Individual and collective encoding of risk in animal groups

Abstract

The need to make fast decisions under risky and uncertain conditions is a widespread problem in the natural world. While there has been extensive work on how individual organisms dynamically modify their behavior to respond appropriately to changing environmental conditions (and how this is encoded in the brain), we know remarkably little about the corresponding aspects of collective information processing in animal groups. For example, many groups appear to show increased “sensitivity” in the presence of perceived threat, as evidenced by the increased frequency and magnitude of repeated cascading waves of behavioral change often observed in fish schools and bird flocks under such circumstances. How such context-dependent changes in collective sensitivity are mediated, however, is unknown. Here we address this question using schooling fish as a model system, focusing on 2 nonexclusive hypotheses: 1) that changes in collective responsiveness result from changes in how individuals respond to social cues (i.e., changes to the properties of the “nodes” in the social network), and 2) that they result from changes made to the structural connectivity of the network itself (i.e., the computation is encoded in the “edges” of the network). We find that despite the fact that perceived risk increases the probability for individuals to initiate an alarm, the context-dependent change in collective sensitivity predominantly results not from changes in how individuals respond to social cues, but instead from how individuals modify the spatial structure, and correspondingly the topology of the network of interactions, within the group. Risk is thus encoded as a collective property, emphasizing that in group-living species individual fitness can depend strongly on coupling between scales of behavioral organization.

Vortex phase matching as a strategy for schooling in robots and in fish

“…In addition, and perhaps most importantly, it suggests that despite the complexity of fluid dynamics, and of real organisms, there may be a surprisingly simple rule (as evident by the simple linear relationship between phase difference Φ and front-back distance D), that if adopted by real fish, would allow them to continuously obtain hydrodynamic benefits from near neighbours, such as to minimise energetic costs. This is not to say that real fish would be expected to exhibit such a rule all the time -they face many other challenges such as to obtain food and avoid predators, as well as to move to gain and utilise social information 38,44,45 -but since this is both simple and robust, it opens up the possibility that it may be a previously undiscovered, but general, behavioural strategy.…”

Social networks predict the life and death of honey bees

“…Social interaction networks have been used to investigate e.g. pair-bonding (Psorakis et al 2012), inter-group brokering (Lusseau et al 2004), offspring survival (Cheney et al 2016), cultural spread (Aplin et al 2015), policing behavior (Flack et al 2006), leadership in mobile groups (Strandburg-Peshkin et al 2018), organization of food retrieval (Planckaert et al 2019), and the propagation of alarm response (Rosenthal et al 2015; Sosna et al 2019). As our ability to collect detailed social network data increases, so too does our need to develop tools for understanding the significance and functional consequences of these networks (Gomez-Marin et al 2014).…”

The wisdom of stalemates: consensus and clustering as filtering mechanisms for improving collective accuracy

“…A dominant early explanation for this tendency was that the close proximity resulted from competition for low-risk places within the group [2]. More recent work has demonstrated, however, that moving closer alters the network of social interactions in a way that increases collective responsiveness [35,36,37]. Highly responsive dense groups are characterized by social networks with fewer neighbors and higher clustering [30,31].…”