Individual variations lead to universal and cross-species patterns of social behavior

Abstract

The duration of interaction events in a society is a fundamental measure of its collective nature and potentially reflects variability in individual behavior. Here we performed a high-throughput measurement of trophallaxis and face-to-face event durations experienced by a colony of honeybees over their entire lifetimes. The interaction time distribution is heavy-tailed, as previously reported for human face-to-face interactions. We developed a theory of pair interactions that takes into account individual variability and predicts the scaling behavior for both bee and extant human datasets. The individual variability of worker honeybees was nonzero but less than that of humans, possibly reflecting their greater genetic relatedness. Our work shows how individual differences can lead to universal patterns of behavior that transcend species and specific mechanisms for social interactions.

Dual‐fluorescence imaging and automated trophallaxis detection for studying multi‐nutrient regulation in superorganisms

“…As both precision and recall are important, we evaluate the overall performance of our algorithm using the F 1 score. Our high F 1 score (0.87) surpasses that of the current state‐of‐the‐art method used for trophallaxis detection in bees ( F 1 = 0.64 in Choi et al., 2020; Gernat et al., 2018), and is comparable to a recently reported advancement in bee trophallaxis detection ( F 1 = 0.88 in Hocke, 2018). A snapshot from a frame displaying our automatic trophallaxis detections is presented in Figure 5a.…”

“…All of our scripts and the image library are now available in a public repository (Baltiansky et al, 2021). The performance of our detection equates to that of the best automatic detection methods currently available for bee trophallaxis (Gernat et al, 2018;Choi et al, 2020;Hocke, 2018;Blut et al, 2017). Additionally, our approach holds another advantage: while existing methods rely firstly on information obtained from the tags that are used to track the insects, in our approach the first step is the image-based detection of touching heads.…”

“…So far, researchers have managed to track trophallactic interaction networks in social insect colonies in great detail, but the actual flow of food throughout the network remained elusive (Bles et al., 2018; Choi et al., 2020; Gernat et al., 2018; Planckaert et al., 2019; Quevillon et al., 2015; Quque et al, 2020; Sendova‐Franks et al., 2010; Wikle et al., 2019). In the past, measuring food inside individual crops required to remove a sample of individuals from the colony (Cassill & Tschinkel, 1996, 1999; Howard & Tschinkel, 1980; Howard & Tschinkel, 1981b; Sorensen et al, 1985; Wilson & Eisner, 1957).…”

“…Accepted Article Quque et al, 2020;Planckaert et al, 2019;Bles, Deneubourg, and Nicolis, 2018;Quevillon et al, 2015;Wikle, Hanks, and Hughes, 2019;Gernat et al, 2018;Choi et al, 2020). In the past, measuring food inside individual crops required to remove a sample of individuals from the colony (Sorensen, Busch, and Vinson, 1985;Howard and Tschinkel, 1980;Howard and Tschinkel, 1981b;Wilson and Eisner, 1957;Cassill and Tschinkel, 1999;Cassill and Tschinkel, 1996).…”

A spanning-tree method for constructing temporal networks

“…The time interval or waiting time between two consecutive events, i.e. the interevent time (IET), is a pivotal statistic to measure temporal structure because of its profound impact on interaction patterns [4][5][6][7] and dynamical processes [8][9][10][11][12] in real-world systems. The empirical data, such as email and mobile communication [13][14][15], epidemic spreading [16][17][18], and human mobility [19,20], exhibits non-Poissonian and heterogeneous activity patterns, known as burstiness [4,21], due to the possible high or low priority of tasks [21,22] or circadian cycles of human activities [14].…”