Dynamic balance and reliability of a stochastic ecosystem with Markov switching

Abstract A stochastic predator–prey system with Markov switching is explored. We have developed a new chasing technique to efficiently solve the Fokker–Planck–Kolmogorov and backward Kolmogorov equations. Dynamic balance and reliability of the switching system are evaluated via stationary probability density function and first-passage failure theory, taking into account factors such as switching frequencies, noise intensities, and initial conditions. Results reveal that Markov switching leads to stochastic P-bifurcation, enhancing dynamic balance and reducing white-noise-induced oscillations. But frequent switching can heighten initial value dependence, harming reliability. Further, the influence of the subsystem on the switching system is not proportional to its action probabilities. Monte Carlo simulations validate the findings, offering an in-depth exploration of these dynamics.