A framework for modelling desert locust population dynamics and large-scale dispersal.

IF 3.8 2区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

PLoS Computational Biology Pub Date : 2024-12-19 eCollection Date: 2024-12-01 DOI:10.1371/journal.pcbi.1012562

Renata Retkute, William Thurston, Keith Cressman, Christopher A Gilligan

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引用次数: 0

Abstract

There is an urgent need for mathematical models that can be used to inform the deployment of surveillance, early warning and management systems for transboundary pest invasions. This is especially important for desert locust, one of the most dangerous migratory pests for smallholder farmers. During periods of desert locust upsurges and plagues, gregarious adult locusts form into swarms that are capable of long-range dispersal. Here we introduce a novel integrated modelling framework for use in predicting gregarious locust populations. The framework integrates the selection of breeding sites, maturation through egg, hopper and adult stages and swarm dispersal in search of areas suitable for feeding and breeding. Using a combination of concepts from epidemiological modelling, weather and environment data, together with an atmospheric transport model for swarm movement we provide a tool to forecast short- and long-term swarm movements. A principal aim of the framework is to provide a practical starting point for use in the next upsurge.

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模拟沙漠蝗虫种群动态和大规模扩散的框架。

目前迫切需要数学模型，以便为跨界有害生物入侵的监测、预警和管理系统的部署提供信息。这对沙漠蝗尤其重要，沙漠蝗是小农最危险的迁徙害虫之一。在沙漠蝗灾和蝗灾期间，群居的成年蝗虫形成能够远距离传播的蝗群。在这里，我们介绍了一种用于预测群居蝗虫种群的新型综合建模框架。该框架整合了繁殖地点的选择，通过卵，跳跃和成虫阶段的成熟以及寻找适合喂养和繁殖的区域的群体分散。结合流行病学模型、天气和环境数据的概念，以及蜂群运动的大气运输模型，我们提供了一种预测短期和长期蜂群运动的工具。该框架的一个主要目的是为下一次高潮提供一个实际的起点。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY

CiteScore

7.10

自引率

4.70%

发文量

820

审稿时长

2.5 months

期刊介绍： PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.