Stochastic Bifurcation in Single-Species Model Induced by {\alpha}-Stable Levy Noise

TL;DR

This paper investigates stochastic bifurcation phenomena in single-species population models driven by {b5}-stable Levy noise, revealing how parameter variations influence the stationary distributions and induce transitions in their shapes.

Contribution

It introduces the analysis of stochastic P-bifurcation in population models with Levy noise, highlighting the effects of key parameters on stationary probability densities.

Findings

Bifurcation parameters significantly alter the shape of stationary densities.

Increased stability index sharpens the density peaks around equilibrium.

Transitions from unimodal to flat densities indicate stochastic bifurcation occurrences.

Abstract

Bifurcation analysis has many applications in different scientific fields, such as electronics, biology, ecology, and economics. In population biology, deterministic methods of bifurcation are commonly used. In contrast, stochastic bifurcation techniques are infrequently employed. Here we establish stochastic P-bifurcation behavior of (i) a growth model with state-dependent birth rate and constant death rate, and (ii) a logistic growth model with state-dependent carrying capacity, both of which are driven by multiplicative symmetric stable Levy noise. Transcritical bifurcation occurs in the deterministic counterpart of the first model, while saddle-node bifurcation takes place in the logistic growth model. We focus on the impact of the variations of the growth rate, the per capita daily adult mortality rate, the stability index, and the noise intensity on the stationary probability density functions of the associated non-local Fokker-Planck equation. In the first model, the bifurcation parameter is the ratio of the population birth rate to the population death rate. In the second model, the bifurcation parameter corresponds to the sensitivity of carrying capacity to change in the size of the population near equilibrium. In each case, we show that as the value of the bifurcation parameter increases, the shape of the steady-state probability density function changes and that both stochastic models exhibit stochastic P-bifurcation. The unimodal density functions become more peaked around deterministic equilibrium points as the stability index increases. While an increase in any one of the other parameters has an effect on the stationary probability density function. That means the geometry of the density function changes from unimodal to flat, and its peak appears in the middle of the domain, which means a transition occurs.