Population size in stochastic discrete-time ecological dynamics.

IF 2.3 4区 数学 Q2 BIOLOGY
Alexandru Hening, Siddharth Sabharwal
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引用次数: 0

Abstract

We study how environmental stochasticity influences the long-term population size in certain one- and two-species models. The difficulty is that even when one can prove that there is coexistence, it is usually impossible to say anything about the invariant probability measure which describes the coexisting species. We are able to circumvent this problem for some important ecological models by noticing that the per-capita growth rates at stationarity are zero, something which can sometimes yield information about the invariant probability measure. For more complicated models we use a recent result by Cuello to explore how small noise influences the population size. We are able to show that environmental fluctuations can decrease, increase, or leave unchanged the expected population size. The results change according to the dynamical model and, within a fixed model, also according to which parameters (growth rate, carrying capacity, etc) are affected by environmental fluctuations. Moreover, we show that not only do things change if we introduce noise differently in a model, but it also matters what one takes as the deterministic 'no-noise' baseline for comparison.

随机离散时间生态动力学中的种群大小。
我们研究了环境随机性如何在一定的单物种和双物种模型中影响长期种群规模。困难在于,即使人们能够证明存在共存,通常也不可能对描述共存物种的不变概率度量作出任何说明。通过注意到平稳状态下的人均增长率为零,我们能够在一些重要的生态模型中规避这个问题,这有时可以产生关于不变概率度量的信息。对于更复杂的模型,我们使用Cuello最近的结果来探索小噪声如何影响种群大小。我们能够证明,环境波动可以减少、增加或保持不变的预期人口规模。结果根据动态模型而变化,在固定模型内,也根据环境波动对参数(增长率、承载能力等)的影响而变化。此外,我们表明,如果我们在模型中引入不同的噪声,不仅会发生变化,而且还会影响人们将什么作为确定性的“无噪声”基线进行比较。
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
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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