Individual and collective encoding of risk in animal groups Matthew M. G. Sosnaa,1, Colin R. Twomeyb, Joseph Bak-Colemana, Winnie Poelc,d, Bryan C. Danielse, Pawel Romanczukc,d, and Iain D. Couzinf,g,h,1 aDepartment of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544; bDepartment of Biology, University of Pennsylvania, Philadelphia, PA 19104; cInstitute for Theoretical Biology, Department of Biology, Humboldt Universitat¨ zu Berlin, D-10099 Berlin, Germany; dBernstein Center for Computational Neuroscience Berlin, Humboldt Universitat¨ zu Berlin, D-10115 Berlin, Germany; eArizona State University-Santa Fe Institute (ASU–SFI) Center for Biosocial Complex Systems, Arizona State University, Tempe, AZ 85287; fDepartment of Collective Behaviour, Max Planck Institute of Animal Behavior, D-78547 Konstanz, Germany; gDepartment of Biology, University of Konstanz, D-78547 Konstanz, Germany; and hCentre for the Advanced Study of Collective Behaviour, University of Konstanz, D-78547 Konstanz, Germany Edited by Gene E. Robinson, University of Illinois at Urbana–Champaign, Urbana, IL, and approved August 28, 2019 (received for review April 1, 2019) The need to make fast decisions under risky and uncertain condi- For example, if we consider an individual in isolation, it must tions is a widespread problem in the natural world. While there base its decisions on sensory inputs and previous experience, has been extensive work on how individual organisms dynami- which may also be modulated by physiological state. However, cally modify their behavior to respond appropriately to changing it is clearly the individual that is “responsible” for the decision. environmental conditions (and how this is encoded in the brain), If we consider instead individuals embedded in a social net- we know remarkably little about the corresponding aspects of work, another possibility is introduced: As in other information- collective information processing in animal groups. For example, processing networks, such as neural circuits, computation may many groups appear to show increased “sensitivity” in the pres- be affected by changes in the individual components themselves ence of perceived threat, as evidenced by the increased frequency (network “nodes”) and/or by changes in the structural connec- and magnitude of repeated cascading waves of behavioral change tivity (topology) among the components (network “edges”). In often observed in fish schools and bird flocks under such cir- animal groups, individuals often exhibit a highly dynamic group cumstances. How such context-dependent changes in collective structure, with individuals’ spatial positions, orientations, and ECOLOGY sensitivity are mediated, however, is unknown. Here we address sensory neighborhoods changing rapidly (5, 7–9). Yet nonethe- this question using schooling fish as a model system, focusing on less, individuals exhibit the capacity to change, consistently and 2 nonexclusive hypotheses: 1) that changes in collective respon- repeatedly, the topology of their social connectivity by switch- siveness result from changes in how individuals respond to social ing between what is often a relatively small number of group cues (i.e., changes to the properties of the “nodes” in the social structural states (e.g., ref. 9). This presents an additional nuance network), and 2) that they result from changes made to the struc- to understanding collective cognition (10–12), as while individu- tural connectivity of the network itself (i.e., the computation is als may be influenced by the topology of their network, they are encoded in the “edges” of the network). We find that despite the also able to modify this topology through their movements and fact that perceived risk increases the probability for individuals to perception of the environment. initiate an alarm, the context-dependent change in collective sen- sitivity predominantly results not from changes in how individuals Significance respond to social cues, but instead from how individuals modify the spatial structure, and correspondingly the topology of the net- work of interactions, within the group. Risk is thus encoded as a Many biological systems exhibit an emergent ability to pro- collective property, emphasizing that in group-living species indi- cess information about their environment. This collective cog- vidual fitness can depend strongly on coupling between scales of nition emerges as a result of both the behavior of system com- behavioral organization. ponents and their interactions, yet the relative importance of the two is often hard to disentangle. Here, we combined experiments and modeling to examine how fish schools col- group structure j antipredator behavior j social contagion lectively encode information about the external environment. We demonstrate that risk is predominantly encoded in the key challenge faced by animals is to appropriately adjust physical structure of groups, which individuals modulate in Atheir behavioral responses to changing environmental con- a way that augments or dampens behavioral cascades. We texts (1). To do so, organisms must make probabilistic decisions show that this modulation is necessary for behavioral cas- based on often imperfect or conflicting sensory information. cades to spread and that it allows collective systems to be Longer-term states such as fear or hunger can be considered as responsive to their environments even without changes in a persistent (but updatable) memory stored by the animal that individual computation. modulates the mapping from sensory input to behavioral change. The mechanisms by which individual organisms achieve effec- Author contributions: M.M.G.S., J.B.-C., and I.D.C. designed research; M.M.G.S. per- tive context-dependent behavior have been well studied (2–4), formed research; C.R.T., W.P., B.C.D., and P.R. contributed new reagents/analytic tools; M.M.G.S. and W.P. analyzed data; M.M.G.S., C.R.T., J.B.-C., W.P., B.C.D., P.R., and I.D.C. but what has been comparatively rarely explored is how such wrote the paper; and C.R.T., J.B.-C., W.P., B.C.D., and P.R. developed the mathematical behavioral plasticity is encoded by organisms that live in groups. model and performed and analyzed numerical simulations.y In highly coordinated animal groups, such as many species of The authors declare no conflict of interest.y schooling fish, flocking birds, or herding ungulates, individual This article is a PNAS Direct Submission.y reproductive success is often intimately linked with the func- This open access article is distributed under Creative Commons Attribution-NonCommercial- tional complexity of collective behavior (5, 6). This introduces a NoDerivatives License 4.0 (CC BY-NC-ND).y coupling between individual (“microscopic”) properties and col- 1 To whom correspondence may be addressed. Email: [email protected] or lective (“macroscopic”) behavior, and it is reasonable to expect [email protected] that this coupling will impact how evolution has shaped the This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. mechanisms by which individuals sense and respond to changing 1073/pnas.1905585116/-/DCSupplemental.y environmental conditions. www.pnas.org/cgi/doi/10.1073/pnas.1905585116 PNAS Latest Articles j 1 of 6 Downloaded by guest on September 27, 2021 Here we explore the possibility that information process- AB ing may be facilitated not only by individuals changing their internal behavioral rules/states, as is typically considered in animal behavior, but that, by forming a networked system, indi- viduals can facilitate collective computation by changing the structural topology of the network (their social connectivity), without necessarily adjusting the way they respond to sensory information. We refer to changes in individual behavioral rules 5 cm and states as individuals changing their responsiveness and to changes in group structure as individuals changing their spatial positioning. C Across many animal taxa, group structure is known to be highly 8 sensitive to group members’ perceptions of risk and resources (13–19). These changes have generally been attributed to sim- 7 ple game theoretic considerations (20, 21), where structure is merely a byproduct of individuals acting to maximize their sur- 6 vival (5). But overlooked is the possibility that group structure, as an emergent encoding of the external environment, could itself 5 be an important mechanism by which organisms effectively pro- NND (cm) cess information in a changing world. In this way, the group’s 4 structure could act as a collective memory that modifies future Schreckstoff: 1st exposure decisions, similar to how an individual’s memory guides its own Schreckstoff: 3rd exposure 3 Water behavior (22, 23). To test the relative contributions of group members’ respon- 0 102030405060 siveness vs. spatial positioning to collective information process- Time (min) ing, here we present results from experiments with schooling fish (golden shiners, Notemigonus crysoleucas), known to have highly DE dynamic and self-regulating group structure (9, 16, 19), and use these data to investigate context-dependent changes in individ- 40 1 ual and collective responses to perceived risk. Like many fish 30 0.8 species (3, 24, 25), predation is a source of extremely high mor- 0.6 tality in the wild (26) and juveniles form coordinated schools in 20 0.4 response to this risk. Shiners also exhibit startle responses as an 10 escape behavior (27) that is socially contagious (28). Startles in 0.2 this species occur even in the absence of an external stimulus, 0 0 N visible neighbors N visible 1st 3rd 1st 3rd and these spontaneous false alarms propagate through the group of vision Proportion in the same manner as triggered true alarms (28). In nature, exposure exposure exposure exposure false alarms account for a high proportion of overall alarms (29– Fig. 1. Effect of perceived predation risk on group structure. (A) A subset 32), very likely because there are such considerable costs to not of the school prior to Schreckstoff. Rays (purple) represent a visualization of responding to true threats relative to false alarms (33).