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Abstract:
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Satellite-based telemetry technology continues to advance at a rapid pace, but wildlife researchers often still rely on radio telemetry to collect spatial location data (using "very high frequency" transmitters and receivers). Current commercial and open-source software provide users with estimated locations but, in practice, these estimates are commonly regarded as being known without error. Failure to propagate this error into subsequent modeling steps (e.g., resource selection functions or home range analysis) can have potentially far-reaching implications on inference. We propose a hierarchical Bayesian model for radio telemetry data that accommodates multiple sources of uncertainty, including uncertainty in the observed azimuths. We demonstrate the model's utility by analyzing radio telemetry data collected on Gunnison sage-grouse, a federally threatened species in the western United States.
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