Unbiased likelihood estimation of the Langevin diffusion for animal movement modelling

TL;DR

This paper introduces a bias-reduction method using Brownian bridges for likelihood estimation in Langevin diffusion models, enhancing animal movement analysis from irregular telemetry data.

Contribution

It proposes a novel importance sampling approach with Brownian bridges to improve likelihood approximation, reducing bias in parameter estimates for animal movement models.

Findings

Effectively removed bias in simulation studies.

Improved habitat coefficient precision with coarser, longer-duration data.

Revealed significant differences in habitat preference estimates.

Abstract

An ongoing challenge in animal ecology is developing movement models that account for the autocorrelation, and often temporal irregularity, in telemetry data. Continuous-time Langevin diffusion models have been proposed to model temporally autocorrelated and irregularly sampled data. However, current estimation techniques obtain increasingly biased parameter estimates as the time between observations increases. In this paper, we propose using Brownian bridges in an importance sampling scheme to improve the likelihood approximation of the Langevin diffusion model. In a series of simulation studies, we showed that our approach effectively removed the bias under various scenarios. We found that the precision of the estimated habitat coefficients increased for data spanning a longer duration at a lower frequency than for shorter, more frequently sampled tracks. This suggests that the model may be well suited for modelling tracking data sampled at a coarser resolution, as is common in datasets collected with older generations of animal tags. We illustrated the application of our model using tracking data from Steller sea lions, \textit{Eumetopias jubatus}. We found that the coefficient estimates converged to values significantly different than those estimated in previous studies, suggesting that bias in conventional estimation methods may meaningfully affect ecological conclusions about habitat preference. Together, these improvements broaden the applicability of Langevin diffusion models, thereby improving ecological insight into habitat selection.