Most Probable Dynamics of the Single-Species with Allee Effect under Jump-diffusion Noise

TL;DR

This paper analyzes the most probable transition pathways in a stochastic single-species model with Allee effect under jump-diffusion noise, revealing how noise influences species survival and stability.

Contribution

It introduces a method to determine the most probable paths and stable states in a stochastic population model with non-Gaussian noise, advancing understanding of biological dynamics.

Findings

Most probable stable equilibrium aligns with stationary density maxima

Paths quickly approach the stable state and then plateau

Non-Gaussian noise shifts the maximum density to the stable equilibrium

Abstract

We investigate the most probable phase portrait (MPPP) of a stochastic single-species model with the Allee effect using the non-local Fokker-Planck equation. This stochastic model is driven by non-Gaussian as well as Gaussian noise, and it has three fixed points. One of them is the unstable state which lies between the two stable equilibria. We focus on the transition pathways from the extinction state to the upper fixed stable state for the transcription factor activator in a single-species model. This helps us to study the biological behavior of species. The most probable path is obtained from the solution of the non-local Fokker-Planck equation corresponding to the population system of the single-species model, and the corresponding maximum possible stable equilibrium state is determined. We also obtain the Onsager-Machlup (OM) function for the stochastic model and solve the corresponding most probable paths. The numerical simulation shows that: (i) When non-Gaussian noise is presented in the system, the maximum of the stationary density function is located at the most probable stable equilibrium state; (ii) If the initial value increases from extinction state to the upper stable state, the most probable trajectory goes to the maximal likely equilibrium state, in our case it lies between 9 and 10; (iii) The most probable paths increase to stable state quickly, then maintain a nearly constant level, and approach to the upper stable equilibrium state as time goes on. These numerical experiment findings accelerate growth for further experimental study, in order to achieve good knowledge about dynamical systems in biology.