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Abstract:
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Using GPS data to study the relationship between individuals within species, between individuals from different species or relationship linking animals and their environment are a major aspect of movement ecology. When focusing on the relation between individuals and their environment, the movement might be thought as a solution to a stochastic differential equation whose drift is the gradient of a multimodal potential surface, describing the atractivity of the environment. 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 prove that the usual Euler method performs worse than the other procedures for all sampling schemes
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