New types of high-resolution animal movement data allow for increasingly comprehensive biological inference, but method development to meet the statistical challenges associated with such data is lagging behind. In this contribution, we extend the commonly applied hidden Markov models for step lengths and turning angles to address the specific requirements posed by high-resolution movement data, in particular the very strong within-state correlation induced by the momentum in the movement. The models feature autoregressive components of general order in both the step length and the turning angle variable, with the possibility to automate the selection of the autoregressive degree using a lasso approach. In a simulation study, we identify potential for improved inference when using the new model instead of the commonly applied basic hidden Markov model in cases where there is strong within-state autocorrelation. The practical use of the model is illustrated using high-resolution movement tracks of terns foraging near an anthropogenic structure causing turbulent water flow features.

翻译：新型高分辨率动物运动数据为日益全面的生物学推断提供了可能，但应对此类数据相关统计挑战的方法发展却相对滞后。本文扩展了常用于步长与转向角的隐马尔可夫模型，以解决高分辨率运动数据提出的特定需求，特别是运动动量引起的极强状态内相关性。该模型在步长和转向角变量中均包含一般阶数的自回归分量，并可通过套索方法实现自回归阶数的自动选择。模拟研究表明，在存在强状态内自相关的情况下，使用新模型相较于常用基础隐马尔可夫模型具有提升推断效果的潜力。本文通过分析燕鸥在人为结构（引发湍流水流特征）附近觅食的高分辨率运动轨迹，展示了该模型的实际应用价值。