Total population for a resource-limited single consumer model.

IF 2.3 4区 数学 Q2 BIOLOGY Journal of Mathematical Biology Pub Date : 2025-01-25 DOI:10.1007/s00285-025-02186-0
Xiaoqing He, Wei-Ming Ni, Zihan Ye, Bo Zhang
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Abstract

In the past several decades, much attention has been focused on the effects of dispersal on total populations of species. In Zhang (EL 20:1118-1128, 2017), a rigorous biological experiment was performed to confirm the mathematical conclusion: Dispersal tends to enhance populations under a suitable hypothesis. In addition, mathematical models keeping track of resource dynamics in population growth were also proposed in Zhang (EL 20:1118-1128, 2017) to understand this remarkable phenomenon. In these models, the self-regulated quantity "loss rate" of the population seems, in general, difficult to measure experimentally. Our main goal in this paper is to study the effects of relations between the loss rate and the resources, the role of dispersal, and the impact of their interactions on total populations. We compare the total population for small and large diffusion under various correlations between loss rate and the resources. Biological evidence seems to support some specific correlations between the loss rate and the resources.

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资源有限的单一消费者模型的总人口。
在过去的几十年里,人们一直把注意力集中在物种扩散对总种群的影响上。Zhang (EL 20:1118-1128, 2017)进行了严格的生物学实验,以证实数学结论:在适当的假设下,分散倾向于增强种群。此外,Zhang (EL 20:1118-1128, 2017)还提出了跟踪人口增长中资源动态的数学模型来理解这一显著现象。在这些模型中,总体而言,自我调节的种群数量“损失率”似乎很难通过实验来测量。本文的主要目的是研究种群损失率与资源之间的关系、种群扩散的作用以及它们之间的相互作用对种群总数的影响。我们比较了在损失率和资源之间的各种相关性下,小扩散和大扩散的种群总数。生物学上的证据似乎支持一些特定的相关性在损失率和资源之间。
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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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