Effects of habitat on GPS collar performance: using data screening to reduce location error.
Abstract
Global positioning system (GPS) technology enables researchers to evaluate wildlife movements, space use and resource selection in detail for extended periods of time. Two types of errors, missed location fixes and location error, are inherent to GPS telemetry and can bias location data sets. Habitat characteristics can influence both types of errors, but no studies have reported how continuous ranges of canopy cover and terrain simultaneously affect location error at different positional dilution of precision (PDOP) and signal quality levels. This information can assist in developing a protocol for removing large location errors from GPS data sets. The objectives of this study were to quantify how canopy cover and terrain affected GPS collar performance within a mountainous region of northern Idaho, USA, and evaluate different data-screening options for GPS location data sets from stationary test collars and free-ranging black bears Ursus americanus. Forest predominated throughout the study area. The fix rate for test collars was very high in all habitats (mean=99.5%, standard deviation=0.14, range=97.9-100%) and was not related to canopy cover or terrain obstruction. However, habitat variables strongly influenced location error, PDOP values and proportion of three-dimensional (3-D) fixes. The 95% circular error probable equalled 106.8 m for locations at all test sites, and varied substantially with canopy cover, terrain obstruction and signal quality categories, ranging from 14.3 to 557.0 m. Location errors for two-dimensional (2-D) fixes were more variable at higher PDOP values and were significantly larger compared with 3-D fixes. Data screening increased the accuracy of test collar location data sets by removing large location errors that were associated with high PDOP values. Data-screening options that focused on screening 2-D locations were most effective in reducing location error and retaining the greatest number of locations. For black bear data sets, the four data screening options resulted in data reduction ranging from 8 to 35%. We have demonstrated how location data can be analysed and screened based on 2-D and 3-D fixes in relation to PDOP values to eliminate locations with potentially large location errors. This information can be applied to GPS location data for individual animals to increase data accuracy for analyses.