On the large time behaviour of the solution of an SDE driven by a Poisson Point Process
Résumé
We study a stochastic differential equation driven by a Poisson point process, which models continuous changes in a population's environment, as well as the stochastic fixation of beneficial mutations that might compensate for this change. The fixation probability of a given mutation increases as the phenotypic lag X_t between the population and the optimum grows larger, and successful mutations are assumed to fix instantaneously (leading to an adaptive jump). Our main result is that the process is transient (i.e., continued adaptation is impossible) if the rate of environmental change v exceeds a parameter m, which can be interpreted as the rate of adaptation in case every beneficial mutation gets fixed with probability 1. If v < m, the process is positive recurrent, while in the limiting case m=v, null recurrence or transience depends upon additional technical conditions. We show how our results can be extended to the case of a time varying rate of environmental change.