106 research outputs found
Dynamical modeling of collective behavior from pigeon flight data: flock cohesion and dispersion
Several models of flocking have been promoted based on simulations with
qualitatively naturalistic behavior. In this paper we provide the first direct
application of computational modeling methods to infer flocking behavior from
experimental field data. We show that this approach is able to infer general
rules for interaction, or lack of interaction, among members of a flock or,
more generally, any community. Using experimental field measurements of homing
pigeons in flight we demonstrate the existence of a basic distance dependent
attraction/repulsion relationship and show that this rule is sufficient to
explain collective behavior observed in nature. Positional data of individuals
over time are used as input data to a computational algorithm capable of
building complex nonlinear functions that can represent the system behavior.
Topological nearest neighbor interactions are considered to characterize the
components within this model. The efficacy of this method is demonstrated with
simulated noisy data generated from the classical (two dimensional) Vicsek
model. When applied to experimental data from homing pigeon flights we show
that the more complex three dimensional models are capable of predicting and
simulating trajectories, as well as exhibiting realistic collective dynamics.
The simulations of the reconstructed models are used to extract properties of
the collective behavior in pigeons, and how it is affected by changing the
initial conditions of the system. Our results demonstrate that this approach
may be applied to construct models capable of simulating trajectories and
collective dynamics using experimental field measurements of herd movement.
From these models, the behavior of the individual agents (animals) may be
inferred
Effects of anisotropic interactions on the structure of animal groups
This paper proposes an agent-based model which reproduces different
structures of animal groups. The shape and structure of the group is the effect
of simple interaction rules among individuals: each animal deploys itself
depending on the position of a limited number of close group mates. The
proposed model is shown to produce clustered formations, as well as lines and
V-like formations. The key factors which trigger the onset of different
patterns are argued to be the relative strength of attraction and repulsion
forces and, most important, the anisotropy in their application.Comment: 22 pages, 9 figures. Submitted. v1-v4: revised presentation; extended
simulations; included technical results. v5: added a few clarification
On the duality between interaction responses and mutual positions in flocking and schooling.
Recent research in animal behaviour has contributed to determine how alignment, turning responses, and changes of speed mediate flocking and schooling interactions in different animal species. Here, we propose a complementary approach to the analysis of flocking phenomena, based on the idea that animals occupy preferential, anysotropic positions with respect to their neighbours, and devote a large amount of their interaction responses to maintaining their mutual positions. We test our approach by deriving the apparent alignment and attraction responses from simulated trajectories of animals moving side by side, or one in front of the other. We show that the anisotropic positioning of individuals, in combination with noise, is sufficient to reproduce several aspects of the movement responses observed in real animal groups. This anisotropy at the level of interactions should be considered explicitly in future models of flocking and schooling. By making a distinction between interaction responses involved in maintaining a preferred flock configuration, and interaction responses directed at changing it, our work provides a frame to discriminate movement interactions that signal directional conflict from interactions underlying consensual group motion
Quantifying the interplay between environmental and social effects on aggregated-fish dynamics
Demonstrating and quantifying the respective roles of social interactions and
external stimuli governing fish dynamics is key to understanding fish spatial
distribution. If seminal studies have contributed to our understanding of fish
spatial organization in schools, little experimental information is available
on fish in their natural environment, where aggregations often occur in the
presence of spatial heterogeneities. Here, we applied novel modeling approaches
coupled to accurate acoustic tracking for studying the dynamics of a group of
gregarious fish in a heterogeneous environment. To this purpose, we
acoustically tracked with submeter resolution the positions of twelve small
pelagic fish (Selar crumenophthalmus) in the presence of an anchored floating
object, constituting a point of attraction for several fish species. We
constructed a field-based model for aggregated-fish dynamics, deriving
effective interactions for both social and external stimuli from experiments.
We tuned the model parameters that best fit the experimental data and
quantified the importance of social interactions in the aggregation, providing
an explanation for the spatial structure of fish aggregations found around
floating objects. Our results can be generalized to other gregarious species
and contexts as long as it is possible to observe the fine-scale movements of a
subset of individuals.Comment: 10 pages, 5 figures and 4 supplementary figure
Active Brownian Particles. From Individual to Collective Stochastic Dynamics
We review theoretical models of individual motility as well as collective
dynamics and pattern formation of active particles. We focus on simple models
of active dynamics with a particular emphasis on nonlinear and stochastic
dynamics of such self-propelled entities in the framework of statistical
mechanics. Examples of such active units in complex physico-chemical and
biological systems are chemically powered nano-rods, localized patterns in
reaction-diffusion system, motile cells or macroscopic animals. Based on the
description of individual motion of point-like active particles by stochastic
differential equations, we discuss different velocity-dependent friction
functions, the impact of various types of fluctuations and calculate
characteristic observables such as stationary velocity distributions or
diffusion coefficients. Finally, we consider not only the free and confined
individual active dynamics but also different types of interaction between
active particles. The resulting collective dynamical behavior of large
assemblies and aggregates of active units is discussed and an overview over
some recent results on spatiotemporal pattern formation in such systems is
given.Comment: 161 pages, Review, Eur Phys J Special-Topics, accepte
Bayesian Inference for Identifying Interaction Rules in Moving Animal Groups
The emergence of similar collective patterns from different self-propelled particle models of animal groups points to a restricted set of âuniversalâ classes for these patterns. While universality is interesting, it is often the fine details of animal interactions that are of biological importance. Universality thus presents a challenge to inferring such interactions from macroscopic group dynamics since these can be consistent with many underlying interaction models. We present a Bayesian framework for learning animal interaction rules from fine scale recordings of animal movements in swarms. We apply these techniques to the inverse problem of inferring interaction rules from simulation models, showing that parameters can often be inferred from a small number of observations. Our methodology allows us to quantify our confidence in parameter fitting. For example, we show that attraction and alignment terms can be reliably estimated when animals are milling in a torus shape, while interaction radius cannot be reliably measured in such a situation. We assess the importance of rate of data collection and show how to test different models, such as topological and metric neighbourhood models. Taken together our results both inform the design of experiments on animal interactions and suggest how these data should be best analysed
Trail formation based on directed pheromone deposition
We propose an Individual-Based Model of ant-trail formation. The ants are
modeled as self-propelled particles which deposit directed pheromones and
interact with them through alignment interaction. The directed pheromones
intend to model pieces of trails, while the alignment interaction translates
the tendency for an ant to follow a trail when it meets it. Thanks to adequate
quantitative descriptors of the trail patterns, the existence of a phase
transition as the ant-pheromone interaction frequency is increased can be
evidenced. Finally, we propose both kinetic and fluid descriptions of this
model and analyze the capabilities of the fluid model to develop trail
patterns. We observe that the development of patterns by fluid models require
extra trail amplification mechanisms that are not needed at the
Individual-Based Model level
Fluctuation-Driven Flocking Movement in Three Dimensions and Scale-Free Correlation
Recent advances in the study of flocking behavior have permitted more sophisticated analyses than previously possible. The concepts of âtopological distancesâ and âscale-free correlationsâ are important developments that have contributed to this improvement. These concepts require us to reconsider the notion of a neighborhood when applied to theoretical models. Previous work has assumed that individuals interact with neighbors within a certain radius (called the âmetric distanceâ). However, other work has shown that, assuming topological interactions, starlings interact on average with the six or seven nearest neighbors within a flock. Accounting for this observation, we previously proposed a metric-topological interaction model in two dimensions. The goal of our model was to unite these two interaction components, the metric distance and the topological distance, into one rule. In our previous study, we demonstrated that the metric-topological interaction model could explain a real bird flocking phenomenon called scale-free correlation, which was first reported by Cavagna et al. In this study, we extended our model to three dimensions while also accounting for variations in speed. This three-dimensional metric-topological interaction model displayed scale-free correlation for velocity and orientation. Finally, we introduced an additional new feature of the model, namely, that a flock can store and release its fluctuations
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