Self-organized spatial structures of locust groups emerging from local interaction

Berger, Uta; Hassani, Lalla Mina Idrissi; Ghaout, Saïd; Peters, Ronny; Piou, Cyril

doi:10.1016/j.ecolmodel.2017.07.020

Cited by 12 publications

(19 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Prior works, both for the Australian plague locust and for other locust species, investigate a variety of social mechanisms for collective movement in hopper bands. In two agent-based modeling studies [23,27], pulses are among a handful of aggregate band structures obtained by varying the parameters that model individual locust behavior. A continuum approach in [30] finds traveling pulses in a PDE similar to our Eq (9) but without accounting for resources.…”

Section: Discussionmentioning

confidence: 99%

“…Previous modeling efforts have considered both agent-based and continuous models, see [1] for an excellent overview of locust models. The majority of these have focused on social behavior-notably alignment, attraction, and repulsion with respect to conspecifics [18,[22][23][24][25][26][27]. Many of the agent-based models consider the pause-and-go behavior of locusts [18,26,27], and other insects [28].…”

Section: Plos Computational Biologymentioning

confidence: 99%

“…We are aware of no models of locust band movement that incorporate foraging behavior or food resources. Previous studies such as [23,27] suggest that the formation of sharp asymmetric fronts may be explained solely by social forces. By contrast, our main conclusion is that foraging and resource-mediated stationary/moving transitions produce pulse-shaped density profiles, supporting the observations of hopper bands with dense fronts and the inferences on foraging of Clark [4] and Hunter et al [10].…”

Section: Plos Computational Biologymentioning

confidence: 99%

“…We acknowledge that the efficacy of this model may be improved by adding social interactions-such as alignment, attraction, and repulsion. Additionally, we believe these additions, particularly that of alignment, would play a pivotal role when modeling locust behavior in two-dimensions, as in [23,27].…”

Section: Basic Assumptionsmentioning

confidence: 99%

See 3 more Smart Citations

Agent-based and continuous models of hopper bands for the Australian plague locust: How resource consumption mediates pulse formation and geometry

et al. 2020

View full text Add to dashboard Cite

Locusts are significant agricultural pests. Under favorable environmental conditions flightless juveniles may aggregate into coherent, aligned swarms referred to as hopper bands. These bands are often observed as a propagating wave having a dense front with rapidly decreasing density in the wake. A tantalizing and common observation is that these fronts slow and steepen in the presence of green vegetation. This suggests the collective motion of the band is mediated by resource consumption. Our goal is to model and quantify this effect. We focus on the Australian plague locust, for which excellent field and experimental data is available. Exploiting the alignment of locusts in hopper bands, we concentrate solely on the density variation perpendicular to the front. We develop two models in tandem; an agent-based model that tracks the position of individuals and a partial differential equation model that describes locust density. In both these models, locust are either stationary (and feeding) or moving. Resources decrease with feeding. The rate at which locusts transition between moving and stationary (and vice versa) is enhanced (diminished) by resource abundance. This effect proves essential to the formation, shape, and speed of locust hopper bands in our models. From the biological literature we estimate ranges for the ten input parameters of our models. Sobol sensitivity analysis yields insight into how the band's collective characteristics vary with changes in the input parameters. By examining 4.4 million parameter combinations, we identify biologically consistent parameters that reproduce field observations. We thus demonstrate that resource-dependent behavior can explain the density distribution observed in locust hopper bands. This work suggests that feeding behaviors should be an intrinsic part of future modeling efforts.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Plos Computational Biologymentioning

confidence: 99%

Section: Plos Computational Biologymentioning

confidence: 99%

Section: Basic Assumptionsmentioning

confidence: 99%

See 2 more Smart Citations

Agent-based and continuous models of hopper bands for the Australian plague locust: How resource consumption mediates pulse formation and geometry

et al. 2020

View full text Add to dashboard Cite

show abstract

“…In addition, non-mean-field models have been considered to approximate other ABMs of locust behaviour. For example, the ABMs in [18] describe self-organizing locust behaviours through rules governing locust attraction, repulsion, and alignment during foraging and invasion. By simulating the ABM over many different parameter values, Dkhili et al [18] discovered three distinct population patterns (spot, band and ribbon formations).…”

Section: Introductionmentioning

confidence: 99%

Learning differential equation models from stochastic agent-based model simulations

Nardini

Baker

Simpson

et al. 2021

J. R. Soc. Interface.

View full text Add to dashboard Cite

Agent-based models provide a flexible framework that is frequently used for modelling many biological systems, including cell migration, molecular dynamics, ecology and epidemiology. Analysis of the model dynamics can be challenging due to their inherent stochasticity and heavy computational requirements. Common approaches to the analysis of agent-based models include extensive Monte Carlo simulation of the model or the derivation of coarse-grained differential equation models to predict the expected or averaged output from the agent-based model. Both of these approaches have limitations, however, as extensive computation of complex agent-based models may be infeasible, and coarse-grained differential equation models can fail to accurately describe model dynamics in certain parameter regimes. We propose that methods from the equation learning field provide a promising, novel and unifying approach for agent-based model analysis. Equation learning is a recent field of research from data science that aims to infer differential equation models directly from data. We use this tutorial to review how methods from equation learning can be used to learn differential equation models from agent-based model simulations. We demonstrate that this framework is easy to use, requires few model simulations, and accurately predicts model dynamics in parameter regions where coarse-grained differential equation models fail to do so. We highlight these advantages through several case studies involving two agent-based models that are broadly applicable to biological phenomena: a birth–death–migration model commonly used to explore cell biology experiments and a susceptible–infected–recovered model of infectious disease spread.

show abstract

Upwind flight partially explains the migratory routes of locust swarms

Sorel,

Gay,

Vernier

et al. 2024

Ecological Modelling

View full text Add to dashboard Cite

Self-organized spatial structures of locust groups emerging from local interaction

Cited by 12 publications

References 67 publications

Agent-based and continuous models of hopper bands for the Australian plague locust: How resource consumption mediates pulse formation and geometry

Agent-based and continuous models of hopper bands for the Australian plague locust: How resource consumption mediates pulse formation and geometry

Learning differential equation models from stochastic agent-based model simulations

Upwind flight partially explains the migratory routes of locust swarms

Contact Info

Product

Resources

About