Estimation of animal location from radio telemetry data with temporal dependencies

Abstract

We evaluate the performance of several estimators of animal location when data arise from radio telemetry studies. We assume that error-prone bearings are taken at regular time intervals from known locations, that observations may be frequent enough to introduce temporal dependencies, and that animals remain within well-defined home ranges. We simulated data to examine several factors including home range shape, dependency between successive locations, sample size and strategy, and bearing error. Simulated data, supplemented by data based on actual paths for Rocky Mountain elk (Cervus elaphus nelsoni), were used to evaluate alternative location estimators: Lenth’s maximum likelihood estimator (MLE) that assumes independence; Pace’s moving average version of the MLE that introduces a post hoc dependence in the data; and three estimators from state-space models that explicitly model animal movement and dependencies. The state-space models differed in the assumed model of animal movement and included a simple normal random walk, a variation of the random walk that includes a centralizing parameter, and a discretetime version of the Ornstein-Uhlenbeck model for movement within an elliptical home range. Estimates were evaluated in terms of location error and estimator precision. The independent MLE had the poorest performance under most scenarios, particularly when actual locations were dependent. The centrally biased random walk estimator generally had smallest location errors and best precision, whether or not data displayed dependencies. Rather than assuming independence or discarding observations to achieve independence, estimation of location would be served better by accounting for potential dependencies. Methods based on simple models of animal movement may vastly improve estimates, and models that recognize an animal’s sense of home territory are preferred.