Effects of GPS Error on Animal Home Range Estimates

This study examined how variables related to habitat cover types can affect the positional accuracy of Global Positioning System (GPS) data and, subsequently, how wildlife home range analysis can be influenced when utilizing this inaccurate data. This study focused on measuring GPS accuracy relative to five habitat variables: open canopy, sparse canopy, dense canopy, open water, and building proximity. The study took place in Hillsborough County, in residential areas that contain all of these habitat types. Five GPS devices, designed for wildlife tracking purposes, were used to collect the data needed for this study. GPS data was collected under the aforementioned scenarios in order to induce error into the data sets. Each data set was defined as a 1-hour data collecting period, with a fix rate of 60 seconds, which resulted in 60 points per sample. The samples were analyzed to determine the magnitude of effect the five variables have on the positional accuracy of the data. Thirty samples were collected for each of the following scenarios: (1) open grassland with uninhibited canopy closure, (2) sparse vegetation canopy closure, (3) dense vegetation canopy closure, (4) close proximity to buildings (\u3c2 m), and (5) open water with uninhibited canopy closure. Then, GPS errors (in terms of mean and maximum distance from the mean center of each sample) were calculated for each sample using a geographic information system (GIS). Confidence intervals were calculated for each scenario in order to evaluate and compare the levels of error. Finally, this data was used to assess the effect of positional uncertainty on home range estimation through the use of a minimum convex polygon home range estimation technique. Open grassland and open water cover types were found to introduce the least amount of positional uncertainty into the data sets. The sparse coverage cover type introduces a higher degree of error into data sets, while the dense coverage and building proximity cover types introduce the greatest amount of positional uncertainty into the data sets. When used to create minimum convex polygon home range estimates, these data sets show that the home range estimates are significantly larger when the positional error is unaccounted for as opposed to when it is factored into the home range estimate