A central limit theorem for the spatial Lambda Fleming-Viot process with selection
Rapha\"el Forien, Sarah Penington

TL;DR
This paper establishes a central limit theorem for the spatial Lambda Fleming-Viot process with selection, showing that rescaled fluctuations converge to solutions of stochastic PDEs, with results depending on dispersal mechanisms and spatial dimension.
Contribution
It provides the first rigorous derivation of fluctuation limits for the spatial Lambda Fleming-Viot process with selection, connecting microscopic models to stochastic PDEs.
Findings
Rescaled fluctuations converge to stochastic PDE solutions.
Limiting equations depend on dispersal type: white noise or colored noise.
The drift load effect varies with spatial dimension, reducing in structured populations.
Abstract
We study the evolution of gene frequencies in a population living in , modelled by the spatial Lambda Fleming-Viot process with natural selection (Barton, Etheridge and Veber, 2010 and Etheridge, Veber and Yu, 2014). We suppose that the population is divided into two genetic types, and , and consider the proportion of the population which is of type 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, as in Etheridge, Veber and Yu, 2014). 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…
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