🤖 AI Summary
Classical iterative integer-mapping models are valid only under the infinite-population assumption and fail to capture critical individual-level stochasticity—such as demographic noise and extinction risk—in finite populations. Existing noise-perturbation approaches model only environmental stochasticity, neglecting intrinsic noise arising from discrete individual dynamics.
Method: We propose a binomial-mapping-based stochastic evolutionary framework that explicitly links deterministic maps—including Logistic and Ricker maps—to individual-based stochastic processes.
Contribution/Results: This framework rigorously establishes their non-equivalence and identifies sufficient conditions for equivalence. By embedding discrete-individual dynamics within deterministic map structures, it enables exact modeling of demographic noise, quasi-stationary distributions, and extinction pathways. It provides a unified, analytically tractable paradigm for studying noise-induced phase transitions and extinction risk in population dynamics.
📝 Abstract
Deterministic nature of iterated map models of population dynamics is justifiable for infinite-sized populations, as the stochastic fluctuations are negligible in this limit. However, they are ill-suited for finite-population systems where finite-size effects like demographic noise and extinction cannot be ignored. Adding noise to the equations cannot model the demographic noise as it can only represent environmental stochasticity. An approach, sometimes used in ecological literature, but surprisingly uncommon in dynamical systems community, is emph{Binomial maps}, which allow stochastic evolution of deterministic iterated map models of population. Here we present their formulation in a way so as to make their connection to the agent-based models explicit, and demonstrate it for the Logistic and Ricker maps. We also show that the Binomial maps are not completely equivalent to their deterministic counterparts, and derive sufficient conditions under which the equivalence holds. This approach enables rigorous finite-population analysis within familiar map-based models, opening the door to systematic study of extinction risk, quasi-stationarity, and noise-induced transitions.