Collective behavior and predation success in a predator-prey model inspired by hunting bats

Lin, Yanping; Abaid, Nicole

doi:10.1103/physreve.88.062724

Cited by 17 publications

(18 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These rules are realized in models by building either discrete decision-making [23,29,30] or potential functions [31]. Agent-based modeling is also applied to study multi-agent systems in other disciplines, such as population dynamics [32,33], predation-prey interactions [34,35,36], cell chain migration [37], and disease or parasite transmission [38,39].…”

Section: Introductionmentioning

confidence: 99%

Modeling perspectives on echolocation strategies inspired by bats flying in groups

Lin

Abaid

2015

Journal of Theoretical Biology

Self Cite

View full text Add to dashboard Cite

Bats navigating with echolocation - which is a type of active sensing achieved by interpreting echoes resulting from self-generated ultrasonic pulses - exhibit unique behaviors during group flight. While bats may benefit from eavesdropping on their peers׳ echolocation, they also potentially suffer from confusion between their own and peers׳ pulses, caused by an effect called frequency jamming. This hardship of group flight is supported by experimental observations of bats simplifying their sound-scape by shifting their pulse frequencies or suppressing echolocation altogether. Here, we investigate eavesdropping and varying pulse emission rate from a modeling perspective to understand these behaviors׳ potential benefits and detriments. We define an agent-based model of echolocating bats avoiding collisions in a three-dimensional tunnel. Through simulation, we show that bats with reasonably accurate eavesdropping can reduce collisions compared to those neglecting information from peers. In large populations, bats minimize frequency jamming by decreasing pulse emission rate, while collision risk increases; conversely, increasing pulse emission rate minimizes collisions by allowing more sensing information generated per bat. These strategies offer benefits for both biological and engineered systems, since frequency jamming is a concern in systems using active sensing.

show abstract

Section: Introductionmentioning

confidence: 99%

Modeling perspectives on echolocation strategies inspired by bats flying in groups

Lin

Abaid

2015

Journal of Theoretical Biology

Self Cite

View full text Add to dashboard Cite

show abstract

“…This results relates to wall-following behavior we discuss for the Vicsek Model set in a disk and square domain (Chapter 3). Handegard et al (2012) and Lin and Abaid (2013) extend the analysis of swarms to include predator-prey relationship between a schooling fish prey and their predators.…”

Section: Datamentioning

confidence: 99%

“…However, it is a common experience that most swarms moving in real space show spatial coherence for security reasons as discussed by Handegard et al (2012) and Lin and Abaid (2013) or directly resulting from the organisms' kinetic motions by Katz et al (2011) and Herbert-Read et al (2011). Hence, to overcome the deficiency of the Vicsek Model, Grégoire and Chaté (2004) and Chaté et al (2008) Mogilner et al (2003), the Attraction-Repulsion Model generally converges to a common velocity and the domain of the group is uniformly bounded with a common distance between particles.…”

mentioning

confidence: 99%

Swarming in bounded domains

Armbruster

Motsch

Thatcher

2017

Physica D: Nonlinear Phenomena

View full text Add to dashboard Cite

Swarms of animals, fish, birds, locusts etc. are a common occurrence but their coherence and method of organization poses a major question for mathematics and biology. The Vicsek and the Attraction-Repulsion are two models that have been proposed to explain the emergence of collective motion. A major issue for the Vicsek Model is that its particles are not attracted to each other, leaving the swarm with alignment in velocity but without spatial coherence. Restricting the particles to a bounded domain generates global spatial coherence of swarms while maintaining velocity alignment. While individual particles are specularly reflected at the boundary, the swarm as a whole is not. As a result, new dynamical swarming solutions are found.The Attraction-Repulsion Model set with a long-range attraction and short-range repulsion interaction potential typically stabilizes to a well-studied flock steady state solution.The particles for a flock remain spatially coherent but have no spatial bound and explore all space. A bounded domain with specularly reflecting walls traps the particles within a specific region. A fundamental refraction law for a swarm impacting on a planar boundary is derived. The swarm reflection varies from specular for a swarm dominated by kinetic energy to inelastic for a swarm dominated by potential energy. Inelastic collisions lead to alignment with the wall and to damped pulsating oscillations of the swarm. The fundamental refraction law provides a one-dimensional iterative map that allows for a prediction and analysis of the trajectory of the center of mass of a flock in a channel and a square domain.The extension of the wall collisions to a scattering experiment is conducted by setting two identical flocks to collide. The two particle dynamics is studied analytically and shows

show abstract

“…From fish schools to bird flocks, insect swarms, and bacteria, macroscopically ordered states often emerge from the interactions of nearby individuals (Ballerini et al 2008;Couzin and Krause 2003;DeLellis et al 2014;Kelley and Ouellette 2012;Lin and Abaid 2013;Rusconi et al 2014;Saintillan and Shelley 2012;Tsang and Kanso 2014). Such states can be quite resilient to fluctuations (Ballerini et al 2008;Heylighen 2001); for example, an initial maneuver by a single bird has been shown to lead to wave-like propagation throughout the flock (Potts 1984), without affecting the group cohesion.…”

Section: Introductionmentioning

confidence: 97%

Collective Dynamics in the Vicsek and Vectorial Network Models Beyond Uniform Additive Noise

2015

View full text Add to dashboard Cite

In this work, we analyze the coordination of interacting individuals in two nonlinear dynamical models that are subject to a new form of noise. Specifically, we propose extensions both to the classical Vicsek model, whereby each individual averages the orientation of its geographically proximal neighbors, and to the vectorial network model, in which the selection of neighbors is random and independent of the group geometric configuration. In the traditional forms of these models, the update rule for the individuals' orientations is affected by additive uniform noise. Motivated by biological groups in which individuals' turn rates exhibit sporadic and large changes, we extend the uniform additive noise model to a turn rate stochastic process. Through comprehensive numerical simulations, we demonstrate the impact of such occasional large deviations (intensity and frequency), along with the role of the neighbors' selection process, on the coordination of the group. In addition, we establish a closed-form expression for the group polarization for the vectorial network model in the vicinity of an ordered state.

show abstract

Collective behavior and predation success in a predator-prey model inspired by hunting bats

Cited by 17 publications

References 59 publications

Modeling perspectives on echolocation strategies inspired by bats flying in groups

Modeling perspectives on echolocation strategies inspired by bats flying in groups

Swarming in bounded domains

Collective Dynamics in the Vicsek and Vectorial Network Models Beyond Uniform Additive Noise

Contact Info

Product

Resources

About