Accounting for location uncertainty in azimuthal telemetry data improves ecological inference.
Brian D Gerber, Mevin B Hooten, Christopher P Peck, Mindy B Rice, James H Gammonley, Anthony D Apa, Amy J Davis
Author Information
Brian D Gerber: 1Colorado Cooperative Fish and Wildlife Research Unit, Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, 80523 CO USA. ORCID
Mevin B Hooten: 3U.S. Geological Survey, Colorado Cooperative Fish and Wildlife Research Unit, Departments of Fish, Wildlife, and Conservation Biology and Statistics, Colorado State University, Fort Collins, 80523 CO USA.
Christopher P Peck: 1Colorado Cooperative Fish and Wildlife Research Unit, Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, 80523 CO USA.
Mindy B Rice: Colorado Division of Parks and Wildlife, 317 West Prospect, Fort Collins, 80526 CO USA.
James H Gammonley: Colorado Division of Parks and Wildlife, 317 West Prospect, Fort Collins, 80526 CO USA.
Anthony D Apa: Colorado Division of Parks and Wildlife, 711 Independent Avenue, Grand Junction, 81505 CO USA.
Amy J Davis: 6National Wildlife Research Center, United States Department of Agriculture, 4101 Laporte Avenue, Fort Collins, 80521 CO USA.
BACKGROUND: Characterizing animal space use is critical for understanding ecological relationships. Animal telemetry technology has revolutionized the fields of ecology and conservation biology by providing high quality spatial data on animal movement. Radio-telemetry with very high frequency (VHF) radio signals continues to be a useful technology because of its low cost, miniaturization, and low battery requirements. Despite a number of statistical developments synthetically integrating animal location estimation and uncertainty with spatial process models using satellite telemetry data, we are unaware of similar developments for azimuthal telemetry data. As such, there are few statistical options to handle these unique data and no synthetic framework for modeling animal location uncertainty and accounting for it in ecological models.We developed a hierarchical modeling framework to provide robust animal location estimates from one or more intersecting or non-intersecting azimuths. We used our azimuthal telemetry model (ATM) to account for azimuthal uncertainty with covariates and propagate location uncertainty into spatial ecological models. We evaluate the ATM with commonly used estimators (Lenth (1981) maximum likelihood and M-Estimators) using simulation. We also provide illustrative empirical examples, demonstrating the impact of ignoring location uncertainty within home range and resource selection analyses. We further use simulation to better understand the relationship among location uncertainty, spatial covariate autocorrelation, and resource selection inference. RESULTS: We found the ATM to have good performance in estimating locations and the only model that has appropriate measures of coverage. Ignoring animal location uncertainty when estimating resource selection or home ranges can have pernicious effects on ecological inference. Home range estimates can be overly confident and conservative when ignoring location uncertainty and resource selection coefficients can lead to incorrect inference and over confidence in the magnitude of selection. Furthermore, our simulation study clarified that incorporating location uncertainty helps reduce bias in resource selection coefficients across all levels of covariate spatial autocorrelation. CONCLUSION: The ATM can accommodate one or more azimuths when estimating animal locations, regardless of how they intersect; this ensures that all data collected are used for ecological inference. Our findings and model development have important implications for interpreting historical analyses using this type of data and the future design of radio-telemetry studies.
