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

Bernoff, Andrew J.; Culshaw-Maurer, Michael; Everett, Rebecca A.; Hohn, Maryann E.; Strickland, Christopher; Weinburd, Jasper

doi:10.1371/journal.pcbi.1007820

Cited by 21 publications

(18 citation statements)

References 45 publications

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“…For simplicity, the Topaz model focused on inter-locust interactions and ignored interactions between locusts and the environment. While there exists some continuum models of locust food interactions to investigate the effect of food on peak locust density [35] or to consider hopper band movement [36], we are not aware of any studies that consider locustlocust and locust-food interactions as well as gregarisation in a continuum setting.…”

Section: Introductionmentioning

confidence: 99%

Modelling locust foraging: How and why food affects group formation

et al. 2021

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Locusts are short horned grasshoppers that exhibit two behaviour types depending on their local population density. These are: solitarious, where they will actively avoid other locusts, and gregarious where they will seek them out. It is in this gregarious state that locusts can form massive and destructive flying swarms or plagues. However, these swarms are usually preceded by the aggregation of juvenile wingless locust nymphs. In this paper we attempt to understand how the distribution of food resources affect the group formation process. We do this by introducing a multi-population partial differential equation model that includes non-local locust interactions, local locust and food interactions, and gregarisation. Our results suggest that, food acts to increase the maximum density of locust groups, lowers the percentage of the population that needs to be gregarious for group formation, and decreases both the required density of locusts and time for group formation around an optimal food width. Finally, by looking at foraging efficiency within the numerical experiments we find that there exists a foraging advantage to being gregarious.

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Section: Introductionmentioning

confidence: 99%

Modelling locust foraging: How and why food affects group formation

et al. 2021

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“…There is a wide literature demonstrating that coarse graining approaches can be used to derive DE models that approximate ABM dynamics [3,9,12,17,35,60,61]. Such DE models are advantageous because they are usually simple to solve (either analytically or numerically), interpretable, and provide insight into how changing ABM parameters can lead to emergent behaviours.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…Previous ecological studies have demonstrated some of the advantages of both extensive simulation and model coarse graining for ABM analysis [16]. For example, Bernoff et al [17] model the foraging behaviour of the Australian plaque locust with a discrete and stochastic ABM. In the model, individual locusts forage and feed on a given resource (representative of food) and, in turn, create a spatial gradient of this resource.…”

Section: Introductionmentioning

confidence: 99%

Learning differential equation models from stochastic agent-based model simulations

Nardini

Baker

Simpson

et al. 2021

J. R. Soc. Interface.

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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.

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“…For simplicity the model focused on inter-locust interactions and ignored interactions between locusts and the environment. While there exists some continuum models of locust food interactions to investigate the effect of food on peak locust density [32] or to consider hopper band movement [33], we are not aware of any studies that consider locust-locust and locust-food interactions as well as gregarisation in a continuum setting. This paper is organised as follows: we begin with the derivation of a PDE model in which locusts only interact with food when they come into direct contact with it.…”

Section: The Non-local Aggregation Equation First Proposed By Mogilnmentioning

confidence: 99%

Modelling locust foraging: How and why food affects hopper band formation

Georgiou¹,

Buhl

Green

et al. 2020

Preprint

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Locust swarms are a major threat to agriculture, affecting every continent except Antarctica and impacting the lives of 1 in 10 people. Locusts are short horned grasshoppers that exhibit two behaviour types depending on their local population density. These are; solitarious, where they will actively avoid other locusts, and gregarious where they will seek them out. It is in this gregarious state that locusts can form massive and destructive flying swarms or plagues. However, these swarms are usually preceded by the formation of hopper bands by the juvenile wingless locust nymphs. It is thus important to understand the hopper band formation process to control locust outbreaks.On longer time-scales, environmental conditions such as rain events synchronize locust lifecycles and can lead to repeated outbreaks. On shorter time-scales, changes in resource distributions at both small and large spatial scales have an effect on locust gregarisation. It is these short time-scale locust-resource relationships and their effect on hopper band formation that are of interest.In this paper we investigate not only the effect of food on both the formation and characteristics of locust hopper bands but also a possible evolutionary explanation for gregarisation in this context. We do this by deriving a multi-population aggregation equation that includes non-local inter-individual interactions and local inter-individual and food interactions. By performing a series of numerical experiments we find that there exists an optimal food width for locust hopper band formation, and by looking at foraging efficiency within the model framework we uncover a possible evolutionary reason for gregarisation.Author summaryLocusts are short horned grass hoppers that live in two diametrically opposed behavioural states. In the first, solitarious, they will actively avoid other locusts, whereas the second, gregarious, they will actively seek them out. It is in this gregarious state that locusts form the recognisable and destructive flying adult swarms. However, prior to swarm formation juvenile flightless locusts will form marching hopper bands and make their way from food source to food source. Predicting where these hopper bands might form is key to controlling locust outbreaks.Research has shown that changes in food distributions can affect the transition from solitarious to gregarious. In this paper we construct a mathematical model of locust-locust and locust-food interactions to investigate how and why isolated food distributions affect hopper band formation. Our findings suggest that there is an optimal food width for hopper band formation and that being gregarious increases a locusts ability to forage when food width decreases.

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

Cited by 21 publications

References 45 publications

Modelling locust foraging: How and why food affects group formation

Modelling locust foraging: How and why food affects group formation

Learning differential equation models from stochastic agent-based model simulations

Modelling locust foraging: How and why food affects hopper band formation

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