Stochastic population growth in spatially heterogeneous environments: The density-dependent case
Alexandru Hening, Dang H. Nguyen, George Yin

TL;DR
This paper investigates the long-term dynamics of structured populations in heterogeneous environments using stochastic differential equations, establishing conditions for persistence or extinction based on the stochastic growth rate.
Contribution
It extends existing models by incorporating general density-dependent growth, degeneracy in environmental noise, and proves the robustness of persistence under perturbations.
Findings
Positive growth rate leads to population persistence with a unique invariant measure.
Negative growth rate causes populations to go extinct exponentially fast.
In the two-patch case, increased dispersal decreases the stochastic growth rate.
Abstract
This work is devoted to studying the dynamics of a structured population that is subject to the combined effects of environmental stochasticity, competition for resources, spatio-temporal heterogeneity and dispersal. The population is spread throughout patches whose population abundances are modelled as the solutions of a system of nonlinear stochastic differential equations living on . We prove that , the stochastic growth rate of the total population in the absence of competition, determines the long-term behaviour of the population. The parameter can be expressed as the Lyapunov exponent of an associated linearized system of stochastic differential equations. Detailed analysis shows that if , the population abundances converge polynomially fast to a unique invariant probability measure on , while when , the population abundances of…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsEcosystem dynamics and resilience · Mathematical and Theoretical Epidemiology and Ecology Models · Stochastic processes and statistical mechanics
