Asymptotic properties of the Lotka-Volterra competition and mutualism model under stochastic perturbations.

Leonid Shaikhet, Andrei Korobeinikov
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Abstract

Stochastically perturbed models, where the white noise type stochastic perturbations are proportional to the current system state, the most realistically describe real-life biosystems. However, such models essentially have no equilibrium states apart from one at the origin. This feature makes analysis of such models extremely difficult. Probably, the best result that can be found for such models is finding of accurate estimations of a region in the model phase space that serves as an attractor for model trajectories. In this paper, we consider a classical stochastically perturbed Lotka-Volterra model of competing or symbiotic populations, where the white noise type perturbations are proportional to the current system state. Using the direct Lyapunov method in a combination with a recently developed technique, we establish global asymptotic properties of this model. In order to do this, we, firstly, construct a Lyapunov function that is applicable to the both competing (and globally stable) and symbiotic deterministic Lotka-Volterra models. Then, applying this Lyapunov function to the stochastically perturbed model, we show that solutions with positive initial conditions converge to a certain compact region in the model phase space and oscillate around this region thereafter. The direct Lyapunov method allows to find estimates for this region. We also show that if the magnitude of the noise exceeds a certain critical level, then some or all species extinct via process of the stochastic stabilization ('stabilization by noise'). The approach applied in this paper allows to obtain necessary conditions for the extinction. Sufficient conditions for the extinction (that for this model occurs via the process that is known as the 'stochastic stabilization', or the 'stabilization by noise') are found applying the Khasminskii-type Lyapunov functions.

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随机扰动下 Lotka-Volterra 竞争与互惠模型的渐近特性
随机扰动模型,即白噪声类型的随机扰动与当前系统状态成正比,最真实地描述了现实生活中的生物系统。然而,除了原点的平衡状态外,这类模型基本上没有其他平衡状态。这一特点使得对这类模型的分析极为困难。对于这类模型来说,最好的结果可能就是找到模型相空间中作为模型轨迹吸引子的精确估计区域。在本文中,我们考虑了竞争或共生种群的经典随机扰动 Lotka-Volterra 模型,其中白噪声类型的扰动与当前系统状态成正比。我们利用直接李亚普诺夫方法,结合最近开发的一种技术,建立了该模型的全局渐近特性。为此,我们首先构建了一个适用于竞争(和全局稳定)和共生确定性 Lotka-Volterra 模型的 Lyapunov 函数。然后,我们将这个 Lyapunov 函数应用于随机扰动模型,结果表明,具有正初始条件的解会收敛到模型相空间中的某个紧凑区域,并在该区域附近振荡。直接利用 Lyapunov 方法可以对该区域进行估计。我们还证明,如果噪声的大小超过某个临界水平,那么通过随机稳定过程("噪声稳定"),部分或全部物种就会灭绝。本文采用的方法可以获得物种灭绝的必要条件。利用哈斯明斯基型李亚普诺夫函数,可以找到物种灭绝的充分条件(对该模型而言,物种灭绝是通过 "随机稳定 "或 "噪声稳定 "过程发生的)。
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