Ariana Strandburg-Peshkin

Bio: Ariana Strandburg-Peshkin is an academic researcher from Max Planck Society. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 13, co-authored 19 publications receiving 1267 citations. Previous affiliations of Ariana Strandburg-Peshkin include Kalahari Meerkat Project & Swarthmore College.

Shared decision-making drives collective movement in wild baboons

Abstract: 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.

Challenges and solutions for studying collective animal behaviour in the wild

Abstract: 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’.

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

Abstract: 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 also for prompt splashing.

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

Abstract: 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.

기독교 사역과 Leadership

Abstract: Building Leadership Effectiveness This program encourages leaders to develop practices that transform values into action, vision into realities, obstacles into innovations, and risks into rewards. Participants will be introduced to the five practices of exemplary leadership: modeling the way, inspiring a shared vision, challenging the process, enabling others to act, and encouraging the heart Coaching & Communicating for Performance Coaching & Communicating for Performance is a highly interactive program that will give supervisors and managers the opportunity to build skills that will enable them to share expectations and set objectives for employees, provide constructive feedback, more effectively engage in learning conversations, and coaching opportunities. Skillful Conflict Management for Leaders As a leader, it is important to understand conflict and be effective at conflict management because the way conflict is resolved becomes an integral component of our university’s culture. This series of conflict management sessions help leaders learn and put into practice effective strategies for managing conflict.

Drop Impact on a Solid Surface

Abstract: A drop hitting a solid surface can deposit, bounce, or splash. Splashing arises from the breakup of a fine liquid sheet that is ejected radially along the substrate. Bouncing and deposition depend crucially on the wetting properties of the substrate. In this review, we focus on recent experimental and theoretical studies, which aim at unraveling the underlying physics, characterized by the delicate interplay of not only liquid inertia, viscosity, and surface tension, but also the surrounding gas. The gas cushions the initial contact; it is entrapped in a central microbubble on the substrate; and it promotes the so-called corona splash, by lifting the lamella away from the solid. Particular attention is paid to the influence of surface roughness, natural or engineered to enhance repellency, relevant in many applications.

Constructing, conducting and interpreting animal social network analysis

Abstract: Summary Animal social networks are descriptions of social structure which, aside from their intrinsic interest for understanding sociality, can have significant bearing across many fields of biology. Network analysis provides a flexible toolbox for testing a broad range of hypotheses, and for describing the social system of species or populations in a quantitative and comparable manner. However, it requires careful consideration of underlying assumptions, in particular differentiating real from observed networks and controlling for inherent biases that are common in social data. We provide a practical guide for using this framework to analyse animal social systems and test hypotheses. First, we discuss key considerations when defining nodes and edges, and when designing methods for collecting data. We discuss different approaches for inferring social networks from these data and displaying them. We then provide an overview of methods for quantifying properties of nodes and networks, as well as for testing hypotheses concerning network structure and network processes. Finally, we provide information about assessing the power and accuracy of an observed network. Alongside this manuscript, we provide appendices containing background information on common programming routines and worked examples of how to perform network analysis using the r programming language. We conclude by discussing some of the major current challenges in social network analysis and interesting future directions. In particular, we highlight the under-exploited potential of experimental manipulations on social networks to address research questions.

idTracker: tracking individuals in a group by automatic identification of unmarked animals

Abstract: Animals in groups touch each other, move in paths that cross, and interact in complex ways. Current video tracking methods sometimes switch identities of unmarked individuals during these interactions. These errors propagate and result in random assignments after a few minutes unless manually corrected. We present idTracker, a multitracking algorithm that extracts a characteristic fingerprint from each animal in a video recording of a group. It then uses these fingerprints to identify every individual throughout the video. Tracking by identification prevents propagation of errors, and the correct identities can be maintained indefinitely. idTracker distinguishes animals even when humans cannot, such as for size-matched siblings, and reidentifies animals after they temporarily disappear from view or across different videos. It is robust, easy to use and general. We tested it on fish (Danio rerio and Oryzias latipes), flies (Drosophila melanogaster), ants (Messor structor) and mice (Mus musculus).

Automated image-based tracking and its application in ecology

Abstract: The behavior of individuals determines the strength and outcome of ecological interactions, which drive population, community, and ecosystem organization. Bio-logging, such as telemetry and animal-borne imaging, provides essential individual viewpoints, tracks, and life histories, but requires capture of individuals and is often impractical to scale. Recent developments in automated image-based tracking offers opportunities to remotely quantify and understand individual behavior at scales and resolutions not previously possible, providing an essential supplement to other tracking methodologies in ecology. Automated image-based tracking should continue to advance the field of ecology by enabling better understanding of the linkages between individual and higher-level ecological processes, via high-throughput quantitative analysis of complex ecological patterns and processes across scales, including analysis of environmental drivers.