Habitat selection and space use are fundamental to understanding animal distribution. Traditional methods for quantifying habitat preferences from telemetry data assume regular sampling and negligible measurement error. However, these assumptions are routinely violated in marine systems. Practitioners typically regularize and filter the data before fitting models, but these two-step procedures do not propagate uncertainty from the filtering stage and can yield biased estimates. Habitat-driven Langevin diffusion models offer an elegant alternative, naturally accommodating irregular sampling. However, incorporating measurement error via a state-space formulation is challenging because habitat covariates depend on the latent true locations. We address this using the Laplace approximation to simultaneously integrate over true locations and account for habitat covariates along latent paths, yielding a single-stage framework efficiently implemented in Template Model Builder (TMB). By doing so, we provide the first TMB implementation capable of handling covariates that depend on latent variables, allowing inference via fast and efficient maximum likelihood estimation. Simulations show that our approach outperforms the two-step method, recovering habitat-selection parameters even under substantial measurement error and missing data, with more accurate utilization distributions and trajectory reconstructions. Applied to narwhal (Monodon monoceros) telemetry data, the two-step method substantially shrinks the habitat selection coefficient towards zero, while our unified approach recovers a much stronger signal. Our framework offers a computationally efficient solution to long-standing challenges of measurement error and temporal irregularity in habitat selection inference, applicable across a wide range of taxa and environments.

翻译：栖息地选择和空间利用是理解动物分布的基础。传统上，从遥测数据量化栖息地偏好的方法假定采样规律且测量误差可忽略。然而，这些假设在海洋系统中常常不成立。实际操作中，研究人员通常会在拟合模型前对数据进行规则化处理和滤波，但这种两阶段方法无法传递滤波阶段的误差，可能导致有偏估计。栖息地驱动的朗之万扩散模型提供了一种优雅的替代方案，能自然适应不规则采样。然而，通过状态空间公式化引入测量误差具有挑战性，因为栖息地协变量依赖于潜在的真实位置。我们利用拉普拉斯近似来解决此问题，同时整合真实位置并沿潜在路径考虑栖息地协变量，从而构建出一个单阶段框架，并通过模板模型构建器（TMB）高效实现。通过这种方式，我们提供了首个能够处理依赖于潜在变量的协变量的TMB实现，允许通过快速高效的极大似然估计进行推断。模拟表明，我们的方法优于两阶段方法，即使存在显著测量误差和缺失数据，也能恢复栖息地选择参数，并得到更准确的利用分布和轨迹重建。应用于独角鲸（Monodon monoceros）遥测数据时，两阶段方法将栖息地选择系数大幅压缩至接近零，而我们的统一方法则恢复了更强的信号。我们的框架为栖息地选择推断中测量误差和时间不规则性这一长期挑战提供了计算高效的解决方案，适用于广泛的物种类群和环境条件。