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.

翻译：动物生态学中的一个持续挑战是开发能够解释遥测数据中自相关性（通常还包括时间不规则性）的运动模型。连续时间的Langevin扩散模型已被提出用于建模时间自相关且采样不规则的数据。然而，当前的估计技术随着观测时间间隔增加，会得到越来越有偏的参数估计。本文提出在重要性采样方案中使用布朗桥来改进Langevin扩散模型的似然近似。通过一系列模拟研究，我们证明该方法能在各种场景下有效消除偏差。我们发现，对于持续时间更长、采样频率更低的数据，估计的栖息地系数精度高于那些时间更短、采样更频繁的轨迹。这表明该模型可能特别适用于粗糙分辨率下采样的追踪数据——这在旧世代动物标签收集的数据集中很常见。我们利用斯特勒海狮（*Eumetopias jubatus*）的追踪数据展示了模型的应用。研究发现，系数估计收敛到的值显著不同于以往研究的结果，表明传统估计方法中的偏差可能显著影响关于栖息地偏好的生态学结论。这些改进共同拓宽了Langevin扩散模型的适用性，从而提升了对栖息地选择的生态学认知。