Can We Accurately Characterize Wildlife Resource Use When Telemetry Data Are Imprecise?

Abstract

Telemetry data have been widely used to quantify wildlife habitat relationships despite the fact that these data are inherently imprecise. All telemetry data have positional error, and failure to account for that error can lead to incorrect predictions of wildlife resource use. Several techniques have been used to account for positional error in wildlife studies. These techniques have been described in the literature, but their ability to accurately characterize wildlife resource use has never been tested. We evaluated the performance of techniques commonly used for incorporating telemetry error into studies of wildlife resource use. Our evaluation was based on imprecise telemetry data (mean telemetry error = 174 m, SD = 130 m) typical of field‐based studies. We tested 5 techniques in 10 virtual environments and in one real‐world environment for categorical (i.e., habitat types) and continuous (i.e., distances or elevations) rasters. Technique accuracy varied by patch size for the categorical rasters, with higher accuracy as patch size increased. At the smallest patch size (1 ha), the technique that ignores error performed best on categorical data (0.31 and 0.30 accuracy for virtual and real data, respectively); however, as patch size increased the bivariate‐weighted technique performed better (0.56 accuracy at patch sizes >31 ha) and achieved complete accuracy (i.e., 1.00 accuracy) at smaller patch sizes (472 ha and 1,522 ha for virtual and real data, respectively) than any other technique. We quantified the accuracy of the continuous covariates using the mean absolute difference (MAD) in covariate value between true and estimated locations. We found that average MAD varied between 104 m (ignore telemetry error) and 140 m (rescale the covariate data) for our continuous covariate surfaces across virtual and real data sets. Techniques that rescale continuous covariate data or use a zonal mean on values within a telemetry error polygon were significantly less accurate than other techniques. Although the technique that ignored telemetry error performed best on categorical rasters with smaller average patch sizes (i.e., ≤31 ha) and on continuous rasters in our study, accuracy was so low that the utility of using point‐based approaches for quantifying resource use is questionable when telemetry data are imprecise, particularly for small‐patch habitat relationships.

Could you please phrase “home range” as a question?

“…Lastly, location error may be an important consideration. If conifer stands are small relative to the likely error in VHF location data, then VHF data may provide little power to detect shifts in habitat use relative to snow depth (e.g., see Montgomery et al 2010). Again, pilot data (and more specifics about the study area) would allow one to quantify trade-offs between VHF and GPS techniques more precisely.…”

Opportunities for telemetry techniques in studies on the nutritional ecology of free-ranging domesticated ruminants

“…A previous review highlighted the importance of ensuring that the sample frequency that is used is determined by the experimental question (Swain et al, 2011). If herbivore movement is to be reliably mapped onto the spatial distribution of nutrient resources, then higher sample rate location data will be required to ensure sufficient spatial accuracy for smaller-scale interactions (Schick et al, 2008;Montgomery et al, 2010). Within herbivore studies, there has been an overwhelming focus on patch-and bite-level nutrient selection preferences (OwenSmith et al, 2010); there are significant opportunities to explore large-scale (regional and over several years) nutrient selection preferences within extensive livestock production systems (Hebblewhite and Haydon, 2010;Owen-Smith et al, 2010).…”

“…If the vegetation comprises a mosaic of small patches, however, as is found with clumping grasses, then 25 m 2 patches may be too large to derive meaningful nutrient selection information. Location data collected using telemetry devices provide Nutritional ecology 127 a snapshot of movement, and the less frequently a location point is collected the less likely that it represents a herbivore's movement (Nams, 2006b) or nutrient selection preferences (Schick et al, 2008;Montgomery et al, 2010). The accuracy of location estimates is also determined by the speed at which an animal moves; faster movement between recorded locations creates greater uncertainty (Nams, 2005 and2006a).…”

Screening GPS telemetry data for locations having unacceptable error

“…Conversely, fixes with measurement error are insidious and can evade current data screening and censoring methods. In analyses, data with measurement error can produce spurious inference about behavioral states (Ganskopp and Johnson, 2007), habitat use (Montgomery et al, 2010;Visscher, 2006), home range (Moser and Garton, 2007) and animal movements (Hurford, 2009;Jerde and Visscher, 2005).…”