Ariana Strandburg-Peshkin

Shared decision-making drives collective movement in wild baboons

Baboons follow the pack, not the leader How do groups of animals, including humans, make decisions that affect the entire group? Evidence collected from schooling animals suggests that the process is somewhat democratic, with nearest neighbors and the majority shaping overall collective behavior. In animals with hierarchical social structures such as primates or wolves, however, such democracy may be complicated by dominance. Strandburg-Peshkin et al. monitored all the individuals within a baboon troop continuously over the course of their daily activities. Even within this highly socially structured species, movement decisions emerged via a shared process. Thus, democracy may be an inherent trait of collective behavior. Science, this issue p. 1358 Democracy guides group decisions in baboons, even in the presence of strong social hierarchies. Conflicts of interest about where to go and what to do are a primary challenge of group living. However, it remains unclear how consensus is achieved in stable groups with stratified social relationships. Tracking wild baboons with a high-resolution global positioning system and analyzing their movements relative to one another reveals that a process of shared decision-making governs baboon movement. Rather than preferentially following dominant individuals, baboons are more likely to follow when multiple initiators agree. When conflicts arise over the direction of movement, baboons choose one direction over the other when the angle between them is large, but they compromise if it is not. These results are consistent with models of collective motion, suggesting that democratic collective action emerging from simple rules is widespread, even in complex, socially stratified societies.

Habitat and social factors shape individual decisions and emergent group structure during baboon collective movement

For group-living animals traveling through heterogeneous landscapes, collective movement can be influenced by both habitat structure and social interactions. Yet research in collective behavior has largely neglected habitat influences on movement. Here we integrate simultaneous, high-resolution, tracking of wild baboons within a troop with a 3-dimensional reconstruction of their habitat to identify key drivers of baboon movement. A previously unexplored social influence – baboons’ preference for locations that other troop members have recently traversed – is the most important predictor of individual movement decisions. Habitat is shown to influence movement over multiple spatial scales, from long-range attraction and repulsion from the troop’s sleeping site, to relatively local influences including road-following and a short-range avoidance of dense vegetation. Scaling to the collective level reveals a clear association between habitat features and the emergent structure of the group, highlighting the importance of habitat heterogeneity in shaping group coordination. DOI: http://dx.doi.org/10.7554/eLife.19505.001

Estimating uncertainty and reliability of social network data using Bayesian inference.

Social network analysis provides a useful lens through which to view the structure of animal societies, and as a result its use is increasingly widespread. One challenge that many studies of animal social networks face is dealing with limited sample sizes, which introduces the potential for a high level of uncertainty in estimating the rates of association or interaction between individuals. We present a method based on Bayesian inference to incorporate uncertainty into network analyses. We test the reliability of this method at capturing both local and global properties of simulated networks, and compare it to a recently suggested method based on bootstrapping. Our results suggest that Bayesian inference can provide useful information about the underlying certainty in an observed network. When networks are well sampled, observed networks approach the real underlying social structure. However, when sampling is sparse, Bayesian inferred networks can provide realistic uncertainty estimates around edge weights. We also suggest a potential method for estimating the reliability of an observed network given the amount of sampling performed. This paper highlights how relatively simple procedures can be used to estimate uncertainty and reliability in studies using animal social network analysis.

Both nearest neighbours and long-term affiliates predict individual locations during collective movement in wild baboons

In many animal societies, groups of individuals form stable social units that are shaped by well-delineated dominance hierarchies and a range of affiliative relationships. How do socially complex groups maintain cohesion and achieve collective movement? Using high-resolution GPS tracking of members of a wild baboon troop, we test whether collective movement in stable social groups is governed by interactions among local neighbours (commonly found in groups with largely anonymous memberships), social affiliates, and/or by individuals paying attention to global group structure. We construct candidate movement prediction models and evaluate their ability to predict the future trajectory of focal individuals. We find that baboon movements are best predicted by 4 to 6 neighbours. While these are generally individuals’ nearest neighbours, we find that baboons have distinct preferences for particular neighbours, and that these social affiliates best predict individual location at longer time scales (>10 minutes). Our results support existing theoretical and empirical studies highlighting the importance of local rules in driving collective outcomes, such as collective departures, in primates. We extend previous studies by elucidating the rules that maintain cohesion in baboons ‘on the move’, as well as the different temporal scales of social interactions that are at play.

Inferring influence and leadership in moving animal groups

Collective decision-making is a daily occurrence in the lives of many group-living animals, and can have critical consequences for the fitness of individuals. Understanding how decisions are reached, including who has influence and the mechanisms by which information and preferences are integrated, has posed a fundamental challenge. Here, we provide a methodological framework for studying influence and leadership in groups. We propose that individuals have influence if their actions result in some behavioural change among their group-mates, and are leaders if they consistently influence others. We highlight three components of influence (influence instances, total influence and consistency of influence), which can be assessed at two levels (individual-to-individual and individual-to-group). We then review different methods, ranging from individual positioning within groups to information-theoretic approaches, by which influence has been operationally defined in empirical studies, as well as how such observations can be aggregated to give insight into the underlying decision-making process. We focus on the domain of collective movement, with a particular emphasis on methods that have recently been, or are being, developed to take advantage of simultaneous tracking data. We aim to provide a resource bringing together methodological tools currently available for studying leadership in moving animal groups, as well as to discuss the limitations of current methodologies and suggest productive avenues for future research. This article is part of the theme issue ‘Collective movement ecology’.

As dusk falls : collective decisions about the return to sleeping sites in meerkats

Social animal groups often make consensus decisions about when to return to a sleeping site after a day of foraging. These decisions can depend on extrinsic as well as intrinsic factors, and can range from unshared to shared. Here we investigated how decisions of meerkats, Suricata suricatta, to return to their burrows are coordinated, whether they are shared or monopolized by dominant individuals, and what factors influence the timing and speed of return. Individual meerkats can initiate group movements using ‘lead’ calls, and groups can change foraging patches using ‘move’ calls in a quorum response. We found that both call types could be produced during the return to the burrow, with the probability of move calls increasing as sunset approached, and the probability of lead calls increasing with greater distance to the burrow when sunset was imminent. Dominant and subordinate individuals did not differ significantly in move and lead call rate. Further, the time of return was better predicted by the foraging success of all subordinates in the group (with the group returning later when success was low) than by the foraging success of the dominant individuals. This suggests that decisions to return are shared rather than controlled by dominants. The speed of return depended both on extrinsic factors, such as the presence of pups, the time until sunset and the distance to the burrow, and on intrinsic factors such as satiation. Our results indicate that both the speed and timing of the return depend on urgency, and the higher incidence of lead calls when groups are far away from the burrow near dark suggests a possible change in the decision process from shared to unshared as urgency increases. Our study highlights the impact of time constraints during decision-making processes and in particular on the level of decision sharing.

Coordination Event Detection and Initiator Identification in Time Series Data

Behavior initiation is a form of leadership and is an important aspect of social organization that affects the processes of group formation, dynamics, and decision-making in human societies and other social animal species. In this work, we formalize the Coordination Initiator Inference Problem and propose a simple yet powerful framework for extracting periods of coordinated activity and determining individuals who initiated this coordination, based solely on the activity of individuals within a group during those periods. The proposed approach, given arbitrary individual time series, automatically (1) identifies times of coordinated group activity, (2) determines the identities of initiators of those activities, and (3) classifies the likely mechanism by which the group coordination occurred, all of which are novel computational tasks. We demonstrate our framework on both simulated and real-world data: trajectories tracking of animals as well as stock market data. Our method is competitive with existing global leadership inference methods but provides the first approaches for local leadership and coordination mechanism classification. Our results are consistent with ground-truthed biological data and the framework finds many known events in financial data which are not otherwise reflected in the aggregate NASDAQ index. Our method is easily generalizable to any coordinated time series data from interacting entities.

The wisdom of baboon decisions : Response

We thank K. B. Wray for his Letter, but we disagree with his interpretation of the terminology he uses. Wray presents a false dichotomy between “wisdom of the crowds” and “democratic decision-making.” In the study of collective animal behavior, “shared consensus” (also referred to as “