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 high-resolution GPS 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 compromise if 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.

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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

Mobile animal groups provide some of the most compelling examples of self-organization in the natural world. While field observations of songbird flocks wheeling in the sky or anchovy schools fleeing from predators have inspired considerable interest in the mechanics of collective motion, the challenge of simultaneously monitoring multiple animals in the field has historically limited our capacity to study collective behaviour of wild animal groups with precision. However, recent technological advancements now present exciting opportunities to overcome many of these limitations. Here we review existing methods used to collect data on the movements and interactions of multiple animals in a natural setting. We then survey emerging technologies that are poised to revolutionize the study of collective animal behaviour by extending the spatial and temporal scales of inquiry, increasing data volume and quality, and expediting the post-processing of raw data.This article is part of the theme issue 'Collective movement ecology'.

A liquid drop impacting a solid surface may splash either by emitting a thin liquid sheet that subsequently breaks apart or by promptly ejecting droplets from the advancing liquid-solid contact line. Using high-speed imaging, we show that surface roughness and air pressure influence both mechanisms. Roughness inhibits thin-sheet formation even though it also increases prompt splashing at the advancing contact line. If the air pressure is lowered, droplet ejection is suppressed not only during thin-sheet formation but for prompt splashing as well.PACS numbers: 47.20.Cq, 47.20.Gv, 47.20.Ma,Will a drop hitting a dry surface splash? Different criteria [1][2][3][4][5] have been proposed to predict when such a drop will splash by comparing the roughness of the solid surface with hydrodynamic length scales, which depend on parameters such as the drop velocity, radius, viscosity and surface tension. Several years ago Xu et al. [6,7] found that these criteria ignore a crucial parameter: the ambient gas pressure, P . When a drop splashes on a smooth surface it spreads smoothly forming a lamella before ejecting a thin sheet that subsequently breaks up into secondary droplets. As P is reduced below a threshold pressure, the drop no longer splashes [6][7][8][9][10]. On the other hand, when splashing occurs on a rough surface, no thin sheet is formed and droplets are ejected directly from the advancing liquid-substrate contact line via a "prompt" splash [1][2][3][4]8].It has been suggested that thin-sheet splashes depend on air pressure while prompt splashes do not and depend only on surface roughness [8]. Here we show that the situation is more complex in that both types of splashing depend, albeit in opposite ways, on surface roughness. In particular, we observe four distinct regimes. In agreement with earlier results [4], we observe a thin-sheet splash on very smooth surfaces and a prompt splash on very rough ones. However, at intermediate roughness, we identify two new regimes: at low viscosities both prompt and thin-sheet splashes occur during a single impact, while at high viscosities neither splash is formed. In addition, as found for thin-sheet splashing [6], we find that a drop deposits smoothly on a rough surface if P is low enough. Clearly, the role of both air pressure and substrate roughness must be considered in all cases.The experiments were conducted with silicone oil (PDMS, Clearco Products) with kinematic viscosity ν ranging from 5 cSt to 14.4 cSt and surface tension σ between 19.7 dyn/cm and 20.8 dyn/cm. The basic results were replicated using water/glycerin mixtures with a similar viscosity range but higher surface tension: σ=67 dyn/cm. Low-viscosity impacts were studied with ethanol. Drops with reproducible diameter D=3.1 mm were produced using a syringe pump (Razel Scientific, Model R99-E) and released in a chamber from a height above a substrate. This height set the impact velocity u 0 which was varied between 2.7 m/s and 4.1 m/s. These parameters determine the Reynolds number Re=Du 0 /ν giving the rati...

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 if their actions result in some behavioural change among their group-mates, and are 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'.

Highlights d Meerkats avoid overlapping conspecifics during group vocal interactions d Vocal turn-taking is achieved by simple individual-level rules d Turn-taking is maintained in multi-participant groups of more than three individuals d Turn-taking might facilitate social bonding through cooperative interaction

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.