Rieber Camille J, Hefley Trevor J, Haukos David A
Department of Statistics and Kansas Cooperative Fish and Wildlife Research Unit Kansas State University Manhattan Kansas USA.
Department of Statistics Kansas State University Manhattan Kansas USA.
Ecol Evol. 2024 Jun 2;14(6):e11447. doi: 10.1002/ece3.11447. eCollection 2024 Jun.
Wildlife telemetry data may be used to answer a diverse range of questions relevant to wildlife ecology and management. One challenge to modeling telemetry data is that animal movement often varies greatly in pattern over time, and current continuous-time modeling approaches to handle such nonstationarity require bespoke and often complex models that may pose barriers to practitioner implementation. We demonstrate a novel application of treed Gaussian process (TGP) modeling, a Bayesian machine learning approach that automatically captures the nonstationarity and abrupt transitions present in animal movement. The machine learning formulation of TGPs enables modeling to be nearly automated, while their Bayesian formulation allows for the derivation of movement descriptors with associated uncertainty measures. We demonstrate the use of an existing R package to implement TGPs using the familiar Markov chain Monte Carlo algorithm. We then use estimated movement trajectories to derive movement descriptors that can be compared across individuals and populations. We applied the TGP model to a case study of lesser prairie-chickens () to demonstrate the benefits of TGP modeling and compared distance traveled and residence times across lesser prairie-chicken individuals and populations. For broad usability, we outline all steps necessary for practitioners to specify relevant movement descriptors (e.g., turn angles, speed, contact points) and apply TGP modeling and trajectory comparison to their own telemetry datasets. Combining the predictive power of machine learning and the statistical inference of Bayesian methods to model movement trajectories allows for the estimation of statistically comparable movement descriptors from telemetry studies. Our use of an accessible R package allows practitioners to model trajectories and estimate movement descriptors, facilitating the use of telemetry data to answer applied management questions.
野生动物遥测数据可用于回答一系列与野生动物生态和管理相关的问题。对遥测数据进行建模面临的一个挑战是，动物的运动模式往往会随时间发生很大变化，而当前用于处理这种非平稳性的连续时间建模方法需要定制且通常很复杂的模型，这可能会给从业者的实施带来障碍。我们展示了树状高斯过程（TGP）建模的一种新应用，这是一种贝叶斯机器学习方法，能够自动捕捉动物运动中存在的非平稳性和突然转变。TGP的机器学习公式使建模几乎可以自动化，而其贝叶斯公式则允许推导带有相关不确定性度量的运动描述符。我们展示了如何使用现有的R包，通过熟悉的马尔可夫链蒙特卡罗算法来实现TGP。然后，我们使用估计的运动轨迹来推导可以在个体和种群之间进行比较的运动描述符。我们将TGP模型应用于小草原榛鸡的案例研究，以展示TGP建模的优势，并比较了小草原榛鸡个体和种群之间的移动距离和停留时间。为了广泛的可用性，我们概述了从业者指定相关运动描述符（如转弯角度、速度、接触点）并将TGP建模和轨迹比较应用于自己的遥测数据集所需的所有步骤。将机器学习的预测能力和贝叶斯方法的统计推断相结合来对运动轨迹进行建模，可以从遥测研究中估计出具有统计可比性的运动描述符。我们使用一个易于使用的R包，使从业者能够对轨迹进行建模并估计运动描述符，便于利用遥测数据回答实际管理问题。
