Stochastic differential equation based on a multimodal potential to model movement data in ecology

TL;DR

This paper introduces a novel stochastic differential equation model with a multimodal potential surface to better capture movement data in ecology, comparing inference methods through simulations and real vessel trajectory data.

Contribution

It presents a new flexible movement model based on SDEs with multimodal potentials and evaluates multiple inference methods, including a Monte Carlo EM approach, for ecological data.

Findings

Euler method performs worse than other inference procedures.

Monte Carlo EM approach provides more accurate parameter estimates.

Model effectively captures complex movement patterns in ecological data.

Abstract

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.