  This paper proposes a new model for individuals movement in ecology. The
movement process is defined as a solution to a stochastic differential equation
whose drift is the gradient of a multimodal potential surface. This offers a
new flexible approach among the popular potential based movement models in
ecology. To perform parameter inference, the widely used Euler method is
compared with two other pseudo-likelihood procedures and with a Monte Carlo
Expectation Maximization approach based on exact simulation of diffusions.
Performances of all methods are assessed with simulated data and with a data
set of fishing vessels trajectories. We show that the usual Euler method
performs worse than the other procedures for all sampling schemes.
