有反捕食者的部分捕食者-捕食者模型中猎物避难所和恐惧程度的作用

IF 3.1 3区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Journal of Computational Science Pub Date : 2024-07-09 DOI:10.1016/j.jocs.2024.102385
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引用次数: 0

摘要

通过考虑各种功能反应和生态效应,生态模型是研究捕食者和猎物物种之间相互作用的有效工具。利用计算或数学模型,我们可以确定特定人类干预或动物行为对这些物种进化的影响。猎物因捕食者的存在而产生的恐惧感对它们之间的互动动态起着至关重要的作用。该手稿重点开发和研究了一种新型猎物-捕食者模型,该模型考虑了捕食恐惧、猎物避难和反捕食者效应。卡普托分数导数被用于模型的构建和分析,该模型结合了记忆效应等生态学原理,以提高我们对物种关系的理解。本研究对所提模型的稳定性、拟合度和解的唯一性等方面进行了研究。我们进行了大量的数值模拟来支持理论结果。我们还提供了该模型的图形结果,其中包括广泛的分数阶值。我们还讨论了恐惧水平、猎物增长率、饱和率和猎物拒绝对求解行为的影响。
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Role of prey refuge and fear level in fractional prey–predator model with anti-predator

Ecological modeling is an effective tool for studying the interactions between predator and prey species by considering various functional responses and ecological effects. Employing computational or mathematical models allows us to determine the impact of specific human interventions or animal behaviors on the evolution of these species. The fear experienced by prey due to the presence of predators plays a crucial role in shaping the dynamics of their interactions. The manuscript focuses on developing and examining a novel prey–predator model that considers predation fear, prey refuge, and anti-predator effects. Caputo fractional derivative is utilized in the construction and analysis of the model, which integrates ecological principles like memory effects to improve our comprehension of species relationship. The study investigates aspects such as stability, well-posedness, and solution uniqueness for the proposed model. We carried out extensive numerical simulation to support theoretical results. Graphical results are provided for the model encompassing a broad spectrum of fractional order values. The effect of the fear level, growth rate of prey, saturation rate and prey refuse on the behavior of the solution are discussed.

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来源期刊
Journal of Computational Science
Journal of Computational Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
5.50
自引率
3.00%
发文量
227
审稿时长
41 days
期刊介绍: Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory. The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation. This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods. Computational science typically unifies three distinct elements: • Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous); • Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems; • Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).
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