recruits to novel locations rely upon dance information rather than olfactory cues that could otherwise guide them to the same resource. Conversely, during reactivation to known sites, dances are relatively less important, as foragers are primarily guided by olfactory information. By disentangling the contributions of multiple information networks, the contexts in which dance communication truly matters amid a complex system full of redundancy can now be identified.

Social networks predict the life and death of honey bees

“…In this study, we extract network age from daily aggregated interaction networks, and thereby disregard potentially relevant intraday information, which could reveal further differences between individuals (e.g., the temporal aspects of intraday interaction networks can disentangle the contribution of different modes of interactions (Hasenjager et al 2020)). While we observe thousands of individuals and many overlapping cohorts, there is no straight-forward extension of the method to extract a common embedding of social networks that do not share individuals (e.g., over different experimental treatments or repetitions).…”

“…With the advent of automated tracking, there has been renewed interest in how interactions change within colonies (Blut et al 2017; Mersch et al 2013), how spatial position predicts task allocation (Crall et al 2018), and how spreading dynamics occur in social networks (Gernat et al 2018). Despite extensive work on the social physiology of honey bee colonies (Seeley 1995), few works have studied interaction networks from a colony-wide or temporal perspective (Gernat et al 2018; Hasenjager et al 2020). While there is considerable variance in task allocation, even among bees of the same age, it is unknown to what extent this variation is reflected in the social networks.…”

Detecting and quantifying social transmission using network‐based diffusion analysis

“…Canteloup, Hoppitt, and van de Waal (2020) show how NBDA can be extended to test for such effects. It may also make sense to compare models with different observation networks representing different types of observations (Hasenjager, Hoppitt, et al, 2020; Box 3) to determine which network (or combination of networks) best explains the diffusion data (see Section 7). See Hobaiter et al.…”

“…Since its initial development, NBDA has enabled investigation of social transmission across diverse taxa under both wild and captive conditions (cetaceans: Allen, Weinrich, Hoppitt, & Rendell, 2013; Wild et al., 2019; primates: Hobaiter, Poisot, Zuberbühler, Hoppitt, & Gruber, 2014; Kendal et al., 2010; Schnoell & Fichtel, 2012; songbirds: Aplin, Farine, Morand‐Ferron, & Sheldon, 2012; teleost fish: Atton, Hoppitt, Webster, Galef, & Laland, 2012; invertebrates: Hasenjager, Hoppitt, & Leadbeater, 2020). This has resulted in several extensions to this approach, such as using dynamic networks to account for changes in social relationships over time (Hobaiter et al., 2014), inclusion of multiple network types to evaluate how transmission is influenced by different types of relationship (Farine, Aplin, Sheldon, & Hoppitt, 2015), and incorporation of learning tasks that require multiple steps to complete (Atton et al., 2012).…”

Analyzing, Exploring, and Visualizing Complex Networks via Hypergraphs using SimpleHypergraphs.jl

“…In particular, over the last twenty years, the interests of research have focused on complex networks, namely networks whose structure is irregular, complex and dynamically evolving in time [26,42,67]. Complex networks naturally model many real-world scenarios, such as social interactions [31,55], biological [40,41,62] and economical [37,70] systems, Internet [36], and the World Wide Web [63], just to name a few examples. Traditionally, these networks are described using graphs, where nodes represent elements of the network, and edges represent relationships between some pairs of elements.…”