268 research outputs found

    Robust permanence for interacting structured populations

    Get PDF
    The dynamics of interacting structured populations can be modeled by dxidt=Ai(x)xi\frac{dx_i}{dt}= A_i (x)x_i where xiRnix_i\in \R^{n_i}, x=(x1,,xk)x=(x_1,\dots,x_k), and Ai(x)A_i(x) are matrices with non-negative off-diagonal entries. These models are permanent if there exists a positive global attractor and are robustly permanent if they remain permanent following perturbations of Ai(x)A_i(x). Necessary and sufficient conditions for robust permanence are derived using dominant Lyapunov exponents λi(μ)\lambda_i(\mu) of the Ai(x)A_i(x) with respect to invariant measures μ\mu. The necessary condition requires maxiλi(μ)>0\max_i \lambda_i(\mu)>0 for all ergodic measures with support in the boundary of the non-negative cone. The sufficient condition requires that the boundary admits a Morse decomposition such that maxiλi(μ)>0\max_i \lambda_i(\mu)>0 for all invariant measures μ\mu supported by a component of the Morse decomposition. When the Morse components are Axiom A, uniquely ergodic, or support all but one population, the necessary and sufficient conditions are equivalent. Applications to spatial ecology, epidemiology, and gene networks are given

    Generalized Urn Models of Evolutionary Processes

    Full text link
    Generalized Polya urn models can describe the dynamics of finite populations of interacting genotypes. Three basic questions these models can address are: Under what conditions does a population exhibit growth? On the event of growth, at what rate does the population increase? What is the long-term behavior of the distribution of genotypes? To address these questions, we associate a mean limit ordinary differential equation (ODE) with the urn model. Previously, it has been shown that on the event of population growth, the limiting distribution of genotypes is a connected internally chain recurrent set for the mean limit ODE. To determine when growth and convergence occurs with positive probability, we prove two results. First, if the mean limit ODE has an ``attainable'' attractor at which growth is expected, then growth and convergence toward this attractor occurs with positive probability. Second, the population distribution almost surely does not converge to sets where growth is not expecte

    Species coexistence in stochastic environments

    Get PDF
    corecore