ROBUST ESTIMATES OF WILDLIFE LOCATION USING TELEMETRY DATA

The location of wildlife is frequently determined using telemetry data gathered at short intervals. If radio transmissions are reflected, as often occurs in mountainous regions, then existing location estimation techniques are unreliable. We explore the effects of gross observation errors upon current analyses and suggest an alternative analysis based on robust state-space time-series modeling. We determine location estimates and their precisions, both for simulated and real mule-deer data, using current and robust procedures. Implementation and specification of filter parameters are also discussed. We conclude that the proposed filter-smoother is similar to the Gaussian filter-smoother when data are not greatly contaminated and that the robust version improves upon location estimates when contamination is large.