Creation of prompt and thin-sheet splashing by varying surface roughness or increasing air pressure

A liquid drop impacting a solid surface may splash 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 air pressure and surface roughness influence both splash mechanisms. Roughness increases prompt splashing at the advancing contact line but inhibits the formation of the thin sheet. If the air pressure is lowered, droplet ejection is suppressed not only during thin-sheet formation but for prompt splashing as well. The threshold pressure depends on impact velocity, liquid viscosity and surface roughness

Long-distance vocalizations of spotted hyenas contain individual, but not group, signatures

In animal societies, identity signals are common, mediate interactions within groups, and allow individuals to discriminate group-mates from out-group competitors. However, individual recognition becomes increasingly challenging as group size increases and as signals must be transmitted over greater distances. Group vocal signatures may evolve when successful in-group/out-group distinctions are at the crux of fitness-relevant decisions, but group signatures alone are insufficient when differentiated within-group relationships are important for decision-making. Spotted hyenas are social carnivores that live in stable clans of less than 125 individuals composed of multiple unrelated matrilines. Clan members cooperate to defend resources and communal territories from neighbouring clans and other mega carnivores; this collective defence is mediated by long-range (up to 5 km range) recruitment vocalizations, called whoops. Here, we use machine learning to determine that spotted hyena whoops contain individual but not group signatures, and that fundamental frequency features which propagate well are critical for individual discrimination. For effective clan-level cooperation, hyenas face the cognitive challenge of remembering and recognizing individual voices at long range. We show that serial redundancy in whoop bouts increases individual classification accuracy and thus extended call bouts used by hyenas probably evolved to overcome the challenges of communicating individual identity at long distance

Disentangling influence over group speed and direction reveals multiple patterns of influence in moving meerkat groups

Animals that travel together in groups must constantly come to consensus about both the direction and speed of movement, often simultaneously. Contributions to collective decisions may vary among group members, yet inferring who has influence over group decisions is challenging, largely due to the multifaceted nature of influence. Here we collected high-resolution GPS data from five habituated meerkat groups in their natural habitat during foraging and developed a method to quantify individual influence over both group direction and speed. We find that individual influence over direction and speed are correlated, but also exhibit substantial variation. Comparing patterns across social statuses reveals that dominant females have higher influence than other individuals over both group direction and speed. Individuals with high influence also tend to spend more time in the front of the group. We discuss our results in light of meerkat life-history and current literature on influence during group movement. Our method provides a general approach which can be applied to disentangle individual influence over group direction and speed in a wide range of species with cohesive movement, emphasizing the importance of integrating multiple lines of inquiry when inferring influence in moving animal groups

Link updating strategies influence consensus decisions as a function of the direction of communication

Consensus decision-making in social groups strongly depends on communication links that determine to whom individuals send, and from whom they receive, information. Here, we ask how consensus decisions are affected by strategic updating of links and how this effect varies with the direction of communication. We quantified the co-evolution of link and opinion dynamics in a large population with binary opinions using mean-field numerical simulations of two voter-like models of opinion dynamics: an Incoming model (where individuals choose who to receive opinions from) and an Outgoing model (where individuals choose who to send opinions to). We show that individuals can bias group-level outcomes in their favor by breaking disagreeing links while receiving opinions (Incoming Model) and retaining disagreeing links while sending opinions (Outgoing Model). Importantly, these biases can help the population avoid stalemates and achieve consensus. However, the role of disagreement avoidance is diluted in the presence of strong preferences - highly stubborn individuals can shape decisions to favor their preferences, giving rise to non-consensus outcomes. We conclude that collectively changing communication structures can bias consensus decisions, as a function of the strength of preferences and the direction of communication

Signalling in groups: New tools for the integration of animal communication and collective movement

Investigations of collective movement and animal communication have often followed distinct, though complementary, trajectories. Both subfields are deeply concerned with how information flows between individuals and shapes subsequent behaviour. Collective movement has largely focused on the dynamics of passive, cue-mediated group coordination, while animal communication has primarily examined the content and function of active dyadic signal exchanges in sender–receiver frameworks. However, in many social groups, network-wide signalling and collective movement decisions are tightly linked. Here we discuss opportunities afforded by using multi-sensor tracking tags to simultaneously monitor the fine-scale movements and vocalisations of entire social groups. We highlight how such data can elucidate the role of vocal signals in individual and collective movement while illuminating the structures of entire vocal-interaction sequences at previously unexamined timescales and across entire communication networks. We identify practical and analytical challenges associated with these new tools and datasets, and present avenues for addressing them. We specifically address issues associated with the deployment and synchronisation of multiple tags, the processing and interpretation of resulting multidimensional datasets, and the benefits of combining tag-based data collection with experimental approaches. Finally, we argue that a comparative approach employing consistent methodologies across a range of environments, populations and systems is needed to shed light on the evolutionary ecology of communication and collective behaviour

A practical guide for generating unsupervised, spectrogram-based latent space representations of animal vocalizations

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Thomas, M., Jensen, F. H., Averly, B., Demartsev, V., Manser, M. B., Sainburg, T., Roch, M. A., & Strandburg-Peshkin, A. A practical guide for generating unsupervised, spectrogram-based latent space representations of animal vocalizations. The Journal of Animal Ecology, 91(8), (2022): 1567– 1581, https://doi.org/10.1111/1365-2656.13754.1. Background: The manual detection, analysis and classification of animal vocalizations in acoustic recordings is laborious and requires expert knowledge. Hence, there is a need for objective, generalizable methods that detect underlying patterns in these data, categorize sounds into distinct groups and quantify similarities between them. Among all computational methods that have been proposed to accomplish this, neighbourhood-based dimensionality reduction of spectrograms to produce a latent space representation of calls stands out for its conceptual simplicity and effectiveness. 2. Goal of the study/what was done: Using a dataset of manually annotated meerkat Suricata suricatta vocalizations, we demonstrate how this method can be used to obtain meaningful latent space representations that reflect the established taxonomy of call types. We analyse strengths and weaknesses of the proposed approach, give recommendations for its usage and show application examples, such as the classification of ambiguous calls and the detection of mislabelled calls. 3. What this means: All analyses are accompanied by example code to help researchers realize the potential of this method for the study of animal vocalizations.This work was supported by HFSP Research Grant RGP0051/2019 to ASP, MBM and MAR, and funded by the Deutsche Forschungsgemeinschaft (DFG) under Germany's Excellence Strategy (EXC-2117-422037984). ASP received additional funding from the Gips-Schüle Stiftung, the Zukunftskolleg at the University of Konstanz and the Max-Planck-Institute of Animal Behaviour. VD was funded by the Minerva Stiftung and Alexander von Humboldt Foundation

Signalling in groups : new tools for the integration of animal communication and collective movement

Investigations of collective movement and animal communication have often followed distinct, though complementary, trajectories. Both subfields are deeply concerned with how information flows between individuals and shapes subsequent behaviour. Collective movement has largely focused on the dynamics of passive, cue-mediated group coordination, while animal communication has primarily examined the content and function of active dyadic signal exchanges in sender–receiver frameworks. However, in many social groups, network-wide signalling and collective movement decisions are tightly linked. Here we discuss opportunities afforded by using multi-sensor tracking tags to simultaneously monitor the fine-scale movements and vocalisations of entire social groups. We highlight how such data can elucidate the role of vocal signals in individual and collective movement while illuminating the structures of entire vocal-interaction sequences at previously unexamined timescales and across entire communication networks. We identify practical and analytical challenges associated with these new tools and datasets, and present avenues for addressing them. We specifically address issues associated with the deployment and synchronisation of multiple tags, the processing and interpretation of resulting multidimensional datasets, and the benefits of combining tag-based data collection with experimental approaches. Finally, we argue that a comparative approach employing consistent methodologies across a range of environments, populations and systems is needed to shed light on the evolutionary ecology of communication and collective behaviour.Alexander von Humboldt-Stiftung; Centre for the Advanced Study of Collective Behaviour; Human Frontier Science Program; Minerva Foundation; Gips-Schüle Foundation. Open Access funding enabled and organized by Projekt DEAL.http://www.wileyonlinelibrary.com/journal/mee3hj2022Mammal Research Institut

From fish schools to primate societies: The dynamics of collective movement in animal groups

Animals that live in groups face a dual challenge of effectively exploiting their environment while at the same time maintaining cohesion with other group members. Maintaining cohesion requires group members to come to consensus about when and where to move, despite the fact that they may not always agree. In this thesis, I investigate how individuals in groups make movement decisions, and how these individual decisions scale up to group-level properties. Using a laboratory experiment with golden shiners (Notemigonus crysoleucas), I first investigate the interaction network over which information spreads, finding that decisions are better predicted by whom individuals can see rather than whom they are close to, with potential consequences for the global spread of information (Chapter 2). I then investigate collective movement behavior in the wild using high-resolution GPS data from members of a troop of olive baboons (Papio anubis). I first show that baboons are consistent in the spatial positions they occupy within the group, and that the observed patterns may be understood based on a very simple mechanism by which individuals maintain cohesion with different numbers of their neighbors (Chapter 3). By quantifying how group members move relative to one another, I then show that baboon movement decisions are consistent with a shared decision-making process, rather than despotic leadership by dominant individuals, and that the patterns of decision-making are consistent with simple models of collective motion (Chapter 4). Finally, by incorporating a fine-scale, three-dimensional reconstruction of the habitat through which the baboons move, I show that habitat structure, in addition to social factors, also exerts an important influence on individual movement decisions, resulting in changes in the emergent structure and movement of the group (Chapter 5). Taken together, these results highlight that by combining high-resolution animal tracking, remote sensing, and analytical methods, we can begin to extend our understanding of collective animal movement from laboratory studies to complex animal societies living in the wild