Technological Advances Increase Fix‐Success for White‐Tailed Deer GPS Collars

The incorporation of quick fix pseudoranging (QFP) technology into global positioning system (GPS) collars has the potential to increase location-fix success for terrestrial species. Units enabled with QFP obtain the data necessary to calculate an accurate GPS location-fix during post-processing with just a 3-5 second view of a satellite constellation. Fix-success rate is one of the main sources of error in GPS telemetry studies, creating an inherent bias resulting in misleading data interpretations and incorrect inferences about habitat use. During winter 2017-2018, we captured 20 adult female white-tailed deer (Odocoileus virginianus) and fitted them with QFP-enabled GPS collars on 2 study areas (10/site) in northcentral and northeastern Minnesota, with an additional 40 collars (20/site) deployed during winter 2018-2019. Prior to deployment of the GPS collars on free-ranging deer, we conducted stationary tests to evaluate location-fix-success and spatial accuracy of 48 (of the 60) collars distributed among 4 different vegetative cover types. We recovered 100% of the expected 3,024 location-fixes during the stationary tests. The overall mean location error of the GPS collars was 5.7 m (+/- 0.15 [SE], range = 0-189 m), with errors in dense conifer (10.3 +/- 0.52, range = 0-189 m) being greater than in hardwood stands (6.2 +/- 0.22, range = 0-91 m), browse patches (3.2 +/- 0.08, range = 0-26 m), and openings (3.2 +/- 0.08, range = 0-32 m). Similarly, we evaluated the performance of 30 collars deployed on free-ranging deer. We deployed collars for a mean 79.5 days (+/- 7.22, range = 1.8-153.8 days) with a fix-success rate of 100% (34,758 location-fixes); QFP accounted for 11.3% of the 34,758 location-fixes. Our results suggest that the addition of QFP capabilities effectively increased fix-success of GPS collars on free-ranging deer by 11%. An increase in fix-success rate can be of considerable value to movement, distribution, and habitat selection studies, where bias associated with missing data can create unreliable or even false inferences. (c) 2021 The Wildlife Society.