Dang H. Nguyen

TL;DR: This work extends results on two dimensional Lotka-Volterra models, two dimensional predator-prey models, $n$ dimensional simple food chains, and two predator and one prey models, and shows how one can use the methods to classify the dynamics of any two-dimensional stochastic Kolmogorov system satisfying some mild assumptions.

TL;DR: In this article, sufficient conditions for the coexistence and exclusion of a stochastic competitive Lotka-Volterra model are derived, and convergence in distribution of positive solutions of the model is also established.

TL;DR: It is proved that the transition probabilities converge in total variation norm to the invariant measure and it is shown that the rate is not too far from exponential in that the convergence speed is of the form of a polynomial of any degree.

TL;DR: In this article, the authors studied the dynamics of two-dimensional stochastic populations with white noise and gave sharp conditions under which the populations converge exponentially fast to their unique stationary distribution and under which some populations go extinct exponentially fast.

Abstract: We study the persistence and extinction of species in a simple food chain that is modelled by a Lotka–Volterra system with environmental stochasticity. There exist sharp results for deterministic Lotka–Volterra systems in the literature but few for their stochastic counterparts. The food chain we analyze consists of one prey and $$n-1$$ predators. The jth predator eats the $$j-1$$ th species and is eaten by the $$j+1$$ th predator; this way each species only interacts with at most two other species—the ones that are immediately above or below it in the trophic chain. We show that one can classify, based on an explicit quantity depending on the interaction coefficients of the system, which species go extinct and which converge to their unique invariant probability measure. Our work can be seen as a natural extension of the deterministic results of Gard and Hallam ’79 to a stochastic setting. As one consequence we show that environmental stochasticity makes species more likely to go extinct. However, if the environmental fluctuations are small, persistence in the deterministic setting is preserved in the stochastic system. Our analysis also shows that the addition of a new apex predator makes, as expected, the different species more prone to extinction. Another novelty of our analysis is the fact that we can describe the behavior of the system when the noise is degenerate. This is relevant because of the possibility of strong correlations between the effects of the environment on the different species.