Basis Function Models for Animal Movement
针对卫星遥测数据质量不一、规模大的问题，提出一种灵活的连续时间随机积分方程框架，通过降秩协方差参数化模拟动物轨迹行为，并用北美骡鹿和美洲狮数据验证了方法的并行计算和非平稳空间建模能力。
Advances in satellite-based data collection techniques have served as a catalyst for new statistical methodology to analyze these data. In wildlife ecological studies, satellite-based data and methodology have provided a wealth of information about animal space use and the investigation of individual-based animal–environment relationships. With the technology for data collection improving dramatically over time, we are left with massive archives of historical animal telemetry data of varying quality. While many contemporary statistical approaches for inferring movement behavior are specified in discrete time, we develop a flexible continuous-time stochastic integral equation framework that is amenable to reduced-rank second-order covariance parameterizations. We demonstrate how the associated first-order basis functions can be constructed to mimic behavioral characteristics in realistic trajectory processes using telemetry data from mule deer and mountain lion individuals in western North America. Our approach is parallelizable and provides inference for heterogenous trajectories using nonstationary spatial modeling techniques that are feasible for large telemetry datasets. Supplementary materials for this article are available online.