Linking Long-Distance Animal Movement Behavior and Landscapes Using Multiscale Functional Models

• Activity Number: 675
• Type: Topic Contributed
• Date/Time: Thursday, August 13, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Mevin Hooten*
• Companies: Colorado State University
• Keywords: spatial ; Bayesian ; time series ; ecology ; telemetry data ; nonparametric

Abstract

Advances in animal telemetry data collection and methodology have provided a wealth of information about animal space use and the investigation of animal-environment relationships. While the technology for data collection is improving dramatically over time, we are left with massive archives of historical animal telemetry data of varying quality. However, many contemporary statistical approaches for inferring movement behavior are designed for newer data that are very accurate and high-resolution. From a scientific perspective, the behaviors we are interested in learning about may be nonstationary and occur across multiple scales. We describe a statistical modeling approach that uses multiple historical data sources in an explicitly multiscale framework to better understand animal spatial behavior. The models we describe are fast to implement, accessible to ecologists, easily generalized, and properly account for the uncertainty associated with telemetry data and the movement process. We apply this methodology to the study of Colorado predators for the identification of corridors and barriers to long-distance movement events.