Removing GPS collar bias in habitat selection studies

Abstract

Summary 1.Compared to traditional radio-collars, global positioning system (GPS) collars provide finer spatial resolution and collect locations across a broader range of spatial and temporal conditions. However, data from GPS collars are biased because vegetation and terrain interfere with the satellite signals necessary to acquire a location. Analyses of habitat selection generally proceed without correcting for this known sampling bias. We documented the effects of bias in resource selection functions (RSF) and compared the effectiveness of two bias-correction techniques. 2. The effects of environmental conditions on the probability of a GPS collar collecting a location were modelled for three brands of collar using data collected in 24-h trials at 194 test locations. The best-supported model was used to create GPS-biased data from unbiased animal locations. These data were used to assess the effects of bias given data losses in the range of 10-40% at both 1-and 6-h sampling intensities. We compared the sign, value and significance of coefficients derived using biased and unbiased data. 3. With 6-h locations we observed type II error rates of 30-40% given as little as a 10% data loss. Biased data also produced coefficients that were significantly more negative than unbiased estimates. Increasing the sampling intensity from 6-to 1-h locations eliminated type II errors but increased the magnitude of coefficient bias. No type I errors or changes in sign were observed. 4. We applied sample weighting and iterative simulation given a 30% data loss. For a biased vegetation type, simulation reduced more type II errors than weighting, most probably because the original sample size was re-established. However, selection for areas near trails, which was influenced by a biased vegetation type, showed fewer type II errors after weighting existing animal locations than after simulation. Both techniques corrected 100% and ≥ 80% of the biased coefficients at the 6-and 1-h sampling intensities, respectively. 5. Synthesis and applications. This study demonstrates that GPS error is predictable and biases the coefficients of resource selection models dependant upon the GPS sampling intensity and the level of data loss. We provide effective alternatives for correcting bias and discuss applying corrections under different sampling designs.

Interactions of wolves, mountain caribou and an increased moose-hunting quota --- Primary-prey management as an approach to caribou recovery.

“…One hundred GPS locations were dropped due to poor accuracy. GPS-fix success may vary among vegetation cover types (Frair et al 2004) and with changes in animal behaviour , which can affects selection coefficients. Unfortunately, there is little consensus on how to deal with these issues , particularly when many categorical aspects of the terrain and habitat are being sampled.…”

Home range sizes and space use of African elephants (Loxodonta africana) in the Southern Kenya and Northern Tanzania borderland landscape

“…Dataset that had GPS errors like missing coordinates were removed from the dataset before analysis. The causes of GPS errors are: Temporal malfunction of the GPS collars (Gala, 2014), canopy cover (Jiang et al, 2008;Sager-Fradkin et al, 2007;Heard et al, 2008), topography (terrain and slope; Hebblewhite et al, 2007;Frair et al, 2004) and collar orientation (Sager-Fradkin et al, 2007;Heard et al, 2008;Moen et al, 1996;Frair et al, 2010). The data available for analysis after screening ranged between 58 and 92% (Table 2), which is within acceptable range to characterize wildlife movement patterns and make sound inference (Frair et al, 2010).…”

Defining Landscapes and Scales to Model Landscape–Organism Interactions

“…Indeed, poor precision itself can result in attenuation bias, which can lead to incorrect conclusions [44,45]. That said, in the case of radiotelemetry, measurement error associated with fix success is often predictable, and consequently tools exist to overcome this bias in habitat selection modeling using statistical corrections [33,[46][47][48][49].…”