Inferring individual rules from collective behavior

Abstract

Social organisms form striking aggregation patterns, displaying cohesion, polarization, and collective intelligence. Determining how they do so in nature is challenging; a plethora of simulation studies displaying life-like swarm behavior lack rigorous comparison with actual data because collecting field data of sufficient quality has been a bottleneck. Here, we bridge this gap by gathering and analyzing a high-quality dataset of flocking surf scoters, forming wellspaced groups of hundreds of individuals on the water surface. By reconstructing each individual's position, velocity, and trajectory, we generate spatial and angular neighbor-distribution plots, revealing distinct concentric structure in positioning, a preference for neighbors directly in front, and strong alignment with neighbors on each side. We fit data to zonal interaction models and characterize which individual interaction forces suffice to explain observed spatial patterns. Results point to strong short-range repulsion, intermediaterange alignment, and longer-range attraction (with circular zones), as well as a weak but significant frontal-sector interaction with one neighbor. A best-fit model with such interactions accounts well for observed group structure, whereas absence or alteration in any one of these rules fails to do so. We find that important features of observed flocking surf scoters can be accounted for by zonal models with specific, well-defined rules of interaction.any social organisms exhibit cohesive, self-organized group motion with visually compelling aggregation patterns (1). Investigating how behavior at one scale (the individual) engenders behavior on the higher scale (e.g., the swarm) has spawned a rich field of research, driven largely by simulation and modeling of both physical (2-4) and biological (5-9) systems. One consensus is that even simple individual rules can give rise to complex group behavior. However, whether/which groups have leaders, how neighbors are surveyed by each member, what is the relative effect of local versus global information, and whether interactions affect speed, turning angles, acceleration, or all of the above is unclear for most flocks. What underlying rules of behavior are at play in real swarms or flocks remains a longstanding question that motivates our study.Given the rich diversity of theoretical models that assume distinct forces and reactions, it is of great interest to assess which correspond to actual behavior in nature. Yet simulation studies alone cannot tackle this question because patterns similar to observations can be generated by different model mechanisms. Instead, this question requires a close comparison between real and model social aggregates and measurement of what individuals are actually doing within large groups, as is our focus here.Technological difficulties in tracking individuals accurately in fast-moving groups presents a challenge (10). Recovering the spatial structure of large groups is hampered by obstruction of the view into the group interior (occlusion) in...

From behavioural analyses to models of collective motion in fish schools

“…To be more than a powerful visualization tool, this approach has to demonstrate its ability to lead to a model that would reproduce in turn the experimental data. A partial answer is given by the work done by Lukeman et al [82], that quantitatively reproduces at the collective level the behaviour of surf scoters, thanks to a selfpropelled particles model with an additional frontal preference hypothesis. However, without an agreement of the model at the individual level, it is difficult to evaluate to which extent the extracted behavioural algorithm corresponds to the actual behaviour-in this case, the value of the frontal preference hypothesis.…”

“…Besides their success in linking functional properties at the school level to behavioural mechanisms at the individual scale and some experimental success [82,83], it is unlikely that the self-propelled particles framework, as we defined it, has the ability to lead to models accurately reproducing experimental data of species, notably in explaining the experimental speed distributions and consequently the mechanisms responsible for speed synchronization. In particular, a series of simulation studies have shown that the mean speed of an individual could be considered as an emergent property as well [51,69,74].…”

“…A recent experimental work [82] brought an evidence of frontal preference in the influential neighbours of an assembly of Surf Scoters swimming against the drift currents. A social force model with A/R/A rules supplemented with weak frontal preference (an early modelling hypothesis [48]) has shown a good agreement between observed and predicted structure (radial distribution, angular preferred position).…”

The role of individuality in collective group movement

“…This in turn allows groups to maintain their coherence and enables group members to realize the benefits of group living [7]. To date, however, most studies of collective behaviour have assumed that group members are identical in their movements and responses to their neighbours [2][3][4] (but see [8][9][10] for theoretical predictions and [11,12] for empirical observations about individual differences in groups). This common assumption of homogeneity contrasts with a large and growing body of work documenting consistent inter-individual differences in behaviour [13][14][15][16][17][18] and evidence that differences in the social affiliations between group members, and individual differences, can affect leadership and the collective decision-making process [12,19,20].…”

“…Group-level properties, such as the collective movements of animal groups, emerge when individuals respond to the location and movements of their near neighbours [2][3][4]. This responsiveness to the behaviour of neighbours allows consensus to be reached over the timing and direction of group movements [5,6].…”