A central limit theorem for the spatial Λ-Fleming-Viot process with selection

Raphael Forien, Sarah Penington

Research output: Contribution to journalArticlepeer-review

7 Citations (SciVal)


We study the evolution of gene frequencies in a population living in Rd, modelled by the spatial Λ-Fleming-Viot process with natural selection. We suppose that the population is divided into two genetic types, a and A, and consider the proportion of the population which is of type a at each spatial location. If we let both the selection intensity and the fraction of individuals replaced during reproduction events tend to zero, the process can be rescaled so as to converge to the solution to a reaction-diffusion equation (typically the Fisher-KPP equation). We show that the rescaled fluctuations converge in distribution to the solution to a linear stochastic partial differential equation. Depending on whether offspring dispersal is only local or if large scale extinction-recolonization events are allowed to take place, the limiting equation is either the stochastic heat equation with a linear drift term driven by space-time white noise or the corresponding fractional heat equation driven by a coloured noise which is white in time. If individuals are diploid (i.e. either AA, Aa or aa) and if natural selection favours heterozygous (Aa) individuals, a stable intermediate gene frequency is maintained in the population. We give estimates for the asymptotic effect of random fluctuations around the equilibrium frequency on the local average fitness in the population. In particular, we find that the size of this effect - known as the drift load - depends crucially on the dimension d of the space in which the population evolves, and is reduced relative to the case without spatial structure.
Original languageEnglish
Pages (from-to)1-68
JournalElectronic Journal of Probability
Early online date17 Jan 2017
Publication statusPublished - 2017


Dive into the research topics of 'A central limit theorem for the spatial Λ-Fleming-Viot process with selection'. Together they form a unique fingerprint.

Cite this