Walk this way: modeling foraging ant dynamics in multiple food source environments

IF 2.2 4区 数学 Q2 BIOLOGY
Sean Hartman, Shawn D. Ryan, Bhargav R. Karamched
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引用次数: 0

Abstract

Foraging for resources is an essential process for the daily life of an ant colony. What makes this process so fascinating is the self-organization of ants into trails using chemical pheromone in the absence of direct communication. Here we present a stochastic lattice model that captures essential features of foraging ant dynamics inspired by recent agent-based models while forgoing more detailed interactions that may not be essential to trail formation. Nevertheless, our model’s results coincide with those presented in more sophisticated theoretical models and experiments. Furthermore, it captures the phenomenon of multiple trail formation in environments with multiple food sources. This latter phenomenon is not described well by other more detailed models. We complement the stochastic lattice model by describing a macroscopic PDE which captures the basic structure of lattice model. The PDE provides a continuum framework for the first-principle interactions described in the stochastic lattice model and is amenable to analysis. Linear stability analysis of this PDE facilitates a computational study of the impact various parameters impart on trail formation. We also highlight universal features of the modeling framework that may allow this simple formation to be used to study complex systems beyond ants.

Abstract Image

走这条路:多食物源环境中的觅食蚂蚁动态建模
觅食是蚁群日常生活中必不可少的过程。这一过程的迷人之处在于,在没有直接交流的情况下,蚂蚁利用化学信息素自行组织成蚁巢轨迹。在这里,我们提出了一个随机晶格模型,该模型捕捉到了蚂蚁觅食动力学的基本特征,其灵感来自于最新的基于代理的模型,同时放弃了更多细节上的相互作用,而这些相互作用对于踪迹的形成可能并不重要。尽管如此,我们的模型结果与更复杂的理论模型和实验结果不谋而合。此外,它还捕捉到了在有多个食物源的环境中形成多条足迹的现象。其他更详细的模型并不能很好地描述后一种现象。我们通过描述一个宏观的 PDE 来补充随机晶格模型,它捕捉到了晶格模型的基本结构。该 PDE 为随机晶格模型中描述的第一原理相互作用提供了一个连续框架,并且易于分析。对该 PDE 的线性稳定性分析有助于计算研究各种参数对轨迹形成的影响。我们还强调了建模框架的普遍特征,这些特征可能会使这种简单的形成方法用于研究蚂蚁以外的复杂系统。
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来源期刊
CiteScore
3.30
自引率
5.30%
发文量
120
审稿时长
6 months
期刊介绍: The Journal of Mathematical Biology focuses on mathematical biology - work that uses mathematical approaches to gain biological understanding or explain biological phenomena. Areas of biology covered include, but are not restricted to, cell biology, physiology, development, neurobiology, genetics and population genetics, population biology, ecology, behavioural biology, evolution, epidemiology, immunology, molecular biology, biofluids, DNA and protein structure and function. All mathematical approaches including computational and visualization approaches are appropriate.
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