5,535 research outputs found
Suppressing escape events in maps of the unit interval with demographic noise
We explore the properties of discrete-time stochastic processes with a
bounded state space, whose deterministic limit is given by a map of the unit
interval. We find that, in the mesoscopic description of the system, the large
jumps between successive iterates of the process can result in probability
leaking out of the unit interval, despite the fact that the noise is
multiplicative and vanishes at the boundaries. By including higher-order terms
in the mesoscopic expansion, we are able to capture the non-Gaussian nature of
the noise distribution near the boundaries, but this does not preclude the
possibility of a trajectory leaving the interval. We propose a number of
prescriptions for treating these escape events, and we compare the results with
those obtained for the metastable behavior of the microscopic model, where
escape events are not possible. We find that, rather than truncating the noise
distribution, censoring this distribution to prevent escape events leads to
results which are more consistent with the microscopic model. The addition of
higher moments to the noise distribution does not increase the accuracy of the
final results, and it can be replaced by the simpler Gaussian noise.Comment: 14 pages, 13 figure
Competitive Lotka-Volterra Population Dynamics with Jumps
This paper considers competitive Lotka-Volterra population dynamics with
jumps. The contributions of this paper are as follows. (a) We show stochastic
differential equation (SDE) with jumps associated with the model has a unique
global positive solution; (b) We discuss the uniform boundedness of th
moment with and reveal the sample Lyapunov exponents; (c) Using a
variation-of-constants formula for a class of SDEs with jumps, we provide
explicit solution for 1-dimensional competitive Lotka-Volterra population
dynamics with jumps, and investigate the sample Lyapunov exponent for each
component and the extinction of our -dimensional model.Comment: 25 page
Sufficient and necessary conditions of stochastic permanence and extinction for stochastic logistic populations under regime switching
In this paper, we prove that a stochastic logistic population under regime switching controlled by a Markov chain is either stochastically permanent or extinctive, and we obtain the sufficient and necessary conditions for stochastic permanence and extinction under some assumptions. In the case of stochastic permanence we estimate the limit of the average in time of the sample path of the solution by two constants related to the stationary probability distribution of the Markov chain and the parameters of the subsystems of the population model. Finally, we illustrate our conclusions through two examples
Some stochastic models for structured populations : scaling limits and long time behavior
The first chapter concerns monotype population models. We first study general
birth and death processes and we give non-explosion and extinction criteria,
moment computations and a pathwise representation. We then show how different
scales may lead to different qualitative approximations, either ODEs or SDEs.
The prototypes of these equations are the logistic (deterministic) equation and
the logistic Feller diffusion process. The convergence in law of the sequence
of processes is proved by tightness-uniqueness argument. In these large
population approximations, the competition between individuals leads to
nonlinear drift terms. We then focus on models without interaction but
including exceptional events due either to demographic stochasticity or to
environmental stochasticity. In the first case, an individual may have a large
number of offspring and we introduce the class of continuous state branching
processes. In the second case, catastrophes may occur and kill a random
fraction of the population and the process enjoys a quenched branching
property. We emphasize on the study of the Laplace transform, which allows us
to classify the long time behavior of these processes. In the second chapter,
we model structured populations by measure-valued stochastic differential
equations. Our approach is based on the individual dynamics. The individuals
are characterized by parameters which have an influence on their survival or
reproduction ability. Some of these parameters can be genetic and are
inheritable except when mutations occur, but they can also be a space location
or a quantity of parasites. The individuals compete for resources or other
environmental constraints. We describe the population by a point measure-valued
Markov process. We study macroscopic approximations of this process depending
on the interplay between different scalings and obtain in the limit either
integro-differential equations or reaction-diffusion equations or nonlinear
super-processes. In each case, we insist on the specific techniques for the
proof of convergence and for the study of the limiting model. The limiting
processes offer different models of mutation-selection dynamics. Then, we study
two-level models motivated by cell division dynamics, where the cell population
is discrete and characterized by a trait, which may be continuous. In 1
particular, we finely study a process for parasite infection and the trait is
the parasite load. The latter grows following a Feller diffusion and is
randomly shared in the two daughter cells when the cell divides. Finally, we
focus on the neutral case when the rate of division of cells is constant but
the trait evolves following a general Markov process and may split in a random
number of cells. The long time behavior of the structured population is then
linked and derived from the behavior a well chosen SDE (monotype population)
Influence of stochastic domain growth on pattern nucleation for diffusive systems with internal noise
Numerous mathematical models exploring the emergence of complexity within developmental biology incorporate diffusion as the dominant mechanism of transport. However, self-organizing paradigms can exhibit the biologically undesirable property of extensive sensitivity, as illustrated by the behavior of the French-flag model in response to intrinsic noise and Turingâs model when subjected to fluctuations in initial conditions. Domain growth is known to be a stabilizing factor for the latter, though the interaction of intrinsic noise and domain growth is underexplored, even in the simplest of biophysical settings. Previously, we developed analytical Fourier methods and a description of domain growth that allowed us to characterize the effects of deterministic domain growth on stochastically diffusing systems. In this paper we extend our analysis to encompass stochastically growing domains. This form of growth can be used only to link the meso- and macroscopic domains as the âbox-splittingâ form of growth on the microscopic scale has an ill-defined thermodynamic limit. The extension is achieved by allowing the simulated particles to undergo random walks on a discretized domain, while stochastically controlling the length of each discretized compartment. Due to the dependence of diffusion on the domain discretization, we find that the description of diffusion cannot be uniquely derived. We apply these analytical methods to two justified descriptions, where it is shown that, under certain conditions, diffusion is able to support a consistent inhomogeneous state that is far removed from the deterministic equilibrium, without additional kinetics. Finally, a logistically growing domain is considered. Not only does this show that we can deal with nonmonotonic descriptions of stochastic growth, but it is also seen that diffusion on a stationary domain produces different effects to diffusion on a domain that is stationary âon average.
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