  Having a precise knowledge of the dispersal ability of a population in a
heterogeneous environment is of critical importance in agroecology and
conservation biology as it can provide management tools to limit the effects of
pests or to increase the survival of endangered species. In this paper, we
propose a mechanistic-statistical method to estimate space-dependent diffusion
parameters of spatially-explicit models based on stochastic differential
equations, using genetic data. Dividing the total population into
subpopulations corresponding to different habitat patches with known allele
frequencies, the expected proportions of individuals from each subpopulation at
each position is computed by solving a system of reaction-diffusion equations.
Modelling the capture and genotyping of the individuals with a statistical
approach, we derive a numerically tractable formula for the likelihood function
associated with the diffusion parameters.
  In a simulated environment made of three types of regions, each associated
with a different diffusion coefficient, we successfully estimate the diffusion
parameters with a maximum-likelihood approach. Although higher genetic
differentiation among subpopulations leads to more accurate estimations, once a
certain level of differentiation has been reached, the finite size of the
genotyped population becomes the limiting factor for accurate estimation.
