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Stochastic differential equation based on a multimodal potential to model movement data in ecology
Author(s) -
Gloaguen Pierre,
Etienne MariePierre,
Le Corff Sylvain
Publication year - 2018
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/rssc.12251
Subject(s) - stochastic differential equation , inference , monte carlo method , computer science , ecology , mathematics , mathematical optimization , maximization , differential equation , differential (mechanical device) , sampling (signal processing) , artificial intelligence , statistics , engineering , mathematical analysis , filter (signal processing) , computer vision , biology , aerospace engineering
Summary The 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 pseudolikelihood 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 vessel trajectories. We show that the usual Euler method performs worse than the other procedures for all sampling schemes.