Basis Function Models for Animal Movement

• 成果类型: Article
• 署名作者: Hooten, Mevin B.; Johnson, Devin S.
• 署名单位: Colorado State University System; Colorado State University Fort Collins; United States Department of the Interior; United States Geological Survey; National Oceanic Atmospheric Admin (NOAA) - USA
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2016.1246250
• 发表日期: 2017
• 页码: 578-589
• 关键词: chronic wasting disease; resource selection; telemetry data; FRAMEWORK; inference; discrete; SPACE; regression; absence

摘要

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.